Repository: VainF/Diff-Pruning Branch: main Commit: 71e922e54275 Files: 439 Total size: 8.8 MB Directory structure: gitextract_bsd98edx/ ├── .gitignore ├── LICENSE ├── README.md ├── ddpm_exp/ │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ ├── calc_fid.py │ ├── compute_flops.py │ ├── compute_pruned_ssim_curve.py │ ├── compute_ssim.py │ ├── compute_ssim_vis.py │ ├── configs/ │ │ ├── bedroom.yml │ │ ├── celeba.yml │ │ ├── church.yml │ │ ├── cifar10.yml │ │ └── cifar10_pruning.yml │ ├── datasets/ │ │ ├── __init__.py │ │ ├── celeba.py │ │ ├── ffhq.py │ │ ├── lsun.py │ │ ├── utils.py │ │ └── vision.py │ ├── draw_ssim_pruned_curve.py │ ├── extract_cifar10.py │ ├── fid_score.py │ ├── finetune.py │ ├── finetune_simple.py │ ├── functions/ │ │ ├── __init__.py │ │ ├── ckpt_util.py │ │ ├── denoising.py │ │ └── losses.py │ ├── inception.py │ ├── main.py │ ├── models/ │ │ ├── diffusion.py │ │ └── ema.py │ ├── prune.py │ ├── prune_kd.py │ ├── prune_ssim.py │ ├── prune_test.py │ ├── runners/ │ │ ├── __init__.py │ │ ├── diffusion.py │ │ └── diffusion_simple.py │ ├── scripts/ │ │ ├── finetune_bedroom_ddpm.sh │ │ ├── finetune_celeba_ddpm.sh │ │ ├── finetune_celeba_ddpm_kd.sh │ │ ├── finetune_church_ddpm.sh │ │ ├── finetune_cifar_ddpm.sh │ │ ├── finetune_cifar_ddpm_kd.sh │ │ ├── finetune_cifar_ddpm_random.sh │ │ ├── finetune_cifar_ddpm_taylor.sh │ │ ├── old/ │ │ │ ├── run_bedroom_sample_pratrained.sh │ │ │ ├── run_celeba_pruning_scratch.sh │ │ │ ├── run_celeba_pruning_taylor.sh │ │ │ ├── run_celeba_sample_pratrained.sh │ │ │ ├── run_church_pruning_taylor.sh │ │ │ ├── run_cifar_pruning_first_order_taylor.sh │ │ │ ├── run_cifar_pruning_magnitude.sh │ │ │ ├── run_cifar_pruning_random.sh │ │ │ ├── run_cifar_pruning_random_kd.sh │ │ │ ├── run_cifar_pruning_scratch.sh │ │ │ ├── run_cifar_pruning_second_order_taylor.sh │ │ │ ├── run_cifar_pruning_taylor.sh │ │ │ ├── run_cifar_pruning_taylor_kd.sh │ │ │ └── run_cifar_train.sh │ │ ├── prune_bedroom_ddpm.sh │ │ ├── prune_bedroom_ddpm_test.sh │ │ ├── prune_celeba_ddpm.sh │ │ ├── prune_celeba_ddpm_ssim.sh │ │ ├── prune_church_ddpm.sh │ │ ├── prune_church_ddpm_test.sh │ │ ├── prune_cifar_ddpm.sh │ │ ├── prune_cifar_ddpm_ssim.sh │ │ ├── prune_cifar_ddpm_test.sh │ │ ├── run_celeba.sh │ │ ├── sample_bedroom_ddpm_pretrained.sh │ │ ├── sample_bedroom_ddpm_pruning.sh │ │ ├── sample_celeba_ddpm_pruning.sh │ │ ├── sample_celeba_pretrained.sh │ │ ├── sample_church_ddpm_pruning.sh │ │ ├── sample_church_ddpm_pruning_old.sh │ │ ├── sample_church_ddpm_test.sh │ │ ├── sample_church_pretrained.sh │ │ ├── sample_cifar_ddpm_pruning.sh │ │ ├── sample_cifar_pretrained.sh │ │ ├── simple_celeba_our.sh │ │ └── simple_cifar_our.sh │ ├── tools/ │ │ ├── extract_cifar10.py │ │ └── transform_weights.py │ ├── torch_pruning/ │ │ ├── __init__.py │ │ ├── _helpers.py │ │ ├── dependency.py │ │ ├── importance.py │ │ ├── ops.py │ │ ├── pruner/ │ │ │ ├── __init__.py │ │ │ ├── algorithms/ │ │ │ │ ├── __init__.py │ │ │ │ ├── batchnorm_scale_pruner.py │ │ │ │ ├── group_norm_pruner.py │ │ │ │ ├── magnitude_based_pruner.py │ │ │ │ ├── metapruner.py │ │ │ │ ├── scaling_factor_pruner.py │ │ │ │ ├── scheduler.py │ │ │ │ └── taylor_pruner.py │ │ │ └── function.py │ │ └── utils/ │ │ ├── __init__.py │ │ ├── op_counter.py │ │ └── utils.py │ └── utils.py ├── ddpm_prune.py ├── ddpm_sample.py ├── ddpm_train.py ├── diffusers/ │ ├── __init__.py │ ├── commands/ │ │ ├── __init__.py │ │ ├── diffusers_cli.py │ │ └── env.py │ ├── configuration_utils.py │ ├── dependency_versions_check.py │ ├── dependency_versions_table.py │ ├── experimental/ │ │ ├── README.md │ │ ├── __init__.py │ │ └── rl/ │ │ ├── __init__.py │ │ └── value_guided_sampling.py │ ├── image_processor.py │ ├── loaders.py │ ├── models/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── attention_flax.py │ │ ├── attention_processor.py │ │ ├── autoencoder_kl.py │ │ ├── controlnet.py │ │ ├── controlnet_flax.py │ │ ├── cross_attention.py │ │ ├── dual_transformer_2d.py │ │ ├── embeddings.py │ │ ├── embeddings_flax.py │ │ ├── modeling_flax_pytorch_utils.py │ │ ├── modeling_flax_utils.py │ │ ├── modeling_pytorch_flax_utils.py │ │ ├── modeling_utils.py │ │ ├── prior_transformer.py │ │ ├── resnet.py │ │ ├── resnet_flax.py │ │ ├── t5_film_transformer.py │ │ ├── transformer_2d.py │ │ ├── transformer_temporal.py │ │ ├── unet_1d.py │ │ ├── unet_1d_blocks.py │ │ ├── unet_2d.py │ │ ├── unet_2d_blocks.py │ │ ├── unet_2d_blocks_flax.py │ │ ├── unet_2d_condition.py │ │ ├── unet_2d_condition_flax.py │ │ ├── unet_3d_blocks.py │ │ ├── unet_3d_condition.py │ │ ├── vae.py │ │ ├── vae_flax.py │ │ └── vq_model.py │ ├── optimization.py │ ├── pipeline_utils.py │ ├── pipelines/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── alt_diffusion/ │ │ │ ├── __init__.py │ │ │ ├── modeling_roberta_series.py │ │ │ ├── pipeline_alt_diffusion.py │ │ │ └── pipeline_alt_diffusion_img2img.py │ │ ├── audio_diffusion/ │ │ │ ├── __init__.py │ │ │ ├── mel.py │ │ │ └── pipeline_audio_diffusion.py │ │ ├── audioldm/ │ │ │ ├── __init__.py │ │ │ └── pipeline_audioldm.py │ │ ├── controlnet/ │ │ │ ├── __init__.py │ │ │ ├── multicontrolnet.py │ │ │ ├── pipeline_controlnet.py │ │ │ ├── pipeline_controlnet_img2img.py │ │ │ ├── pipeline_controlnet_inpaint.py │ │ │ └── pipeline_flax_controlnet.py │ │ ├── dance_diffusion/ │ │ │ ├── __init__.py │ │ │ └── pipeline_dance_diffusion.py │ │ ├── ddim/ │ │ │ ├── __init__.py │ │ │ └── pipeline_ddim.py │ │ ├── ddpm/ │ │ │ ├── __init__.py │ │ │ └── pipeline_ddpm.py │ │ ├── deepfloyd_if/ │ │ │ ├── __init__.py │ │ │ ├── pipeline_if.py │ │ │ ├── pipeline_if_img2img.py │ │ │ ├── pipeline_if_img2img_superresolution.py │ │ │ ├── pipeline_if_inpainting.py │ │ │ ├── pipeline_if_inpainting_superresolution.py │ │ │ ├── pipeline_if_superresolution.py │ │ │ ├── safety_checker.py │ │ │ ├── timesteps.py │ │ │ └── watermark.py │ │ ├── dit/ │ │ │ ├── __init__.py │ │ │ └── pipeline_dit.py │ │ ├── latent_diffusion/ │ │ │ ├── __init__.py │ │ │ ├── pipeline_latent_diffusion.py │ │ │ └── pipeline_latent_diffusion_superresolution.py │ │ ├── latent_diffusion_uncond/ │ │ │ ├── __init__.py │ │ │ └── pipeline_latent_diffusion_uncond.py │ │ ├── onnx_utils.py │ │ ├── paint_by_example/ │ │ │ ├── __init__.py │ │ │ ├── image_encoder.py │ │ │ └── pipeline_paint_by_example.py │ │ ├── pipeline_flax_utils.py │ │ ├── pipeline_utils.py │ │ ├── pndm/ │ │ │ ├── __init__.py │ │ │ └── pipeline_pndm.py │ │ ├── repaint/ │ │ │ ├── __init__.py │ │ │ └── pipeline_repaint.py │ │ ├── score_sde_ve/ │ │ │ ├── __init__.py │ │ │ └── pipeline_score_sde_ve.py │ │ ├── semantic_stable_diffusion/ │ │ │ ├── __init__.py │ │ │ └── pipeline_semantic_stable_diffusion.py │ │ ├── spectrogram_diffusion/ │ │ │ ├── __init__.py │ │ │ ├── continous_encoder.py │ │ │ ├── midi_utils.py │ │ │ ├── notes_encoder.py │ │ │ └── pipeline_spectrogram_diffusion.py │ │ ├── stable_diffusion/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── convert_from_ckpt.py │ │ │ ├── pipeline_cycle_diffusion.py │ │ │ ├── pipeline_flax_stable_diffusion.py │ │ │ ├── pipeline_flax_stable_diffusion_controlnet.py │ │ │ ├── pipeline_flax_stable_diffusion_img2img.py │ │ │ ├── pipeline_flax_stable_diffusion_inpaint.py │ │ │ ├── pipeline_onnx_stable_diffusion.py │ │ │ ├── pipeline_onnx_stable_diffusion_img2img.py │ │ │ ├── pipeline_onnx_stable_diffusion_inpaint.py │ │ │ ├── pipeline_onnx_stable_diffusion_inpaint_legacy.py │ │ │ ├── pipeline_onnx_stable_diffusion_upscale.py │ │ │ ├── pipeline_stable_diffusion.py │ │ │ ├── pipeline_stable_diffusion_attend_and_excite.py │ │ │ ├── pipeline_stable_diffusion_controlnet.py │ │ │ ├── pipeline_stable_diffusion_depth2img.py │ │ │ ├── pipeline_stable_diffusion_diffedit.py │ │ │ ├── pipeline_stable_diffusion_image_variation.py │ │ │ ├── pipeline_stable_diffusion_img2img.py │ │ │ ├── pipeline_stable_diffusion_inpaint.py │ │ │ ├── pipeline_stable_diffusion_inpaint_legacy.py │ │ │ ├── pipeline_stable_diffusion_instruct_pix2pix.py │ │ │ ├── pipeline_stable_diffusion_k_diffusion.py │ │ │ ├── pipeline_stable_diffusion_latent_upscale.py │ │ │ ├── pipeline_stable_diffusion_model_editing.py │ │ │ ├── pipeline_stable_diffusion_panorama.py │ │ │ ├── pipeline_stable_diffusion_pix2pix_zero.py │ │ │ ├── pipeline_stable_diffusion_sag.py │ │ │ ├── pipeline_stable_diffusion_upscale.py │ │ │ ├── pipeline_stable_unclip.py │ │ │ ├── pipeline_stable_unclip_img2img.py │ │ │ ├── safety_checker.py │ │ │ ├── safety_checker_flax.py │ │ │ └── stable_unclip_image_normalizer.py │ │ ├── stable_diffusion_safe/ │ │ │ ├── __init__.py │ │ │ ├── pipeline_stable_diffusion_safe.py │ │ │ └── safety_checker.py │ │ ├── stochastic_karras_ve/ │ │ │ ├── __init__.py │ │ │ └── pipeline_stochastic_karras_ve.py │ │ ├── text_to_video_synthesis/ │ │ │ ├── __init__.py │ │ │ ├── pipeline_text_to_video_synth.py │ │ │ └── pipeline_text_to_video_zero.py │ │ ├── unclip/ │ │ │ ├── __init__.py │ │ │ ├── pipeline_unclip.py │ │ │ ├── pipeline_unclip_image_variation.py │ │ │ └── text_proj.py │ │ ├── versatile_diffusion/ │ │ │ ├── __init__.py │ │ │ ├── modeling_text_unet.py │ │ │ ├── pipeline_versatile_diffusion.py │ │ │ ├── pipeline_versatile_diffusion_dual_guided.py │ │ │ ├── pipeline_versatile_diffusion_image_variation.py │ │ │ └── pipeline_versatile_diffusion_text_to_image.py │ │ └── vq_diffusion/ │ │ ├── __init__.py │ │ └── pipeline_vq_diffusion.py │ ├── schedulers/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── scheduling_ddim.py │ │ ├── scheduling_ddim_flax.py │ │ ├── scheduling_ddim_inverse.py │ │ ├── scheduling_ddpm.py │ │ ├── scheduling_ddpm_flax.py │ │ ├── scheduling_deis_multistep.py │ │ ├── scheduling_dpmsolver_multistep.py │ │ ├── scheduling_dpmsolver_multistep_flax.py │ │ ├── scheduling_dpmsolver_multistep_inverse.py │ │ ├── scheduling_dpmsolver_sde.py │ │ ├── scheduling_dpmsolver_singlestep.py │ │ ├── scheduling_euler_ancestral_discrete.py │ │ ├── scheduling_euler_discrete.py │ │ ├── scheduling_heun_discrete.py │ │ ├── scheduling_ipndm.py │ │ ├── scheduling_k_dpm_2_ancestral_discrete.py │ │ ├── scheduling_k_dpm_2_discrete.py │ │ ├── scheduling_karras_ve.py │ │ ├── scheduling_karras_ve_flax.py │ │ ├── scheduling_lms_discrete.py │ │ ├── scheduling_lms_discrete_flax.py │ │ ├── scheduling_pndm.py │ │ ├── scheduling_pndm_flax.py │ │ ├── scheduling_repaint.py │ │ ├── scheduling_sde_ve.py │ │ ├── scheduling_sde_ve_flax.py │ │ ├── scheduling_sde_vp.py │ │ ├── scheduling_unclip.py │ │ ├── scheduling_unipc_multistep.py │ │ ├── scheduling_utils.py │ │ ├── scheduling_utils_flax.py │ │ └── scheduling_vq_diffusion.py │ ├── training_utils.py │ └── utils/ │ ├── __init__.py │ ├── accelerate_utils.py │ ├── constants.py │ ├── deprecation_utils.py │ ├── doc_utils.py │ ├── dummy_flax_and_transformers_objects.py │ ├── dummy_flax_objects.py │ ├── dummy_note_seq_objects.py │ ├── dummy_onnx_objects.py │ ├── dummy_pt_objects.py │ ├── dummy_torch_and_librosa_objects.py │ ├── dummy_torch_and_scipy_objects.py │ ├── dummy_torch_and_torchsde_objects.py │ ├── dummy_torch_and_transformers_and_k_diffusion_objects.py │ ├── dummy_torch_and_transformers_and_onnx_objects.py │ ├── dummy_torch_and_transformers_objects.py │ ├── dummy_transformers_and_torch_and_note_seq_objects.py │ ├── dynamic_modules_utils.py │ ├── hub_utils.py │ ├── import_utils.py │ ├── logging.py │ ├── model_card_template.md │ ├── outputs.py │ ├── pil_utils.py │ ├── testing_utils.py │ └── torch_utils.py ├── fid_score.py ├── inception.py ├── ldm_exp/ │ ├── LICENSE │ ├── README.md │ ├── configs/ │ │ ├── autoencoder/ │ │ │ ├── autoencoder_kl_16x16x16.yaml │ │ │ ├── autoencoder_kl_32x32x4.yaml │ │ │ ├── autoencoder_kl_64x64x3.yaml │ │ │ └── autoencoder_kl_8x8x64.yaml │ │ ├── latent-diffusion/ │ │ │ ├── celebahq-ldm-vq-4.yaml │ │ │ ├── cin-ldm-vq-f8.yaml │ │ │ ├── cin256-v2.yaml │ │ │ ├── ffhq-ldm-vq-4.yaml │ │ │ ├── lsun_bedrooms-ldm-vq-4.yaml │ │ │ ├── lsun_churches-ldm-kl-8.yaml │ │ │ └── txt2img-1p4B-eval.yaml │ │ └── retrieval-augmented-diffusion/ │ │ └── 768x768.yaml │ ├── environment.yaml │ ├── fid_score.py │ ├── inception.py │ ├── ldm/ │ │ ├── lr_scheduler.py │ │ ├── models/ │ │ │ ├── autoencoder.py │ │ │ └── diffusion/ │ │ │ ├── __init__.py │ │ │ ├── classifier.py │ │ │ ├── ddim.py │ │ │ ├── ddpm.py │ │ │ └── plms.py │ │ ├── modules/ │ │ │ ├── __init__.py │ │ │ ├── attention.py │ │ │ ├── diffusionmodules/ │ │ │ │ ├── __init__.py │ │ │ │ ├── model.py │ │ │ │ ├── openaimodel.py │ │ │ │ └── util.py │ │ │ ├── distributions/ │ │ │ │ ├── __init__.py │ │ │ │ └── distributions.py │ │ │ ├── ema.py │ │ │ ├── encoders/ │ │ │ │ ├── __init__.py │ │ │ │ └── modules.py │ │ │ ├── image_degradation/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bsrgan.py │ │ │ │ ├── bsrgan_light.py │ │ │ │ └── utils_image.py │ │ │ ├── losses/ │ │ │ │ ├── __init__.py │ │ │ │ ├── contperceptual.py │ │ │ │ └── vqperceptual.py │ │ │ └── x_transformer.py │ │ └── util.py │ ├── main.py │ ├── models/ │ │ ├── first_stage_models/ │ │ │ ├── kl-f16/ │ │ │ │ └── config.yaml │ │ │ ├── kl-f32/ │ │ │ │ └── config.yaml │ │ │ ├── kl-f4/ │ │ │ │ └── config.yaml │ │ │ ├── kl-f8/ │ │ │ │ └── config.yaml │ │ │ ├── vq-f16/ │ │ │ │ └── config.yaml │ │ │ ├── vq-f4/ │ │ │ │ └── config.yaml │ │ │ ├── vq-f4-noattn/ │ │ │ │ └── config.yaml │ │ │ ├── vq-f8/ │ │ │ │ └── config.yaml │ │ │ └── vq-f8-n256/ │ │ │ └── config.yaml │ │ └── ldm/ │ │ ├── bsr_sr/ │ │ │ └── config.yaml │ │ ├── celeba256/ │ │ │ └── config.yaml │ │ ├── cin256/ │ │ │ └── config.yaml │ │ ├── ffhq256/ │ │ │ └── config.yaml │ │ ├── inpainting_big/ │ │ │ └── config.yaml │ │ ├── layout2img-openimages256/ │ │ │ └── config.yaml │ │ ├── lsun_beds256/ │ │ │ └── config.yaml │ │ ├── lsun_churches256/ │ │ │ └── config.yaml │ │ ├── semantic_synthesis256/ │ │ │ └── config.yaml │ │ ├── semantic_synthesis512/ │ │ │ └── config.yaml │ │ └── text2img256/ │ │ └── config.yaml │ ├── notebook_helpers.py │ ├── profile_ldm.py │ ├── profile_ldm_pretrained.py │ ├── profile_model.py │ ├── prune_ldm.py │ ├── prune_ldm_no_grad.py │ ├── run.sh │ ├── sample_for_FID.py │ ├── sample_imagenet.py │ ├── sample_pruned.py │ ├── scripts/ │ │ ├── download_first_stages.sh │ │ ├── download_models.sh │ │ ├── inpaint.py │ │ ├── knn2img.py │ │ ├── latent_imagenet_diffusion.ipynb │ │ ├── sample_diffusion.py │ │ ├── train_searcher.py │ │ └── txt2img.py │ ├── setup.py │ ├── test_criterion.py │ └── test_diffusion.py ├── ldm_prune.py ├── requirements.txt ├── scripts/ │ ├── finetune_ddpm_cifar10.sh │ ├── prune_ddpm_cifar10.sh │ ├── prune_ddpm_ema_bedroom_random.sh │ ├── prune_ddpm_ema_church_random.sh │ ├── prune_ldm.sh │ ├── sample_ddpm_cifar10_pretrained.sh │ ├── sample_ddpm_cifar10_pretrained_distributed.sh │ └── sample_ddpm_cifar10_pruned.sh ├── tools/ │ ├── convert_cifar10_ddpm_ema.sh │ ├── convert_ddpm_original_checkpoint_to_diffusers_cifar10.py │ ├── convert_ldm_original_checkpoint_to_diffusers.py │ ├── ddpm_cifar10_config.json │ ├── extract_cifar10.py │ └── ldm_unet_config.json └── utils.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Initially taken from Github's Python gitignore file run2 run pretrained # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class checkpoints *.ckpt # C extensions *.so cache # tests and logs tests/fixtures/cached_*_text.txt logs/ lightning_logs/ lang_code_data/ docker doc _typos.toml data docs ldm_generated_image.png *.log # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # vscode .vs .vscode # Pycharm .idea # TF code tensorflow_code # Models proc_data # examples runs /runs_old /wandb /examples/runs /examples/**/*.args /examples/rag/sweep # data /data serialization_dir # emacs *.*~ debug.env # vim .*.swp #ctags tags # pre-commit .pre-commit* # .lock *.lock # DS_Store (MacOS) .DS_Store # RL pipelines may produce mp4 outputs *.mp4 # dependencies /transformers # ruff .ruff_cache wandb __pycache__ ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2023] [Gongfan Fang] 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. ================================================ FILE: README.md ================================================ # Diff-Pruning: Structural Pruning for Diffusion Models
## Update Check our latest work [DeepCache](https://horseee.github.io/Diffusion_DeepCache/), a **training-free and almost loessless** method for diffusion model acceleration. It can be viewed as a special pruning technique that dynamically drops deep layers and only runs shallow ones during inference. ## Introduction > **Structural Pruning for Diffusion Models** [[arxiv]](https://arxiv.org/abs/2305.10924) > *[Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)* > *National University of Singapore* This work presents *Diff-Pruning*, an efficient structrual pruning method for diffusion models. Our empirical assessment highlights two primary features: 1) ``Efficiency``: It enables approximately a 50% reduction in FLOPs at a mere 10% to 20% of the original training expenditure; 2) ``Consistency``: The pruned diffusion models inherently preserve generative behavior congruent with the pre-trained ones.
### Supported Methods - [x] Magnitude Pruning - [x] Random Pruning - [x] Taylor Pruning - [x] Diff-Pruning (A taylor-based method proposed in our paper) ### TODO List - [ ] Support more diffusion models from Diffusers - [ ] Upload checkpoints of pruned models - [ ] Training scripts for CelebA-HQ, LSUN Church & LSUN Bedroom - [ ] Align the performance with the [DDIM Repo](https://github.com/ermongroup/ddim). ## Our Exp Code (Unorganized) ### Pruning with DDIM codebase This example shows how to prune a DDPM model pre-trained on CIFAR-10 using the [DDIM codebase](https://github.com/ermongroup/ddim). Since that [Huggingface Diffusers](https://github.com/huggingface/diffusers) do not support [``skip_type='quad'``](https://github.com/ermongroup/ddim/issues/3) in DDIM, you may get slightly worse FID scores with Diffusers for both pre-trained models (FID=4.5) and pruned models (FID=5.6). We are working on this to implement the quad strategy for Diffusers. For reproducibility, we provide our original **but unorganized** exp code for the paper in [ddpm_exp](ddpm_exp). ```bash cd ddpm_exp # Prune & Finetune bash scripts/simple_cifar_our.sh 0.05 # the pre-trained model and data will be automatically prepared # Sampling bash scripts/sample_cifar_ddpm_pruning.sh run/finetune_simple_v2/cifar10_ours_T=0.05.pth/logs/post_training/ckpt_100000.pth run/sample ``` For FID, please refer to [this section](https://github.com/VainF/Diff-Pruning#4-fid-score). Output: ``` Found 49984 files. 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 391/391 [00:49<00:00, 7.97it/s] FID: 5.242662673752534 ``` ### Pruning with LDM codebase Please check [ldm_exp/run.sh](ldm_exp/run.sh) for an example of pruning a pre-trained LDM model on ImageNet. This codebase is still unorganized. We will clean it up in the future. ## Pruning with Huggingface Diffusers The following pipeline prunes a pre-trained DDPM on CIFAR-10 with [Huggingface Diffusers](https://github.com/huggingface/diffusers). ### 0. Requirements, Data and Pretrained Model * Requirements ```bash pip install -r requirements.txt ``` * Data Download and extract CIFAR-10 images to *data/cifar10_images* for training and evaluation. ```bash python tools/extract_cifar10.py --output data ``` * Pretrained Models The following script will download an official DDPM model and convert it to the format of Huggingface Diffusers. You can find the converted model at *pretrained/ddpm_ema_cifar10*. It is an EMA version of [google/ddpm-cifar10-32](https://huggingface.co/google/ddpm-cifar10-32) ```bash bash tools/convert_cifar10_ddpm_ema.sh ``` (Optional) You can also download a pre-converted model using wget ```bash wget https://github.com/VainF/Diff-Pruning/releases/download/v0.0.1/ddpm_ema_cifar10.zip ``` ### 1. Pruning Create a pruned model at *run/pruned/ddpm_cifar10_pruned* ```bash bash scripts/prune_ddpm_cifar10.sh 0.3 # pruning ratio = 30\% ``` ### 2. Finetuning (Post-Training) Finetune the model and save it at *run/finetuned/ddpm_cifar10_pruned_post_training* ```bash bash scripts/finetune_ddpm_cifar10.sh ``` ### 3. Sampling **Pruned:** Sample and save images to *run/sample/ddpm_cifar10_pruned* ```bash bash scripts/sample_ddpm_cifar10_pruned.sh ``` **Pretrained:** Sample and save images to *run/sample/ddpm_cifar10_pretrained* ```bash bash scripts/sample_ddpm_cifar10_pretrained.sh ``` ### 4. FID Score This script was modified from https://github.com/mseitzer/pytorch-fid. ```bash # pre-compute the stats of CIFAR-10 dataset python fid_score.py --save-stats data/cifar10_images run/fid_stats_cifar10.npz --device cuda:0 --batch-size 256 ``` ```bash # Compute the FID score of sampled images python fid_score.py run/sample/ddpm_cifar10_pruned run/fid_stats_cifar10.npz --device cuda:0 --batch-size 256 ``` ### 5. (Optional) Distributed Training and Sampling with Accelerate This project supports distributed training and sampling. ```bash python -m torch.distributed.launch --nproc_per_node=8 --master_port 22222 --use_env ... ``` A multi-processing example can be found at [scripts/sample_ddpm_cifar10_pretrained_distributed.sh](scripts/sample_ddpm_cifar10_pretrained_distributed.sh). ## Prune Pre-trained DPMs from [HuggingFace Diffusers](https://huggingface.co/models?library=diffusers) ### :rocket: [Denoising Diffusion Probabilistic Models (DDPMs)](https://arxiv.org/abs/2006.11239) Example: [google/ddpm-ema-bedroom-256](https://huggingface.co/google/ddpm-ema-bedroom-256) ```bash python ddpm_prune.py \ --dataset "" \ --model_path google/ddpm-ema-bedroom-256 \ --save_path run/pruned/ddpm_ema_bedroom_256_pruned \ --pruning_ratio 0.05 \ --pruner "" \ --batch_size 4 \ --thr 0.05 \ --device cuda:0 \ ``` The ``dataset`` and ``thr`` arguments only work for taylor & diff-pruning. ### :rocket: [Latent Diffusion Models (LDMs)](https://arxiv.org/abs/2112.10752) Example: [CompVis/ldm-celebahq-256](https://huggingface.co/CompVis/ldm-celebahq-256) ```bash python ldm_prune.py \ --model_path CompVis/ldm-celebahq-256 \ --save_path run/pruned/ldm_celeba_pruned \ --pruning_ratio 0.05 \ --pruner "" \ --device cuda:0 \ --batch_size 4 \ ``` ## Results * **DDPM on Cifar-10, CelebA and LSUN**
* **Conditional LDM on ImageNet-1K 256** We also have some results on Conditional LDM for ImageNet-1K 256x256, where we finetune a pruned LDM for only 4 epochs. Will release the training script soon.
## Acknowledgement This project is heavily based on [Diffusers](https://github.com/huggingface/diffusers), [Torch-Pruning](https://github.com/VainF/Torch-Pruning), [pytorch-fid](https://github.com/mseitzer/pytorch-fid). Our experiments were conducted on [ddim](https://github.com/ermongroup/ddim) and [LDM](https://github.com/CompVis/latent-diffusion). ## Citation If you find this work helpful, please cite: ``` @inproceedings{fang2023structural, title={Structural pruning for diffusion models}, author={Gongfan Fang and Xinyin Ma and Xinchao Wang}, booktitle={Advances in Neural Information Processing Systems}, year={2023}, } ``` ``` @inproceedings{fang2023depgraph, title={Depgraph: Towards any structural pruning}, author={Fang, Gongfan and Ma, Xinyin and Song, Mingli and Mi, Michael Bi and Wang, Xinchao}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={16091--16101}, year={2023} } ``` ================================================ FILE: ddpm_exp/.gitignore ================================================ .vscode __pycache__ *.log run data *.png ================================================ FILE: ddpm_exp/LICENSE ================================================ MIT License Copyright (c) 2020 Jiaming Song Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ddpm_exp/README.md ================================================ # Denoising Diffusion Implicit Models (DDIM) [Jiaming Song](http://tsong.me), [Chenlin Meng](http://cs.stanford.edu/~chenlin) and [Stefano Ermon](http://cs.stanford.edu/~ermon), Stanford Implements sampling from an implicit model that is trained with the same procedure as [Denoising Diffusion Probabilistic Model](https://hojonathanho.github.io/diffusion/), but costs much less time and compute if you want to sample from it (click image below for a video demo): ![](http://img.youtube.com/vi/WCKzxoSduJQ/0.jpg) ## **Integration with 🤗 Diffusers library** DDIM is now also available in 🧨 Diffusers and accesible via the [DDIMPipeline](https://huggingface.co/docs/diffusers/api/pipelines/ddim). Diffusers allows you to test DDIM in PyTorch in just a couple lines of code. You can install diffusers as follows: ``` pip install diffusers torch accelerate ``` And then try out the model with just a couple lines of code: ```python from diffusers import DDIMPipeline model_id = "google/ddpm-cifar10-32" # load model and scheduler ddim = DDIMPipeline.from_pretrained(model_id) # run pipeline in inference (sample random noise and denoise) image = ddim(num_inference_steps=50).images[0] # save image image.save("ddim_generated_image.png") ``` More DDPM/DDIM models compatible with hte DDIM pipeline can be found directly [on the Hub](https://huggingface.co/models?library=diffusers&sort=downloads&search=ddpm) To better understand the DDIM scheduler, you can check out [this introductionary google colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) The DDIM scheduler can also be used with more powerful diffusion models such as [Stable Diffusion](https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#stable-diffusion-pipelines) You simply need to [accept the license on the Hub](https://huggingface.co/runwayml/stable-diffusion-v1-5), login with `huggingface-cli login` and install transformers: ``` pip install transformers ``` Then you can run: ```python from diffusers import StableDiffusionPipeline, DDIMScheduler ddim = DDIMScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler") pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=ddim) image = pipeline("An astronaut riding a horse.").images[0] image.save("astronaut_riding_a_horse.png") ``` ## Running the Experiments The code has been tested on PyTorch 1.6. ### Train a model Training is exactly the same as DDPM with the following: ``` python main.py --config {DATASET}.yml --exp {PROJECT_PATH} --doc {MODEL_NAME} --ni ``` ### Sampling from the model #### Sampling from the generalized model for FID evaluation ``` python main.py --config {DATASET}.yml --exp {PROJECT_PATH} --doc {MODEL_NAME} --sample --fid --timesteps {STEPS} --eta {ETA} --ni ``` where - `ETA` controls the scale of the variance (0 is DDIM, and 1 is one type of DDPM). - `STEPS` controls how many timesteps used in the process. - `MODEL_NAME` finds the pre-trained checkpoint according to its inferred path. If you want to use the DDPM pretrained model: ``` python main.py --config {DATASET}.yml --exp {PROJECT_PATH} --use_pretrained --sample --fid --timesteps {STEPS} --eta {ETA} --ni ``` the `--use_pretrained` option will automatically load the model according to the dataset. We provide a CelebA 64x64 model [here](https://drive.google.com/file/d/1R_H-fJYXSH79wfSKs9D-fuKQVan5L-GR/view?usp=sharing), and use the DDPM version for CIFAR10 and LSUN. If you want to use the version with the larger variance in DDPM: use the `--sample_type ddpm_noisy` option. #### Sampling from the model for image inpainting Use `--interpolation` option instead of `--fid`. #### Sampling from the sequence of images that lead to the sample Use `--sequence` option instead. The above two cases contain some hard-coded lines specific to producing the image, so modify them according to your needs. ## References and Acknowledgements ``` @article{song2020denoising, title={Denoising Diffusion Implicit Models}, author={Song, Jiaming and Meng, Chenlin and Ermon, Stefano}, journal={arXiv:2010.02502}, year={2020}, month={October}, abbr={Preprint}, url={https://arxiv.org/abs/2010.02502} } ``` This implementation is based on / inspired by: - [https://github.com/hojonathanho/diffusion](https://github.com/hojonathanho/diffusion) (the DDPM TensorFlow repo), - [https://github.com/pesser/pytorch_diffusion](https://github.com/pesser/pytorch_diffusion) (PyTorch helper that loads the DDPM model), and - [https://github.com/ermongroup/ncsnv2](https://github.com/ermongroup/ncsnv2) (code structure). ================================================ FILE: ddpm_exp/calc_fid.py ================================================ from cleanfid import fid import argparse parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument('--path1', type=str, required=True, help='Path to the images') parser.add_argument('--path2', type=str, required=True, help='Path to the images') args = parser.parse_args() if args.path2=="cifar10": score = fid.compute_fid(args.dir, dataset_name="cifar10", dataset_res=32, dataset_split="train") else: score = fid.compute_fid(args.path1, args.path2) print("FID: ", score) ================================================ FILE: ddpm_exp/compute_flops.py ================================================ import torch import random, os import argparse from PIL import Image import torchvision import numpy as np import pytorch_msssim from utils import UnlabeledImageFolder from tqdm import tqdm import torch_pruning as tp parser = argparse.ArgumentParser() parser.add_argument('--restore_from', type=str, required=True) args = parser.parse_args() model = torch.load(args.restore_from, map_location='cpu')[0] example_inputs = {'x': torch.randn(1, 3, 32, 32), 't': torch.ones(1)} macs, params = tp.utils.count_ops_and_params(model, example_inputs) print("model: {}, macs: {} G, params: {} M".format(args.restore_from, macs/1e9, params/1e6)) ================================================ FILE: ddpm_exp/compute_pruned_ssim_curve.py ================================================ import pytorch_msssim import os import torch from PIL import Image import torchvision base_folder_name = 'run/prune_ssim_2/0' folder_name = [os.path.join('run/prune_ssim_2', '{}'.format(k)) for k in range(50, 1000+1, 50)] n_samples = 32 # test ssim for each folder folder_ssim = [] for f in folder_name: ssim_list = [] for img_id in range(n_samples): img1 = Image.open(os.path.join(base_folder_name, f'{img_id}.png')) img2 = Image.open(os.path.join(f, f'{img_id}.png')) img1_tensor = torchvision.transforms.ToTensor()(img1) img2_tensor = torchvision.transforms.ToTensor()(img2) img1_tensor = img1_tensor.unsqueeze(0) img2_tensor = img2_tensor.unsqueeze(0) ssim = pytorch_msssim.ssim(img1_tensor, img2_tensor, data_range=1.0, size_average=True) ssim_list.append(ssim) ssim = sum(ssim_list) / len(ssim_list) folder_ssim.append(ssim.item()) print(folder_ssim) ================================================ FILE: ddpm_exp/compute_ssim.py ================================================ import torch import random, os import argparse from PIL import Image import torchvision import numpy as np import pytorch_msssim from utils import UnlabeledImageFolder from tqdm import tqdm parser = argparse.ArgumentParser() parser.add_argument('--path', type=str, required=True, nargs='+') args = parser.parse_args() # generate radom index nrow = 16 img_index = random.sample(list(range(50000)), nrow*nrow) path1 = args.path[0] path2 = args.path[1] print(path1, path2) img_dst1 = UnlabeledImageFolder(path1, transform=torchvision.transforms.ToTensor(), exts=["png"]) img_dst2 = UnlabeledImageFolder(path2, transform=torchvision.transforms.ToTensor(), exts=["png"]) print(len(img_dst1), len(img_dst2)) loader1 = torch.utils.data.DataLoader( img_dst1, batch_size=100, shuffle=False, num_workers=4, drop_last=False, ) loader2 = torch.utils.data.DataLoader( img_dst2, batch_size=100, shuffle=False, num_workers=4, drop_last=False, ) with torch.no_grad(): ssim_list = [] mse_list = [] for i, (img1, img2) in tqdm(enumerate(zip(loader1, loader2))): ssim = pytorch_msssim.ssim(img1.cuda(), img2.cuda(), data_range=1.0, size_average=False) ssim_list.append(ssim.cpu()) mse = torch.nn.functional.mse_loss(img1.cuda(), img2.cuda(), reduction='none').mean(dim=(1,2,3)) mse_list.append(mse.cpu()) ssim = torch.cat(ssim_list, dim=0) mse = torch.cat(mse_list, dim=0) ssim_avg = ssim.mean() mse_avg = mse.mean() print("path1: {}, path2: {}, ssim: {}, mse: {}".format(path1, path2, ssim_avg, mse_avg)) ================================================ FILE: ddpm_exp/compute_ssim_vis.py ================================================ import torch import random, os import argparse from PIL import Image import torchvision import numpy as np import pytorch_msssim from utils import UnlabeledImageFolder from tqdm import tqdm img_ids = [159, 149, 144, 127, 86, 41] image_folder1 = 'run/sample_v2/bedroom_250k/image_samples/images/0' image_folder2 = 'run/sample_v2/bedroom_official/image_samples/images/0' base_img_id = 0 ssim_list = [] for iid in img_ids: img1 = Image.open(os.path.join(image_folder1, f'{iid}.png')) img2 = Image.open(os.path.join(image_folder2, f'{iid}.png')) img1_tensor = torchvision.transforms.ToTensor()(img1).unsqueeze(0) img2_tensor = torchvision.transforms.ToTensor()(img2).unsqueeze(0) ssim = pytorch_msssim.ssim(img1_tensor, img2_tensor, data_range=1.0, size_average=True) ssim_list.append(ssim.item()) print(ssim_list) ================================================ FILE: ddpm_exp/configs/bedroom.yml ================================================ data: dataset: "LSUN" category: "bedroom" image_size: 256 channels: 3 logit_transform: false uniform_dequantization: false gaussian_dequantization: false random_flip: true rescaled: true num_workers: 32 model: type: "simple" in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 1, 2, 2, 4, 4] num_res_blocks: 2 attn_resolutions: [16, ] dropout: 0.0 var_type: fixedsmall ema_rate: 0.999 ema: True resamp_with_conv: True diffusion: beta_schedule: linear beta_start: 0.0001 beta_end: 0.02 num_diffusion_timesteps: 1000 training: batch_size: 8 n_epochs: 10000 n_iters: 5000000 snapshot_freq: 5000 validation_freq: 2000 sampling: batch_size: 16 last_only: True optim: weight_decay: 0.000 optimizer: "Adam" lr: 0.000002 beta1: 0.9 amsgrad: false eps: 0.00000001 ================================================ FILE: ddpm_exp/configs/celeba.yml ================================================ data: dataset: "CELEBA" image_size: 64 channels: 3 logit_transform: false uniform_dequantization: false gaussian_dequantization: false random_flip: true rescaled: true num_workers: 4 model: type: "simple" in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 2, 2, 2, 4] num_res_blocks: 2 attn_resolutions: [16, ] dropout: 0.1 var_type: fixedlarge ema_rate: 0.9999 ema: True resamp_with_conv: True diffusion: beta_schedule: linear beta_start: 0.0001 beta_end: 0.02 num_diffusion_timesteps: 1000 training: batch_size: 96 # 128 n_epochs: 10000 n_iters: 5000000 snapshot_freq: 5000 validation_freq: 20000 sampling: batch_size: 32 last_only: True optim: weight_decay: 0.000 optimizer: "Adam" lr: 0.0002 beta1: 0.9 amsgrad: false eps: 0.00000001 grad_clip: 1.0 ================================================ FILE: ddpm_exp/configs/church.yml ================================================ data: dataset: "LSUN" category: "church_outdoor" image_size: 256 channels: 3 logit_transform: false uniform_dequantization: false gaussian_dequantization: false random_flip: true rescaled: true num_workers: 32 model: type: "simple" in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 1, 2, 2, 4, 4] num_res_blocks: 2 attn_resolutions: [16, ] dropout: 0.0 var_type: fixedsmall ema_rate: 0.999 ema: True resamp_with_conv: True diffusion: beta_schedule: linear beta_start: 0.0001 beta_end: 0.02 num_diffusion_timesteps: 1000 training: batch_size: 8 # 64 n_epochs: 10000 n_iters: 5000000 snapshot_freq: 5000 validation_freq: 2000 sampling: batch_size: 16 last_only: True optim: weight_decay: 0.000 optimizer: "Adam" lr: 0.00002 beta1: 0.9 amsgrad: false eps: 0.00000001 ================================================ FILE: ddpm_exp/configs/cifar10.yml ================================================ data: dataset: "CIFAR10" image_size: 32 channels: 3 logit_transform: false uniform_dequantization: false gaussian_dequantization: false random_flip: true rescaled: true num_workers: 4 model: type: "simple" in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 2, 2, 2] num_res_blocks: 2 attn_resolutions: [16, ] dropout: 0.1 var_type: fixedlarge ema_rate: 0.9999 ema: True resamp_with_conv: True diffusion: beta_schedule: linear beta_start: 0.0001 beta_end: 0.02 num_diffusion_timesteps: 1000 training: batch_size: 128 n_epochs: 10000 n_iters: 5000000 snapshot_freq: 5000 validation_freq: 2000 sampling: batch_size: 64 last_only: True optim: weight_decay: 0.000 optimizer: "Adam" lr: 0.0002 beta1: 0.9 amsgrad: false eps: 0.00000001 grad_clip: 1.0 ================================================ FILE: ddpm_exp/configs/cifar10_pruning.yml ================================================ data: dataset: "CIFAR10" image_size: 32 channels: 3 logit_transform: false uniform_dequantization: false gaussian_dequantization: false random_flip: true rescaled: true num_workers: 4 model: type: "simple" in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 2, 2, 2] num_res_blocks: 2 attn_resolutions: [16, ] dropout: 0.1 var_type: fixedlarge ema_rate: 0.9999 ema: True resamp_with_conv: True diffusion: beta_schedule: linear beta_start: 0.0001 beta_end: 0.02 num_diffusion_timesteps: 1000 training: batch_size: 128 n_epochs: 10000 n_iters: 5000000 snapshot_freq: 5000 validation_freq: 2000 sampling: batch_size: 64 last_only: True optim: weight_decay: 0.000 optimizer: "Adam" lr: 0.00002 beta1: 0.9 amsgrad: false eps: 0.00000001 grad_clip: 1.0 ================================================ FILE: ddpm_exp/datasets/__init__.py ================================================ import os import torch import numbers import torchvision.transforms as transforms import torchvision.transforms.functional as F from torchvision.datasets import CIFAR10 from datasets.celeba import CelebA from datasets.ffhq import FFHQ from datasets.lsun import LSUN from torch.utils.data import Subset import numpy as np class Crop(object): def __init__(self, x1, x2, y1, y2): self.x1 = x1 self.x2 = x2 self.y1 = y1 self.y2 = y2 def __call__(self, img): return F.crop(img, self.x1, self.y1, self.x2 - self.x1, self.y2 - self.y1) def __repr__(self): return self.__class__.__name__ + "(x1={}, x2={}, y1={}, y2={})".format( self.x1, self.x2, self.y1, self.y2 ) def get_dataset(args, config): if config.data.random_flip is False: tran_transform = test_transform = transforms.Compose( [transforms.Resize(config.data.image_size), transforms.ToTensor()] ) else: tran_transform = transforms.Compose( [ transforms.Resize(config.data.image_size), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), ] ) test_transform = transforms.Compose( [transforms.Resize(config.data.image_size), transforms.ToTensor()] ) if config.data.dataset == "CIFAR10": dataset = CIFAR10( os.path.join('data', "cifar10"), train=True, download=True, transform=tran_transform, ) test_dataset = CIFAR10( os.path.join('data', "cifar10"), train=False, download=True, transform=test_transform, ) elif config.data.dataset == "CELEBA": cx = 89 cy = 121 x1 = cy - 64 x2 = cy + 64 y1 = cx - 64 y2 = cx + 64 if config.data.random_flip: dataset = CelebA( root=os.path.join("data", "celeba"), split="train", transform=transforms.Compose( [ Crop(x1, x2, y1, y2), transforms.Resize(config.data.image_size), transforms.RandomHorizontalFlip(), transforms.ToTensor(), ] ), download=False, ) else: dataset = CelebA( root=os.path.join("data", "celeba"), split="train", transform=transforms.Compose( [ Crop(x1, x2, y1, y2), transforms.Resize(config.data.image_size), transforms.ToTensor(), ] ), download=False, ) test_dataset = CelebA( root=os.path.join("data", "celeba"), split="test", transform=transforms.Compose( [ Crop(x1, x2, y1, y2), transforms.Resize(config.data.image_size), transforms.ToTensor(), ] ), download=True, ) elif config.data.dataset == "LSUN": train_folder = "{}_train".format(config.data.category) val_folder = "{}_val".format(config.data.category) if config.data.random_flip: dataset = LSUN( root=os.path.join("data", "lsun"), classes=[train_folder], transform=transforms.Compose( [ transforms.Resize(config.data.image_size), transforms.CenterCrop(config.data.image_size), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), ] ), ) else: dataset = LSUN( root=os.path.join("data", "lsun"), classes=[train_folder], transform=transforms.Compose( [ transforms.Resize(config.data.image_size), transforms.CenterCrop(config.data.image_size), transforms.ToTensor(), ] ), ) test_dataset = LSUN( root=os.path.join("data", "lsun"), classes=[val_folder], transform=transforms.Compose( [ transforms.Resize(config.data.image_size), transforms.CenterCrop(config.data.image_size), transforms.ToTensor(), ] ), ) elif config.data.dataset == "FFHQ": if config.data.random_flip: dataset = FFHQ( path=os.path.join("data", "FFHQ"), transform=transforms.Compose( [transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor()] ), resolution=config.data.image_size, ) else: dataset = FFHQ( path=os.path.join("data", "FFHQ"), transform=transforms.ToTensor(), resolution=config.data.image_size, ) num_items = len(dataset) indices = list(range(num_items)) random_state = np.random.get_state() np.random.seed(2019) np.random.shuffle(indices) np.random.set_state(random_state) train_indices, test_indices = ( indices[: int(num_items * 0.9)], indices[int(num_items * 0.9) :], ) test_dataset = Subset(dataset, test_indices) dataset = Subset(dataset, train_indices) else: dataset, test_dataset = None, None return dataset, test_dataset def logit_transform(image, lam=1e-6): image = lam + (1 - 2 * lam) * image return torch.log(image) - torch.log1p(-image) def data_transform(config, X): if config.data.uniform_dequantization: X = X / 256.0 * 255.0 + torch.rand_like(X) / 256.0 if config.data.gaussian_dequantization: X = X + torch.randn_like(X) * 0.01 if config.data.rescaled: X = 2 * X - 1.0 elif config.data.logit_transform: X = logit_transform(X) if hasattr(config, "image_mean"): return X - config.image_mean.to(X.device)[None, ...] return X def inverse_data_transform(config, X): if hasattr(config, "image_mean"): X = X + config.image_mean.to(X.device)[None, ...] if config.data.logit_transform: X = torch.sigmoid(X) elif config.data.rescaled: X = (X + 1.0) / 2.0 return torch.clamp(X, 0.0, 1.0) ================================================ FILE: ddpm_exp/datasets/celeba.py ================================================ import torch import os import PIL from .vision import VisionDataset from .utils import download_file_from_google_drive, check_integrity class CelebA(VisionDataset): """`Large-scale CelebFaces Attributes (CelebA) Dataset `_ Dataset. Args: root (string): Root directory where images are downloaded to. split (string): One of {'train', 'valid', 'test'}. Accordingly dataset is selected. target_type (string or list, optional): Type of target to use, ``attr``, ``identity``, ``bbox``, or ``landmarks``. Can also be a list to output a tuple with all specified target types. The targets represent: ``attr`` (np.array shape=(40,) dtype=int): binary (0, 1) labels for attributes ``identity`` (int): label for each person (data points with the same identity are the same person) ``bbox`` (np.array shape=(4,) dtype=int): bounding box (x, y, width, height) ``landmarks`` (np.array shape=(10,) dtype=int): landmark points (lefteye_x, lefteye_y, righteye_x, righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y) Defaults to ``attr``. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.ToTensor`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. """ base_folder = "Img" # There currently does not appear to be a easy way to extract 7z in python (without introducing additional # dependencies). The "in-the-wild" (not aligned+cropped) images are only in 7z, so they are not available # right now. file_list = [ # File ID MD5 Hash Filename ("0B7EVK8r0v71pZjFTYXZWM3FlRnM", "00d2c5bc6d35e252742224ab0c1e8fcb", "img_align_celeba.zip"), # ("0B7EVK8r0v71pbWNEUjJKdDQ3dGc", "b6cd7e93bc7a96c2dc33f819aa3ac651", "img_align_celeba_png.7z"), # ("0B7EVK8r0v71peklHb0pGdDl6R28", "b6cd7e93bc7a96c2dc33f819aa3ac651", "img_celeba.7z"), ("0B7EVK8r0v71pblRyaVFSWGxPY0U", "75e246fa4810816ffd6ee81facbd244c", "list_attr_celeba.txt"), ("1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS", "32bd1bd63d3c78cd57e08160ec5ed1e2", "identity_CelebA.txt"), ("0B7EVK8r0v71pbThiMVRxWXZ4dU0", "00566efa6fedff7a56946cd1c10f1c16", "list_bbox_celeba.txt"), ("0B7EVK8r0v71pd0FJY3Blby1HUTQ", "cc24ecafdb5b50baae59b03474781f8c", "list_landmarks_align_celeba.txt"), # ("0B7EVK8r0v71pTzJIdlJWdHczRlU", "063ee6ddb681f96bc9ca28c6febb9d1a", "list_landmarks_celeba.txt"), ("0B7EVK8r0v71pY0NSMzRuSXJEVkk", "d32c9cbf5e040fd4025c592c306e6668", "list_eval_partition.txt"), ] def __init__(self, root, split="train", target_type="attr", transform=None, target_transform=None, download=False): import pandas super(CelebA, self).__init__(root) self.split = split if isinstance(target_type, list): self.target_type = target_type else: self.target_type = [target_type] self.transform = transform self.target_transform = target_transform #if download: # self.download() #if not self._check_integrity(): # raise RuntimeError('Dataset not found or corrupted.' + # ' You can use download=True to download it') self.transform = transform self.target_transform = target_transform if split.lower() == "train": split = 0 elif split.lower() == "valid": split = 1 elif split.lower() == "test": split = 2 else: raise ValueError('Wrong split entered! Please use split="train" ' 'or split="valid" or split="test"') with open(os.path.join(self.root, 'Eval', "list_eval_partition.txt"), "r") as f: splits = pandas.read_csv(f, delim_whitespace=True, header=None, index_col=0) with open(os.path.join(self.root, 'Anno', "identity_CelebA.txt"), "r") as f: self.identity = pandas.read_csv(f, delim_whitespace=True, header=None, index_col=0) with open(os.path.join(self.root, 'Anno', "list_bbox_celeba.txt"), "r") as f: self.bbox = pandas.read_csv(f, delim_whitespace=True, header=1, index_col=0) with open(os.path.join(self.root, 'Anno', "list_landmarks_align_celeba.txt"), "r") as f: self.landmarks_align = pandas.read_csv(f, delim_whitespace=True, header=1) with open(os.path.join(self.root, 'Anno', "list_attr_celeba.txt"), "r") as f: self.attr = pandas.read_csv(f, delim_whitespace=True, header=1) mask = (splits[1] == split) self.filename = splits[mask].index.values self.identity = torch.as_tensor(self.identity[mask].values) self.bbox = torch.as_tensor(self.bbox[mask].values) self.landmarks_align = torch.as_tensor(self.landmarks_align[mask].values) self.attr = torch.as_tensor(self.attr[mask].values) self.attr = (self.attr + 1) // 2 # map from {-1, 1} to {0, 1} def _check_integrity(self): for (_, md5, filename) in self.file_list: fpath = os.path.join(self.root, self.base_folder, filename) _, ext = os.path.splitext(filename) # Allow original archive to be deleted (zip and 7z) # Only need the extracted images if ext not in [".zip", ".7z"] and not check_integrity(fpath, md5): return False # Should check a hash of the images return os.path.isdir(os.path.join(self.root, self.base_folder, "img_align_celeba")) def download(self): import zipfile if self._check_integrity(): print('Files already downloaded and verified') return for (file_id, md5, filename) in self.file_list: download_file_from_google_drive(file_id, os.path.join(self.root, self.base_folder), filename, md5) with zipfile.ZipFile(os.path.join(self.root, self.base_folder, "img_align_celeba.zip"), "r") as f: f.extractall(os.path.join(self.root, self.base_folder)) def __getitem__(self, index): X = PIL.Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", self.filename[index])) target = [] for t in self.target_type: if t == "attr": target.append(self.attr[index, :]) elif t == "identity": target.append(self.identity[index, 0]) elif t == "bbox": target.append(self.bbox[index, :]) elif t == "landmarks": target.append(self.landmarks_align[index, :]) else: raise ValueError("Target type \"{}\" is not recognized.".format(t)) target = tuple(target) if len(target) > 1 else target[0] if self.transform is not None: X = self.transform(X) if self.target_transform is not None: target = self.target_transform(target) return X, target def __len__(self): return len(self.attr) def extra_repr(self): lines = ["Target type: {target_type}", "Split: {split}"] return '\n'.join(lines).format(**self.__dict__) ================================================ FILE: ddpm_exp/datasets/ffhq.py ================================================ from io import BytesIO import lmdb from PIL import Image from torch.utils.data import Dataset class FFHQ(Dataset): def __init__(self, path, transform, resolution=8): self.env = lmdb.open( path, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False, ) if not self.env: raise IOError('Cannot open lmdb dataset', path) with self.env.begin(write=False) as txn: self.length = int(txn.get('length'.encode('utf-8')).decode('utf-8')) self.resolution = resolution self.transform = transform def __len__(self): return self.length def __getitem__(self, index): with self.env.begin(write=False) as txn: key = f'{self.resolution}-{str(index).zfill(5)}'.encode('utf-8') img_bytes = txn.get(key) buffer = BytesIO(img_bytes) img = Image.open(buffer) img = self.transform(img) target = 0 return img, target ================================================ FILE: ddpm_exp/datasets/lsun.py ================================================ from .vision import VisionDataset from PIL import Image import os import os.path import io from collections.abc import Iterable import pickle from torchvision.datasets.utils import verify_str_arg, iterable_to_str class LSUNClass(VisionDataset): def __init__(self, root, transform=None, target_transform=None): import lmdb super(LSUNClass, self).__init__( root, transform=transform, target_transform=target_transform ) self.env = lmdb.open( root, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False, ) with self.env.begin(write=False) as txn: self.length = txn.stat()["entries"] root_split = root.split("/") cache_file = os.path.join("/".join(root_split[:-1]), f"_cache_{root_split[-1]}") if os.path.isfile(cache_file): self.keys = pickle.load(open(cache_file, "rb")) else: with self.env.begin(write=False) as txn: self.keys = [key for key, _ in txn.cursor()] pickle.dump(self.keys, open(cache_file, "wb")) def __getitem__(self, index): img, target = None, None env = self.env with env.begin(write=False) as txn: imgbuf = txn.get(self.keys[index]) buf = io.BytesIO() buf.write(imgbuf) buf.seek(0) img = Image.open(buf).convert("RGB") if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target def __len__(self): return self.length class LSUN(VisionDataset): """ `LSUN `_ dataset. Args: root (string): Root directory for the database files. classes (string or list): One of {'train', 'val', 'test'} or a list of categories to load. e,g. ['bedroom_train', 'church_outdoor_train']. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. """ def __init__(self, root, classes="train", transform=None, target_transform=None): super(LSUN, self).__init__( root, transform=transform, target_transform=target_transform ) self.classes = self._verify_classes(classes) # for each class, create an LSUNClassDataset self.dbs = [] for c in self.classes: self.dbs.append( LSUNClass(root=root + "/" + c + "_lmdb", transform=transform) ) self.indices = [] count = 0 for db in self.dbs: count += len(db) self.indices.append(count) self.length = count def _verify_classes(self, classes): categories = [ "bedroom", "bridge", "church_outdoor", "classroom", "conference_room", "dining_room", "kitchen", "living_room", "restaurant", "tower", ] dset_opts = ["train", "val", "test"] try: verify_str_arg(classes, "classes", dset_opts) if classes == "test": classes = [classes] else: classes = [c + "_" + classes for c in categories] except ValueError: if not isinstance(classes, Iterable): msg = ( "Expected type str or Iterable for argument classes, " "but got type {}." ) raise ValueError(msg.format(type(classes))) classes = list(classes) msg_fmtstr = ( "Expected type str for elements in argument classes, " "but got type {}." ) for c in classes: verify_str_arg(c, custom_msg=msg_fmtstr.format(type(c))) c_short = c.split("_") category, dset_opt = "_".join(c_short[:-1]), c_short[-1] msg_fmtstr = "Unknown value '{}' for {}. Valid values are {{{}}}." msg = msg_fmtstr.format( category, "LSUN class", iterable_to_str(categories) ) verify_str_arg(category, valid_values=categories, custom_msg=msg) msg = msg_fmtstr.format(dset_opt, "postfix", iterable_to_str(dset_opts)) verify_str_arg(dset_opt, valid_values=dset_opts, custom_msg=msg) return classes def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: Tuple (image, target) where target is the index of the target category. """ target = 0 sub = 0 for ind in self.indices: if index < ind: break target += 1 sub = ind db = self.dbs[target] index = index - sub if self.target_transform is not None: target = self.target_transform(target) img, _ = db[index] return img, target def __len__(self): return self.length def extra_repr(self): return "Classes: {classes}".format(**self.__dict__) ================================================ FILE: ddpm_exp/datasets/utils.py ================================================ import os import os.path import hashlib import errno from torch.utils.model_zoo import tqdm def gen_bar_updater(): pbar = tqdm(total=None) def bar_update(count, block_size, total_size): if pbar.total is None and total_size: pbar.total = total_size progress_bytes = count * block_size pbar.update(progress_bytes - pbar.n) return bar_update def check_integrity(fpath, md5=None): if md5 is None: return True if not os.path.isfile(fpath): return False md5o = hashlib.md5() with open(fpath, 'rb') as f: # read in 1MB chunks for chunk in iter(lambda: f.read(1024 * 1024), b''): md5o.update(chunk) md5c = md5o.hexdigest() if md5c != md5: return False return True def makedir_exist_ok(dirpath): """ Python2 support for os.makedirs(.., exist_ok=True) """ try: os.makedirs(dirpath) except OSError as e: if e.errno == errno.EEXIST: pass else: raise def download_url(url, root, filename=None, md5=None): """Download a file from a url and place it in root. Args: url (str): URL to download file from root (str): Directory to place downloaded file in filename (str, optional): Name to save the file under. If None, use the basename of the URL md5 (str, optional): MD5 checksum of the download. If None, do not check """ from six.moves import urllib root = os.path.expanduser(root) if not filename: filename = os.path.basename(url) fpath = os.path.join(root, filename) makedir_exist_ok(root) # downloads file if os.path.isfile(fpath) and check_integrity(fpath, md5): print('Using downloaded and verified file: ' + fpath) else: try: print('Downloading ' + url + ' to ' + fpath) urllib.request.urlretrieve( url, fpath, reporthook=gen_bar_updater() ) except OSError: if url[:5] == 'https': url = url.replace('https:', 'http:') print('Failed download. Trying https -> http instead.' ' Downloading ' + url + ' to ' + fpath) urllib.request.urlretrieve( url, fpath, reporthook=gen_bar_updater() ) def list_dir(root, prefix=False): """List all directories at a given root Args: root (str): Path to directory whose folders need to be listed prefix (bool, optional): If true, prepends the path to each result, otherwise only returns the name of the directories found """ root = os.path.expanduser(root) directories = list( filter( lambda p: os.path.isdir(os.path.join(root, p)), os.listdir(root) ) ) if prefix is True: directories = [os.path.join(root, d) for d in directories] return directories def list_files(root, suffix, prefix=False): """List all files ending with a suffix at a given root Args: root (str): Path to directory whose folders need to be listed suffix (str or tuple): Suffix of the files to match, e.g. '.png' or ('.jpg', '.png'). It uses the Python "str.endswith" method and is passed directly prefix (bool, optional): If true, prepends the path to each result, otherwise only returns the name of the files found """ root = os.path.expanduser(root) files = list( filter( lambda p: os.path.isfile(os.path.join(root, p)) and p.endswith(suffix), os.listdir(root) ) ) if prefix is True: files = [os.path.join(root, d) for d in files] return files def download_file_from_google_drive(file_id, root, filename=None, md5=None): """Download a Google Drive file from and place it in root. Args: file_id (str): id of file to be downloaded root (str): Directory to place downloaded file in filename (str, optional): Name to save the file under. If None, use the id of the file. md5 (str, optional): MD5 checksum of the download. If None, do not check """ # Based on https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url import requests url = "https://docs.google.com/uc?export=download" root = os.path.expanduser(root) if not filename: filename = file_id fpath = os.path.join(root, filename) makedir_exist_ok(root) if os.path.isfile(fpath) and check_integrity(fpath, md5): print('Using downloaded and verified file: ' + fpath) else: session = requests.Session() response = session.get(url, params={'id': file_id}, stream=True) token = _get_confirm_token(response) if token: params = {'id': file_id, 'confirm': token} response = session.get(url, params=params, stream=True) _save_response_content(response, fpath) def _get_confirm_token(response): for key, value in response.cookies.items(): if key.startswith('download_warning'): return value return None def _save_response_content(response, destination, chunk_size=32768): with open(destination, "wb") as f: pbar = tqdm(total=None) progress = 0 for chunk in response.iter_content(chunk_size): if chunk: # filter out keep-alive new chunks f.write(chunk) progress += len(chunk) pbar.update(progress - pbar.n) pbar.close() ================================================ FILE: ddpm_exp/datasets/vision.py ================================================ import os import torch import torch.utils.data as data class VisionDataset(data.Dataset): _repr_indent = 4 def __init__(self, root, transforms=None, transform=None, target_transform=None): if isinstance(root, torch._six.string_classes): root = os.path.expanduser(root) self.root = root has_transforms = transforms is not None has_separate_transform = transform is not None or target_transform is not None if has_transforms and has_separate_transform: raise ValueError("Only transforms or transform/target_transform can " "be passed as argument") # for backwards-compatibility self.transform = transform self.target_transform = target_transform if has_separate_transform: transforms = StandardTransform(transform, target_transform) self.transforms = transforms def __getitem__(self, index): raise NotImplementedError def __len__(self): raise NotImplementedError def __repr__(self): head = "Dataset " + self.__class__.__name__ body = ["Number of datapoints: {}".format(self.__len__())] if self.root is not None: body.append("Root location: {}".format(self.root)) body += self.extra_repr().splitlines() if hasattr(self, 'transform') and self.transform is not None: body += self._format_transform_repr(self.transform, "Transforms: ") if hasattr(self, 'target_transform') and self.target_transform is not None: body += self._format_transform_repr(self.target_transform, "Target transforms: ") lines = [head] + [" " * self._repr_indent + line for line in body] return '\n'.join(lines) def _format_transform_repr(self, transform, head): lines = transform.__repr__().splitlines() return (["{}{}".format(head, lines[0])] + ["{}{}".format(" " * len(head), line) for line in lines[1:]]) def extra_repr(self): return "" class StandardTransform(object): def __init__(self, transform=None, target_transform=None): self.transform = transform self.target_transform = target_transform def __call__(self, input, target): if self.transform is not None: input = self.transform(input) if self.target_transform is not None: target = self.target_transform(target) return input, target def _format_transform_repr(self, transform, head): lines = transform.__repr__().splitlines() return (["{}{}".format(head, lines[0])] + ["{}{}".format(" " * len(head), line) for line in lines[1:]]) def __repr__(self): body = [self.__class__.__name__] if self.transform is not None: body += self._format_transform_repr(self.transform, "Transform: ") if self.target_transform is not None: body += self._format_transform_repr(self.target_transform, "Target transform: ") return '\n'.join(body) ================================================ FILE: ddpm_exp/draw_ssim_pruned_curve.py ================================================ import matplotlib.pyplot as plt import numpy as np plt.style.use('seaborn-whitegrid') ssim = [0.7881933450698853, 0.8069899082183838, 0.8119480609893799, 0.8162015080451965, 0.8389594554901123, 0.8415904641151428, 0.8398601412773132, 0.8351159691810608, 0.8382353186607361, 0.8380391001701355, 0.8358467221260071, 0.8335589170455933, 0.8339887857437134, 0.8341929316520691, 0.8322316408157349, 0.8351540565490723, 0.8365049958229065, 0.8395034074783325, 0.8369854092597961, 0.8361033797264099] loss = [1701.1046142578125, 1480.7890625, 1344.899658203125, 1244.06982421875, 1163.198486328125, 1095.482421875, 1037.3287353515625, 986.4912109375, 941.4730224609375, 901.2019653320312, 864.8944091796875, 831.9296875, 801.815185546875, 774.1593017578125, 748.635498046875, 724.9940795898438, 703.0166015625, 682.5120239257812, 663.32568359375, 645.3184204101562, 628.3749389648438, 612.4005126953125, 597.3052978515625, 583.0064697265625, 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0.11029437184333801, 0.10888860374689102, 0.10724224895238876, 0.10557863116264343, 0.10384707897901535, 0.10236769914627075, 0.1006280779838562, 0.09903483092784882, 0.09761733561754227, 0.09611119329929352, 0.09474831074476242, 0.09324538707733154, 0.0919327437877655, 0.09046762436628342, 0.08917433023452759, 0.08783926069736481, 0.08677000552415848, 0.08565567433834076, 0.08473670482635498, 0.08394858241081238, 0.08294089138507843, 0.08191481232643127, 0.08059291541576385, 0.07923152297735214, 0.07800912857055664, 0.07714410126209259, 0.07613521814346313, 0.07499660551548004, 0.0738428384065628, 0.0728812888264656, 0.07185603678226471, 0.07075358927249908, 0.06998489797115326, 0.06904087215662003, 0.06817013025283813, 0.0675143226981163, 0.06662875413894653, 0.06585747003555298, 0.06498762965202332, 0.06416307389736176, 0.06331884860992432, 0.06252430379390717, 0.06164319068193436, 0.0608375146985054, 0.06004999577999115, 0.05958651006221771, 0.05899893864989281, 0.05812441185116768, 0.05736687779426575, 0.05654726177453995, 0.05582256615161896, 0.055398836731910706, 0.05473468452692032, 0.05418632924556732, 0.053625207394361496, 0.05313240364193916, 0.05263800173997879, 0.05228545516729355] loss = loss / np.max(loss) stage = list(range(50, 1000+1, 50)) # Set the font size plt.rcParams['font.family'] = 'serif' plt.rcParams['font.size'] = 23 # Define custom colors in a dark color scheme color_blue = '#002147' color_red = '#8C1515' background_color = '#F0F0F0' # Set the figure size plt.figure(figsize=(10, 4)) # Set the background color plt.rcParams['axes.facecolor'] = background_color # Plot SSIM with markers ax1 = plt.gca() ax1.set_ylabel('SSIM') ax1.plot(stage, ssim, marker='o', linestyle='-', label='SSIM', color=color_blue) # Plot Loss with markers ax2 = plt.twinx() ax2.set_ylabel('Relative Loss') ax2.plot(np.arange(len(loss)), loss, label='Relative Loss', color=color_red) # Set x-axis label ax1.set_xlabel('Steps') # Add vertical dashed line and text ax1.axvline(x=300, color='#777777aa', linestyle='dashed') ax2.text(300 + 10, 0.35, 'Threshold $\mathcal{T}$', rotation=90, color='gray', fontsize=12) # Adjust the alignment of twin axes ax1.spines['left'].set_color(color_blue) ax1.spines['right'].set_visible(False) ax1.yaxis.label.set_color(color_blue) ax1.tick_params(axis='y', colors=color_blue) ax2.spines['right'].set_color(color_red) ax2.spines['left'].set_visible(False) ax2.yaxis.label.set_color(color_red) ax2.tick_params(axis='y', colors=color_red) # Set the y-axis limits for Loss ax2.set_ylim([0, np.max(loss)]) # Show legend in one box lines, labels = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax2.legend(lines + lines2, labels + labels2, loc='lower right') # Set the title # Add grid plt.grid(color='white', linestyle='-', linewidth=0.5) # Set the layout and padding plt.tight_layout(pad=1.0) # Save the figure plt.title("CIFAR-10") plt.savefig('run/ssim_loss.pdf', dpi=300) plt.savefig('run/ssim_loss.png', dpi=300) plt.close() ssim = [0.5799465179443359, 0.6136730909347534, 0.6535662412643433, 0.6940350532531738, 0.7176114916801453, 0.7297082543373108, 0.7317281365394592, 0.7415740489959717, 0.731893002986908, 0.7338791489601135, 0.7469093799591064, 0.7643880248069763, 0.7528172135353088, 0.7652474045753479, 0.7676505446434021, 0.7659737467765808, 0.7663692831993103, 0.7921077609062195, 0.7951106429100037, 0.7900822162628174] loss = [4539.2880859375, 3768.095947265625, 3357.234375, 3075.648681640625, 2858.8349609375, 2681.1640625, 2529.66748046875, 2396.486083984375, 2277.68798828125, 2170.572265625, 2073.0498046875, 1983.8221435546875, 1901.969482421875, 1826.51416015625, 1756.9361572265625, 1692.67529296875, 1632.9080810546875, 1577.036865234375, 1524.709228515625, 1475.60205078125, 1429.496826171875, 1386.111572265625, 1345.2410888671875, 1306.699462890625, 1270.2772216796875, 1235.816650390625, 1203.143310546875, 1172.09619140625, 1142.5673828125, 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list(range(50, 1000+1, 50)) # Define custom colors in a dark color scheme color_blue = '#002147' color_red = '#8C1515' # Set the figure size plt.figure(figsize=(10, 4)) # Plot SSIM with markers ax1 = plt.gca() ax1.set_ylabel('SSIM') ax1.plot(stage, ssim, marker='o', linestyle='-', label='SSIM', color=color_blue) # Plot Loss with markers ax2 = plt.twinx() ax2.set_ylabel('Relative Loss') ax2.plot(np.arange(len(loss)), loss, label='Relative Loss', color=color_red) # Set x-axis label ax1.set_xlabel('Steps') ax1.axvline(x=950, color='#777777aa', linestyle='dashed') ax2.text(950 + 10, 0.35, 'Threshold $\mathcal{T}$', rotation=90, color='gray', fontsize=12) ax2.set_ylim([0, np.max(loss)]) # Show legend in one box lines, labels = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax2.legend(lines + lines2, labels + labels2, loc='lower right') # Adjust the alignment of twin axes ax1.spines['left'].set_color(color_blue) ax1.spines['right'].set_visible(False) ax1.yaxis.label.set_color(color_blue) ax1.tick_params(axis='y', colors=color_blue) ax2.spines['right'].set_color(color_red) ax2.spines['left'].set_visible(False) ax2.yaxis.label.set_color(color_red) ax2.tick_params(axis='y', colors=color_red) # Set the title # Add grid plt.grid(color='white', linestyle='-', linewidth=0.5) # Set the layout and padding plt.tight_layout(pad=1.0) plt.title("CeleA-HQ") # Save the figure plt.savefig('run/ssim_loss2.pdf', dpi=300) plt.savefig('run/ssim_loss2.png', dpi=300) plt.close() ================================================ FILE: ddpm_exp/extract_cifar10.py ================================================ import os import torchvision from torchvision.datasets import CIFAR10 from tqdm import tqdm # Define the path to the folder where the images will be saved save_path = 'data/cifar10/images' # Create the folder if it doesn't exist if not os.path.exists(save_path): os.makedirs(save_path) # Load the CIFAR10 dataset dataset = CIFAR10(root='data/cifar10', train=True, download=True) # Loop through the dataset and save each image to the folder for i in tqdm(range(len(dataset))): image, label = dataset[i] image_name = f'{i}.png' image_path = os.path.join(save_path, image_name) image.save(image_path) ================================================ FILE: ddpm_exp/fid_score.py ================================================ """Calculates the Frechet Inception Distance (FID) to evalulate GANs The FID metric calculates the distance between two distributions of images. Typically, we have summary statistics (mean & covariance matrix) of one of these distributions, while the 2nd distribution is given by a GAN. When run as a stand-alone program, it compares the distribution of images that are stored as PNG/JPEG at a specified location with a distribution given by summary statistics (in pickle format). The FID is calculated by assuming that X_1 and X_2 are the activations of the pool_3 layer of the inception net for generated samples and real world samples respectively. See --help to see further details. Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead of Tensorflow Copyright 2018 Institute of Bioinformatics, JKU Linz 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 os import pathlib from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser import numpy as np import torch import torchvision.transforms as TF from PIL import Image from scipy import linalg from torch.nn.functional import adaptive_avg_pool2d try: from tqdm import tqdm except ImportError: # If tqdm is not available, provide a mock version of it def tqdm(x): return x from inception import InceptionV3 parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument('--batch-size', type=int, default=50, help='Batch size to use') parser.add_argument('--dataset_name', type=str, default=None) parser.add_argument('--num-workers', type=int, help=('Number of processes to use for data loading. ' 'Defaults to `min(8, num_cpus)`')) parser.add_argument('--device', type=str, default=None, help='Device to use. Like cuda, cuda:0 or cpu') parser.add_argument('--dims', type=int, default=2048, choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), help=('Dimensionality of Inception features to use. ' 'By default, uses pool3 features')) parser.add_argument('--num_samples', type=int, default=None, help=('Number of samples for FID estimation')) parser.add_argument('--res', type=int, default=None, help=('Resolutions of samples for FID estimation')) parser.add_argument('--save-stats', action='store_true', help=('Generate an npz archive from a directory of samples. ' 'The first path is used as input and the second as output.')) parser.add_argument('path', type=str, nargs=2, help=('Paths to the generated images or ' 'to .npz statistic files')) IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm', 'tif', 'tiff', 'webp'} class ImagePathDataset(torch.utils.data.Dataset): def __init__(self, files, transforms=None): self.files = files self.transforms = transforms def __len__(self): return len(self.files) def __getitem__(self, i): path = self.files[i] img = Image.open(path).convert('RGB') if self.transforms is not None: img = self.transforms(img) return img def get_activations(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, res=None, dataset_name=None): """Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : Batch size of images for the model to process at once. Make sure that the number of samples is a multiple of the batch size, otherwise some samples are ignored. This behavior is retained to match the original FID score implementation. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- A numpy array of dimension (num images, dims) that contains the activations of the given tensor when feeding inception with the query tensor. """ model.eval() if batch_size > len(files): print(('Warning: batch size is bigger than the data size. ' 'Setting batch size to data size')) batch_size = len(files) if res is None: trans = TF.ToTensor() else: if dataset_name == 'celeba': from datasets import Crop cx = 89 cy = 121 x1 = cy - 64 x2 = cy + 64 y1 = cx - 64 y2 = cx + 64 trans = TF.Compose([ Crop(x1, x2, y1, y2), TF.Resize(res), TF.ToTensor(), ]) else: trans = TF.Compose([ TF.Resize(res), TF.CenterCrop(res), TF.ToTensor() ]) dataset = ImagePathDataset(files, transforms=trans) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers) pred_arr = np.empty((len(files), dims)) start_idx = 0 for batch in tqdm(dataloader): batch = batch.to(device) with torch.no_grad(): pred = model(batch)[0] # If model output is not scalar, apply global spatial average pooling. # This happens if you choose a dimensionality not equal 2048. if pred.size(2) != 1 or pred.size(3) != 1: pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) pred = pred.squeeze(3).squeeze(2).cpu().numpy() pred_arr[start_idx:start_idx + pred.shape[0]] = pred start_idx = start_idx + pred.shape[0] return pred_arr def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of a layer of the inception net (like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations, precalculated on an representative data set. -- sigma1: The covariance matrix over activations for generated samples. -- sigma2: The covariance matrix over activations, precalculated on an representative data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, \ 'Training and test mean vectors have different lengths' assert sigma1.shape == sigma2.shape, \ 'Training and test covariances have different dimensions' diff = mu1 - mu2 # Product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ('fid calculation produces singular product; ' 'adding %s to diagonal of cov estimates') % eps print(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError('Imaginary component {}'.format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean) def calculate_activation_statistics(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, res=None, dataset_name=None): """Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : The images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the hardware. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the inception model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the inception model. """ act = get_activations(files, model, batch_size, dims, device, num_workers, res=res, dataset_name=dataset_name) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma def compute_statistics_of_path(path, model, batch_size, dims, device, num_workers=1, num_samples=None, res=None, dataset_name=None): if path.endswith('.npz'): with np.load(path) as f: m, s = f['mu'][:], f['sigma'][:] else: path = pathlib.Path(path) files = sorted([file for ext in IMAGE_EXTENSIONS for file in path.glob('**/*.{}'.format(ext))]) if num_samples is not None: #import random #files = random.sample(files, num_samples) files = files[:num_samples] print("Found %d files." % len(files)) m, s = calculate_activation_statistics(files, model, batch_size, dims, device, num_workers, res=res, dataset_name=dataset_name) return m, s def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1, num_samples=None, res=None, dataset_name=None): """Calculates the FID of two paths""" for p in paths: if not os.path.exists(p): raise RuntimeError('Invalid path: %s' % p) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) m2, s2 = compute_statistics_of_path(paths[1], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) fid_value = calculate_frechet_distance(m1, s1, m2, s2) return fid_value def save_fid_stats(paths, batch_size, device, dims, num_workers=1, num_samples=None, res=None, dataset_name=None): """Calculates the FID of two paths""" if not os.path.exists(paths[0]): raise RuntimeError('Invalid path: %s' % paths[0]) if os.path.exists(paths[1]): raise RuntimeError('Existing output file: %s' % paths[1]) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) print(f"Saving statistics for {paths[0]}") m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) np.savez_compressed(paths[1], mu=m1, sigma=s1) def main(): args = parser.parse_args() if args.device is None: device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu') else: device = torch.device(args.device) if args.num_workers is None: try: num_cpus = len(os.sched_getaffinity(0)) except AttributeError: # os.sched_getaffinity is not available under Windows, use # os.cpu_count instead (which may not return the *available* number # of CPUs). num_cpus = os.cpu_count() num_workers = min(num_cpus, 8) if num_cpus is not None else 0 else: num_workers = args.num_workers if args.save_stats: save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers, num_samples=args.num_samples, res=args.res, dataset_name=args.dataset_name) return fid_value = calculate_fid_given_paths(args.path, args.batch_size, device, args.dims, num_workers, num_samples=args.num_samples, res = args.res, dataset_name=args.dataset_name) print('FID: ', fid_value) if __name__ == '__main__': main() ================================================ FILE: ddpm_exp/finetune.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from tqdm import tqdm from runners.diffusion import Diffusion from torchvision import transforms import torchvision from datasets import get_dataset, data_transform, inverse_data_transform from utils import UnlabeledImageFolder from accelerate import Accelerator torch.set_printoptions(sci_mode=False) def parse_args_and_config(accelerator): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=2333, help="Random seed") parser.add_argument("--taylor_batch_size", type=int, default=128, help="batch size for taylor expansion") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--kd", action="store_true", default=False, help="skip according to (uniform or quadratic)", ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--load_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--save_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_ema", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--pruner", type=str, default="taylor", choices=["taylor", "random", "magnitude", "reinit", "first_order_taylor", "second_order_taylor"], ) parser.add_argument( "--restore_from", type=str, default=None, help="Restore from user a checkpoint", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--pruning_ratio", type=float, default=0.0, help="pruning ratio", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) #tb_path = os.path.join(args.exp, "tensorboard", args.doc) if accelerator.is_main_process: if not args.test and not args.sample: if not args.resume_training: if os.path.exists(args.log_path): overwrite = False if args.ni: overwrite = True else: response = input("Folder already exists. Overwrite? (Y/N)") if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.log_path) #shutil.rmtree(tb_path) os.makedirs(args.log_path) #if os.path.exists(tb_path): # shutil.rmtree(tb_path) else: print("Folder exists. Program halted.") sys.exit(0) else: os.makedirs(args.log_path) with open(os.path.join(args.log_path, "config.yml"), "w") as f: yaml.dump(new_config, f, default_flow_style=False) os.makedirs(os.path.join(args.log_path, 'vis'), exist_ok=True) #new_config.tb_logger = tb.SummaryWriter(log_dir=tb_path) # setup logger level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() handler2 = logging.FileHandler(os.path.join(args.log_path, "stdout.txt")) formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) handler2.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.addHandler(handler2) logger.setLevel(level) else: level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.setLevel(level) if args.sample: os.makedirs(os.path.join(args.exp, "image_samples", args.image_folder, str(accelerator.process_index)), exist_ok=True) args.image_folder = os.path.join( args.exp, "image_samples", args.image_folder, str(accelerator.process_index) ) if not os.path.exists(args.image_folder): os.makedirs(args.image_folder) else: if not (args.fid or args.interpolation): overwrite = False if args.ni: overwrite = True else: response = input( f"Image folder {args.image_folder} already exists. Overwrite? (Y/N)" ) if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.image_folder) os.makedirs(args.image_folder) else: print("Output image folder exists. Program halted.") sys.exit(0) # add device #device = #torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") #logging.info("Using device: {}".format(device)) #new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): accelerator = Accelerator() args, config = parse_args_and_config(accelerator) logging.info("Writing log file to {}".format(args.log_path)) logging.info("Exp instance id = {}".format(os.getpid())) logging.info("Exp comment = {}".format(args.comment)) try: runner = Diffusion(args, config) runner.accelerator = accelerator if args.sample: runner.sample() elif args.test: runner.test() else: runner.train(kd=args.kd) except Exception: logging.error(traceback.format_exc()) return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/finetune_simple.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from tqdm import tqdm from runners.diffusion_simple import Diffusion from torchvision import transforms import torchvision from datasets import get_dataset, data_transform, inverse_data_transform from utils import UnlabeledImageFolder torch.set_printoptions(sci_mode=False) def parse_args_and_config(): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=2333, help="Random seed") parser.add_argument("--taylor_batch_size", type=int, default=128, help="batch size for taylor expansion") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--load_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--save_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_ema", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--pruner", type=str, default="taylor", choices=["taylor", "random", "magnitude", "reinit", "first_order_taylor", "second_order_taylor", "ours"], ) parser.add_argument( "--restore_from", type=str, default=None, help="Restore from user a checkpoint", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--thr", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--pruning_ratio", type=float, default=0.0, help="pruning ratio", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) #tb_path = os.path.join(args.exp, "tensorboard", args.doc) if not args.test and not args.sample: if not args.resume_training: if os.path.exists(args.log_path): overwrite = False if args.ni: overwrite = True else: response = input("Folder already exists. Overwrite? (Y/N)") if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.log_path) #shutil.rmtree(tb_path) os.makedirs(args.log_path) #if os.path.exists(tb_path): # shutil.rmtree(tb_path) else: print("Folder exists. Program halted.") sys.exit(0) else: os.makedirs(args.log_path) with open(os.path.join(args.log_path, "config.yml"), "w") as f: yaml.dump(new_config, f, default_flow_style=False) os.makedirs(os.path.join(args.log_path, 'vis'), exist_ok=True) #new_config.tb_logger = tb.SummaryWriter(log_dir=tb_path) # setup logger level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() handler2 = logging.FileHandler(os.path.join(args.log_path, "stdout.txt")) formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) handler2.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.addHandler(handler2) logger.setLevel(level) else: level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.setLevel(level) if args.sample: os.makedirs(os.path.join(args.exp, "image_samples"), exist_ok=True) args.image_folder = os.path.join( args.exp, "image_samples", args.image_folder ) if not os.path.exists(args.image_folder): os.makedirs(args.image_folder) else: if not (args.fid or args.interpolation): overwrite = False if args.ni: overwrite = True else: response = input( f"Image folder {args.image_folder} already exists. Overwrite? (Y/N)" ) if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.image_folder) os.makedirs(args.image_folder) else: print("Output image folder exists. Program halted.") sys.exit(0) # add device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") logging.info("Using device: {}".format(device)) new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): args, config = parse_args_and_config() logging.info("Writing log file to {}".format(args.log_path)) logging.info("Exp instance id = {}".format(os.getpid())) logging.info("Exp comment = {}".format(args.comment)) try: runner = Diffusion(args, config) if args.pruning_ratio > 0 and args.load_pruned_model is None: # Dataset print(config) dataset, _ = get_dataset(args, config) print(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.taylor_batch_size, shuffle=True, num_workers=4, drop_last=True ) from models.diffusion import AttnBlock import torch_pruning as tp print("Pruning ...") model = runner.model model.to(runner.device) example_inputs = {'x': torch.randn(1, 3, config.data.image_size, config.data.image_size).to(runner.device), 't': torch.ones(1).to(runner.device)} if args.pruner == 'taylor': imp = tp.importance.TaylorImportance() elif args.pruner == 'first_order_taylor': imp = tp.importance.FullTaylorImportance(order=1) elif args.pruner == 'second_order_taylor': imp = tp.importance.FullTaylorImportance(order=2) elif args.pruner == 'random' or args.pruner == 'reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() elif args.pruner == 'ours': imp = tp.importance.TaylorImportance() ignored_layers = [model.conv_out] channel_groups = {} iterative_steps = 1 pruner = tp.pruner.MagnitudePruner( model, example_inputs, importance=imp, iterative_steps=iterative_steps, channel_groups =channel_groups, ch_sparsity=args.pruning_ratio, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear] ) base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs) if 'taylor' in args.pruner or args.pruner=='ours': x = next(iter(train_dataloader)) if isinstance(x, (list, tuple)): x = x[0] x = x.to(runner.device) x = data_transform(config, x) n = x.size(0) e = torch.randn_like(x) b = runner.betas #t = torch.randint( # low=0, high=runner.num_timesteps, size=(n // 2 + 1,) #).to(runner.device) #t = torch.cat([t, runner.num_timesteps - t - 1], dim=0)[:n] from functions.losses import loss_registry model.zero_grad() max_loss = 0 for step_k in tqdm(range(1000)): t = torch.ones(n, dtype=torch.long).to(runner.device)*step_k loss = loss_registry[config.model.type](model, x, t, e, b) if args.pruner == 'ours': if loss>max_loss: max_loss = loss if loss {:.4f} M".format(base_nparams/1e6, nparams/1e6)) print("#MACs: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) del pruner # Save pruned model print("Saving pruned model as {}".format(os.path.join(args.log_path, "pruned_model.pth"))) torch.save( model, os.path.join(args.log_path, "pruned_model.pth"), ) if args.load_pruned_model is not None: print("Loading pruned model from {}".format(args.load_pruned_model)) model = torch.load(args.load_pruned_model, map_location='cpu') runner.model = model print(step_k) if args.sample: runner.sample() elif args.test: runner.test() else: runner.train() except Exception: logging.error(traceback.format_exc()) return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/functions/__init__.py ================================================ import torch.optim as optim def get_optimizer(config, parameters): if config.optim.optimizer == 'Adam': return optim.Adam(parameters, lr=config.optim.lr, weight_decay=config.optim.weight_decay, betas=(config.optim.beta1, 0.999), amsgrad=config.optim.amsgrad, eps=config.optim.eps) elif config.optim.optimizer == 'RMSProp': return optim.RMSprop(parameters, lr=config.optim.lr, weight_decay=config.optim.weight_decay) elif config.optim.optimizer == 'SGD': return optim.SGD(parameters, lr=config.optim.lr, momentum=0.9) else: raise NotImplementedError( 'Optimizer {} not understood.'.format(config.optim.optimizer)) ================================================ FILE: ddpm_exp/functions/ckpt_util.py ================================================ import os, hashlib import requests from tqdm import tqdm URL_MAP = { "cifar10": "https://heibox.uni-heidelberg.de/f/869980b53bf5416c8a28/?dl=1", "ema_cifar10": "https://heibox.uni-heidelberg.de/f/2e4f01e2d9ee49bab1d5/?dl=1", "lsun_bedroom": "https://heibox.uni-heidelberg.de/f/f179d4f21ebc4d43bbfe/?dl=1", "ema_lsun_bedroom": "https://heibox.uni-heidelberg.de/f/b95206528f384185889b/?dl=1", "lsun_cat": "https://heibox.uni-heidelberg.de/f/fac870bd988348eab88e/?dl=1", "ema_lsun_cat": "https://heibox.uni-heidelberg.de/f/0701aac3aa69457bbe34/?dl=1", "lsun_church": "https://heibox.uni-heidelberg.de/f/2711a6f712e34b06b9d8/?dl=1", "ema_lsun_church": "https://heibox.uni-heidelberg.de/f/44ccb50ef3c6436db52e/?dl=1", } CKPT_MAP = { "cifar10": "diffusion_cifar10_model/model-790000.ckpt", "ema_cifar10": "ema_diffusion_cifar10_model/model-790000.ckpt", "lsun_bedroom": "diffusion_lsun_bedroom_model/model-2388000.ckpt", "ema_lsun_bedroom": "ema_diffusion_lsun_bedroom_model/model-2388000.ckpt", "lsun_cat": "diffusion_lsun_cat_model/model-1761000.ckpt", "ema_lsun_cat": "ema_diffusion_lsun_cat_model/model-1761000.ckpt", "lsun_church": "diffusion_lsun_church_model/model-4432000.ckpt", "ema_lsun_church": "ema_diffusion_lsun_church_model/model-4432000.ckpt", "celeba": "ema_diffusion_celeba_model/model.ckpt", "ema_celeba": "ema_diffusion_celeba_model/model.ckpt", } MD5_MAP = { "cifar10": "82ed3067fd1002f5cf4c339fb80c4669", "ema_cifar10": "1fa350b952534ae442b1d5235cce5cd3", "lsun_bedroom": "f70280ac0e08b8e696f42cb8e948ff1c", "ema_lsun_bedroom": "1921fa46b66a3665e450e42f36c2720f", "lsun_cat": "bbee0e7c3d7abfb6e2539eaf2fb9987b", "ema_lsun_cat": "646f23f4821f2459b8bafc57fd824558", "lsun_church": "eb619b8a5ab95ef80f94ce8a5488dae3", "ema_lsun_church": "fdc68a23938c2397caba4a260bc2445f", } def download(url, local_path, chunk_size=1024): os.makedirs(os.path.split(local_path)[0], exist_ok=True) with requests.get(url, stream=True) as r: total_size = int(r.headers.get("content-length", 0)) with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: with open(local_path, "wb") as f: for data in r.iter_content(chunk_size=chunk_size): if data: f.write(data) pbar.update(chunk_size) def md5_hash(path): with open(path, "rb") as f: content = f.read() return hashlib.md5(content).hexdigest() def get_ckpt_path(name, root=None, check=False): if 'church_outdoor' in name: name = name.replace('church_outdoor', 'church') #assert name in URL_MAP # Modify the path when necessary cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("./run/cache")) root = ( root if root is not None else os.path.join(cachedir, "diffusion_models_converted") ) path = os.path.join(root, CKPT_MAP[name]) if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) download(URL_MAP[name], path) md5 = md5_hash(path) assert md5 == MD5_MAP[name], md5 return path ================================================ FILE: ddpm_exp/functions/denoising.py ================================================ import torch def compute_alpha(beta, t): beta = torch.cat([torch.zeros(1).to(beta.device), beta], dim=0) a = (1 - beta).cumprod(dim=0).index_select(0, t + 1).view(-1, 1, 1, 1) return a def generalized_steps(x, seq, model, b, **kwargs): with torch.no_grad(): n = x.size(0) seq_next = [-1] + list(seq[:-1]) x0_preds = [] xs = [x] for i, j in zip(reversed(seq), reversed(seq_next)): t = (torch.ones(n) * i).to(x.device) next_t = (torch.ones(n) * j).to(x.device) at = compute_alpha(b, t.long()) at_next = compute_alpha(b, next_t.long()) xt = xs[-1].to('cuda') et = model(xt, t) x0_t = (xt - et * (1 - at).sqrt()) / at.sqrt() x0_preds.append(x0_t.to('cpu')) c1 = ( kwargs.get("eta", 0) * ((1 - at / at_next) * (1 - at_next) / (1 - at)).sqrt() ) c2 = ((1 - at_next) - c1 ** 2).sqrt() xt_next = at_next.sqrt() * x0_t + c1 * torch.randn_like(x) + c2 * et xs.append(xt_next.to('cpu')) return xs, x0_preds def ddpm_steps(x, seq, model, b, **kwargs): with torch.no_grad(): n = x.size(0) seq_next = [-1] + list(seq[:-1]) xs = [x] x0_preds = [] betas = b for i, j in zip(reversed(seq), reversed(seq_next)): t = (torch.ones(n) * i).to(x.device) next_t = (torch.ones(n) * j).to(x.device) at = compute_alpha(betas, t.long()) atm1 = compute_alpha(betas, next_t.long()) beta_t = 1 - at / atm1 x = xs[-1].to('cuda') output = model(x, t.float()) e = output x0_from_e = (1.0 / at).sqrt() * x - (1.0 / at - 1).sqrt() * e x0_from_e = torch.clamp(x0_from_e, -1, 1) x0_preds.append(x0_from_e.to('cpu')) mean_eps = ( (atm1.sqrt() * beta_t) * x0_from_e + ((1 - beta_t).sqrt() * (1 - atm1)) * x ) / (1.0 - at) mean = mean_eps noise = torch.randn_like(x) mask = 1 - (t == 0).float() mask = mask.view(-1, 1, 1, 1) logvar = beta_t.log() sample = mean + mask * torch.exp(0.5 * logvar) * noise xs.append(sample.to('cpu')) return xs, x0_preds ================================================ FILE: ddpm_exp/functions/losses.py ================================================ import torch def noise_estimation_loss(model, x0: torch.Tensor, t: torch.LongTensor, e: torch.Tensor, b: torch.Tensor, keepdim=False): a = (1-b).cumprod(dim=0).index_select(0, t).view(-1, 1, 1, 1) x = x0 * a.sqrt() + e * (1.0 - a).sqrt() output = model(x, t.float()) if keepdim: return (e - output).square().sum(dim=(1, 2, 3)) else: return (e - output).square().sum(dim=(1, 2, 3)).mean(dim=0) def noise_estimation_kd_loss(model, teacher, x0: torch.Tensor, t: torch.LongTensor, e: torch.Tensor, b: torch.Tensor, keepdim=False): a = (1-b).cumprod(dim=0).index_select(0, t).view(-1, 1, 1, 1) x = x0 * a.sqrt() + e * (1.0 - a).sqrt() output = model(x, t.float()) with torch.no_grad(): teacher_output = teacher(x, t.float()) if keepdim: return 0.7*(teacher_output - output).square().sum(dim=(1, 2, 3)) + 0.3 * (e - output).square().sum(dim=(1, 2, 3)) else: return 0.7*(teacher_output - output).square().sum(dim=(1, 2, 3)).mean(dim=0) + 0.3 * (e - output).square().sum(dim=(1, 2, 3)).mean(dim=0) loss_registry = { 'simple': noise_estimation_loss, } ================================================ FILE: ddpm_exp/inception.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torchvision try: from torchvision.models.utils import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url # Inception weights ported to Pytorch from # http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 class InceptionV3(nn.Module): """Pretrained InceptionV3 network returning feature maps""" # Index of default block of inception to return, # corresponds to output of final average pooling DEFAULT_BLOCK_INDEX = 3 # Maps feature dimensionality to their output blocks indices BLOCK_INDEX_BY_DIM = { 64: 0, # First max pooling features 192: 1, # Second max pooling featurs 768: 2, # Pre-aux classifier features 2048: 3 # Final average pooling features } def __init__(self, output_blocks=(DEFAULT_BLOCK_INDEX,), resize_input=True, normalize_input=True, requires_grad=False, use_fid_inception=True): """Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, scales the input from range (0, 1) to the range the pretrained Inception network expects, namely (-1, 1) requires_grad : bool If true, parameters of the model require gradients. Possibly useful for finetuning the network use_fid_inception : bool If true, uses the pretrained Inception model used in Tensorflow's FID implementation. If false, uses the pretrained Inception model available in torchvision. The FID Inception model has different weights and a slightly different structure from torchvision's Inception model. If you want to compute FID scores, you are strongly advised to set this parameter to true to get comparable results. """ super(InceptionV3, self).__init__() self.resize_input = resize_input self.normalize_input = normalize_input self.output_blocks = sorted(output_blocks) self.last_needed_block = max(output_blocks) assert self.last_needed_block <= 3, \ 'Last possible output block index is 3' self.blocks = nn.ModuleList() if use_fid_inception: inception = fid_inception_v3() else: inception = _inception_v3(weights='DEFAULT') # Block 0: input to maxpool1 block0 = [ inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block0)) # Block 1: maxpool1 to maxpool2 if self.last_needed_block >= 1: block1 = [ inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block1)) # Block 2: maxpool2 to aux classifier if self.last_needed_block >= 2: block2 = [ inception.Mixed_5b, inception.Mixed_5c, inception.Mixed_5d, inception.Mixed_6a, inception.Mixed_6b, inception.Mixed_6c, inception.Mixed_6d, inception.Mixed_6e, ] self.blocks.append(nn.Sequential(*block2)) # Block 3: aux classifier to final avgpool if self.last_needed_block >= 3: block3 = [ inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, nn.AdaptiveAvgPool2d(output_size=(1, 1)) ] self.blocks.append(nn.Sequential(*block3)) for param in self.parameters(): param.requires_grad = requires_grad def forward(self, inp): """Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index """ outp = [] x = inp if self.resize_input: x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) if self.normalize_input: x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) for idx, block in enumerate(self.blocks): x = block(x) if idx in self.output_blocks: outp.append(x) if idx == self.last_needed_block: break return outp def _inception_v3(*args, **kwargs): """Wraps `torchvision.models.inception_v3`""" try: version = tuple(map(int, torchvision.__version__.split('.')[:2])) except ValueError: # Just a caution against weird version strings version = (0,) # Skips default weight inititialization if supported by torchvision # version. See https://github.com/mseitzer/pytorch-fid/issues/28. if version >= (0, 6): kwargs['init_weights'] = False # Backwards compatibility: `weights` argument was handled by `pretrained` # argument prior to version 0.13. if version < (0, 13) and 'weights' in kwargs: if kwargs['weights'] == 'DEFAULT': kwargs['pretrained'] = True elif kwargs['weights'] is None: kwargs['pretrained'] = False else: raise ValueError( 'weights=={} not supported in torchvision {}'.format( kwargs['weights'], torchvision.__version__ ) ) del kwargs['weights'] return torchvision.models.inception_v3(*args, **kwargs) def fid_inception_v3(): """Build pretrained Inception model for FID computation The Inception model for FID computation uses a different set of weights and has a slightly different structure than torchvision's Inception. This method first constructs torchvision's Inception and then patches the necessary parts that are different in the FID Inception model. """ inception = _inception_v3(num_classes=1008, aux_logits=False, weights=None) inception.Mixed_5b = FIDInceptionA(192, pool_features=32) inception.Mixed_5c = FIDInceptionA(256, pool_features=64) inception.Mixed_5d = FIDInceptionA(288, pool_features=64) inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) inception.Mixed_7b = FIDInceptionE_1(1280) inception.Mixed_7c = FIDInceptionE_2(2048) state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True) inception.load_state_dict(state_dict) return inception class FIDInceptionA(torchvision.models.inception.InceptionA): """InceptionA block patched for FID computation""" def __init__(self, in_channels, pool_features): super(FIDInceptionA, self).__init__(in_channels, pool_features) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionC(torchvision.models.inception.InceptionC): """InceptionC block patched for FID computation""" def __init__(self, in_channels, channels_7x7): super(FIDInceptionC, self).__init__(in_channels, channels_7x7) def forward(self, x): branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionE_1(torchvision.models.inception.InceptionE): """First InceptionE block patched for FID computation""" def __init__(self, in_channels): super(FIDInceptionE_1, self).__init__(in_channels) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionE_2(torchvision.models.inception.InceptionE): """Second InceptionE block patched for FID computation""" def __init__(self, in_channels): super(FIDInceptionE_2, self).__init__(in_channels) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) # Patch: The FID Inception model uses max pooling instead of average # pooling. This is likely an error in this specific Inception # implementation, as other Inception models use average pooling here # (which matches the description in the paper). branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) ================================================ FILE: ddpm_exp/main.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from runners.diffusion import Diffusion torch.set_printoptions(sci_mode=False) def parse_args_and_config(): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=1234, help="Random seed") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) tb_path = os.path.join(args.exp, "tensorboard", args.doc) if not args.test and not args.sample: if not args.resume_training: if os.path.exists(args.log_path): overwrite = False if args.ni: overwrite = True else: response = input("Folder already exists. Overwrite? (Y/N)") if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.log_path) shutil.rmtree(tb_path) os.makedirs(args.log_path) if os.path.exists(tb_path): shutil.rmtree(tb_path) else: print("Folder exists. Program halted.") sys.exit(0) else: os.makedirs(args.log_path) with open(os.path.join(args.log_path, "config.yml"), "w") as f: yaml.dump(new_config, f, default_flow_style=False) new_config.tb_logger = tb.SummaryWriter(log_dir=tb_path) # setup logger level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() handler2 = logging.FileHandler(os.path.join(args.log_path, "stdout.txt")) formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) handler2.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.addHandler(handler2) logger.setLevel(level) else: level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.setLevel(level) if args.sample: os.makedirs(os.path.join(args.exp, "image_samples"), exist_ok=True) args.image_folder = os.path.join( args.exp, "image_samples", args.image_folder ) if not os.path.exists(args.image_folder): os.makedirs(args.image_folder) else: if not (args.fid or args.interpolation): overwrite = False if args.ni: overwrite = True else: response = input( f"Image folder {args.image_folder} already exists. Overwrite? (Y/N)" ) if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.image_folder) os.makedirs(args.image_folder) else: print("Output image folder exists. Program halted.") sys.exit(0) # add device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") logging.info("Using device: {}".format(device)) new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): args, config = parse_args_and_config() logging.info("Writing log file to {}".format(args.log_path)) logging.info("Exp instance id = {}".format(os.getpid())) logging.info("Exp comment = {}".format(args.comment)) try: runner = Diffusion(args, config) if args.sample: runner.sample() elif args.test: runner.test() else: runner.train() except Exception: logging.error(traceback.format_exc()) return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/models/diffusion.py ================================================ import math import torch import torch.nn as nn 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 x*torch.sigmoid(x) def Normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): x = torch.nn.functional.interpolate( x, scale_factor=2.0, mode="nearest") if self.with_conv: x = self.conv(x) return x class Downsample(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.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: pad = (0, 1, 0, 1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) 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, temb_channels=512): 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.norm1 = Normalize(in_channels) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) self.temb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout) self.conv2 = torch.nn.Conv2d(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 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb): h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] h = self.norm2(h) h = nonlinearity(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): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.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) q = q.permute(0, 2, 1) # b,hw,c k = k.reshape(b, c, h*w) # b,c,hw w_ = torch.bmm(q, k) # 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) w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] h_ = torch.bmm(v, w_) h_ = h_.reshape(b, c, h, w) h_ = self.proj_out(h_) return x+h_ class Model(nn.Module): def __init__(self, config): super().__init__() self.config = config ch, out_ch, ch_mult = config.model.ch, config.model.out_ch, tuple(config.model.ch_mult) num_res_blocks = config.model.num_res_blocks attn_resolutions = config.model.attn_resolutions dropout = config.model.dropout in_channels = config.model.in_channels resolution = config.data.image_size resamp_with_conv = config.model.resamp_with_conv num_timesteps = config.diffusion.num_diffusion_timesteps if config.model.type == 'bayesian': self.logvar = nn.Parameter(torch.zeros(num_timesteps)) 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 # timestep embedding self.temb = nn.Module() self.temb.dense = nn.ModuleList([ torch.nn.Linear(self.ch, self.temb_ch), torch.nn.Linear(self.temb_ch, self.temb_ch), ]) # downsampling self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+ch_mult self.down = nn.ModuleList() block_in = None 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(AttnBlock(block_in)) 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 = AttnBlock(block_in) 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(AttnBlock(block_in)) 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 = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, x, t): assert x.shape[2] == x.shape[3] == self.resolution # timestep embedding temb = get_timestep_embedding(t, self.ch) temb = self.temb.dense[0](temb) temb = nonlinearity(temb) temb = self.temb.dense[1](temb) # 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 ================================================ FILE: ddpm_exp/models/ema.py ================================================ import torch.nn as nn class EMAHelper(object): def __init__(self, mu=0.999): self.mu = mu self.shadow = [] def to(self, device=None) -> None: self.shadow = [ p.to(device=device) for p in self.shadow ] def copy_to(self, parameters) -> None: parameters = list(parameters) for s_param, param in zip(self.shadow, parameters): param.data.copy_(s_param.to(param.device).data) def store(self, parameters) -> None: r""" Args: Save the current parameters for restoring later. parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.temp_stored_params = [param.detach().cpu().clone() for param in parameters] def restore(self, parameters) -> None: if self.temp_stored_params is None: raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`") for c_param, param in zip(self.temp_stored_params, parameters): param.data.copy_(c_param.data) self.temp_stored_params = None def register(self, module): for param in module.parameters(): if param.requires_grad: self.shadow.append(param.data.clone()) def update(self, module): for i, (shadow_param, param) in enumerate(zip(self.shadow, module.parameters())): if param.requires_grad: shadow_param.data = ( 1. - self.mu) * param.data + self.mu * shadow_param.data #if i==0: # print(shadow_param.flatten()[0]) def ema(self, module): for shadow_param, param in zip(self.shadow, module.parameters()): if param.requires_grad: param.data.copy_(shadow_param.data) #def ema_copy(self, module): # if isinstance(module, nn.parallel.DistributedDataParallel): # from copy import deepcopy # inner_module = module.module # module_copy = deepcopy(inner_module).to(inner_module.config.device) ## module_copy.load_state_dict(inner_module.state_dict()) # module_copy = nn.DistributedDataParallel(module_copy) # else: # module_copy = deepcopy(inner_module).to(module.config.device) # module_copy.load_state_dict(module.state_dict()) # # module_copy = copy.deepcopy(module) # self.ema(module_copy) # return module_copy def state_dict(self): return self.shadow def load_state_dict(self, state_dict): if isinstance(state_dict, list): self.shadow = state_dict else: self.shadow = state_dict.values() ================================================ FILE: ddpm_exp/prune.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from tqdm import tqdm from runners.diffusion import Diffusion from torchvision import transforms import torchvision from datasets import get_dataset, data_transform, inverse_data_transform import torchvision.utils as tvu from utils import UnlabeledImageFolder torch.set_printoptions(sci_mode=False) def parse_args_and_config(): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=2333, help="Random seed") parser.add_argument("--taylor_batch_size", type=int, default=128, help="batch size for taylor expansion") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--load_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--save_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_generated_samples", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_ema", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--pruner", type=str, default="taylor", choices=["taylor", "random", "magnitude", "reinit", "first_order_taylor", "second_order_taylor", 'abs_taylor', 'fisher', 'ours'], ) parser.add_argument( "--restore_from", type=str, default=None, help="Restore from user a checkpoint", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--thr", type=float, default=0.01, help="eta used to control the variances of sigma", ) parser.add_argument( "--pruning_ratio", type=float, default=0.0, help="pruning ratio", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) # add device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") logging.info("Using device: {}".format(device)) new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): args, config = parse_args_and_config() runner = Diffusion(args, config) if args.pruning_ratio > 0 and args.load_pruned_model is None: # Dataset print(config) dataset, _ = get_dataset(args, config) print(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.taylor_batch_size, shuffle=True, num_workers=4, drop_last=True ) from models.diffusion import AttnBlock import torch_pruning as tp print("Pruning ...") model = runner.model.eval() model.to(runner.device) example_inputs = {'x': torch.randn(1, 3, config.data.image_size, config.data.image_size).to(runner.device), 't': torch.ones(1).to(runner.device)} if args.pruner == 'taylor': imp = tp.importance.TaylorImportance() elif args.pruner == 'first_order_taylor': imp = tp.importance.FullTaylorImportance(order=1) elif args.pruner == 'second_order_taylor': imp = tp.importance.FullTaylorImportance(order=2) elif args.pruner == 'random' or args.pruner == 'reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() elif args.pruner == 'abs_taylor': imp = tp.importance.AbsTaylorImportance() elif args.pruner == 'fisher': imp = tp.importance.FisherImportance() elif args.pruner == 'ours': imp = tp.importance.TaylorImportance() ignored_layers = [model.conv_out] channel_groups = {} iterative_steps = 1 pruner = tp.pruner.MagnitudePruner( model, example_inputs, importance=imp, iterative_steps=iterative_steps, channel_groups =channel_groups, ch_sparsity=args.pruning_ratio, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear] ) #torch.manual_seed(10) base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs) image_path = args.save_pruned_model.replace('.pth', '') n = config.sampling.batch_size noise = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=runner.device, ) if 'taylor' in args.pruner or 'fisher' in args.pruner or 'ours' in args.pruner: x = iter(train_dataloader).next() if isinstance(x, (list, tuple)): x = x[0] x = x.to(runner.device) x = data_transform(config, x) x = x.to(runner.device) n = x.size(0) e = torch.randn_like(x) b = runner.betas from functions.losses import noise_estimation_loss model.zero_grad() max_loss = 0 for step_k in tqdm(range(0, 1000)): t = torch.ones(n, dtype=torch.long).to(runner.device) * step_k loss = noise_estimation_loss(model, x, t, e, b) if args.pruner == 'ours': if loss>max_loss: max_loss = loss if loss {:.4f} M".format(base_nparams/1e6, nparams/1e6)) print("#MACs: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) del pruner # Save pruned model os.makedirs(os.path.dirname(args.save_pruned_model), exist_ok=True) print("Saving pruned model as {}".format(args.save_pruned_model)) torch.save( model, args.save_pruned_model ) with torch.no_grad(): n = config.sampling.batch_size x = runner.sample_image(noise, model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, image_path+'-pruned.png') return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/prune_kd.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from tqdm import tqdm from runners.diffusion_kd import Diffusion from torchvision import transforms import torchvision from utils import UnlabeledImageFolder torch.set_printoptions(sci_mode=False) def parse_args_and_config(): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=2333, help="Random seed") parser.add_argument("--taylor_batch_size", type=int, default=128, help="batch size for taylor expansion") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--load_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--save_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_ema", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--pruner", type=str, default="taylor", choices=["taylor", "random", "magnitude", "reinit", "first_order_taylor", "second_order_taylor"], ) parser.add_argument( "--restore_from", type=str, default=None, help="Restore from user a checkpoint", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--pruning_ratio", type=float, default=0.0, help="pruning ratio", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) #tb_path = os.path.join(args.exp, "tensorboard", args.doc) if not args.test and not args.sample: if not args.resume_training: if os.path.exists(args.log_path): overwrite = False if args.ni: overwrite = True else: response = input("Folder already exists. Overwrite? (Y/N)") if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.log_path) #shutil.rmtree(tb_path) os.makedirs(args.log_path) #if os.path.exists(tb_path): # shutil.rmtree(tb_path) else: print("Folder exists. Program halted.") sys.exit(0) else: os.makedirs(args.log_path) with open(os.path.join(args.log_path, "config.yml"), "w") as f: yaml.dump(new_config, f, default_flow_style=False) os.makedirs(os.path.join(args.log_path, 'vis'), exist_ok=True) #new_config.tb_logger = tb.SummaryWriter(log_dir=tb_path) # setup logger level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() handler2 = logging.FileHandler(os.path.join(args.log_path, "stdout.txt")) formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) handler2.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.addHandler(handler2) logger.setLevel(level) else: level = getattr(logging, args.verbose.upper(), None) if not isinstance(level, int): raise ValueError("level {} not supported".format(args.verbose)) handler1 = logging.StreamHandler() formatter = logging.Formatter( "%(levelname)s - %(filename)s - %(asctime)s - %(message)s" ) handler1.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler1) logger.setLevel(level) if args.sample: os.makedirs(os.path.join(args.exp, "image_samples"), exist_ok=True) args.image_folder = os.path.join( args.exp, "image_samples", args.image_folder ) if not os.path.exists(args.image_folder): os.makedirs(args.image_folder) else: if not (args.fid or args.interpolation): overwrite = False if args.ni: overwrite = True else: response = input( f"Image folder {args.image_folder} already exists. Overwrite? (Y/N)" ) if response.upper() == "Y": overwrite = True if overwrite: shutil.rmtree(args.image_folder) os.makedirs(args.image_folder) else: print("Output image folder exists. Program halted.") sys.exit(0) # add device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") logging.info("Using device: {}".format(device)) new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): args, config = parse_args_and_config() logging.info("Writing log file to {}".format(args.log_path)) logging.info("Exp instance id = {}".format(os.getpid())) logging.info("Exp comment = {}".format(args.comment)) try: runner = Diffusion(args, config) if args.pruning_ratio > 0 and args.load_pruned_model is None: # Dataset print(config) if config.data.dataset.lower() == 'cifar10': augmentations = transforms.Compose( [ transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) dataset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=False, transform=augmentations) else: augmentations = transforms.Compose( [ transforms.Resize(256, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(256), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) if 'celeba' in config.data.dataset.lower(): dataset = UnlabeledImageFolder(args.dataset, transform=augmentations, exts=['png']) else: dataset = UnlabeledImageFolder(args.dataset, transform=augmentations, exts=['webp']) print(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.taylor_batch_size, shuffle=True, num_workers=4, drop_last=True ) from models.diffusion import AttnBlock import torch_pruning as tp print("Pruning ...") model = runner.model model.to(runner.device) example_inputs = {'x': torch.randn(1, 3, 32, 32).to(runner.device), 't': torch.ones(1).to(runner.device)} if args.pruner == 'taylor': imp = tp.importance.TaylorImportance() elif args.pruner == 'first_order_taylor': imp = tp.importance.FullTaylorImportance(order=1) elif args.pruner == 'second_order_taylor': imp = tp.importance.FullTaylorImportance(order=2) elif args.pruner == 'random' or args.pruner == 'reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() ignored_layers = [model.conv_out] channel_groups = {} iterative_steps = 1 pruner = tp.pruner.MagnitudePruner( model, example_inputs, importance=imp, iterative_steps=iterative_steps, channel_groups =channel_groups, ch_sparsity=args.pruning_ratio, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear] ) base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs) if 'taylor' in args.pruner: x = iter(train_dataloader).next() if isinstance(x, (list, tuple)): x = x[0] x = x.to(runner.device) n = x.size(0) e = torch.randn_like(x) b = runner.betas t = torch.randint( low=0, high=runner.num_timesteps, size=(n // 2 + 1,) ).to(runner.device) t = torch.cat([t, runner.num_timesteps - t - 1], dim=0)[:n] from functions.losses import loss_registry model.zero_grad() for step_k in tqdm(range(1000)): loss = loss_registry[config.model.type](model, x, t, e, b) loss.backward() print("============ Before Pruning ============") print(model) for g in pruner.step(interactive=True): g.prune() if args.pruner == 'reinit': def reset_parameters(model): for m in model.modules(): if hasattr(m, 'reset_parameters'): m.reset_parameters() model.apply(reset_parameters) macs, nparams = tp.utils.count_ops_and_params(model, example_inputs) print("============ After Pruning ============") print(model) print("#Params: {:.4f} M => {:.4f} M".format(base_nparams/1e6, nparams/1e6)) print("#MACs: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) del pruner # Save pruned model print("Saving pruned model as {}".format(os.path.join(args.log_path, "pruned_model.pth"))) torch.save( model, os.path.join(args.log_path, "pruned_model.pth"), ) if args.load_pruned_model is not None: print("Loading pruned model from {}".format(args.load_pruned_model)) model = torch.load(args.load_pruned_model, map_location='cpu') runner.model = model if args.sample: runner.sample() elif args.test: runner.test() else: runner.train() except Exception: logging.error(traceback.format_exc()) return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/prune_ssim.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from tqdm import tqdm from runners.diffusion import Diffusion from torchvision import transforms import torchvision from datasets import get_dataset, data_transform, inverse_data_transform import torchvision.utils as tvu from utils import UnlabeledImageFolder torch.set_printoptions(sci_mode=False) def parse_args_and_config(): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=2333, help="Random seed") parser.add_argument("--taylor_batch_size", type=int, default=128, help="batch size for taylor expansion") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--load_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--save_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_generated_samples", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_ema", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--pruner", type=str, default="taylor", choices=["taylor", "random", "magnitude", "reinit", "first_order_taylor", "second_order_taylor", 'abs_taylor', 'fisher', 'ours'], ) parser.add_argument( "--restore_from", type=str, default=None, help="Restore from user a checkpoint", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--pruning_ratio", type=float, default=0.0, help="pruning ratio", ) parser.add_argument( "--stage", type=int, default=0, help="pruning ratio", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) # add device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") logging.info("Using device: {}".format(device)) new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): args, config = parse_args_and_config() runner = Diffusion(args, config) # Dataset print(config) dataset, _ = get_dataset(args, config) print(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.taylor_batch_size, shuffle=True, num_workers=4, drop_last=True ) from models.diffusion import AttnBlock import torch_pruning as tp print("Pruning ...") model = runner.model.eval() model.to(runner.device) example_inputs = {'x': torch.randn(1, 3, config.data.image_size, config.data.image_size).to(runner.device), 't': torch.ones(1).to(runner.device)} if args.pruner == 'taylor': imp = tp.importance.TaylorImportance() elif args.pruner == 'first_order_taylor': imp = tp.importance.FullTaylorImportance(order=1) elif args.pruner == 'second_order_taylor': imp = tp.importance.FullTaylorImportance(order=2) elif args.pruner == 'random' or args.pruner == 'reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() elif args.pruner == 'abs_taylor': imp = tp.importance.AbsTaylorImportance() elif args.pruner == 'fisher': imp = tp.importance.FisherImportance() elif args.pruner == 'ours': imp = tp.importance.TaylorImportance() ignored_layers = [model.conv_out] channel_groups = {} iterative_steps = 1 pruner = tp.pruner.MagnitudePruner( model, example_inputs, importance=imp, iterative_steps=iterative_steps, channel_groups =channel_groups, ch_sparsity=args.pruning_ratio, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear] ) #torch.manual_seed(10) base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs) image_path = args.save_pruned_model.replace('.pth', '') n = config.sampling.batch_size noise = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=runner.device, ) loss_list = [] if args.pruning_ratio > 0 and args.stage>0 and args.load_pruned_model is None: if 'taylor' in args.pruner or 'fisher' in args.pruner or 'ours' in args.pruner: x = iter(train_dataloader).next() if isinstance(x, (list, tuple)): x = x[0] x = x.to(runner.device) x = data_transform(config, x) x = x.to(runner.device) n = x.size(0) e = torch.randn_like(x) b = runner.betas alphas = 1.0 - b alphas_cumprod = alphas.cumprod(dim=0) weights = ((1 - alphas) / (torch.sqrt( alphas_cumprod * (1 - alphas_cumprod) ) + 1e-8)).clamp(min=1) print(weights) from functions.losses import loss_registry model.zero_grad() if args.pruner=='abs_taylor': imp.set_model(model) max_loss = 0 for step_k in tqdm(range(0, args.stage)): t = torch.ones(n, dtype=torch.long).to(runner.device) * step_k loss = loss_registry[config.model.type](model, x, t, e, b) #if loss>max_loss: # max_loss = loss #if loss {:.4f} M".format(base_nparams/1e6, nparams/1e6)) print("#MACs: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) del pruner # Save pruned model #os.makedirs(os.path.dirname(args.save_pruned_model), exist_ok=True) #print("Saving pruned model as {}".format(args.save_pruned_model)) #torch.save( # model, # args.save_pruned_model #) os.makedirs('run/prune_ssim_2/{}'.format(args.stage), exist_ok=True) os.makedirs('run/vis_2', exist_ok=True) with torch.no_grad(): n = config.sampling.batch_size x = runner.sample_image(noise, model) x = inverse_data_transform(config, x) for i, xi in enumerate(x): tvu.save_image(xi, 'run/prune_ssim_2/{}/{}.png'.format(args.stage, i)) grid = tvu.make_grid(x) tvu.save_image(grid, 'run/vis_2/pruned-{}.png'.format(args.stage)) print(loss_list) if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/prune_test.py ================================================ import argparse import traceback import shutil import logging import yaml import sys import os import torch import numpy as np import torch.utils.tensorboard as tb from tqdm import tqdm from runners.diffusion import Diffusion from torchvision import transforms import torchvision from datasets import get_dataset, data_transform, inverse_data_transform import torchvision.utils as tvu from utils import UnlabeledImageFolder torch.set_printoptions(sci_mode=False) def parse_args_and_config(): parser = argparse.ArgumentParser(description=globals()["__doc__"]) parser.add_argument( "--config", type=str, required=True, help="Path to the config file" ) parser.add_argument("--seed", type=int, default=2333, help="Random seed") parser.add_argument("--taylor_batch_size", type=int, default=128, help="batch size for taylor expansion") parser.add_argument( "--exp", type=str, default="exp", help="Path for saving running related data." ) parser.add_argument( "--doc", type=str, required=True, help="A string for documentation purpose. " "Will be the name of the log folder.", ) parser.add_argument( "--comment", type=str, default="", help="A string for experiment comment" ) parser.add_argument( "--load_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--save_pruned_model", type=str, default=None, help="load pruned models" ) parser.add_argument( "--verbose", type=str, default="info", help="Verbose level: info | debug | warning | critical", ) parser.add_argument("--test", action="store_true", help="Whether to test the model") parser.add_argument( "--sample", action="store_true", help="Whether to produce samples from the model", ) parser.add_argument("--fid", action="store_true") parser.add_argument("--interpolation", action="store_true") parser.add_argument( "--resume_training", action="store_true", help="Whether to resume training" ) parser.add_argument( "-i", "--image_folder", type=str, default="images", help="The folder name of samples", ) parser.add_argument( "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_generated_samples", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument( "--use_ema", action="store_true", help="No interaction. Suitable for Slurm Job launcher", ) parser.add_argument("--use_pretrained", action="store_true") parser.add_argument( "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)", ) parser.add_argument( "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)", ) parser.add_argument( "--pruner", type=str, default="taylor", choices=["taylor", "random", "magnitude", "reinit", "first_order_taylor", "second_order_taylor", 'abs_taylor', 'fisher', 'ours'], ) parser.add_argument( "--restore_from", type=str, default=None, help="Restore from user a checkpoint", ) parser.add_argument( "--timesteps", type=int, default=1000, help="number of steps involved" ) parser.add_argument( "--eta", type=float, default=0.0, help="eta used to control the variances of sigma", ) parser.add_argument( "--pruning_ratio", type=float, default=0.0, help="pruning ratio", ) parser.add_argument("--sequence", action="store_true") args = parser.parse_args() args.log_path = os.path.join(args.exp, "logs", args.doc) # parse config file with open(os.path.join("configs", args.config), "r") as f: config = yaml.safe_load(f) new_config = dict2namespace(config) # add device device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") logging.info("Using device: {}".format(device)) new_config.device = device # set random seed torch.manual_seed(args.seed) np.random.seed(args.seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed) torch.backends.cudnn.benchmark = True return args, new_config def dict2namespace(config): namespace = argparse.Namespace() for key, value in config.items(): if isinstance(value, dict): new_value = dict2namespace(value) else: new_value = value setattr(namespace, key, new_value) return namespace def main(): args, config = parse_args_and_config() runner = Diffusion(args, config) torch.manual_seed(10) if True or args.pruning_ratio > 0 and args.load_pruned_model is None: # Dataset print(config) dataset, _ = get_dataset(args, config) print(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.taylor_batch_size, shuffle=True, num_workers=4, drop_last=True ) from models.diffusion import AttnBlock import torch_pruning as tp print("Pruning ...") model = runner.model.eval() model.to(runner.device) example_inputs = {'x': torch.randn(1, 3, config.data.image_size, config.data.image_size).to(runner.device), 't': torch.ones(1).to(runner.device)} if args.pruner == 'taylor': imp = tp.importance.TaylorImportance() elif args.pruner == 'first_order_taylor': imp = tp.importance.FullTaylorImportance(order=1) elif args.pruner == 'second_order_taylor': imp = tp.importance.FullTaylorImportance(order=2) elif args.pruner == 'random' or args.pruner == 'reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() elif args.pruner == 'abs_taylor': imp = tp.importance.AbsTaylorImportance() elif args.pruner == 'fisher': imp = tp.importance.FisherImportance() elif args.pruner == 'ours': imp = tp.importance.TaylorImportance() ignored_layers = [model.conv_out, model.temb] channel_groups = {} iterative_steps = 1 pruner = tp.pruner.MagnitudePruner( model, example_inputs, importance=imp, iterative_steps=iterative_steps, channel_groups =channel_groups, ch_sparsity=args.pruning_ratio, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear] ) #torch.manual_seed(10) base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs) image_path = args.save_pruned_model.replace('.pth', '') n = config.sampling.batch_size noise = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=runner.device, ) if 'taylor' in args.pruner or 'fisher' in args.pruner or 'ours' in args.pruner: if args.use_generated_samples: with torch.no_grad(): x = runner.sample_image(noise, model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, image_path+'-generated.png') else: x = iter(train_dataloader).next() if isinstance(x, (list, tuple)): x = x[0] x = x.to(runner.device) x = data_transform(config, x) x = x.to(runner.device) n = x.size(0) e = torch.randn_like(x) b = runner.betas alphas = 1.0 - b alphas_cumprod = alphas.cumprod(dim=0) #weights = torch.ones(1000) # torch.linspace(100, 1, 1000) #((1 - alphas) / (torch.sqrt( alphas_cumprod * (1 - alphas_cumprod) ) + 1e-8)).clamp(min=1) #print(weights) #t = torch.randint( # low=0, high=runner.num_timesteps, size=(n // 2 + 1,) #).to(runner.device) #t = torch.cat([t, runner.num_timesteps - t - 1], dim=0)[:n] from functions.losses import loss_registry model.zero_grad() if args.pruner=='abs_taylor': imp.set_model(model) for step_k in tqdm(range(0, 400)): t = torch.ones(n, dtype=torch.long).to(runner.device) * step_k loss = loss_registry[config.model.type](model, x, t, e, b) #if args.pruner == 'ours': # loss = weights[step_k] * loss loss.backward() print("============ Before Pruning ============") print(model) for g in pruner.step(interactive=True): g.prune() if args.pruner == 'reinit': def reset_parameters(model): for m in model.modules(): if hasattr(m, 'reset_parameters'): m.reset_parameters() model.apply(reset_parameters) macs, nparams = tp.utils.count_ops_and_params(model, example_inputs) print("============ After Pruning ============") print(model) print("#Params: {:.4f} M => {:.4f} M".format(base_nparams/1e6, nparams/1e6)) print("#MACs: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) del pruner # Save pruned model os.makedirs(os.path.dirname(args.save_pruned_model), exist_ok=True) print("Saving pruned model as {}".format(args.save_pruned_model)) torch.save( model, args.save_pruned_model ) with torch.no_grad(): n = config.sampling.batch_size #x = torch.randn( # n, # config.data.channels, # config.data.image_size, # config.data.image_size, # device=runner.device, #) x = runner.sample_image(noise, model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, image_path+'-pruned.png') return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: ddpm_exp/runners/__init__.py ================================================ ================================================ FILE: ddpm_exp/runners/diffusion.py ================================================ import os import logging import time import glob import numpy as np import tqdm import torch import torch.utils.data as data from models.diffusion import Model from models.ema import EMAHelper from functions import get_optimizer from functions.losses import noise_estimation_loss, noise_estimation_kd_loss from datasets import get_dataset, data_transform, inverse_data_transform from functions.ckpt_util import get_ckpt_path import torchvision.utils as tvu from accelerate import Accelerator def torch2hwcuint8(x, clip=False): if clip: x = torch.clamp(x, -1, 1) x = (x + 1.0) / 2.0 return x def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps): def sigmoid(x): return 1 / (np.exp(-x) + 1) if beta_schedule == "quad": betas = ( np.linspace( beta_start ** 0.5, beta_end ** 0.5, num_diffusion_timesteps, dtype=np.float64, ) ** 2 ) elif beta_schedule == "linear": betas = np.linspace( beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64 ) elif beta_schedule == "const": betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64) elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1 betas = 1.0 / np.linspace( num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64 ) elif beta_schedule == "sigmoid": betas = np.linspace(-6, 6, num_diffusion_timesteps) betas = sigmoid(betas) * (beta_end - beta_start) + beta_start else: raise NotImplementedError(beta_schedule) assert betas.shape == (num_diffusion_timesteps,) return betas class Diffusion(object): def __init__(self, args, config, device=None): self.args = args self.config = config if device is None: device = ( torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") ) self.device = device self.model_var_type = config.model.var_type betas = get_beta_schedule( beta_schedule=config.diffusion.beta_schedule, beta_start=config.diffusion.beta_start, beta_end=config.diffusion.beta_end, num_diffusion_timesteps=config.diffusion.num_diffusion_timesteps, ) betas = self.betas = torch.from_numpy(betas).float().to(self.device) self.num_timesteps = betas.shape[0] alphas = 1.0 - betas alphas_cumprod = alphas.cumprod(dim=0) alphas_cumprod_prev = torch.cat( [torch.ones(1).to(device), alphas_cumprod[:-1]], dim=0 ) posterior_variance = ( betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod) ) if self.model_var_type == "fixedlarge": self.logvar = betas.log() # torch.cat( # [posterior_variance[1:2], betas[1:]], dim=0).log() elif self.model_var_type == "fixedsmall": self.logvar = posterior_variance.clamp(min=1e-20).log() self.build_model() def build_model(self): args, config = self.args, self.config model = Model(config) if args.load_pruned_model is not None: print("Loading pruned model from {}".format(args.load_pruned_model)) states = torch.load(args.load_pruned_model, map_location='cpu') if isinstance(states, torch.nn.Module): # a simple pruned model model = torch.load(args.load_pruned_model, map_location='cpu') elif isinstance(states, list): # pruned model and training states model = states[0] if args.use_ema and self.config.model.ema: print("Loading EMA") ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) ema_helper.load_state_dict(states[-1]) ema_helper.ema(model) self.ema_helper = ema_helper else: self.ema_helper = None else: raise NotImplementedError self.model = model elif args.restore_from is not None and os.path.isfile(args.restore_from): ckpt = args.restore_from print("Loading checkpoint {}".format(ckpt)) states = torch.load( ckpt, map_location='cpu', ) if isinstance(states[0], torch.nn.Module): model = states[0] model = model.to(self.device) else: model = model.to(self.device) model.load_state_dict(states[0], strict=True) if args.use_ema and self.config.model.ema: print("Loading EMA") ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) ema_helper.load_state_dict(states[-1]) ema_helper.ema(model) self.ema_helper = ema_helper else: self.ema_helper = None elif not self.args.use_pretrained: if getattr(self.config.sampling, "ckpt_id", None) is None: ckpt = os.path.join(self.args.log_path, "ckpt.pth") states = torch.load( ckpt, map_location=self.config.device, ) else: ckpt = os.path.join( self.args.log_path, f"ckpt_{self.config.sampling.ckpt_id}.pth" ) states = torch.load( ckpt, map_location=self.config.device, ) print("Loading checkpoint {}".format(ckpt)) if isinstance(states[0], torch.nn.Module): model = states[0] model = model.to(self.device) else: model = model.to(self.device) model.load_state_dict(states[0], strict=True) if args.use_ema and self.config.model.ema: ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) ema_helper.load_state_dict(states[-1]) ema_helper.ema(model) self.ema_helper = ema_helper else: self.ema_helper = None else: # This used the pretrained DDPM model, see https://github.com/pesser/pytorch_diffusion if self.config.data.dataset == "CIFAR10": name = "cifar10" elif self.config.data.dataset == "LSUN": name = f"lsun_{self.config.data.category}" elif self.config.data.dataset == "CELEBA": name = 'celeba' ckpt = get_ckpt_path(f"ema_{name}") print("Loading checkpoint {}".format(ckpt)) states = torch.load(ckpt, map_location=self.device) if isinstance(states, (list,tuple)): model.load_state_dict(states[0]) else: model.load_state_dict(states) model.to(self.device) self.model = model def train(self, kd=False): accelerator = self.accelerator device = accelerator.device if kd: teacher = Model(self.config) if self.config.data.dataset == "CIFAR10": name = "cifar10" elif self.config.data.dataset == "LSUN": name = f"lsun_{self.config.data.category}" elif self.config.data.dataset == 'CELEBA': name = "celeba" ckpt = get_ckpt_path(f"ema_{name}") print("Loading teacher from {}".format(ckpt)) states = torch.load(ckpt, map_location='cpu') if isinstance(states, list): teacher.load_state_dict(states[0]) else: teacher.load_state_dict() teacher.to(accelerator.device) self.teacher = teacher.eval() args, config = self.args, self.config dataset, test_dataset = get_dataset(args, config) train_loader = data.DataLoader( dataset, batch_size=config.training.batch_size, shuffle=True, num_workers=config.data.num_workers, ) model = self.model optimizer = get_optimizer(self.config, model.parameters()) if args.use_ema and self.config.model.ema: ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) else: ema_helper = None start_epoch, step = 0, 0 if self.args.resume_training: states = torch.load(os.path.join(self.args.log_path, "ckpt.pth")) if isinstance(states[0], torch.nn.Module): self.model = model = states[0] optimizer = get_optimizer(self.config, model.parameters()) # rebuild the optimizer else: model.load_state_dict(states[0]) states[1]["param_groups"][0]["eps"] = self.config.optim.eps optimizer.load_state_dict(states[1]) start_epoch = states[2] step = states[3] if self.config.model.ema: ema_helper.load_state_dict(states[4]) model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader) self.model = model if ema_helper is not None: ema_helper.to(accelerator.device) if accelerator.is_main_process: unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.eval() if ema_helper is not None: ema_helper.store(unwrapped_model.parameters()) ema_helper.copy_to(unwrapped_model.parameters()) with torch.no_grad(): n = config.sampling.batch_size x = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=accelerator.device, ) x = self.sample_image(x, unwrapped_model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, os.path.join(args.log_path, 'vis', 'Init.png')) #tb_logger.add_image('Before Training', grid, global_step=0) if args.use_ema: ema_helper.restore(unwrapped_model.parameters()) for epoch in range(start_epoch, self.config.training.n_epochs): data_start = time.time() data_time = 0 for i, (x, y) in enumerate(train_loader): n = x.size(0) data_time += time.time() - data_start model.train() step += 1 x = x.to(self.device) x = data_transform(self.config, x) e = torch.randn_like(x) b = self.betas # antithetic sampling t = torch.randint( low=0, high=self.num_timesteps, size=(n // 2 + 1,) ).to(self.device) t = torch.cat([t, self.num_timesteps - t - 1], dim=0)[:n] if kd: loss = noise_estimation_kd_loss(model, teacher, x, t, e, b) else: loss = noise_estimation_loss(model, x, t, e, b) #tb_logger.add_scalar("loss", loss, global_step=step) if step % 100 == 0: logging.info( f"step: {step} (Ep={epoch}/{self.config.training.n_epochs}, Iter={i}/{len(train_loader)}), loss: {loss.item()}, data time: {data_time / (i+1)}" ) optimizer.zero_grad() accelerator.backward(loss) try: if accelerator.sync_gradients: accelerator.clip_grad_norm_(model.parameters(), config.optim.grad_clip) except Exception: pass optimizer.step() if args.use_ema and self.config.model.ema: ema_helper.update(model) accelerator.wait_for_everyone() if accelerator.is_main_process: if step % self.config.training.snapshot_freq == 0 or step == 1: unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.eval() unwrapped_model.zero_grad() states = [ unwrapped_model, optimizer.state_dict(), epoch, step, ] if args.use_ema and self.config.model.ema: states.append(ema_helper.state_dict()) torch.save( states, os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)), ) torch.save(states, os.path.join(self.args.log_path, "ckpt.pth")) data_start = time.time() accelerator.wait_for_everyone() # Sampling for visualization if accelerator.is_main_process: unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.eval() if ema_helper is not None: ema_helper.store(unwrapped_model.parameters()) ema_helper.copy_to(unwrapped_model.parameters()) with torch.no_grad(): n = config.sampling.batch_size x = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=accelerator.device, ) x = self.sample_image(x, model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, os.path.join(args.log_path, 'vis', 'epoch-{}.png'.format(epoch))) if args.use_ema: ema_helper.restore(unwrapped_model.parameters()) accelerator.end_training() def sample(self): accelerator = self.accelerator model = self.model model.to(accelerator.device) model.eval() if self.args.fid: self.sample_fid(model) elif self.args.interpolation: self.sample_interpolation(model) elif self.args.sequence: self.sample_sequence(model) else: raise NotImplementedError("Sample procedeure not defined") def sample_fid(self, model): args = self.args accelerator = self.accelerator import torch torch.manual_seed(accelerator.process_index+args.seed) import random random.seed(accelerator.process_index+args.seed) import numpy as np np.random.seed(accelerator.process_index+args.seed) config = self.config img_id = len(glob.glob(f"{self.args.image_folder}/*")) print(f"starting from image {img_id}") total_n_samples = 50000 n_rounds = (total_n_samples - img_id) // config.sampling.batch_size #os.makedirs(os.path.join(self.args.image_folder, '{}'.format(accelerator.process_index)), exist_ok=True) with torch.no_grad(): for _ in tqdm.tqdm( range(n_rounds), desc="Generating image samples for FID evaluation." ): n = config.sampling.batch_size x = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=accelerator.device, ) x = self.sample_image(x, model) x = inverse_data_transform(config, x) for i in range(n): tvu.save_image( x[i], os.path.join(self.args.image_folder, f"{img_id}.png") ) img_id += 1 def sample_sequence(self, model): config = self.config x = torch.randn( 8, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) # NOTE: This means that we are producing each predicted x0, not x_{t-1} at timestep t. with torch.no_grad(): _, x = self.sample_image(x, model, last=False) x = [inverse_data_transform(config, y) for y in x] for i in range(len(x)): for j in range(x[i].size(0)): tvu.save_image( x[i][j], os.path.join(self.args.image_folder, f"{j}_{i}.png") ) def sample_interpolation(self, model): config = self.config def slerp(z1, z2, alpha): theta = torch.acos(torch.sum(z1 * z2) / (torch.norm(z1) * torch.norm(z2))) return ( torch.sin((1 - alpha) * theta) / torch.sin(theta) * z1 + torch.sin(alpha * theta) / torch.sin(theta) * z2 ) z1 = torch.randn( 1, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) z2 = torch.randn( 1, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) alpha = torch.arange(0.0, 1.01, 0.1).to(z1.device) z_ = [] for i in range(alpha.size(0)): z_.append(slerp(z1, z2, alpha[i])) x = torch.cat(z_, dim=0) xs = [] # Hard coded here, modify to your preferences with torch.no_grad(): for i in range(0, x.size(0), 8): xs.append(self.sample_image(x[i : i + 8], model)) x = inverse_data_transform(config, torch.cat(xs, dim=0)) for i in range(x.size(0)): tvu.save_image(x[i], os.path.join(self.args.image_folder, f"{i}.png")) def sample_image(self, x, model, last=True): try: skip = self.args.skip except Exception: skip = 1 if self.args.sample_type == "generalized": if self.args.skip_type == "uniform": skip = self.num_timesteps // self.args.timesteps seq = range(0, self.num_timesteps, skip) elif self.args.skip_type == "quad": seq = ( np.linspace( 0, np.sqrt(self.num_timesteps * 0.8), self.args.timesteps ) ** 2 ) seq = [int(s) for s in list(seq)] else: raise NotImplementedError from functions.denoising import generalized_steps xs = generalized_steps(x, seq, model, self.betas, eta=self.args.eta) x = xs elif self.args.sample_type == "ddpm_noisy": if self.args.skip_type == "uniform": skip = self.num_timesteps // self.args.timesteps seq = range(0, self.num_timesteps, skip) elif self.args.skip_type == "quad": seq = ( np.linspace( 0, np.sqrt(self.num_timesteps * 0.8), self.args.timesteps ) ** 2 ) seq = [int(s) for s in list(seq)] else: raise NotImplementedError from functions.denoising import ddpm_steps x = ddpm_steps(x, seq, model, self.betas) else: raise NotImplementedError if last: x = x[0][-1] return x def test(self): pass ================================================ FILE: ddpm_exp/runners/diffusion_simple.py ================================================ import os import logging import time import glob import numpy as np import tqdm import torch import torch.utils.data as data from models.diffusion import Model from models.ema import EMAHelper from functions import get_optimizer from functions.losses import loss_registry from datasets import get_dataset, data_transform, inverse_data_transform from functions.ckpt_util import get_ckpt_path import torchvision.utils as tvu def torch2hwcuint8(x, clip=False): if clip: x = torch.clamp(x, -1, 1) x = (x + 1.0) / 2.0 return x def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps): def sigmoid(x): return 1 / (np.exp(-x) + 1) if beta_schedule == "quad": betas = ( np.linspace( beta_start ** 0.5, beta_end ** 0.5, num_diffusion_timesteps, dtype=np.float64, ) ** 2 ) elif beta_schedule == "linear": betas = np.linspace( beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64 ) elif beta_schedule == "const": betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64) elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1 betas = 1.0 / np.linspace( num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64 ) elif beta_schedule == "sigmoid": betas = np.linspace(-6, 6, num_diffusion_timesteps) betas = sigmoid(betas) * (beta_end - beta_start) + beta_start else: raise NotImplementedError(beta_schedule) assert betas.shape == (num_diffusion_timesteps,) return betas class Diffusion(object): def __init__(self, args, config, device=None): self.args = args self.config = config if device is None: device = ( torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") ) self.device = device self.model_var_type = config.model.var_type betas = get_beta_schedule( beta_schedule=config.diffusion.beta_schedule, beta_start=config.diffusion.beta_start, beta_end=config.diffusion.beta_end, num_diffusion_timesteps=config.diffusion.num_diffusion_timesteps, ) betas = self.betas = torch.from_numpy(betas).float().to(self.device) self.num_timesteps = betas.shape[0] alphas = 1.0 - betas alphas_cumprod = alphas.cumprod(dim=0) alphas_cumprod_prev = torch.cat( [torch.ones(1).to(device), alphas_cumprod[:-1]], dim=0 ) posterior_variance = ( betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod) ) if self.model_var_type == "fixedlarge": self.logvar = betas.log() # torch.cat( # [posterior_variance[1:2], betas[1:]], dim=0).log() elif self.model_var_type == "fixedsmall": self.logvar = posterior_variance.clamp(min=1e-20).log() self.build_model() def build_model(self): args, config = self.args, self.config model = Model(config) if args.restore_from is not None and os.path.isfile(args.restore_from): ckpt = args.restore_from print("Loading checkpoint {}".format(ckpt)) states = torch.load( ckpt, map_location=self.config.device, ) if isinstance(states[0], torch.nn.Module): model = states[0] model = model.to(self.device) else: model = model.to(self.device) model.load_state_dict(states[0], strict=True) if args.use_ema and self.config.model.ema: print("Loading EMA") ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) ema_helper.load_state_dict(states[-1]) ema_helper.ema(model) self.ema_helper = ema_helper else: self.ema_helper = None elif not self.args.use_pretrained: if getattr(self.config.sampling, "ckpt_id", None) is None: ckpt = os.path.join(self.args.log_path, "ckpt.pth") states = torch.load( ckpt, map_location=self.config.device, ) else: ckpt = os.path.join( self.args.log_path, f"ckpt_{self.config.sampling.ckpt_id}.pth" ) states = torch.load( ckpt, map_location=self.config.device, ) print("Loading checkpoint {}".format(ckpt)) if isinstance(states[0], torch.nn.Module): model = states[0] model = model.to(self.device) else: model = model.to(self.device) model.load_state_dict(states[0], strict=True) if args.use_ema and self.config.model.ema: ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) ema_helper.load_state_dict(states[-1]) ema_helper.ema(model) self.ema_helper = ema_helper else: self.ema_helper = None else: # This used the pretrained DDPM model, see https://github.com/pesser/pytorch_diffusion if self.config.data.dataset == "CIFAR10": name = "cifar10" elif self.config.data.dataset == "LSUN": name = f"lsun_{self.config.data.category}" elif self.config.data.dataset == "CELEBA": name = 'celeba' ckpt = get_ckpt_path(f"ema_{name}") print("Loading checkpoint {}".format(ckpt)) states = torch.load(ckpt, map_location=self.device) if isinstance(states, (list,tuple)): model.load_state_dict(states[0]) else: model.load_state_dict(states) model.to(self.device) self.model = model def train(self): args, config = self.args, self.config dataset, test_dataset = get_dataset(args, config) train_loader = data.DataLoader( dataset, batch_size=config.training.batch_size, shuffle=True, num_workers=config.data.num_workers, ) model = self.model model = model.to(self.device) #model = torch.nn.DataParallel(model) optimizer = get_optimizer(self.config, model.parameters()) if args.use_ema and self.config.model.ema: ema_helper = EMAHelper(mu=self.config.model.ema_rate) ema_helper.register(model) else: ema_helper = None #ema_helper = self.ema_helper start_epoch, step = 0, 0 if self.args.resume_training: states = torch.load(os.path.join(self.args.log_path, "ckpt.pth")) model.load_state_dict(states[0]) states[1]["param_groups"][0]["eps"] = self.config.optim.eps optimizer.load_state_dict(states[1]) start_epoch = states[2] step = states[3] if self.config.model.ema: ema_helper.load_state_dict(states[4]) model.eval() with torch.no_grad(): n = config.sampling.batch_size x = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) x = self.sample_image(x, model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, os.path.join(args.log_path, 'vis', 'Init.png')) #tb_logger.add_image('Before Training', grid, global_step=0) for epoch in range(start_epoch, self.config.training.n_epochs): data_start = time.time() data_time = 0 for i, (x, y) in enumerate(train_loader): n = x.size(0) data_time += time.time() - data_start model.train() step += 1 x = x.to(self.device) x = data_transform(self.config, x) e = torch.randn_like(x) b = self.betas # antithetic sampling t = torch.randint( low=0, high=self.num_timesteps, size=(n // 2 + 1,) ).to(self.device) t = torch.cat([t, self.num_timesteps - t - 1], dim=0)[:n] loss = loss_registry[config.model.type](model, x, t, e, b) #tb_logger.add_scalar("loss", loss, global_step=step) if step % 100 == 0: logging.info( f"step: {step} (Ep={epoch}/{self.config.training.n_epochs}, Iter={i}/{len(train_loader)}), loss: {loss.item()}, data time: {data_time / (i+1)}" ) optimizer.zero_grad() loss.backward() try: torch.nn.utils.clip_grad_norm_( model.parameters(), config.optim.grad_clip ) except Exception: pass optimizer.step() if args.use_ema and self.config.model.ema: ema_helper.update(model) if step % self.config.training.snapshot_freq == 0 or step == 1: model.zero_grad() states = [ model, optimizer.state_dict(), epoch, step, ] if args.use_ema and self.config.model.ema: states.append(ema_helper.state_dict()) torch.save( states, os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)), ) torch.save(states, os.path.join(self.args.log_path, "ckpt.pth")) data_start = time.time() # Sampling for visualization model.eval() with torch.no_grad(): n = config.sampling.batch_size x = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) x = self.sample_image(x, model) x = inverse_data_transform(config, x) grid = tvu.make_grid(x) tvu.save_image(grid, os.path.join(args.log_path, 'vis', 'epoch-{}.png'.format(epoch))) def sample(self): model = self.model #Model(self.config) model.to(self.device) #model = torch.nn.DataParallel(model) model.eval() if self.args.fid: self.sample_fid(model) elif self.args.interpolation: self.sample_interpolation(model) elif self.args.sequence: self.sample_sequence(model) else: raise NotImplementedError("Sample procedeure not defined") def sample_fid(self, model): import torch torch.manual_seed(0) import random random.seed(0) import numpy as np np.random.seed(0) config = self.config img_id = len(glob.glob(f"{self.args.image_folder}/*")) print(f"starting from image {img_id}") total_n_samples = 50000 n_rounds = (total_n_samples - img_id) // config.sampling.batch_size with torch.no_grad(): for _ in tqdm.tqdm( range(n_rounds), desc="Generating image samples for FID evaluation." ): n = config.sampling.batch_size x = torch.randn( n, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) x = self.sample_image(x, model) x = inverse_data_transform(config, x) for i in range(n): tvu.save_image( x[i], os.path.join(self.args.image_folder, f"{img_id}.png") ) img_id += 1 def sample_sequence(self, model): config = self.config x = torch.randn( 8, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) # NOTE: This means that we are producing each predicted x0, not x_{t-1} at timestep t. with torch.no_grad(): _, x = self.sample_image(x, model, last=False) x = [inverse_data_transform(config, y) for y in x] for i in range(len(x)): for j in range(x[i].size(0)): tvu.save_image( x[i][j], os.path.join(self.args.image_folder, f"{j}_{i}.png") ) def sample_interpolation(self, model): config = self.config def slerp(z1, z2, alpha): theta = torch.acos(torch.sum(z1 * z2) / (torch.norm(z1) * torch.norm(z2))) return ( torch.sin((1 - alpha) * theta) / torch.sin(theta) * z1 + torch.sin(alpha * theta) / torch.sin(theta) * z2 ) z1 = torch.randn( 1, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) z2 = torch.randn( 1, config.data.channels, config.data.image_size, config.data.image_size, device=self.device, ) alpha = torch.arange(0.0, 1.01, 0.1).to(z1.device) z_ = [] for i in range(alpha.size(0)): z_.append(slerp(z1, z2, alpha[i])) x = torch.cat(z_, dim=0) xs = [] # Hard coded here, modify to your preferences with torch.no_grad(): for i in range(0, x.size(0), 8): xs.append(self.sample_image(x[i : i + 8], model)) x = inverse_data_transform(config, torch.cat(xs, dim=0)) for i in range(x.size(0)): tvu.save_image(x[i], os.path.join(self.args.image_folder, f"{i}.png")) def sample_image(self, x, model, last=True): try: skip = self.args.skip except Exception: skip = 1 if self.args.sample_type == "generalized": if self.args.skip_type == "uniform": skip = self.num_timesteps // self.args.timesteps seq = range(0, self.num_timesteps, skip) elif self.args.skip_type == "quad": seq = ( np.linspace( 0, np.sqrt(self.num_timesteps * 0.8), self.args.timesteps ) ** 2 ) seq = [int(s) for s in list(seq)] else: raise NotImplementedError from functions.denoising import generalized_steps xs = generalized_steps(x, seq, model, self.betas, eta=self.args.eta) x = xs elif self.args.sample_type == "ddpm_noisy": if self.args.skip_type == "uniform": skip = self.num_timesteps // self.args.timesteps seq = range(0, self.num_timesteps, skip) elif self.args.skip_type == "quad": seq = ( np.linspace( 0, np.sqrt(self.num_timesteps * 0.8), self.args.timesteps ) ** 2 ) seq = [int(s) for s in list(seq)] else: raise NotImplementedError from functions.denoising import ddpm_steps x = ddpm_steps(x, seq, model, self.betas) else: raise NotImplementedError if last: x = x[0][-1] return x def test(self): pass ================================================ FILE: ddpm_exp/scripts/finetune_bedroom_ddpm.sh ================================================ python -m torch.distributed.launch --nproc_per_node=6 --master_port 22223 --use_env finetune.py \ --config bedroom.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune/bedroom_ddpm_$1_0.3_finetuned-continue-v4-2e-5 \ --doc post_training \ --skip_type uniform \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --load_pruned_model run/finetune/bedroom_ddpm_taylor_0.3_finetuned-continue-v3-2e-6/logs/post_training/ckpt_65000.pth \ ================================================ FILE: ddpm_exp/scripts/finetune_celeba_ddpm.sh ================================================ python finetune.py \ --config celeba.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_final/celeba_T=$1_finetuned \ --doc post_training \ --skip_type uniform \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --load_pruned_model "run/pruned_final/celeba_T=$1.pth" \ ================================================ FILE: ddpm_exp/scripts/finetune_celeba_ddpm_kd.sh ================================================ python finetune.py \ --config celeba.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_v2/celeba_ddpm_$1_0.3_finetuned_kd \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --kd \ --load_pruned_model run/pruned/celeba_ddpm_$1_0.3.pth \ ================================================ FILE: ddpm_exp/scripts/finetune_church_ddpm.sh ================================================ python -m torch.distributed.launch --nproc_per_node=4 --master_port 22223 --use_env finetune.py \ --config church.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune/church_ddpm_$1_0.3_finetuned \ --doc post_training \ --skip_type uniform \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --load_pruned_model run/pruned/church_ddpm_$1_0.3.pth \ ================================================ FILE: ddpm_exp/scripts/finetune_cifar_ddpm.sh ================================================ python finetune.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_v3/cifar10_ddpm_$1_finetuned_0.05T.pth \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --load_pruned_model run/pruned_v5/cifar10_pruned_$1.pth \ ================================================ FILE: ddpm_exp/scripts/finetune_cifar_ddpm_kd.sh ================================================ python finetune.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_v2/cifar10_ddpm_$1_0.3_finetuned_kd \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --kd \ --load_pruned_model run/pruned/cifar10_pruned_$1_0.3.pth \ ================================================ FILE: ddpm_exp/scripts/finetune_cifar_ddpm_random.sh ================================================ python -m torch.distributed.launch --nproc_per_node=2 --master_port 22223 --use_env finetune.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune/cifar10_pruned_random_0.3_finetuned\ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner random \ --load_pruned_model run/pruned/cifar10_pruned_random_0.3.pth \ ================================================ FILE: ddpm_exp/scripts/finetune_cifar_ddpm_taylor.sh ================================================ python finetune.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune/cifar10_pruned_taylor_0.3_real_x_finetuned \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner taylor \ --load_pruned_model run/pruned/cifar10_pruned_taylor_0.3_real_x.pth \ ================================================ FILE: ddpm_exp/scripts/old/run_bedroom_sample_pratrained.sh ================================================ python prune.py \ --config bedroom.yml \ --exp run/ddim_bedroom_official \ --sample \ --use_pretrained \ --timesteps 50 \ --eta 0 \ --ni \ --doc 50steps_quad \ --skip_type quad \ --pruning_ratio 0.0 \ --fid \ --use_ema ================================================ FILE: ddpm_exp/scripts/old/run_celeba_pruning_scratch.sh ================================================ python prune.py \ --config celeba.yml \ --exp run/ddim_celeba_pruning_reinit \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner reinit \ --taylor_batch_size 96 \ ================================================ FILE: ddpm_exp/scripts/old/run_celeba_pruning_taylor.sh ================================================ python prune.py \ --config celeba.yml \ --exp run/ddim_celeba_pruning_taylor \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner taylor \ --taylor_batch_size 96 \ ================================================ FILE: ddpm_exp/scripts/old/run_celeba_sample_pratrained.sh ================================================ python prune.py \ --config celeba.yml \ --exp run/ddim_celeba_official \ --sample \ --use_pretrained \ --timesteps 100 \ --eta 0 \ --ni \ --doc 100steps_quad \ --skip_type quad \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from run/cache/diffusion_models_converted/celeba/ckpt.pth \ ================================================ FILE: ddpm_exp/scripts/old/run_church_pruning_taylor.sh ================================================ python prune.py \ --config church.yml \ --exp run/ddim_church_pruning_taylor \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner random \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_first_order_taylor.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_first_order_taylor \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner first_order_taylor \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_magnitude.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_magnitude \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner magnitude \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_random.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_random \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner random \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_random_kd.sh ================================================ python prune_kd.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_random \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner random \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_scratch.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_reinit \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner reinit \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_second_order_taylor.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_second_order_taylor \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner second_order_taylor \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_taylor.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_taylor \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner taylor \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_pruning_taylor_kd.sh ================================================ python prune_kd.py \ --config cifar10.yml \ --exp run/ddim_cifar10_pruning_taylor_kd \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner taylor \ ================================================ FILE: ddpm_exp/scripts/old/run_cifar_train.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/ddim_cifar10_train_v2 \ --use_pretrained \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training_with_0.2_pruning_ratio_v2 \ --skip_type quad \ --pruning_ratio 0.2 \ --use_ema \ ================================================ FILE: ddpm_exp/scripts/prune_bedroom_ddpm.sh ================================================ #!/bin/bash # Execute the Python script with the provided arguments python prune.py \ --config "bedroom.yml" \ --timesteps "100" \ --eta "0" \ --ni \ --doc "post_training" \ --skip_type "quad" \ --pruning_ratio "0.3" \ --use_ema \ --use_pretrained \ --pruner "$1" \ --save_pruned_model "run/pruned/bedroom_ddpm_$1_0.3.pth" \ --taylor_batch_size "4" ================================================ FILE: ddpm_exp/scripts/prune_bedroom_ddpm_test.sh ================================================ #!/bin/bash # Execute the Python script with the provided arguments python prune_test.py \ --config "bedroom.yml" \ --timesteps "100" \ --eta "0" \ --ni \ --doc "post_training" \ --skip_type "quad" \ --pruning_ratio "0.05" \ --use_ema \ --use_pretrained \ --pruner "$1" \ --save_pruned_model "run/pruned_test/bedroom_ddpm_$1.pth" \ --taylor_batch_size "4" ================================================ FILE: ddpm_exp/scripts/prune_celeba_ddpm.sh ================================================ #!/bin/bash # Execute the Python script with the provided arguments python prune.py \ --config "celeba.yml" \ --timesteps "100" \ --eta "0" \ --ni \ --doc "post_training" \ --skip_type "quad" \ --pruning_ratio "0.3" \ --use_ema \ --use_pretrained \ --pruner "ours" \ --save_pruned_model "run/pruned_final/celeba_T=$1.pth" \ --taylor_batch_size "64" \ --thr "$1" ================================================ FILE: ddpm_exp/scripts/prune_celeba_ddpm_ssim.sh ================================================ python prune_ssim.py \ --config celeba.yml \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.15 \ --use_ema \ --use_pretrained \ --stage $1 \ --pruner "ours" \ --save_pruned_model run/pruned_v4/celeba_pruned.pth \ --taylor_batch_size 64 ================================================ FILE: ddpm_exp/scripts/prune_church_ddpm.sh ================================================ #!/bin/bash # Execute the Python script with the provided arguments python prune.py \ --config "church.yml" \ --timesteps "100" \ --eta "0" \ --ni \ --doc "post_training" \ --skip_type "quad" \ --pruning_ratio "0.3" \ --use_ema \ --use_pretrained \ --pruner "$1" \ --save_pruned_model "run/pruned/church_ddpm_$1_0.3.pth" \ --taylor_batch_size "2" ================================================ FILE: ddpm_exp/scripts/prune_church_ddpm_test.sh ================================================ #!/bin/bash # Execute the Python script with the provided arguments python prune_test.py \ --config "church.yml" \ --timesteps "100" \ --eta "0" \ --ni \ --doc "post_training" \ --skip_type "quad" \ --pruning_ratio "0.05" \ --use_ema \ --use_pretrained \ --pruner "$1" \ --save_pruned_model "run/pruned_test/church_ddpm_$1.pth" \ --taylor_batch_size "4" ================================================ FILE: ddpm_exp/scripts/prune_cifar_ddpm.sh ================================================ python prune.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner "$1" \ --save_pruned_model run/pruned_v5/cifar10_pruned_$1_$2.pth \ --thr $2 \ ================================================ FILE: ddpm_exp/scripts/prune_cifar_ddpm_ssim.sh ================================================ python prune_ssim.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.2 \ --use_ema \ --use_pretrained \ --stage $1 \ --pruner "ours" \ --save_pruned_model run/pruned_v4/cifar10_pruned.pth \ ================================================ FILE: ddpm_exp/scripts/prune_cifar_ddpm_test.sh ================================================ python prune_test.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --pruner "$1" \ --save_pruned_model run/pruned_test/cifar10_pruned_$1_0.2.pth \ ================================================ FILE: ddpm_exp/scripts/run_celeba.sh ================================================ #!/bin/bash # Execute the Python script with the provided arguments python prune.py \ --config "celeba.yml" \ --timesteps "100" \ --eta "0" \ --ni \ --doc "post_training" \ --skip_type "quad" \ --pruning_ratio "0.3" \ --use_ema \ --use_pretrained \ --pruner "ours" \ --save_pruned_model "run/pruned_final/celeba_T=$1.pth" \ --taylor_batch_size "64" \ --thr "$1" python finetune.py \ --config celeba.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_final/celeba_T=$1_finetuned \ --doc post_training \ --skip_type uniform \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --load_pruned_model "run/pruned_final/celeba_T=$1.pth" \ ================================================ FILE: ddpm_exp/scripts/sample_bedroom_ddpm_pretrained.sh ================================================ python -m torch.distributed.launch --nproc_per_node=1 --master_port 22200 --use_env finetune.py \ --config bedroom.yml \ --exp $1 \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --use_pretrained \ ================================================ FILE: ddpm_exp/scripts/sample_bedroom_ddpm_pruning.sh ================================================ python -m torch.distributed.launch --nproc_per_node=4 --master_port 22223 --use_env finetune.py \ --config bedroom.yml \ --exp $2 \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from $1 \ ================================================ FILE: ddpm_exp/scripts/sample_celeba_ddpm_pruning.sh ================================================ python finetune.py \ --config celeba.yml \ --exp $2 \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from $1 \ ================================================ FILE: ddpm_exp/scripts/sample_celeba_pretrained.sh ================================================ python prune.py \ --config celeba.yml \ --exp run/sample/ddim_celeba_official \ --sample \ --use_pretrained \ --timesteps 100 \ --eta 0 \ --ni \ --doc official \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema ================================================ FILE: ddpm_exp/scripts/sample_church_ddpm_pruning.sh ================================================ python finetune.py \ --config church.yml \ --exp run/sample/church_ddpm_350k \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from run/finetune_v2/church_pruned_taylor_0.3_finetuned/logs/post_training/ckpt_350000.pth \ ================================================ FILE: ddpm_exp/scripts/sample_church_ddpm_pruning_old.sh ================================================ python -m torch.distributed.launch --nproc_per_node=4 --master_port 22221 --use_env finetune.py \ --config church.yml \ --exp $2 \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from $1 \ ================================================ FILE: ddpm_exp/scripts/sample_church_ddpm_test.sh ================================================ python finetune.py \ --config church.yml \ --exp run/sample/church_ddpm_350k \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from run/finetune_v2/church_pruned_taylor_0.3_finetuned/logs/post_training/ckpt_350000.pth \ ================================================ FILE: ddpm_exp/scripts/sample_church_pretrained.sh ================================================ python prune.py \ --config church.yml \ --exp run/sample/ddim_church_official \ --sample \ --use_pretrained \ --timesteps 100 \ --eta 0 \ --ni \ --doc official \ --skip_type uniform \ --pruning_ratio 0.0 \ --fid \ --use_ema ================================================ FILE: ddpm_exp/scripts/sample_cifar_ddpm_pruning.sh ================================================ python finetune.py \ --config cifar10.yml \ --exp "$2" \ --sample \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample \ --skip_type quad \ --pruning_ratio 0.0 \ --fid \ --use_ema \ --restore_from "$1" \ ================================================ FILE: ddpm_exp/scripts/sample_cifar_pretrained.sh ================================================ python prune.py \ --config cifar10.yml \ --exp run/sample/ddim_cifar10_official \ --sample \ --use_pretrained \ --timesteps 100 \ --eta 0 \ --ni \ --doc sample_100k \ --skip_type quad \ --pruning_ratio 0.0 \ --fid \ --use_ema ================================================ FILE: ddpm_exp/scripts/simple_celeba_our.sh ================================================ python finetune_simple.py \ --config celeba.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_simple/celeba_ours_T=$1.pth \ --doc post_training \ --skip_type uniform \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --thr $1 \ --pruner ours \ --taylor_batch_size 64 ================================================ FILE: ddpm_exp/scripts/simple_cifar_our.sh ================================================ python finetune_simple.py \ --config cifar10.yml \ --timesteps 100 \ --eta 0 \ --ni \ --exp run/finetune_simple_v2/cifar10_ours_T=$1.pth \ --doc post_training \ --skip_type quad \ --pruning_ratio 0.3 \ --use_ema \ --use_pretrained \ --thr $1 \ --pruner ours ================================================ FILE: ddpm_exp/tools/extract_cifar10.py ================================================ import os import torchvision from torchvision.datasets import CIFAR10 from tqdm import tqdm # Define the path to the folder where the images will be saved save_path = 'data/cifar10/images' # Create the folder if it doesn't exist if not os.path.exists(save_path): os.makedirs(save_path) # Load the CIFAR10 dataset dataset = CIFAR10(root='data/cifar10', train=True, download=True) # Loop through the dataset and save each image to the folder for i in tqdm(range(len(dataset))): image, label = dataset[i] image_name = f'{i}.png' image_path = os.path.join(save_path, image_name) image.save(image_path) ================================================ FILE: ddpm_exp/tools/transform_weights.py ================================================ import torch state = torch.load("model.ckpt.old") old_dict = state[0] print(state[0].keys()) state[0] = {pname.replace("module.", ''): pval for pname, pval in old_dict.items()} print(state[0].keys()) torch.save(state, "model.ckpt") ================================================ FILE: ddpm_exp/torch_pruning/__init__.py ================================================ from .dependency import * from .pruner import * from . import _helpers, utils, importance ================================================ FILE: ddpm_exp/torch_pruning/_helpers.py ================================================ import torch.nn as nn import numpy as np import torch from operator import add from numbers import Number def is_scalar(x): if isinstance(x, torch.Tensor): return len(x.shape) == 0 elif isinstance(x, Number): return True elif isinstance(x, (list, tuple)): return False return False class _FlattenIndexMapping(object): def __init__(self, stride=1, reverse=False): self._stride = stride self.reverse = reverse def __call__(self, idxs): new_idxs = [] if self.reverse == True: for i in idxs: new_idxs.append(i // self._stride) new_idxs = list(set(new_idxs)) else: for i in idxs: new_idxs.extend( list(range(i * self._stride, (i + 1) * self._stride))) return new_idxs class _ConcatIndexMapping(object): def __init__(self, offset, reverse=False): self.offset = offset self.reverse = reverse def __call__(self, idxs): if self.reverse == True: new_idxs = [ i - self.offset[0] for i in idxs if (i >= self.offset[0] and i < self.offset[1]) ] else: new_idxs = [i + self.offset[0] for i in idxs] return new_idxs class _SplitIndexMapping(object): def __init__(self, offset, reverse=False): self.offset = offset self.reverse = reverse def __call__(self, idxs): if self.reverse == True: new_idxs = [i + self.offset[0] for i in idxs] else: new_idxs = [ i - self.offset[0] for i in idxs if (i >= self.offset[0] and i < self.offset[1]) ] return new_idxs class _GroupConvIndexMapping(object): def __init__(self, in_channels, out_channels, groups, reverse=False): self.in_channels = in_channels self.out_channels = out_channels self.groups = groups self.reverse = reverse def __call__(self, idxs): if self.reverse == True: new_idxs = [i + self.offset[0] for i in idxs] else: group_histgram = np.histogram( idxs, bins=self.groups, range=(0, self.out_channels) ) max_group_size = int(group_histgram.max()) return new_idxs class ScalarSum: def __init__(self): self._results = {} def update(self, metric_name, metric_value): if metric_name not in self._results: self._results[metric_name] = 0 self._results[metric_name] += metric_value def results(self): return self._results def reset(self): self._results = {} class VectorSum: def __init__(self): self._results = {} def update(self, metric_name, metric_value): if metric_name not in self._results: self._results[metric_name] = metric_value if isinstance(metric_value, torch.Tensor): self._results[metric_name] += metric_value elif isinstance(metric_value, list): self._results[metric_name] = list( map(add, self._results[metric_name], metric_value) ) def results(self): return self._results def reset(self): self._results = {} ================================================ FILE: ddpm_exp/torch_pruning/dependency.py ================================================ import typing import warnings from numbers import Number from collections import namedtuple import torch import torch.nn as nn from .pruner import function from . import _helpers, utils, ops __all__ = ["Dependency", "Group", "DependencyGraph"] class Node(object): """ Nodes of DepGraph """ def __init__(self, module: nn.Module, grad_fn, name: str = None): # For Computational Graph (Tracing) self.inputs = [] self.outputs = [] self.module = module self.grad_fn = grad_fn self._name = name self.type = ops.module2type(module) self.class_type = module.__class__ # For Dependency Graph self.dependencies = [] # Adjacency List self.enable_index_mapping = True self.pruning_dim = -1 @property def name(self): if self._name is None: return str(self.module) else: fmt = self._name if self.type != ops.OPTYPE.PARAMETER: fmt += " ({})".format(str(self.module)) return fmt def add_input(self, node, allow_dumplicated=False): #if node not in self.inputs: if allow_dumplicated is True: self.inputs.append(node) else: if node not in self.inputs: self.inputs.append(node) def add_output(self, node, allow_dumplicated=False): if allow_dumplicated is True: self.outputs.append(node) else: if node not in self.outputs: self.outputs.append(node) def __repr__(self): return "".format(self.name) def __str__(self): return "".format(self.name) def details(self): fmt = "-" * 32 + "\n" fmt += "\n".format(self.name) fmt += " " * 4 + "IN:\n" for in_node in self.inputs: fmt += " " * 8 + "{}\n".format(in_node) fmt += " " * 4 + "OUT:\n" for out_node in self.outputs: fmt += " " * 8 + "{}\n".format(out_node) fmt += " " * 4 + "DEP:\n" for dep in self.dependencies: fmt += " " * 8 + "{}\n".format(dep) fmt += "\tEnable_index_mapping={}\n".format( self.enable_index_mapping) fmt = "-" * 32 + "\n" return fmt class Edge(): # for readability pass class Dependency(Edge): def __init__( self, trigger: typing.Callable, handler: typing.Callable, source: Node, target: Node, ): """Layer dependency (Edge of DepGraph) in structral neural network pruning. Args: trigger (Callable): a pruning function that triggers this dependency handler (Callable): a pruning function that can fix the broken dependency source (Node): the source node pruned by the trigger function target (Node): the target node pruned by the handler function index_mapping (Callable): a callable function for index mapping """ self.trigger = trigger self.handler = handler self.source = source self.target = target self.index_mapping = [None, None] def __call__(self, idxs: list): self.handler.__self__.pruning_dim = self.target.pruning_dim result = self.handler( self.target.module, idxs, ) return result def __repr__(self): return str(self) def __str__(self): return "{} on {} => {} on {}".format( "None" if self.trigger is None else self.trigger.__name__, self.source.name, self.handler.__name__, self.target.name, ) def is_triggered_by(self, pruning_fn): return pruning_fn == self.trigger def __eq__(self, other): return ( self.source == other.source and self.trigger == other.trigger and self.handler == other.handler and self.target == other.target ) def __hash__(self): return hash((self.source, self.target, self.trigger, self.handler)) GroupItem = namedtuple('GroupItem', ['dep', 'idxs']) class Group(object): """A group that contains dependencies and pruning indices. Each element is defined as a namedtuple('GroupItem', ['dep', 'idxs']). A group is a iterable list [ [Dep1, Indices1], [Dep2, Indices2], ..., [DepK, IndicesK] ] """ def __init__(self): self._group = list() self._DG = None # for group.prune(idxs=NEW_IDXS) def prune(self, idxs=None, record_history=True): """Prune all coupled layers in the group """ if idxs is not None: module = self._group[0].dep.target.module pruning_fn = self._group[0].dep.handler new_group = self._DG.get_pruning_group(module, pruning_fn, idxs) new_group.prune() else: for dep, idxs in self._group: if dep.target.type == ops.OPTYPE.PARAMETER: old_parameter = dep.target.module name = self._DG._param_to_name[old_parameter] self._DG._param_to_name.pop(old_parameter) pruned_parameter = dep(idxs) path = name.split('.') module = self._DG.model for p in path[:-1]: module = getattr(module, p) setattr(module, path[-1], pruned_parameter) self._DG._param_to_name[pruned_parameter] = name self._DG.module2node[pruned_parameter] = self._DG.module2node.pop(old_parameter) self._DG.module2node[pruned_parameter].module = pruned_parameter else: dep(idxs) if record_history: root_module, pruning_fn, root_pruning_idx = self[0][0].target.module, self[0][0].trigger, self[0][1] root_module_name = self._DG._module2name[root_module] self._DG._pruning_history.append([root_module_name, self._DG.is_out_channel_pruning_fn(pruning_fn), root_pruning_idx]) def add_dep(self, dep, idxs): self._group.append(GroupItem(dep=dep, idxs=idxs)) def __getitem__(self, k): return self._group[k] @property def items(self): return self._group def has_dep(self, dep): for _dep, _ in self._group: if dep == _dep: return True return False def has_pruning_op(self, dep, idxs): for _dep, _idxs in self._group: if ( _dep.target == dep.target and _dep.handler == dep.handler and _idxs == idxs ): return True return False def __len__(self): return len(self._group) def add_and_merge(self, dep, idxs): for i, (_dep, _idxs) in enumerate(self._group): if _dep.target == dep.target and _dep.handler == dep.handler: self._group[i] = (_dep, list(set(_idxs + idxs))) return self.add_dep(dep, idxs) def __str__(self): fmt = "" fmt += "\n" + "-" * 32 + "\n" fmt += " " * 10 + "Pruning Group" fmt += "\n" + "-" * 32 + "\n" for i, (dep, idxs) in enumerate(self._group): fmt += "[{}] {}, #idxs={}\n".format(i, dep, len(idxs)) fmt += "-" * 32 + "\n" return fmt def details(self): fmt = "" fmt += "\n" + "-" * 32 + "\n" fmt += " " * 10 + "Pruning Group" fmt += "\n" + "-" * 32 + "\n" for i, (dep, idxs) in enumerate(self._group): if i==0: fmt += "[{}] {}, idxs={} (Pruning Root)\n".format(i, dep, idxs) else: fmt += "[{}] {}, idxs={}\n".format(i, dep, idxs) fmt += "-" * 32 + "\n" return fmt def exec(self): """old interface, replaced by group.prune()""" self.prune() def __call__(self): return self.prune() UnwrappedParameters = namedtuple('UnwrappedParameters', ['parameters', 'pruning_dim']) class DependencyGraph(object): def __init__(self): _dummy_pruners = { ops.OPTYPE.CONCAT: ops.ConcatPruner(), ops.OPTYPE.SPLIT: ops.SplitPruner(), ops.OPTYPE.ELEMENTWISE: ops.ElementWisePruner(), ops.OPTYPE.RESHAPE: ops.ReshapePruner(), ops.OPTYPE.CUSTOMIZED: None, } self.REGISTERED_PRUNERS = function.PrunerBox.copy() # shallow copy self.REGISTERED_PRUNERS.update(_dummy_pruners) self.CUSTOMIZED_PRUNERS = {} self.IGNORED_LAYERS = [] # cache self._in_channel_pruning_fn = set([p.prune_in_channels for p in self.REGISTERED_PRUNERS.values() if p is not None] + [p.prune_in_channels for p in self.CUSTOMIZED_PRUNERS.values() if p is not None]) self._out_channel_pruning_fn = set([p.prune_out_channels for p in self.REGISTERED_PRUNERS.values() if p is not None] + [p.prune_out_channels for p in self.CUSTOMIZED_PRUNERS.values() if p is not None]) self._op_id = 0 # Pruning History self._pruning_history = [] def pruning_history(self): return self._pruning_history def load_pruning_history(self, pruning_history): self._pruning_history = pruning_history for module_name, is_out_channel_pruning, pruning_idx in self._pruning_history: module = self.model for n in module_name.split('.'): module = getattr(module, n) pruner = self.get_pruner_of_module(module) if is_out_channel_pruning: pruning_fn = pruner.prune_out_channels else: pruning_fn = pruner.prune_in_channels group = self.get_pruning_group(module, pruning_fn, pruning_idx) group.prune(record_history=False) def build_dependency( self, model: torch.nn.Module, example_inputs: typing.Union[torch.Tensor, typing.Sequence], forward_fn: typing.Callable[[ torch.nn.Module, typing.Union[torch.Tensor, typing.Sequence]], torch.Tensor] = None, output_transform: typing.Callable = None, unwrapped_parameters: typing.Dict[nn.Parameter, int] = None, customized_pruners: typing.Dict[typing.Any, function.BasePruningFunc] = None, verbose: bool = True, ): """Build a dependency graph through tracing. Args: model (class): the model to be pruned. example_inputs (torch.Tensor or List): dummy inputs for tracing. forward_fn (Callable): a function to run the model with example_inputs, which should return a reduced tensor for backpropagation. output_transform (Callable): a function to transform network outputs. unwrapped_parameters (List): unwrapped nn.parameters defined by parameters. customized_pruners (typing.Dict[typing.Any, function.BasePruningFunc]): pruners for customized layers. verbose (bool): verbose mode. """ self.verbose = verbose self.model = model self._module2name = {module: name for ( name, module) in model.named_modules()} # Register customized pruners if customized_pruners is not None: for customized_module, customized_pruner in customized_pruners.items(): self.register_customized_layer( customized_module, customized_pruner) # Ignore all sub-modules of customized layers for layer_type in self.CUSTOMIZED_PRUNERS.keys(): for m in self.model.modules(): if isinstance(m, layer_type): for sub_module in m.modules(): if sub_module != m: self.IGNORED_LAYERS.append(sub_module) # Detect unwrapped nn.parameters wrapped_parameters = [] prunable_module_types = self.REGISTERED_PRUNERS.keys() for m in self.model.modules(): op_type = ops.module2type(m) if ( op_type in prunable_module_types and op_type!=ops.OPTYPE.ELEMENTWISE ) or m.__class__ in self.CUSTOMIZED_PRUNERS.keys(): wrapped_parameters.extend(list(m.parameters())) unwrapped_detected = [] _param_to_name = {} for name, p in self.model.named_parameters(): is_wrapped = False for p_wrapped in wrapped_parameters: if p is p_wrapped: is_wrapped = True break if not is_wrapped: unwrapped_detected.append(p) _param_to_name[p] = name if unwrapped_parameters is None: unwrapped_parameters = [] self._param_to_name = _param_to_name unwrapped_detected = list( set(unwrapped_detected) - set([p for (p, _) in unwrapped_parameters]) ) if len(unwrapped_detected)>0 and self.verbose: warnings.warn("Unwrapped parameters detected: {}.\n Torch-Pruning will prune the last non-singleton dimension of a parameter. If you wish to customize this behavior, please provide an unwrapped_parameters argument.".format([_param_to_name[p] for p in unwrapped_detected])) for p in unwrapped_detected: # get the last dimension that >1 def last_non_singleton_dim(tensor): non_singleton_dims = [i for i, s in enumerate(tensor.shape) if s > 1] return non_singleton_dims[-1] if non_singleton_dims else None pruning_dim = last_non_singleton_dim(p) if pruning_dim is not None: unwrapped_parameters.append( UnwrappedParameters(parameters=p, pruning_dim=pruning_dim) ) # prune the last non-singleton dim by daufault self.unwrapped_parameters = unwrapped_parameters # Build computational graph by tracing. self.module2node = self._trace( model, example_inputs, forward_fn, output_transform=output_transform ) # Build dependency graph self._build_dependency(self.module2node) # Init Shape information self._init_shape_information() # Update index mapping for torch.cat/split/chunck/... self.update_index_mapping() return self def register_customized_layer( self, layer_type: typing.Type, layer_pruner: function.BasePruningFunc, ): """Register a customized pruner Args: layer_type (class): the type of target layer pruner (tp.pruner.BasePruningFunc): a pruner for the specified layer type. """ self.CUSTOMIZED_PRUNERS[layer_type] = layer_pruner # Update cache self._in_channel_pruning_fn = set([p.prune_in_channels for p in self.REGISTERED_PRUNERS.values() if p is not None] + [p.prune_in_channels for p in self.CUSTOMIZED_PRUNERS.values() if p is not None]) self._out_channel_pruning_fn = set([p.prune_out_channels for p in self.REGISTERED_PRUNERS.values() if p is not None] + [p.prune_out_channels for p in self.CUSTOMIZED_PRUNERS.values() if p is not None]) def check_pruning_group(self, group: Group) -> bool: """check the group to avoid over-pruning. Return True if there are sufficient prunable elements. Args: group (Group): a depenedency group """ for dep, idxs in group: if self.is_out_channel_pruning_fn(dep.handler): prunable_chs = self.get_out_channels( dep.target.module) if prunable_chs is None: continue if prunable_chs <= len(idxs): return False if self.is_in_channel_pruning_fn(dep.handler): prunable_in_chs = self.get_in_channels( dep.target.module) if prunable_in_chs is None: continue if prunable_in_chs <= len(idxs): return False return True def is_out_channel_pruning_fn(self, fn: typing.Callable) -> bool: return (fn in self._out_channel_pruning_fn) def is_in_channel_pruning_fn(self, fn: typing.Callable) -> bool: return (fn in self._in_channel_pruning_fn) def get_pruning_plan(self, module: nn.Module, pruning_fn: typing.Callable, idxs: typing.Union[list, tuple]) -> Group: """ An alias of DependencyGraph.get_pruning_group for compatibility. """ return self.get_pruning_group(module, pruning_fn, idxs) def get_pruning_group( self, module: nn.Module, pruning_fn: typing.Callable, idxs: typing.Union[list, tuple], ) -> Group: """Get the pruning group of pruning_fn. Args: module (nn.Module): the to-be-pruned module/layer. pruning_fn (Callable): the pruning function. idxs (list or tuple): the indices of channels/dimensions. """ if isinstance(module, ops.TORCH_CONV) and module.groups == module.out_channels: pruning_fn = function.prune_depthwise_conv_out_channels if isinstance(idxs, Number): idxs = [idxs] self.update_index_mapping() group = Group() # the user pruning operation root_node = self.module2node[module] group.add_dep( Dependency(pruning_fn, pruning_fn, source=root_node, target=root_node), idxs ) visited_node = set() def _fix_dependency_graph_non_recursive(dep, idxs): processing_stack = [(dep, idxs)] while len(processing_stack) > 0: dep, idxs = processing_stack.pop(-1) node, fn = dep.target, dep.handler visited_node.add(node) #print(dep) #print(node.dependencies) for new_dep in node.dependencies: if new_dep.is_triggered_by(fn): new_indices = idxs for mapping in new_dep.index_mapping: if mapping is not None: new_indices = mapping(new_indices) #print(new_dep, new_dep.index_mapping) #print(len(new_indices), new_indices) #print() if len(new_indices) == 0: continue if (new_dep.target in visited_node) and group.has_pruning_op( new_dep, new_indices ): continue else: group.add_dep(new_dep, new_indices) processing_stack.append( (new_dep, new_indices) ) _fix_dependency_graph_non_recursive(*group[0]) # merge pruning ops merged_group = Group() for dep, idxs in group.items: merged_group.add_and_merge(dep, idxs) merged_group._DG = self return merged_group def get_all_groups(self, ignored_layers=[], root_module_types=(ops.TORCH_CONV, ops.TORCH_LINEAR)): visited_layers = [] ignored_layers = ignored_layers+self.IGNORED_LAYERS for m in list(self.module2node.keys()): if m in ignored_layers: continue if not isinstance(m, tuple(root_module_types)): continue pruner = self.get_pruner_of_module(m) if pruner is None or pruner.get_out_channels(m) is None: continue if m in visited_layers: continue layer_channels = pruner.get_out_channels(m) group = self.get_pruning_group( m, pruner.prune_out_channels, list(range(layer_channels))) prunable_group = True for dep, _ in group: module = dep.target.module pruning_fn = dep.handler if self.is_out_channel_pruning_fn(pruning_fn): visited_layers.append(module) if module in ignored_layers: prunable_group = False if prunable_group: yield group def get_pruner_of_module(self, module): p = self.CUSTOMIZED_PRUNERS.get(module.__class__, None) if p is None: p = self.REGISTERED_PRUNERS.get(ops.module2type(module), None) return p def get_out_channels(self, module_or_node): if isinstance(module_or_node, Node): module = module_or_node.module pruning_dim = module_or_node.pruning_dim else: module = module_or_node pruning_dim = self.module2node[module].pruning_dim p = self.get_pruner_of_module(module) p.pruning_dim = pruning_dim if p is None: return None return p.get_out_channels(module) def get_in_channels(self, module_or_node): if isinstance(module_or_node, Node): module = module_or_node.module pruning_dim = module_or_node.pruning_dim else: module = module_or_node pruning_dim = self.module2node[module].pruning_dim p = self.get_pruner_of_module(module) p.pruning_dim = pruning_dim if p is None: return None return p.get_in_channels(module) def _infer_out_channels_recursively(self, node: Node): """ infer the number of output channels recursively """ ch = self.get_out_channels(node) if ch is None: ch = 0 for in_node in node.inputs: if node.type == ops.OPTYPE.CONCAT: sub_ch = self._infer_out_channels_recursively(in_node) if sub_ch is None: return None ch += sub_ch else: if in_node.type == ops.OPTYPE.SPLIT and in_node.module.split_sizes is not None: for i, split_out_node in enumerate(in_node.outputs): if split_out_node == node: ch = in_node.module.split_sizes[i] else: ch = self._infer_out_channels_recursively(in_node) if ch == 0: return None return ch def _infer_in_channels_recursively(self, node: Node): """ infer the number of input channels recursively """ ch = self.get_in_channels(node) if ch is None: ch = 0 for out_node in node.outputs: if node.type == ops.OPTYPE.SPLIT: sub_ch = self._infer_in_channels_recursively(out_node) if sub_ch is None: return None ch += sub_ch else: ch = self._infer_in_channels_recursively(out_node) if ch == 0: return None return ch def _build_dependency(self, module2node): for _, node in module2node.items(): ########################################### # Rule 1) - Inter-layer Dependency ########################################### for in_node in node.inputs: handler = self.get_pruner_of_module(in_node.module).prune_out_channels trigger = self.get_pruner_of_module(node.module).prune_in_channels dep = Dependency( trigger=trigger, handler=handler, source=node, target=in_node ) node.dependencies.append(dep) for out_node in node.outputs: trigger = self.get_pruner_of_module(node.module).prune_out_channels handler = self.get_pruner_of_module(out_node.module).prune_in_channels dep = Dependency( trigger=trigger, handler=handler, source=node, target=out_node ) node.dependencies.append(dep) ########################################### # Rule 2) - Intra-layer Dependency ########################################### # This is implictly implemented by assigning # prune_out_channels=prune_in_channels in tp.pruner.function.BasePruningFunc def _trace(self, model, example_inputs, forward_fn, output_transform): """ Tracing the model as a graph """ model.eval() gradfn2module = {} visited = {} self._2d_4d = True # only for pytorch<=1.8 def _record_grad_fn(module, inputs, outputs): if module not in visited: visited[module] = 1 else: visited[module] += 1 if isinstance(module, nn.Linear) and len(outputs.shape)==3: self._2d_4d=False if isinstance(outputs, tuple): outputs = outputs[0] if isinstance(outputs, torch.nn.utils.rnn.PackedSequence): outputs = outputs.data gradfn2module[outputs.grad_fn] = module registered_types = tuple(ops.type2class( t) for t in self.REGISTERED_PRUNERS.keys()) + tuple(self.CUSTOMIZED_PRUNERS.keys()) hooks = [ m.register_forward_hook(_record_grad_fn) for m in model.modules() if (isinstance(m, registered_types) and m not in self.IGNORED_LAYERS) ] # Feed forward and record gradient functions of prunable modules if forward_fn is not None: out = forward_fn(model, example_inputs) elif isinstance(example_inputs, dict): out = model(**example_inputs) else: try: out = model(*example_inputs) except: out = model(example_inputs) for hook in hooks: hook.remove() # for recursive models or layers reused = [m for (m, count) in visited.items() if count > 1] # build graph if output_transform is not None: out = output_transform(out) module2node = {} for o in utils.flatten_as_list(out): self._trace_computational_graph( module2node, o.grad_fn, gradfn2module, reused) # TODO: Improving ViT pruning # This is a corner case for pruning ViT, # where concatination of pos_emb and cls_emv is not applied on the feature dim. # Notably, this is not a good practice and will be fixed in the future version if len(self.unwrapped_parameters) > 0: for node in module2node.values(): if node.type in (ops.OPTYPE.CONCAT, ops.OPTYPE.SPLIT): stack = [node] visited = set() while len(stack) > 0: n = stack.pop(-1) visited.add(n) if n.type == ops.OPTYPE.PARAMETER and len(n.module.shape) == 3: node.enable_index_mapping = False break else: for ni in n.inputs: if ni not in visited: stack.append(ni) return module2node def _trace_computational_graph(self, module2node, grad_fn_root, gradfn2module, reused): def create_node_if_not_exists(grad_fn): module = gradfn2module.get(grad_fn, None) if module is not None \ and module in module2node \ and module not in reused: return module2node[module] # 1. link grad_fns and modules if module is None: # a new module if not hasattr(grad_fn, "name"): # we treat all unknwon modules as element-wise operations by default, # which does not modify the #dimension/#channel of features. # If you have some customized layers, please register it with DependencyGraph.register_customized_layer module = ops._ElementWiseOp(self._op_id ,"Unknown") self._op_id+=1 if self.verbose: warnings.warn( "[Warning] Unknown operation {} encountered, which will be handled as an element-wise op".format( str(grad_fn)) ) elif "catbackward" in grad_fn.name().lower(): module = ops._ConcatOp(self._op_id) self._op_id+=1 elif "split" in grad_fn.name().lower(): module = ops._SplitOp(self._op_id) self._op_id+=1 elif "view" in grad_fn.name().lower() or 'reshape' in grad_fn.name().lower(): module = ops._ReshapeOp(self._op_id) self._op_id+=1 else: # treate other ops as element-wise ones, like Add, Sub, Div, Mul. module = ops._ElementWiseOp(self._op_id, grad_fn.name()) self._op_id+=1 gradfn2module[grad_fn] = module # 2. link modules and nodes if module not in module2node: node = Node( module=module, grad_fn=grad_fn, name=self._module2name.get(module, None), ) if ( type(module) in self.CUSTOMIZED_PRUNERS ): # mark it as a customized layer node.type = ops.OPTYPE.CUSTOMIZED module2node[module] = node else: node = module2node[module] return node # non-recursive construction of computational graph processing_stack = [grad_fn_root] visited = set() visited_as_output_node = set() while len(processing_stack) > 0: grad_fn = processing_stack.pop(-1) if grad_fn in visited: continue node = create_node_if_not_exists(grad_fn=grad_fn) if hasattr(grad_fn, "next_functions"): for f in grad_fn.next_functions: if f[0] is not None: if ( hasattr(f[0], "name") and "accumulategrad" in f[0].name().lower() ): # a leaf variable. is_unwrapped_param = False for (j, (p, dim)) in enumerate(self.unwrapped_parameters): if f[0].variable is p: is_unwrapped_param = True gradfn2module[f[0]] = p self._module2name[p] = "UnwrappedParameter_{} ({})".format(j, p.shape) if not is_unwrapped_param: continue input_node = create_node_if_not_exists(f[0]) #allow_dumplicated = False # TODO: support duplicated concat/split like torch.cat([x, x], dim=1) # The following implementation is can achieve this but will introduce some bugs. # will be fixed in the future version #if node.type == ops.OPTYPE.CONCAT: # allow_dumplicated = (node not in visited_as_output_node) # node.add_input(input_node, allow_dumplicated=allow_dumplicated) # input_node.add_output(node, allow_dumplicated=allow_dumplicated) # print(node, node.inputs) #elif input_node.type == ops.OPTYPE.SPLIT: # allow_dumplicated = (node not in visited_as_output_node) # node.add_input(input_node, allow_dumplicated=allow_dumplicated) # input_node.add_output(node, allow_dumplicated=allow_dumplicated) #else: node.add_input(input_node, allow_dumplicated=False) input_node.add_output(node, allow_dumplicated=False) processing_stack.append(f[0]) visited.add(grad_fn) visited_as_output_node.add(node) for (param, dim) in self.unwrapped_parameters: module2node[param].pruning_dim = dim return module2node def update_index_mapping(self): """ update all index mapping after pruning """ # update index mapping for module, node in self.module2node.items(): if node.type == ops.OPTYPE.CONCAT: self._update_concat_index_mapping(node) if node.type == ops.OPTYPE.SPLIT: self._update_split_index_mapping(node) if node.type == ops.OPTYPE.RESHAPE: self._update_reshape_index_mapping(node) def _init_shape_information(self): for module, node in self.module2node.items(): if node.type == ops.OPTYPE.SPLIT: grad_fn = node.grad_fn if hasattr(grad_fn, '_saved_self_sizes'): if hasattr(grad_fn, '_saved_split_sizes') and hasattr(grad_fn, '_saved_dim') : if grad_fn._saved_dim != 1: continue chs = list(grad_fn._saved_split_sizes) node.module.split_sizes = chs elif hasattr(grad_fn, '_saved_split_size') and hasattr(grad_fn, '_saved_dim'): if grad_fn._saved_dim != 1: continue chs = [grad_fn._saved_split_size for _ in range(len(node.outputs))] node.module.split_sizes = chs offsets = [0] for i in range(len(chs)): offsets.append(offsets[i] + chs[i]) node.module.offsets = offsets else: # legency version chs = [] for n in node.outputs: chs.append(self._infer_in_channels_recursively(n)) offsets = [0] for ch in chs: if ch is None: continue offsets.append(offsets[-1] + ch) node.module.split_sizes = chs node.module.offsets = offsets def _update_flatten_index_mapping(self, fc_node: Node): if fc_node.type != ops.OPTYPE.LINEAR: return fc_in_features = fc_node.module.in_features feature_channels = 0 for n in fc_node.inputs: feature_channels = self._infer_out_channels_recursively(n) if feature_channels is not None: # =0 if there is a residual connection to model inputs break if ( feature_channels is None ): # the first layer: https://github.com/VainF/Torch-Pruning/issues/21 return stride = fc_in_features // feature_channels if stride > 1 and fc_in_features % feature_channels == 0: for in_node in fc_node.inputs: for dep in fc_node.dependencies: if dep.target == in_node: dep.index_mapping[0] = _helpers._FlattenIndexMapping( stride=stride, reverse=True ) for dep in in_node.dependencies: if dep.target == fc_node: dep.index_mapping[0] = _helpers._FlattenIndexMapping( stride=stride, reverse=False ) def _update_reshape_index_mapping(self, reshape_node: Node): # Only Supports 2D/4D tensors # TODO: Better support for reshape/view/flatten if hasattr(reshape_node.grad_fn, '_saved_self_sizes'): size = reshape_node.grad_fn._saved_self_sizes if (len(size)!=1 and len(size)!=4): return else: # old pytorch versions if not self._2d_4d: return out_channels = None for n in reshape_node.outputs: out_channels = self._infer_in_channels_recursively(n) if out_channels is not None: # =0 if there is a residual connection to model inputs break in_channels = None for n in reshape_node.inputs: in_channels = self._infer_out_channels_recursively(n) if in_channels is not None: # =0 if there is a residual connection to model inputs break if out_channels is None or in_channels is None: return if out_channels==in_channels: return if hasattr(reshape_node.grad_fn, '_saved_self_sizes'): if len(size)==4 and size[1]*size[2]*size[3]!=out_channels: return # Flatten #print(reshape_node.grad_fn._saved_self_sizes, in_channels, out_channels) if out_channels > in_channels: for in_node in reshape_node.inputs: for dep in reshape_node.dependencies: if dep.target == in_node: dep.index_mapping[0] = _helpers._FlattenIndexMapping( stride=out_channels // in_channels, reverse=True ) for dep in in_node.dependencies: if dep.target == reshape_node: dep.index_mapping[0] = _helpers._FlattenIndexMapping( stride=out_channels // in_channels, reverse=False ) else: # 1D -> 2D for out_node in reshape_node.outputs: for dep in reshape_node.dependencies: if dep.target == out_node: dep.index_mapping[0] = _helpers._FlattenIndexMapping( stride=in_channels // out_channels, reverse=True ) for dep in out_node.dependencies: if dep.target == reshape_node: dep.index_mapping[0] = _helpers._FlattenIndexMapping( stride=in_channels // out_channels, reverse=False ) #print(in_channels, out_channels) #print(reshape_node.grad_fn._saved_self_sizes) #print('------') def _update_concat_index_mapping(self, cat_node: Node): if cat_node.type != ops.OPTYPE.CONCAT: return if cat_node.module.concat_sizes is not None: chs = cat_node.module.concat_sizes else: chs = [] for n in cat_node.inputs: chs.append(self.infer_channels(n, cat_node)) cat_node.module.concat_sizes = chs offsets = [0] for ch in chs: if ch is None: #warnings.warn("Fails to trace the concat operation. It may lead to unexpected results.") return offsets.append(offsets[-1] + ch) cat_node.module.offsets = offsets # no transform if the concat dim is different from the feature dim # TODO: make the messy for loop more efficient addressed_dep = [] for i, in_node in enumerate(cat_node.inputs): for dep in cat_node.dependencies: if any((dep is d) for d in addressed_dep): continue if dep.target == in_node: if cat_node.enable_index_mapping: dep.index_mapping[1] = _helpers._ConcatIndexMapping( offset=offsets[i: i + 2], reverse=True ) addressed_dep.append(dep) break addressed_dep = [] for i, in_node in enumerate(cat_node.inputs): for dep in in_node.dependencies: if any((dep is d) for d in addressed_dep): continue if dep.target == cat_node: if cat_node.enable_index_mapping: dep.index_mapping[1] = _helpers._ConcatIndexMapping( offset=offsets[i: i + 2], reverse=False ) addressed_dep.append(dep) break def _update_split_index_mapping(self, split_node: Node): if split_node.type != ops.OPTYPE.SPLIT: return offsets = split_node.module.offsets if offsets is None: return addressed_dep = [] for i, out_node in enumerate(split_node.outputs): for dep in split_node.dependencies: if any((dep is d) for d in addressed_dep): continue if dep.target == out_node: if split_node.enable_index_mapping: dep.index_mapping[0] = (_helpers._SplitIndexMapping( offset=offsets[i: i + 2], reverse=False )) addressed_dep.append(dep) break addressed_dep = [] for i, out_node in enumerate(split_node.outputs): for dep in out_node.dependencies: if dep.target == split_node: if any((dep is d) for d in addressed_dep): continue if split_node.enable_index_mapping: dep.index_mapping[0] = (_helpers._SplitIndexMapping( offset=offsets[i: i + 2], reverse=True )) addressed_dep.append(dep) break def infer_channels(self, node_1, node_2): if node_1.type == ops.OPTYPE.SPLIT: for i, n in enumerate(node_1.outputs): if n == node_2: return node_1.module.split_sizes[i] return self._infer_out_channels_recursively(node_1) ================================================ FILE: ddpm_exp/torch_pruning/importance.py ================================================ import abc import torch import torch.nn as nn import typing from .pruner import function from ._helpers import _FlattenIndexMapping from . import ops import math class Importance(abc.ABC): """ estimate the importance of a Pruning Group, and return an 1-D per-channel importance score. """ @abc.abstractclassmethod def __call__(self, group)-> torch.Tensor: raise NotImplementedError class MagnitudeImportance(Importance): def __init__(self, p=2, group_reduction="mean", normalizer='mean'): self.p = p self.group_reduction = group_reduction self.normalizer = normalizer def _normalize(self, group_importance, normalizer): if normalizer is None: return group_importance elif isinstance(normalizer, typing.Callable): return normalizer(group_importance) elif normalizer == "sum": return group_importance / group_importance.sum() elif normalizer == "standarization": return (group_importance - group_importance.min()) / (group_importance.max() - group_importance.min()+1e-8) elif normalizer == "mean": return group_importance / group_importance.mean() elif normalizer == "max": return group_importance / group_importance.max() elif normalizer == 'gaussian': return (group_importance - group_importance.mean()) / (group_importance.std()+1e-8) else: raise NotImplementedError def _reduce(self, group_imp): if self.group_reduction == "sum": group_imp = group_imp.sum(dim=0) elif self.group_reduction == "mean": group_imp = group_imp.mean(dim=0) elif self.group_reduction == "max": group_imp = group_imp.max(dim=0)[0] elif self.group_reduction == "prod": group_imp = torch.prod(group_imp, dim=0) elif self.group_reduction=='first': group_imp = group_imp[0] elif self.group_reduction is None: group_imp = group_imp else: raise NotImplementedError return group_imp @torch.no_grad() def __call__(self, group, ch_groups=1): group_imp = [] #Get group norm #print(group.details()) for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) local_norm = w.abs().pow(self.p).sum(1) #if ch_groups>1: # local_norm = local_norm.view(ch_groups, -1).sum(0) # local_norm = local_norm.repeat(ch_groups) group_imp.append(local_norm) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: is_conv_flatten_linear = False if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight).flatten(1) else: w = (layer.weight).transpose(0, 1).flatten(1) #if ch_groups>1 and prune_fn==function.prune_conv_in_channels and layer.groups==1: # standard convs with ch_groups>1 #print(w.shape) # w = w.view(ch_groups, w.shape[0] // ch_groups, w.shape[1]).flatten(1) local_norm = w.abs().pow(self.p).sum(1) #if ch_groups>1: #if len(local_norm)==len(group_imp[0]): #local_norm = local_norm.view(ch_groups, -1).sum(0) # local_norm = local_norm.repeat(ch_groups) local_norm = local_norm[idxs] group_imp.append(local_norm) # BN elif prune_fn == function.prune_batchnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] local_norm = w.abs().pow(self.p) #if ch_groups>1: # local_norm = local_norm.view(ch_groups, -1).sum(0) # local_norm = local_norm.repeat(ch_groups) #print(local_norm.shape) group_imp.append(local_norm) if len(group_imp)==0: return None imp_size = len(group_imp[0]) aligned_group_imp = [] for imp in group_imp: if len(imp)==imp_size: aligned_group_imp.append(imp) group_imp = torch.stack(aligned_group_imp, dim=0) group_imp = self._reduce(group_imp) group_imp = self._normalize(group_imp, self.normalizer) return group_imp class BNScaleImportance(MagnitudeImportance): """Learning Efficient Convolutional Networks through Network Slimming, https://arxiv.org/abs/1708.06519 """ def __init__(self, group_reduction='mean', normalizer='mean'): super().__init__(p=1, group_reduction=group_reduction, normalizer=normalizer) def __call__(self, group, ch_groups=1): group_imp = [] for dep, _ in group: module = dep.target.module if isinstance(module, (ops.TORCH_BATCHNORM)) and module.affine: local_imp = torch.abs(module.weight.data) if ch_groups>1: local_imp = local_imp.view(ch_groups, -1).mean(0) local_imp = local_imp.repeat(ch_groups) group_imp.append(local_imp) if len(group_imp)==0: return None group_imp = torch.stack(group_imp, dim=0) group_imp = self._reduce(group_imp) group_imp = self._normalize(group_imp, self.normalizer) return group_imp class LAMPImportance(MagnitudeImportance): """Layer-adaptive Sparsity for the Magnitude-based Pruning, https://arxiv.org/abs/2010.07611 """ def __init__(self, p=2, group_reduction="mean", normalizer='mean'): super().__init__(p=p, group_reduction=group_reduction, normalizer=normalizer) @torch.no_grad() def __call__(self, group, **kwargs): group_imp = [] for dep, idxs in group: layer = dep.target.module prune_fn = dep.handler if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight)[:, idxs].transpose(0, 1) else: w = (layer.weight)[idxs] local_imp = torch.norm( torch.flatten(w, 1), dim=1, p=self.p) group_imp.append(local_imp) elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight)[idxs].flatten(1) else: w = (layer.weight)[:, idxs].transpose(0, 1).flatten(1) if ( w.shape[0] != group_imp[0].shape[0] ): # for conv-flatten-linear without global pooling w = w.view( group_imp[0].shape[0], w.shape[0] // group_imp[0].shape[0], w.shape[1], ).flatten(1) local_imp = torch.norm(w, dim=1, p=self.p) group_imp.append(local_imp) elif prune_fn == function.prune_batchnorm_out_channels: if layer.affine is not None: w = (layer.weight)[idxs].view(-1, 1) local_imp = torch.norm(w, dim=1, p=self.p) group_imp.append(local_imp) if len(group_imp)==0: return None group_imp = torch.stack(group_imp, dim=0) group_imp = self._reduce(group_imp) group_imp = self._normalize(group_imp, self.normalizer) return self.lamp(group_imp) def lamp(self, imp): argsort_idx = torch.argsort(imp, dim=0, descending=True).tolist() sorted_imp = imp[argsort_idx] cumsum_imp = torch.cumsum(sorted_imp, dim=0) sorted_imp = sorted_imp / cumsum_imp inversed_idx = torch.arange(len(sorted_imp))[ argsort_idx ].tolist() # [0, 1, 2, 3, ..., ] return sorted_imp[inversed_idx] class RandomImportance(Importance): @torch.no_grad() def __call__(self, group, **kwargs): _, idxs = group[0] return torch.rand(len(idxs)) class GroupNormImportance(MagnitudeImportance): def __init__(self, p=2, normalizer='max'): super().__init__(p=p, group_reduction=None, normalizer=normalizer) self.p = p self.normalizer = normalizer @torch.no_grad() def __call__(self, group, ch_groups=1): group_norm = 0 #Get group norm for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, 'transposed') and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) local_norm = w.abs().pow(self.p).sum(1) #print(local_norm.shape, layer, idxs, ch_groups) if ch_groups>1: local_norm = local_norm.view(ch_groups, -1).sum(0) local_norm = local_norm.repeat(ch_groups) group_norm+=local_norm #if layer.bias is not None: # group_norm += layer.bias.data[idxs].pow(2) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: is_conv_flatten_linear = False if hasattr(layer, 'transposed') and layer.transposed: w = (layer.weight).flatten(1) else: w = (layer.weight).transpose(0, 1).flatten(1) if (w.shape[0] != group_norm.shape[0]): if (hasattr(dep, 'index_mapping') and isinstance(dep.index_mapping, _FlattenIndexMapping)): #conv-flatten w = w[idxs].view( group_norm.shape[0], w.shape[0] // group_norm.shape[0], w.shape[1], ).flatten(1) is_conv_flatten_linear = True elif ch_groups>1 and prune_fn==function.prune_conv_in_channels and layer.groups==1: # non-grouped conv with group convs w = w.view(w.shape[0] // group_norm.shape[0], group_norm.shape[0], w.shape[1]).transpose(0, 1).flatten(1) local_norm = w.abs().pow(self.p).sum(1) if ch_groups>1: if len(local_norm)==len(group_norm): local_norm = local_norm.view(ch_groups, -1).sum(0) local_norm = local_norm.repeat(ch_groups) if not is_conv_flatten_linear: local_norm = local_norm[idxs] group_norm += local_norm # BN elif prune_fn == function.prune_batchnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] local_norm = w.abs().pow(self.p) if ch_groups>1: local_norm = local_norm.view(ch_groups, -1).sum(0) local_norm = local_norm.repeat(ch_groups) group_norm += local_norm elif prune_fn == function.prune_lstm_out_channels: _idxs = torch.tensor(idxs) local_norm = 0 local_norm_reverse = 0 num_layers = layer.num_layers expanded_idxs = torch.cat([ _idxs+i*layer.hidden_size for i in range(4) ], dim=0) if layer.bidirectional: postfix = ['', '_reverse'] else: postfix = [''] local_norm+=getattr(layer, 'weight_hh_l0')[expanded_idxs].abs().pow(self.p).sum(1).view(4, -1).sum(0) local_norm+=getattr(layer, 'weight_hh_l0')[:, _idxs].abs().pow(self.p).sum(0) local_norm+=getattr(layer, 'weight_ih_l0')[expanded_idxs].abs().pow(self.p).sum(1).view(4, -1).sum(0) if layer.bidirectional: local_norm_reverse+=getattr(layer, 'weight_hh_l0')[expanded_idxs].abs().pow(self.p).sum(1).view(4, -1).sum(0) local_norm_reverse+=getattr(layer, 'weight_hh_l0')[:, _idxs].abs().pow(self.p).sum(0) local_norm_reverse+=getattr(layer, 'weight_ih_l0')[expanded_idxs].abs().pow(self.p).sum(1).view(4, -1).sum(0) local_norm = torch.cat([local_norm, local_norm_reverse], dim=0) group_norm += local_norm elif prune_fn == function.prune_lstm_in_channels: local_norm=getattr(layer, 'weight_ih_l0')[:, idxs].abs().pow(self.p).sum(0) if layer.bidirectional: local_norm_reverse+=getattr(layer, 'weight_ih_l0_reverse')[:, idxs].abs().pow(self.p).sum(0) local_norm = torch.cat([local_norm, local_norm_reverse], dim=0) group_norm+=local_norm group_imp = group_norm**(1/self.p) group_imp = self._normalize(group_imp, self.normalizer) return group_imp class TaylorImportance(Importance): def __init__(self, group_reduction="mean", normalizer='mean'): self.group_reduction = group_reduction self.normalizer = normalizer def set_model(self, model): self.model = model def _normalize(self, group_importance, normalizer): if normalizer is None: return group_importance elif isinstance(normalizer, typing.Callable): return normalizer(group_importance) elif normalizer == "sum": return group_importance / group_importance.sum() elif normalizer == "standarization": return (group_importance - group_importance.min()) / (group_importance.max() - group_importance.min()+1e-8) elif normalizer == "mean": return group_importance / group_importance.mean() elif normalizer == "max": return group_importance / group_importance.max() elif normalizer == 'gaussian': return (group_importance - group_importance.mean()) / (group_importance.std()+1e-8) else: raise NotImplementedError def _reduce(self, group_imp): if self.group_reduction == "sum": group_imp = group_imp.sum(dim=0) elif self.group_reduction == "mean": group_imp = group_imp.mean(dim=0) elif self.group_reduction == "max": group_imp = group_imp.max(dim=0)[0] elif self.group_reduction == "prod": group_imp = torch.prod(group_imp, dim=0) elif self.group_reduction=='first': group_imp = group_imp[0] elif self.group_reduction is None: group_imp = group_imp else: raise NotImplementedError return group_imp @torch.no_grad() def __call__(self, group, ch_groups=1): group_imp = [] for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) dw= layer.weight.grad.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) dw= layer.weight.grad.data[idxs].flatten(1) wdw = (w*dw).abs().pow(2).sum(1) local_norm = wdw group_imp.append(local_norm) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight).flatten(1)[idxs] dw= (layer.weight.grad).flatten(1)[idxs] else: w = (layer.weight).transpose(0, 1).flatten(1)[idxs] dw= (layer.weight.grad).transpose(0, 1).flatten(1)[idxs] wdw = (w*dw).abs().pow(2).sum(1) local_norm = wdw group_imp.append(local_norm) # BN elif prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] dw= layer.weight.grad.data[idxs] wdw = (w*dw).abs() local_norm = wdw group_imp.append(local_norm) if len(group_imp)==0: return None imp_size = len(group_imp[0]) aligned_group_imp = [] for imp in group_imp: if len(imp)==imp_size: aligned_group_imp.append(imp) group_imp = torch.stack(aligned_group_imp, dim=0) group_imp = group_imp.sum(0) #self._reduce(group_imp) #if ch_groups>1: # group_imp = group_imp.view(ch_groups, -1).mean(0) # group_imp = group_imp.repeat(ch_groups) #group_imp = group_imp.abs() #group_imp = self._normalize(group_imp, self.normalizer) return group_imp class FullTaylorImportance(Importance): def __init__(self, order=1, group_reduction="mean", normalizer='mean'): self.group_reduction = group_reduction self.normalizer = normalizer self.order=order def set_model(self, model): self.model = model def _normalize(self, group_importance, normalizer): if normalizer is None: return group_importance elif isinstance(normalizer, typing.Callable): return normalizer(group_importance) elif normalizer == "sum": return group_importance / group_importance.sum() elif normalizer == "standarization": return (group_importance - group_importance.min()) / (group_importance.max() - group_importance.min()+1e-8) elif normalizer == "mean": return group_importance / group_importance.mean() elif normalizer == "max": return group_importance / group_importance.max() elif normalizer == 'gaussian': return (group_importance - group_importance.mean()) / (group_importance.std()+1e-8) else: raise NotImplementedError def _reduce(self, group_imp): if self.group_reduction == "sum": group_imp = group_imp.sum(dim=0) elif self.group_reduction == "mean": group_imp = group_imp.mean(dim=0) elif self.group_reduction == "max": group_imp = group_imp.max(dim=0)[0] elif self.group_reduction == "prod": group_imp = torch.prod(group_imp, dim=0) elif self.group_reduction=='first': group_imp = group_imp[0] elif self.group_reduction is None: group_imp = group_imp else: raise NotImplementedError return group_imp @torch.no_grad() def __call__(self, group, ch_groups=1): group_imp = [] for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) dw= layer.weight.grad.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) dw= layer.weight.grad.data[idxs].flatten(1) if self.order == 1: wdw = (w*dw).sum(1) elif self.order == 2: wdw = (w*dw).sum(1) + (w*dw).pow(2).sum(1) local_norm = wdw group_imp.append(local_norm) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight).flatten(1)[idxs] dw= (layer.weight.grad).flatten(1)[idxs] else: w = (layer.weight).transpose(0, 1).flatten(1)[idxs] dw= (layer.weight.grad).transpose(0, 1).flatten(1)[idxs] if self.order == 1: wdw = (w*dw).sum(1) elif self.order == 2: wdw = (w*dw).sum(1) + (w*dw).pow(2).sum(1) local_norm = wdw group_imp.append(local_norm) # BN elif prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] dw= layer.weight.grad.data[idxs] if self.order == 1: wdw = (w*dw) elif self.order == 2: wdw = (w*dw) + (w*dw).pow(2) local_norm = wdw group_imp.append(local_norm) if len(group_imp)==0: return None imp_size = len(group_imp[0]) aligned_group_imp = [] for imp in group_imp: if len(imp)==imp_size: aligned_group_imp.append(imp) group_imp = torch.stack(aligned_group_imp, dim=0) group_imp = group_imp.sum(0).abs() #self._reduce(group_imp) return group_imp class AbsTaylorImportance(Importance): def __init__(self, order=1, group_reduction="mean", normalizer='mean'): self.group_reduction = group_reduction self.normalizer = normalizer self.order=order self._accum_abs_grad = {} def set_model(self, model): self.model = model def _normalize(self, group_importance, normalizer): if normalizer is None: return group_importance elif isinstance(normalizer, typing.Callable): return normalizer(group_importance) elif normalizer == "sum": return group_importance / group_importance.sum() elif normalizer == "standarization": return (group_importance - group_importance.min()) / (group_importance.max() - group_importance.min()+1e-8) elif normalizer == "mean": return group_importance / group_importance.mean() elif normalizer == "max": return group_importance / group_importance.max() elif normalizer == 'gaussian': return (group_importance - group_importance.mean()) / (group_importance.std()+1e-8) else: raise NotImplementedError def _reduce(self, group_imp): if self.group_reduction == "sum": group_imp = group_imp.sum(dim=0) elif self.group_reduction == "mean": group_imp = group_imp.mean(dim=0) elif self.group_reduction == "max": group_imp = group_imp.max(dim=0)[0] elif self.group_reduction == "prod": group_imp = torch.prod(group_imp, dim=0) elif self.group_reduction=='first': group_imp = group_imp[0] elif self.group_reduction is None: group_imp = group_imp else: raise NotImplementedError return group_imp def accum_abs_grad(self, model): for name, p in model.named_parameters(): if p.requires_grad: if p not in self._accum_abs_grad: self._accum_abs_grad[name]=(p.grad).abs() else: self._accum_abs_grad[name]+=(p.grad).abs() def assign_abs_grad(self, model): for name, p in model.named_parameters(): if name in self._accum_abs_grad: p.grad = self._accum_abs_grad[name] @torch.no_grad() def __call__(self, group, ch_groups=1): group_imp = [] for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) dw= layer.weight.grad.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) dw= layer.weight.grad.data[idxs].flatten(1) wdw = (w*dw).abs().sum(1) local_norm = wdw group_imp.append(local_norm) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight).flatten(1)[idxs] dw= (layer.weight.grad).flatten(1)[idxs] else: w = (layer.weight).transpose(0, 1).flatten(1)[idxs] dw= (layer.weight.grad).transpose(0, 1).flatten(1)[idxs] wdw = (w * dw).abs().sum(1) local_norm = wdw group_imp.append(local_norm) # BN elif prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] dw= layer.weight.grad.data[idxs] wdw = (w * dw).abs() local_norm = wdw group_imp.append(local_norm) if len(group_imp)==0: return None imp_size = len(group_imp[0]) aligned_group_imp = [] for imp in group_imp: if len(imp)==imp_size: aligned_group_imp.append(imp) group_imp = torch.stack(aligned_group_imp, dim=0) group_imp = group_imp.sum(0) #self._reduce(group_imp) #if ch_groups>1: # group_imp = group_imp.view(ch_groups, -1).mean(0) # group_imp = group_imp.repeat(ch_groups) #group_imp = group_imp.abs() #group_imp = self._normalize(group_imp, self.normalizer) return group_imp class FisherImportance(Importance): def __init__(self, group_reduction="mean", normalizer='mean'): self.group_reduction = group_reduction self.normalizer = normalizer def set_model(self, model): self.model = model def _normalize(self, group_importance, normalizer): if normalizer is None: return group_importance elif isinstance(normalizer, typing.Callable): return normalizer(group_importance) elif normalizer == "sum": return group_importance / group_importance.sum() elif normalizer == "standarization": return (group_importance - group_importance.min()) / (group_importance.max() - group_importance.min()+1e-8) elif normalizer == "mean": return group_importance / group_importance.mean() elif normalizer == "max": return group_importance / group_importance.max() elif normalizer == 'gaussian': return (group_importance - group_importance.mean()) / (group_importance.std()+1e-8) else: raise NotImplementedError def _reduce(self, group_imp): if self.group_reduction == "sum": group_imp = group_imp.sum(dim=0) elif self.group_reduction == "mean": group_imp = group_imp.mean(dim=0) elif self.group_reduction == "max": group_imp = group_imp.max(dim=0)[0] elif self.group_reduction == "prod": group_imp = torch.prod(group_imp, dim=0) elif self.group_reduction=='first': group_imp = group_imp[0] elif self.group_reduction is None: group_imp = group_imp else: raise NotImplementedError return group_imp @torch.no_grad() def __call__(self, group, ch_groups=1): group_imp = [] for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) dw= layer.weight.grad.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) dw= layer.weight.grad.data[idxs].flatten(1) #wdw = dw local_norm = dw.pow(2).sum(1) #if ch_groups>1: # local_norm = local_norm.view(ch_groups, -1).mean(0) # local_norm = local_norm.repeat(ch_groups) group_imp.append(local_norm) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight).flatten(1)[idxs] dw= (layer.weight.grad).flatten(1)[idxs] else: w = (layer.weight).transpose(0, 1).flatten(1)[idxs] dw= (layer.weight.grad).transpose(0, 1).flatten(1)[idxs] local_norm = dw.pow(2).sum(1) #if ch_groups>1: # local_norm = local_norm.view(ch_groups, -1).mean(0) # local_norm = local_norm.repeat(ch_groups) group_imp.append(local_norm) # BN elif prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] dw= layer.weight.grad.data[idxs] wdw = w*dw local_norm = wdw.pow(2) #if ch_groups>1: # local_norm = local_norm.view(ch_groups, -1).mean(0) # local_norm = local_norm.repeat(ch_groups) group_imp.append(local_norm) if len(group_imp)==0: return None imp_size = len(group_imp[0]) aligned_group_imp = [] for imp in group_imp: if len(imp)==imp_size: aligned_group_imp.append(imp) group_imp = torch.stack(aligned_group_imp, dim=0) group_imp = group_imp.sum(0) #self._reduce(group_imp) #if ch_groups>1: # group_imp = group_imp.view(ch_groups, -1).mean(0) # group_imp = group_imp.repeat(ch_groups) #group_imp = group_imp.abs() #group_imp = self._normalize(group_imp, self.normalizer) return group_imp ================================================ FILE: ddpm_exp/torch_pruning/ops.py ================================================ import torch.nn as nn from enum import IntEnum class DummyMHA(nn.Module): def __init__(self): super(DummyMHA, self).__init__() class _CustomizedOp(nn.Module): def __init__(self, op_class): self.op_cls = op_class def __repr__(self): return "CustomizedOp({})".format(str(self.op_cls)) class _ConcatOp(nn.Module): def __init__(self, id): super(_ConcatOp, self).__init__() self.offsets = None self.concat_sizes = None self.id = id def __repr__(self): return "_ConcatOp_{}({})".format(self.id, self.offsets) class _SplitOp(nn.Module): def __init__(self, id): super(_SplitOp, self).__init__() self.offsets = None self.split_sizes = None self.id = id def __repr__(self): return "_SplitOp_{}({})".format(self.id,self.offsets) class _ReshapeOp(nn.Module): def __init__(self, id): super(_ReshapeOp, self).__init__() self.id = id def __repr__(self): return "_Reshape_{}()".format(self.id) class _ElementWiseOp(nn.Module): def __init__(self, id, grad_fn): super(_ElementWiseOp, self).__init__() self._grad_fn = grad_fn self.id = id def __repr__(self): return "_ElementWiseOp_{}({})".format(self.id, self._grad_fn) ###################################################### # Dummy Pruners class DummyPruner(object): def __call__(self, layer, *args, **kargs): return layer def prune_out_channels(self, layer, idxs): return layer prune_in_channels = prune_out_channels def get_out_channels(self, layer): return None def get_in_channels(self, layer): return None class ConcatPruner(DummyPruner): def prune_out_channels(self, layer, idxs): if layer.concat_sizes is None: return new_concat_sizes = layer.concat_sizes.copy() concat_sizes = layer.concat_sizes offsets = [0] for i in range(len(concat_sizes)): offsets.append(offsets[i] + concat_sizes[i]) for idx in idxs: # find the ID of the concat for i in range(len(offsets)-1): if idx >= offsets[i] and idx < offsets[i+1]: concat_sizes[i] -= 1 break new_concat_sizes[i]-=1 layer.concat_sizes = new_concat_sizes offsets = [0] for i in range(len(new_concat_sizes)): offsets.append(offsets[i] + new_concat_sizes[i]) self.offsets = offsets prune_in_channels = prune_out_channels class SplitPruner(DummyPruner): def prune_out_channels(self, layer, idxs): if layer.split_sizes is None: return new_split_sizes = layer.split_sizes.copy() split_sizes = layer.split_sizes #offsets = layer.offsets # accumulate split_sizes offsets = [0] for i in range(len(split_sizes)): offsets.append(offsets[i] + split_sizes[i]) for idx in idxs: # find the ID of the split for i in range(len(offsets)-1): if idx >= offsets[i] and idx < offsets[i+1]: split_sizes[i] -= 1 break new_split_sizes[i]-=1 layer.split_sizes = new_split_sizes offsets = [0] for i in range(len(new_split_sizes)): offsets.append(offsets[i] + new_split_sizes[i]) self.offsets = offsets prune_in_channels = prune_out_channels class ReshapePruner(DummyPruner): pass class ElementWisePruner(DummyPruner): pass # Standard Modules TORCH_CONV = nn.modules.conv._ConvNd TORCH_BATCHNORM = nn.modules.batchnorm._BatchNorm TORCH_LAYERNORM = nn.modules.normalization.LayerNorm TORCH_GROUPNORM = nn.GroupNorm TORCH_INSTANCENORM = nn.modules.instancenorm._InstanceNorm TORCH_PRELU = nn.PReLU TORCH_LINEAR = nn.Linear TORCH_EMBED = nn.Embedding TORCH_PARAMETER = nn.Parameter TORCH_LSTM = nn.LSTM try: TORCH_MHA = nn.MultiheadAttention except: TORCH_MHA = DummyMHA # for pytorch w/o MultiHeadAttention TORCH_OTHERS = None class OPTYPE(IntEnum): CONV = 0 BN = 1 LINEAR = 2 PRELU = 3 DEPTHWISE_CONV = 4 CONCAT = 5 # torch.cat SPLIT = 6 # torch.split CUSTOMIZED = 7 # customized module ELEMENTWISE = 8 # element-wise add, sub, etc. LN = 9 # nn.LayerNorm EMBED = 10 # nn.Embedding PARAMETER = 11 # nn.Parameter MHA = 12 LSTM = 13 RESHAPE = 14 GN = 15 # nn.GroupNorm IN = 16 # nn.InstanceNorm def module2type(module): if isinstance(module, TORCH_CONV): if module.groups == module.out_channels: return OPTYPE.DEPTHWISE_CONV else: return OPTYPE.CONV elif isinstance(module, TORCH_BATCHNORM): return OPTYPE.BN elif isinstance(module, TORCH_PRELU): return OPTYPE.PRELU elif isinstance(module, TORCH_LINEAR): return OPTYPE.LINEAR elif isinstance(module, _ConcatOp): return OPTYPE.CONCAT elif isinstance(module, _SplitOp): return OPTYPE.SPLIT elif isinstance(module, TORCH_LAYERNORM): return OPTYPE.LN elif isinstance(module, TORCH_EMBED): return OPTYPE.EMBED elif isinstance(module, _CustomizedOp): return OPTYPE.CUSTOMIZED elif isinstance(module, nn.Parameter): return OPTYPE.PARAMETER elif isinstance(module, TORCH_MHA): return OPTYPE.MHA elif isinstance(module, TORCH_LSTM): return OPTYPE.LSTM elif isinstance(module, TORCH_GROUPNORM): return OPTYPE.GN elif isinstance(module, TORCH_INSTANCENORM): return OPTYPE.IN elif isinstance(module, _ReshapeOp): return OPTYPE.RESHAPE else: return OPTYPE.ELEMENTWISE def type2class(op_type): if op_type == OPTYPE.CONV or op_type==OPTYPE.DEPTHWISE_CONV: return TORCH_CONV elif op_type == OPTYPE.BN: return TORCH_BATCHNORM elif op_type == OPTYPE.PRELU: return TORCH_PRELU elif op_type == OPTYPE.LINEAR: return TORCH_LINEAR elif op_type == OPTYPE.CONCAT: return _ConcatOp elif op_type == OPTYPE.SPLIT: return _SplitOp elif op_type == OPTYPE.LN: return TORCH_LAYERNORM elif op_type == OPTYPE.EMBED: return TORCH_EMBED elif op_type == OPTYPE.CUSTOMIZED: return _CustomizedOp elif op_type == OPTYPE.PARAMETER: return TORCH_PARAMETER elif op_type == OPTYPE.MHA: return TORCH_MHA elif op_type == OPTYPE.GN: return TORCH_GROUPNORM elif op_type == OPTYPE.IN: return TORCH_INSTANCENORM elif op_type == OPTYPE.LSTM: return TORCH_LSTM elif OPTYPE == OPTYPE.RESHAPE: return _ReshapeOp else: return _ElementWiseOp ================================================ FILE: ddpm_exp/torch_pruning/pruner/__init__.py ================================================ from .function import * from .algorithms import * ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/__init__.py ================================================ from .metapruner import MetaPruner from .magnitude_based_pruner import MagnitudePruner from .batchnorm_scale_pruner import BNScalePruner from .group_norm_pruner import GroupNormPruner from .scaling_factor_pruner import ScalingFactorPruner from .taylor_pruner import TaylorPruner ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/batchnorm_scale_pruner.py ================================================ from numbers import Number from typing import Callable from .metapruner import MetaPruner from .scheduler import linear_scheduler import torch import torch.nn as nn class BNScalePruner(MetaPruner): def __init__( self, model, example_inputs, importance, reg=1e-5, iterative_steps=1, iterative_sparsity_scheduler: Callable = linear_scheduler, ch_sparsity=0.5, ch_sparsity_dict=None, global_pruning=False, max_ch_sparsity=1.0, round_to=None, ignored_layers=None, customized_pruners=None, unwrapped_parameters=None, output_transform=None, ): super(BNScalePruner, self).__init__( model=model, example_inputs=example_inputs, importance=importance, iterative_steps=iterative_steps, iterative_sparsity_scheduler=iterative_sparsity_scheduler, ch_sparsity=ch_sparsity, ch_sparsity_dict=ch_sparsity_dict, global_pruning=global_pruning, max_ch_sparsity=max_ch_sparsity, round_to=round_to, ignored_layers=ignored_layers, customized_pruners=customized_pruners, unwrapped_parameters=unwrapped_parameters, output_transform=output_transform, ) self.reg = reg def regularize(self, model): for m in model.modules(): if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)) and m.affine==True: m.weight.grad.data.add_(self.reg*torch.sign(m.weight.data)) ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/group_norm_pruner.py ================================================ import torch import math from .metapruner import MetaPruner from .scheduler import linear_scheduler from .. import function from ..._helpers import _FlattenIndexMapping class GroupNormPruner(MetaPruner): def __init__( self, model, example_inputs, importance, reg=1e-4, alpha=4, iterative_steps=1, iterative_sparsity_scheduler=linear_scheduler, ch_sparsity=0.5, global_pruning=False, channel_groups=dict(), max_ch_sparsity=1.0, soft_keeping_ratio=0.0, ch_sparsity_dict=None, round_to=None, ignored_layers=None, customized_pruners=None, unwrapped_parameters=None, output_transform=None, ): super(GroupNormPruner, self).__init__( model=model, example_inputs=example_inputs, importance=importance, iterative_steps=iterative_steps, iterative_sparsity_scheduler=iterative_sparsity_scheduler, ch_sparsity=ch_sparsity, ch_sparsity_dict=ch_sparsity_dict, global_pruning=global_pruning, channel_groups=channel_groups, max_ch_sparsity=max_ch_sparsity, round_to=round_to, ignored_layers=ignored_layers, customized_pruners=customized_pruners, unwrapped_parameters=unwrapped_parameters, output_transform=output_transform, ) self.reg = reg self.alpha = alpha self.groups = list(self.DG.get_all_groups()) self.soft_keeping_ratio = soft_keeping_ratio self.cnt = 0 @torch.no_grad() def regularize(self, model, base=16): for i, group in enumerate(self.groups): ch_groups = self.get_channel_groups(group) group_norm = 0 # Get group norm #print(group) for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: w = layer.weight.data[idxs].flatten(1) local_norm = w.pow(2).sum(1) #print(local_norm.shape, layer, idxs, ch_groups) if ch_groups>1: local_norm = local_norm.view(ch_groups, -1).sum(0) local_norm = local_norm.repeat(ch_groups) group_norm+=local_norm #if layer.bias is not None: # group_norm += layer.bias.data[idxs].pow(2) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: w = (layer.weight).transpose(0, 1).flatten(1) if ( w.shape[0] != group_norm.shape[0] ): if hasattr(dep, 'index_mapping') and isinstance(dep.index_mapping, _FlattenIndexMapping): # conv - latten w = w.view( group_norm.shape[0], w.shape[0] // group_norm.shape[0], w.shape[1], ).flatten(1) elif ch_groups>1 and prune_fn==function.prune_conv_in_channels and layer.groups==1: # group conv w = w.view(w.shape[0] // group_norm.shape[0], group_norm.shape[0], w.shape[1]).transpose(0, 1).flatten(1) local_norm = w.pow(2).sum(1) if ch_groups>1: if len(local_norm)==len(group_norm): local_norm = local_norm.view(ch_groups, -1).sum(0) local_norm = local_norm.repeat(ch_groups) group_norm += local_norm[idxs] # BN elif prune_fn == function.prune_batchnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] local_norm = w.pow(2) if ch_groups>1: local_norm = local_norm.view(ch_groups, -1).sum(0) local_norm = local_norm.repeat(ch_groups) group_norm += local_norm #b = layer.bias.data[idxs] #local_norm = b.pow(2) #if ch_groups>1: # local_norm = local_norm.view(ch_groups, -1).sum(0) # local_norm = local_norm.repeat(ch_groups) #group_norm += local_norm current_channels = len(group_norm) if ch_groups>1: group_norm = group_norm.view(ch_groups, -1).sum(0) group_stride = current_channels//ch_groups group_norm = torch.cat([group_norm+group_stride*i for i in range(ch_groups)], 0) group_norm = group_norm.sqrt() base = 16 scale = base**((group_norm.max() - group_norm) / (group_norm.max() - group_norm.min())) #if self.cnt%1000==0: # print("="*15) # print(group) # print("Group {}".format(i)) # print(group_norm) # print(scale) # Update Gradient for dep, idxs in group: layer = dep.target.module prune_fn = dep.handler if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: w = layer.weight.data[idxs] g = w * scale.view( -1, *([1]*(len(w.shape)-1)) ) #/ group_norm.view( -1, *([1]*(len(w.shape)-1)) ) * group_size #group_size #* scale.view( -1, *([1]*(len(w.shape)-1)) ) layer.weight.grad.data[idxs]+=self.reg * g #if layer.bias is not None: # b = layer.bias.data[idxs] # g = b * scale # layer.bias.grad.data[idxs]+=self.reg * g elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: gn = group_norm if hasattr(dep.target, 'index_transform') and isinstance(dep.target.index_transform, _FlattenIndexTransform): gn = group_norm.repeat_interleave(w.shape[1]//group_norm.shape[0]) # regularize input channels if prune_fn==function.prune_conv_in_channels and layer.groups>1: scale = scale[:len(idxs)//ch_groups] idxs = idxs[:len(idxs)//ch_groups] w = layer.weight.data[:, idxs] g = w * scale.view( 1, -1, *([1]*(len(w.shape)-2)) ) #/ gn.view( 1, -1, *([1]*(len(w.shape)-2)) ) * group_size #* scale.view( 1, -1, *([1]*(len(w.shape)-2)) ) layer.weight.grad.data[:, idxs]+=self.reg * g elif prune_fn == function.prune_batchnorm_out_channels: # regularize BN if layer.affine is not None: w = layer.weight.data[idxs] g = w * scale #/ group_norm * group_size layer.weight.grad.data[idxs]+=self.reg * g #b = layer.bias.data[idxs] #g = b * scale #/ group_norm * group_size #layer.bias.grad.data[idxs]+=self.reg * g self.cnt+=1 ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/magnitude_based_pruner.py ================================================ from .metapruner import MetaPruner class MagnitudePruner(MetaPruner): pass ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/metapruner.py ================================================ import torch import torch.nn as nn import typing from .scheduler import linear_scheduler from ..import function from ... import ops, dependency import os import matplotlib.pyplot as plt class MetaPruner: """ Meta Pruner for structural pruning. Args: model (nn.Module): A to-be-pruned model example_inputs (torch.Tensor or List): dummy inputs for graph tracing. importance (Callable): importance estimator. global_pruning (bool): enable global pruning. ch_sparsity (float): global channel sparisty. ch_sparsity_dict (Dict[nn.Module, float]): layer-specific sparsity. iterative_steps (int): number of steps for iterative pruning. iterative_sparsity_scheduler (Callable): scheduler for iterative pruning. max_ch_sparsity (float): maximum channel sparsity. ignored_layers (List[nn.Module]): ignored modules. round_to (int): channel rounding. customized_pruners (dict): a dict containing module-pruner pairs. unwrapped_parameters (list): nn.Parameter that does not belong to any supported layerss. root_module_types (list): types of prunable modules. output_transform (Callable): A function to transform network outputs. """ def __init__( self, # Basic model: nn.Module, example_inputs: torch.Tensor, importance: typing.Callable, # https://pytorch.org/tutorials/intermediate/pruning_tutorial.html#global-pruning. global_pruning: bool = False, ch_sparsity: float = 0.5, # channel/dim sparsity ch_sparsity_dict: typing.Dict[nn.Module, float] = None, max_ch_sparsity: float = 1.0, iterative_steps: int = 1, # for iterative pruning iterative_sparsity_scheduler: typing.Callable = linear_scheduler, ignored_layers: typing.List[nn.Module] = None, # Advanced round_to: int = None, # round channels to 8x, 16x, ... # for grouped channels. channel_groups: typing.Dict[nn.Module, int] = dict(), # pruners for customized layers customized_pruners: typing.Dict[typing.Any, function.BasePruningFunc] = None, # unwrapped nn.Parameters like ViT.pos_emb unwrapped_parameters: typing.List[nn.Parameter] = None, root_module_types: typing.List = [ ops.TORCH_CONV, ops.TORCH_LINEAR, ops.TORCH_LSTM], # root module for each group output_transform: typing.Callable = None, ): self.model = model self.importance = importance self.ch_sparsity = ch_sparsity self.ch_sparsity_dict = ch_sparsity_dict if ch_sparsity_dict is not None else {} self.max_ch_sparsity = max_ch_sparsity self.global_pruning = global_pruning self.channel_groups = channel_groups self.root_module_types = root_module_types self.round_to = round_to # Build dependency graph self.DG = dependency.DependencyGraph().build_dependency( model, example_inputs=example_inputs, output_transform=output_transform, unwrapped_parameters=unwrapped_parameters, customized_pruners=customized_pruners, ) self.ignored_layers = [] if ignored_layers: for layer in ignored_layers: self.ignored_layers.extend(list(layer.modules())) self.iterative_steps = iterative_steps self.iterative_sparsity_scheduler = iterative_sparsity_scheduler self.current_step = 0 # Record initial status self.layer_init_out_ch = {} self.layer_init_in_ch = {} for m in self.DG.module2node.keys(): if ops.module2type(m) in self.DG.REGISTERED_PRUNERS: self.layer_init_out_ch[m] = self.DG.get_out_channels(m) self.layer_init_in_ch[m] = self.DG.get_in_channels(m) # global channel sparsity for each iterative step self.per_step_ch_sparsity = self.iterative_sparsity_scheduler( self.ch_sparsity, self.iterative_steps ) # The customized channel sparsity for different layers self.ch_sparsity_dict = {} if ch_sparsity_dict is not None: for module in ch_sparsity_dict: sparsity = ch_sparsity_dict[module] for submodule in module.modules(): prunable_types = tuple([ops.type2class( prunable_type) for prunable_type in self.DG.REGISTERED_PRUNERS.keys()]) if isinstance(submodule, prunable_types): self.ch_sparsity_dict[submodule] = self.iterative_sparsity_scheduler( sparsity, self.iterative_steps ) # detect group convs & group norms for m in self.model.modules(): if isinstance(m, ops.TORCH_CONV) \ and m.groups > 1 \ and m.groups != m.out_channels: self.channel_groups[m] = m.groups if isinstance(m, ops.TORCH_GROUPNORM): self.channel_groups[m] = m.num_groups if self.global_pruning: initial_total_channels = 0 for group in self.DG.get_all_groups(ignored_layers=self.ignored_layers, root_module_types=self.root_module_types): ch_groups = self.get_channel_groups(group) # utils.count_prunable_out_channels( group[0][0].target.module ) initial_total_channels += (self.DG.get_out_channels( group[0][0].target.module) // ch_groups) self.initial_total_channels = initial_total_channels def pruning_history(self): return self.DG.pruning_history() def load_pruning_history(self, pruning_history): self.DG.load_pruning_history(pruning_history) def get_target_sparsity(self, module): s = self.ch_sparsity_dict.get(module, self.per_step_ch_sparsity)[ self.current_step] return min(s, self.max_ch_sparsity) def reset(self): self.current_step = 0 def regularize(self, model, loss): """ Model regularizor """ pass def step(self, interactive=False): self.current_step += 1 if self.global_pruning: if interactive: return self.prune_global() else: for group in self.prune_global(): group.prune() else: if interactive: return self.prune_local() else: for group in self.prune_local(): group.prune() def estimate_importance(self, group, ch_groups=1): return self.importance(group, ch_groups=ch_groups) def _check_sparsity(self, group): for dep, _ in group: module = dep.target.module pruning_fn = dep.handler if dep.target.type == ops.OPTYPE.PARAMETER: continue if self.DG.is_out_channel_pruning_fn(pruning_fn): target_sparsity = self.get_target_sparsity(module) layer_out_ch = self.DG.get_out_channels(module) if layer_out_ch is None: continue if layer_out_ch < self.layer_init_out_ch[module] * ( 1 - self.max_ch_sparsity ) or layer_out_ch == 1: return False elif self.DG.is_in_channel_pruning_fn(pruning_fn): layer_in_ch = self.DG.get_in_channels(module) if layer_in_ch is None: continue if layer_in_ch < self.layer_init_in_ch[module] * ( 1 - self.max_ch_sparsity ) or layer_in_ch == 1: return False return True def get_channel_groups(self, group): if isinstance(self.channel_groups, int): return self.channel_groups for dep, _ in group: module = dep.target.module if module in self.channel_groups: return self.channel_groups[module] return 1 # no channel grouping def prune_local(self): if self.current_step > self.iterative_steps: return for gi, group in enumerate(self.DG.get_all_groups(ignored_layers=self.ignored_layers, root_module_types=self.root_module_types)): # check pruning rate if self._check_sparsity(group): module = group[0][0].target.module pruning_fn = group[0][0].handler ch_groups = self.get_channel_groups(group) imp = self.estimate_importance(group, ch_groups=ch_groups) if imp is None: continue os.makedirs('run/pruning_logs', exist_ok=True) # draw bar for imp plt.figure() plt.bar(range(len(imp)), imp.cpu().numpy()) plt.savefig(f'run/pruning_logs/imp_{gi}.png') plt.close() current_channels = self.DG.get_out_channels(module) target_sparsity = self.get_target_sparsity(module) n_pruned = current_channels - int( self.layer_init_out_ch[module] * (1 - target_sparsity) ) if self.round_to: n_pruned = n_pruned - (n_pruned % self.round_to) if n_pruned <= 0: continue if ch_groups > 1: group_size = current_channels // ch_groups pruning_idxs = [] n_pruned_per_group = n_pruned // ch_groups # max(1, n_pruned // ch_groups) for chg in range(ch_groups): sub_group_imp = imp[chg*group_size: (chg+1)*group_size] sub_imp_argsort = torch.argsort(sub_group_imp) sub_pruning_idxs = sub_imp_argsort[:n_pruned_per_group]+chg*group_size pruning_idxs.append(sub_pruning_idxs) pruning_idxs = torch.cat(pruning_idxs, 0) else: imp_argsort = torch.argsort(imp) pruning_idxs = imp_argsort[:(n_pruned//ch_groups)] group = self.DG.get_pruning_group( module, pruning_fn, pruning_idxs.tolist()) if self.DG.check_pruning_group(group): yield group def prune_global(self): if self.current_step > self.iterative_steps: return global_importance = [] for group in self.DG.get_all_groups(ignored_layers=self.ignored_layers, root_module_types=self.root_module_types): if self._check_sparsity(group): ch_groups = self.get_channel_groups(group) imp = self.estimate_importance(group, ch_groups=ch_groups) if imp is None: continue if ch_groups > 1: imp = imp[:len(imp)//ch_groups] global_importance.append((group, ch_groups, imp)) imp = torch.cat([local_imp[-1] for local_imp in global_importance], dim=0) target_sparsity = self.per_step_ch_sparsity[self.current_step] n_pruned = len(imp) - int( self.initial_total_channels * (1 - target_sparsity) ) if n_pruned <= 0: return topk_imp, _ = torch.topk(imp, k=n_pruned, largest=False) # global pruning through thresholding thres = topk_imp[-1] for group, ch_groups, imp in global_importance: module = group[0][0].target.module pruning_fn = group[0][0].handler pruning_indices = (imp <= thres).nonzero().view(-1) if ch_groups > 1: group_size = self.DG.get_out_channels(module)//ch_groups pruning_indices = torch.cat( [pruning_indices+group_size*i for i in range(ch_groups)], 0) if self.round_to: n_pruned = len(pruning_indices) n_pruned = n_pruned - (n_pruned % self.round_to) pruning_indices = pruning_indices[:n_pruned] group = self.DG.get_pruning_group( module, pruning_fn, pruning_indices.tolist()) if self.DG.check_pruning_group(group): yield group ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/scaling_factor_pruner.py ================================================ from numbers import Number from typing import Callable from .metapruner import MetaPruner from .scheduler import linear_scheduler import torch import torch.nn as nn from .scheduler import linear_scheduler from ..import function from ... import ops, dependency class ScalingFactorPruner(MetaPruner): def __init__( self, model, example_inputs, importance, reg=1e-5, iterative_steps=1, iterative_sparsity_scheduler: Callable = linear_scheduler, ch_sparsity=0.5, ch_sparsity_dict=None, global_pruning=False, max_ch_sparsity=1.0, round_to=None, channel_groups=None, ignored_layers=None, customized_pruners=None, unwrapped_parameters=None, output_transform=None, ): super(ScalingFactorPruner, self).__init__( model=model, example_inputs=example_inputs, importance=importance, iterative_steps=iterative_steps, iterative_sparsity_scheduler=iterative_sparsity_scheduler, ch_sparsity=ch_sparsity, ch_sparsity_dict=ch_sparsity_dict, global_pruning=global_pruning, max_ch_sparsity=max_ch_sparsity, round_to=round_to, ignored_layers=ignored_layers, customized_pruners=customized_pruners, unwrapped_parameters=unwrapped_parameters, output_transform=output_transform, channel_groups=channel_groups, ) self.reg = reg self.groups = list(self.DG.get_all_groups()) def regularize(self, model): for i, group in enumerate(self.groups): ch_groups = self.get_channel_groups(group) # Get group norm #print(group) group_norm = [] for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler if prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] local_norm = w.pow(2) group_norm.append(local_norm) if len(group_norm)==0: continue group_norm = [gn for gn in group_norm if gn.shape[0]==group_norm[0].shape[0]] group_norm = torch.stack(group_norm, 0).sum(0) group_norm = group_norm.sqrt() base = 16 scale = base**((group_norm.max() - group_norm) / (group_norm.max() - group_norm.min())) # Update Gradient for dep, idxs in group: layer = dep.target.module prune_fn = dep.handler if prune_fn == function.prune_groupnorm_out_channels and len(idxs)==group_norm.shape[0]: # regularize BN if layer.affine is not None: w = layer.weight.data[idxs] g = w * scale #/ group_norm * group_size layer.weight.grad.data[idxs]+=self.reg * g #b = layer.bias.data[idxs] #g = b * scale #/ group_norm * group_size #layer.bias.grad.data[idxs]+=self.reg * g ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/scheduler.py ================================================ def linear_scheduler(ch_sparsity_dict, steps): return [((i) / float(steps)) * ch_sparsity_dict for i in range(steps+1)] ================================================ FILE: ddpm_exp/torch_pruning/pruner/algorithms/taylor_pruner.py ================================================ import torch import math from .metapruner import MetaPruner from .scheduler import linear_scheduler from .. import function from ..._helpers import _FlattenIndexMapping class TaylorPruner(MetaPruner): def __init__( self, model, example_inputs, importance, reg=1e-4, alpha=4, iterative_steps=1, iterative_sparsity_scheduler=linear_scheduler, ch_sparsity=0.5, global_pruning=False, channel_groups=dict(), max_ch_sparsity=1.0, soft_keeping_ratio=0.0, ch_sparsity_dict=None, round_to=None, ignored_layers=None, customized_pruners=None, unwrapped_parameters=None, output_transform=None, ): super(TaylorPruner, self).__init__( model=model, example_inputs=example_inputs, importance=importance, iterative_steps=iterative_steps, iterative_sparsity_scheduler=iterative_sparsity_scheduler, ch_sparsity=ch_sparsity, ch_sparsity_dict=ch_sparsity_dict, global_pruning=global_pruning, channel_groups=channel_groups, max_ch_sparsity=max_ch_sparsity, round_to=round_to, ignored_layers=ignored_layers, customized_pruners=customized_pruners, unwrapped_parameters=unwrapped_parameters, output_transform=output_transform, ) self.reg = reg self.alpha = alpha self.groups = list(self.DG.get_all_groups()) self.soft_keeping_ratio = soft_keeping_ratio self.cnt = 0 @torch.no_grad() def regularize(self, model, base=16): min_avg = 0 cnt = 0 for i, group in enumerate(self.groups): ch_groups = self.get_channel_groups(group) group_imp = [] # Get group norm for dep, idxs in group: idxs.sort() layer = dep.target.module prune_fn = dep.handler # Conv out_channels if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = layer.weight.data.transpose(1, 0)[idxs].flatten(1) dw= layer.weight.grad.data.transpose(1, 0)[idxs].flatten(1) else: w = layer.weight.data[idxs].flatten(1) dw= layer.weight.grad.data[idxs].flatten(1) wdw = w * dw local_norm = wdw.abs().sum(1) group_imp.append(local_norm) # Conv in_channels elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: if hasattr(layer, "transposed") and layer.transposed: w = (layer.weight).flatten(1)[idxs] dw= (layer.weight.grad).flatten(1)[idxs] else: w = (layer.weight).transpose(0, 1).flatten(1)[idxs] dw= (layer.weight.grad).transpose(0, 1).flatten(1)[idxs] wdw = w * dw local_norm = wdw.abs().sum(1) group_imp.append(local_norm) # BN elif prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine: w = layer.weight.data[idxs] dw= layer.weight.grad.data[idxs] wdw = w * dw local_norm = wdw.abs() group_imp.append(local_norm) if len(group_imp)==0: return None imp_size = len(group_imp[0]) aligned_group_imp = [] for imp in group_imp: if len(imp)==imp_size: aligned_group_imp.append(imp) group_imp = torch.stack(aligned_group_imp, dim=0) group_imp = group_imp.sum(0).abs() min_avg+=float(group_imp.min()) cnt+=1 base = 16 scale = base**((group_imp.max() - group_imp) / (group_imp.max() - group_imp.min())) # Update Gradient for dep, idxs in group: layer = dep.target.module prune_fn = dep.handler if prune_fn in [ function.prune_conv_out_channels, function.prune_linear_out_channels, ]: w = layer.weight.data[idxs] g = w * scale.view( -1, *([1]*(len(w.shape)-1)) ) #/ group_norm.view( -1, *([1]*(len(w.shape)-1)) ) * group_size #group_size #* scale.view( -1, *([1]*(len(w.shape)-1)) ) layer.weight.grad.data[idxs]+=self.reg * g elif prune_fn in [ function.prune_conv_in_channels, function.prune_linear_in_channels, ]: w = layer.weight.data[:, idxs] g = w * scale.view( 1, -1, *([1]*(len(w.shape)-2)) ) #/ gn.view( 1, -1, *([1]*(len(w.shape)-2)) ) * group_size #* scale.view( 1, -1, *([1]*(len(w.shape)-2)) ) layer.weight.grad.data[:, idxs]+=self.reg * g elif prune_fn == function.prune_groupnorm_out_channels: # regularize BN if layer.affine is not None: w = layer.weight.data[idxs] g = w * scale #/ group_norm * group_size layer.weight.grad.data[idxs]+=self.reg * g return min_avg / cnt ================================================ FILE: ddpm_exp/torch_pruning/pruner/function.py ================================================ import torch import torch.nn as nn from .. import ops from copy import deepcopy from functools import reduce from operator import mul from abc import ABC, abstractclassmethod, abstractmethod, abstractstaticmethod from typing import Callable, Sequence, Tuple, Dict __all__=[ 'BasePruningFunc', 'PrunerBox', 'prune_conv_out_channels', 'prune_conv_in_channels', 'prune_depthwise_conv_out_channels', 'prune_depthwise_conv_in_channels', 'prune_batchnorm_out_channels', 'prune_batchnorm_in_channels', 'prune_linear_out_channels', 'prune_linear_in_channels', 'prune_prelu_out_channels', 'prune_prelu_in_channels', 'prune_layernorm_out_channels', 'prune_layernorm_in_channels', 'prune_embedding_out_channels', 'prune_embedding_in_channels', 'prune_parameter_out_channels', 'prune_parameter_in_channels', 'prune_multihead_attention_out_channels', 'prune_multihead_attention_in_channels', 'prune_groupnorm_out_channels', 'prune_groupnorm_in_channels', 'prune_instancenorm_out_channels', 'prune_instancenorm_in_channels', ] class BasePruningFunc(ABC): TARGET_MODULES = ops.TORCH_OTHERS # None def __init__(self, pruning_dim=1): self.pruning_dim = pruning_dim @abstractclassmethod def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]): raise NotImplementedError @abstractclassmethod def prune_in_channels(self, layer: nn.Module, idxs: Sequence[int]): raise NotImplementedError @abstractclassmethod def get_out_channels(self, layer: nn.Module): raise NotImplementedError @abstractclassmethod def get_in_channels(self, layer: nn.Module): raise NotImplementedError def check(self, layer, idxs, to_output): if self.TARGET_MODULES is not None: assert isinstance(layer, self.TARGET_MODULES), 'Mismatched pruner {} and module {}'.format( self.__str__, layer) if to_output: prunable_channels = self.get_out_channels(layer) else: prunable_channels = self.get_in_channels(layer) if prunable_channels is not None: assert all(idx < prunable_channels and idx >= 0 for idx in idxs), "All pruning indices should fall into [{}, {})".format(0, prunable_channels) def __call__(self, layer: nn.Module, idxs: Sequence[int], to_output: bool = True, inplace: bool = True, dry_run: bool = False) -> Tuple[nn.Module, int]: idxs.sort() self.check(layer, idxs, to_output) pruning_fn = self.prune_out_channels if to_output else self.prune_in_channels if not inplace: layer = deepcopy(layer) layer = pruning_fn(layer, idxs) return layer class ConvPruner(BasePruningFunc): TARGET_MODULE = ops.TORCH_CONV def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.out_channels)) - set(idxs)) keep_idxs.sort() layer.out_channels = layer.out_channels-len(idxs) if not layer.transposed: if layer.weight.grad is not None: grad = layer.weight.grad.data[keep_idxs] else: grad = None layer.weight = torch.nn.Parameter( layer.weight.data[keep_idxs]) layer.weight.grad = grad else: if layer.weight.grad is not None: grad = layer.weight.grad.data[:, keep_idxs] else: grad = None layer.weight = torch.nn.Parameter( layer.weight.data[:, keep_idxs]) layer.weight.grad = grad if layer.bias is not None: if layer.bias.grad is not None: grad = layer.bias.grad.data[keep_idxs] else: grad = None layer.bias = torch.nn.Parameter(layer.bias.data[keep_idxs]) layer.bias.grad = grad return layer def prune_in_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.in_channels)) - set(idxs)) keep_idxs.sort() layer.in_channels = layer.in_channels - len(idxs) if layer.groups>1: keep_idxs = keep_idxs[:len(keep_idxs)//layer.groups] if not layer.transposed: if layer.weight.grad is not None: grad = layer.weight.grad.data[:, keep_idxs] else: grad = None layer.weight = torch.nn.Parameter( layer.weight.data[:, keep_idxs]) layer.weight.grad = grad else: if layer.weight.grad is not None: grad = layer.weight.grad.data[keep_idxs] else: grad = None layer.weight = torch.nn.Parameter( layer.weight.data[keep_idxs]) layer.weight.grad = grad # no bias pruning because it does not change the output channels return layer def get_out_channels(self, layer): return layer.out_channels def get_in_channels(self, layer): return layer.in_channels class DepthwiseConvPruner(ConvPruner): TARGET_MODULE = ops.TORCH_CONV def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.out_channels)) - set(idxs)) keep_idxs.sort() layer.out_channels = layer.out_channels-len(idxs) layer.in_channels = layer.in_channels-len(idxs) layer.groups = layer.groups-len(idxs) layer.weight = torch.nn.Parameter(layer.weight.data[keep_idxs]) if layer.bias is not None: layer.bias = torch.nn.Parameter(layer.bias.data[keep_idxs]) return layer prune_in_channels = prune_out_channels # def prune_input(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: # return self.prune_output(layer, idxs) class LinearPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_LINEAR def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.out_features)) - set(idxs)) keep_idxs.sort() layer.out_features = layer.out_features-len(idxs) if layer.weight.grad is not None: grad = layer.weight.grad.data[keep_idxs] else: grad = None layer.weight = torch.nn.Parameter(layer.weight.data[keep_idxs]) layer.weight.grad = grad if layer.bias is not None: if layer.bias.grad is not None: grad = layer.bias.grad.data[keep_idxs] else: grad = None layer.bias = torch.nn.Parameter(layer.bias.data[keep_idxs]) layer.bias.grad = grad return layer def prune_in_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.in_features)) - set(idxs)) keep_idxs.sort() layer.in_features = layer.in_features-len(idxs) if layer.weight.grad is not None: grad = layer.weight.grad.data[:, keep_idxs] else: grad = None layer.weight = torch.nn.Parameter( layer.weight.data[:, keep_idxs]) layer.weight.grad = grad return layer def get_out_channels(self, layer): return layer.out_features def get_in_channels(self, layer): return layer.in_features class BatchnormPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_BATCHNORM def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.num_features)) - set(idxs)) keep_idxs.sort() layer.num_features = layer.num_features-len(idxs) layer.running_mean = layer.running_mean.data[keep_idxs] layer.running_var = layer.running_var.data[keep_idxs] if layer.affine: layer.weight = torch.nn.Parameter( layer.weight.data[keep_idxs]) layer.bias = torch.nn.Parameter(layer.bias.data[keep_idxs]) return layer prune_in_channels = prune_out_channels # def prune_in_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: # return self.prune_out_channels(layer=layer, idxs=idxs) def get_out_channels(self, layer): return layer.num_features def get_in_channels(self, layer): return layer.num_features class LayernormPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_LAYERNORM def __init__(self, metrcis=None, pruning_dim=-1): super().__init__(metrcis) self.pruning_dim = pruning_dim def check(self, layer, idxs): layer.dim = self.pruning_dim def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: pruning_dim = self.pruning_dim if len(layer.normalized_shape) < -pruning_dim: return layer num_features = layer.normalized_shape[pruning_dim] keep_idxs = torch.tensor(list(set(range(num_features)) - set(idxs))) keep_idxs.sort() if layer.elementwise_affine: layer.weight = torch.nn.Parameter( layer.weight.data.index_select(pruning_dim, keep_idxs)) layer.bias = torch.nn.Parameter( layer.bias.data.index_select(pruning_dim, keep_idxs)) if pruning_dim != -1: layer.normalized_shape = layer.normalized_shape[:pruning_dim] + ( keep_idxs.size(0), ) + layer.normalized_shape[pruning_dim+1:] else: layer.normalized_shape = layer.normalized_shape[:pruning_dim] + ( keep_idxs.size(0), ) return layer prune_in_channels = prune_out_channels def get_out_channels(self, layer): return layer.normalized_shape[self.pruning_dim] def get_in_channels(self, layer): return layer.normalized_shape[self.pruning_dim] class GroupNormPruner(BasePruningFunc): def prune_out_channels(self, layer: nn.GroupNorm, idxs: list) -> nn.Module: keep_idxs = list(set(range(layer.num_channels)) - set(idxs)) keep_idxs.sort() layer.num_channels = layer.num_channels-len(idxs) if layer.affine: if layer.weight.grad is not None: grad = layer.weight.grad.data[keep_idxs] else: grad = None layer.weight = torch.nn.Parameter( layer.weight.data[keep_idxs]) layer.weight.grad = grad if layer.bias.grad is not None: grad = layer.bias.grad.data[keep_idxs] else: grad = None layer.bias = torch.nn.Parameter(layer.bias.data[keep_idxs]) layer.bias.grad = grad return layer prune_in_channels = prune_out_channels def get_out_channels(self, layer): return layer.num_channels def get_in_channels(self, layer): return layer.num_channels class InstanceNormPruner(BasePruningFunc): def prune_out_channels(self, layer: nn.Module, idxs: Sequence[int]) -> nn.Module: keep_idxs = list(set(range(layer.num_features)) - set(idxs)) keep_idxs.sort() layer.num_features = layer.num_features-len(idxs) if layer.affine: layer.weight = torch.nn.Parameter( layer.weight.data[keep_idxs]) layer.bias = torch.nn.Parameter(layer.bias.data[keep_idxs]) return layer prune_in_channels = prune_out_channels def get_out_channels(self, layer): return layer.num_features def get_in_channels(self, layer): return layer.num_features class PReLUPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_PRELU def prune_out_channels(self, layer: nn.PReLU, idxs: list) -> nn.Module: if layer.num_parameters == 1: return layer keep_idxs = list(set(range(layer.num_parameters)) - set(idxs)) keep_idxs.sort() layer.num_parameters = layer.num_parameters-len(idxs) layer.weight = torch.nn.Parameter(layer.weight.data[keep_idxs]) return layer prune_in_channels = prune_out_channels # def prune_in_channels(self, layer:nn.Module, idxs: Sequence[int]) -> nn.Module: # return self.prune_out_channels(layer=layer, idxs=idxs) def get_out_channels(self, layer): if layer.num_parameters == 1: return None else: return layer.num_parameters def get_in_channels(self, layer): return self.get_out_channels(layer=layer) class EmbeddingPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_EMBED def prune_out_channels(self, layer: nn.Embedding, idxs: list) -> nn.Module: num_features = layer.embedding_dim keep_idxs = list(set(range(num_features)) - set(idxs)) keep_idxs.sort() layer.weight = torch.nn.Parameter( layer.weight.data[:, keep_idxs]) layer.embedding_dim = len(keep_idxs) return layer prune_in_channels = prune_out_channels # def prune_in_channels(self, layer: nn.Embedding, idxs: list)-> nn.Module: # return self.prune_out_channels(layer=layer, idxs=idxs) def get_out_channels(self, layer): return layer.embedding_dim def get_in_channels(self, layer): return self.get_out_channels(layer=layer) class LSTMPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_LSTM def prune_out_channels(self, layer: nn.LSTM, idxs: list) -> nn.Module: assert layer.num_layers==1 num_layers = layer.num_layers num_features = layer.hidden_size keep_idxs = list(set(range(num_features)) - set(idxs)) keep_idxs.sort() keep_idxs = torch.tensor(keep_idxs) expanded_keep_idxs = torch.cat([ keep_idxs+i*num_features for i in range(4) ], dim=0) if layer.bidirectional: postfix = ['', '_reverse'] else: postfix = [''] #for l in range(num_layers): for pf in postfix: setattr(layer, 'weight_hh_l0'+pf, torch.nn.Parameter( getattr(layer, 'weight_hh_l0'+pf).data[expanded_keep_idxs])) if layer.bias: setattr(layer, 'bias_hh_l0'+pf, torch.nn.Parameter( getattr(layer, 'bias_hh_l0'+pf).data[expanded_keep_idxs])) setattr(layer, 'weight_hh_l0'+pf, torch.nn.Parameter( getattr(layer, 'weight_hh_l0'+pf).data[:, keep_idxs])) setattr(layer, 'weight_ih_l0'+pf, torch.nn.Parameter( getattr(layer, 'weight_ih_l0'+pf).data[expanded_keep_idxs])) if layer.bias: setattr(layer, 'bias_ih_l0'+pf, torch.nn.Parameter( getattr(layer, 'bias_ih_l0'+pf).data[expanded_keep_idxs])) layer.hidden_size = len(keep_idxs) def prune_in_channels(self, layer: nn.LSTM, idxs: list): num_features = layer.input_size keep_idxs = list(set(range(num_features)) - set(idxs)) keep_idxs.sort() setattr(layer, 'weight_ih_l0', torch.nn.Parameter( getattr(layer, 'weight_ih_l0').data[:, keep_idxs])) if layer.bidirectional: setattr(layer, 'weight_ih_l0_reverse', torch.nn.Parameter( getattr(layer, 'weight_ih_l0_reverse').data[:, keep_idxs])) layer.input_size = len(keep_idxs) def get_out_channels(self, layer): return layer.hidden_size def get_in_channels(self, layer): return layer.input_size class ParameterPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_PARAMETER def __init__(self, pruning_dim=-1): super().__init__(pruning_dim=pruning_dim) def prune_out_channels(self, tensor, idxs: list) -> nn.Module: keep_idxs = list(set(range(tensor.data.shape[self.pruning_dim])) - set(idxs)) keep_idxs.sort() pruned_parameter = nn.Parameter(torch.index_select( tensor.data, self.pruning_dim, torch.LongTensor(keep_idxs).to(tensor.device))) return pruned_parameter prune_in_channels = prune_out_channels def get_out_channels(self, parameter): return parameter.shape[self.pruning_dim] def get_in_channels(self, parameter): return parameter.shape[self.pruning_dim] class MultiheadAttentionPruner(BasePruningFunc): TARGET_MODULES = ops.TORCH_MHA def check(self, layer, idxs, to_output): super().check(layer, idxs, to_output) assert (layer.embed_dim - len(idxs)) % layer.num_heads == 0, "embed_dim (%d) of MultiheadAttention after pruning must divide evenly by `num_heads` (%d)" % (layer.embed_dim, layer.num_heads) def prune_out_channels(self, layer, idxs: list) -> nn.Module: keep_idxs = list(set(range(layer.embed_dim)) - set(idxs)) keep_idxs.sort() if layer.q_proj_weight is not None: layer.q_proj_weight = nn.Parameter(torch.index_select( layer.q_proj_weight.data, 0, torch.LongTensor(keep_idxs))) if layer.k_proj_weight is not None: layer.q_proj_weight = nn.Parameter(torch.index_select( layer.q_proj_weight.data, 0, torch.LongTensor(keep_idxs))) if layer.v_proj_weight is not None: layer.v_proj_weight = nn.Parameter(torch.index_select( layer.v_proj_weight.data, 0, torch.LongTensor(keep_idxs))) pruning_idxs_repeated = idxs + \ [i+layer.embed_dim for i in idxs] + \ [i+2*layer.embed_dim for i in idxs] keep_idxs_3x_repeated = list( set(range(3*layer.embed_dim)) - set(pruning_idxs_repeated)) keep_idxs_3x_repeated.sort() if layer.in_proj_weight is not None: layer.in_proj_weight = nn.Parameter(torch.index_select( layer.in_proj_weight.data, 0, torch.LongTensor(keep_idxs_3x_repeated))) layer.in_proj_weight = nn.Parameter(torch.index_select( layer.in_proj_weight.data, 1, torch.LongTensor(keep_idxs))) if layer.in_proj_bias is not None: layer.in_proj_bias = nn.Parameter(torch.index_select( layer.in_proj_bias.data, 0, torch.LongTensor(keep_idxs_3x_repeated))) if layer.bias_k is not None: layer.bias_k = nn.Parameter(torch.index_select( layer.bias_k.data, 2, torch.LongTensor(keep_idxs))) if layer.bias_v is not None: layer.bias_v = nn.Parameter(torch.index_select( layer.bias_v.data, 2, torch.LongTensor(keep_idxs))) linear = layer.out_proj keep_idxs = list(set(range(linear.out_features)) - set(idxs)) keep_idxs.sort() linear.out_features = linear.out_features-len(idxs) linear.weight = torch.nn.Parameter( linear.weight.data[keep_idxs]) if linear.bias is not None: linear.bias = torch.nn.Parameter( linear.bias.data[keep_idxs]) keep_idxs = list(set(range(linear.in_features)) - set(idxs)) keep_idxs.sort() linear.in_features = linear.in_features-len(idxs) linear.weight = torch.nn.Parameter( linear.weight.data[:, keep_idxs]) layer.embed_dim = layer.embed_dim - len(idxs) layer.head_dim = layer.embed_dim // layer.num_heads layer.kdim = layer.embed_dim layer.vdim = layer.embed_dim return layer prune_in_channels = prune_out_channels def get_out_channels(self, layer): return layer.embed_dim def get_in_channels(self, layer): return self.get_out_channels(layer) PrunerBox = { ops.OPTYPE.CONV: ConvPruner(), ops.OPTYPE.LINEAR: LinearPruner(), ops.OPTYPE.BN: BatchnormPruner(), ops.OPTYPE.DEPTHWISE_CONV: DepthwiseConvPruner(), ops.OPTYPE.PRELU: PReLUPruner(), ops.OPTYPE.LN: LayernormPruner(), ops.OPTYPE.EMBED: EmbeddingPruner(), ops.OPTYPE.PARAMETER: ParameterPruner(), ops.OPTYPE.MHA: MultiheadAttentionPruner(), ops.OPTYPE.LSTM: LSTMPruner(), ops.OPTYPE.GN: GroupNormPruner(), ops.OPTYPE.IN: InstanceNormPruner(), } # Alias prune_conv_out_channels = PrunerBox[ops.OPTYPE.CONV].prune_out_channels prune_conv_in_channels = PrunerBox[ops.OPTYPE.CONV].prune_in_channels prune_depthwise_conv_out_channels = PrunerBox[ops.OPTYPE.DEPTHWISE_CONV].prune_out_channels prune_depthwise_conv_in_channels = PrunerBox[ops.OPTYPE.DEPTHWISE_CONV].prune_in_channels prune_batchnorm_out_channels = PrunerBox[ops.OPTYPE.BN].prune_out_channels prune_batchnorm_in_channels = PrunerBox[ops.OPTYPE.BN].prune_in_channels prune_linear_out_channels = PrunerBox[ops.OPTYPE.LINEAR].prune_out_channels prune_linear_in_channels = PrunerBox[ops.OPTYPE.LINEAR].prune_in_channels prune_prelu_out_channels = PrunerBox[ops.OPTYPE.PRELU].prune_out_channels prune_prelu_in_channels = PrunerBox[ops.OPTYPE.PRELU].prune_in_channels prune_layernorm_out_channels = PrunerBox[ops.OPTYPE.LN].prune_out_channels prune_layernorm_in_channels = PrunerBox[ops.OPTYPE.LN].prune_in_channels prune_embedding_out_channels = PrunerBox[ops.OPTYPE.EMBED].prune_out_channels prune_embedding_in_channels = PrunerBox[ops.OPTYPE.EMBED].prune_in_channels prune_parameter_out_channels = PrunerBox[ops.OPTYPE.PARAMETER].prune_out_channels prune_parameter_in_channels = PrunerBox[ops.OPTYPE.PARAMETER].prune_in_channels prune_multihead_attention_out_channels = PrunerBox[ops.OPTYPE.MHA].prune_out_channels prune_multihead_attention_in_channels = PrunerBox[ops.OPTYPE.MHA].prune_in_channels prune_lstm_out_channels = PrunerBox[ops.OPTYPE.LSTM].prune_out_channels prune_lstm_in_channels = PrunerBox[ops.OPTYPE.LSTM].prune_in_channels prune_groupnorm_out_channels = PrunerBox[ops.OPTYPE.GN].prune_out_channels prune_groupnorm_in_channels = PrunerBox[ops.OPTYPE.GN].prune_in_channels prune_instancenorm_out_channels = PrunerBox[ops.OPTYPE.IN].prune_out_channels prune_instancenorm_in_channels = PrunerBox[ops.OPTYPE.IN].prune_in_channels ================================================ FILE: ddpm_exp/torch_pruning/utils/__init__.py ================================================ from .utils import * from .op_counter import count_ops_and_params ================================================ FILE: ddpm_exp/torch_pruning/utils/op_counter.py ================================================ ''' This opcounter is adapted from https://github.com/sovrasov/flops-counter.pytorch Copyright (C) 2021 Sovrasov V. - All Rights Reserved * You may use, distribute and modify this code under the * terms of the MIT license. * You should have received a copy of the MIT license with * this file. If not visit https://opensource.org/licenses/MIT ''' import numpy as np import torch.nn as nn import torch @torch.no_grad() def count_ops_and_params(model, example_inputs): global CUSTOM_MODULES_MAPPING model = copy.deepcopy(model) flops_model = add_flops_counting_methods(model) flops_model.eval() flops_model.start_flops_count(ost=sys.stdout, verbose=False, ignore_list=[]) if isinstance(example_inputs, dict): _ = flops_model(**example_inputs) elif isinstance(example_inputs, (tuple, list)): _ = flops_model(*example_inputs) else: _ = flops_model(example_inputs) flops_count, params_count = flops_model.compute_average_flops_cost() flops_model.stop_flops_count() CUSTOM_MODULES_MAPPING = {} return flops_count, params_count def empty_flops_counter_hook(module, input, output): module.__flops__ += 0 def upsample_flops_counter_hook(module, input, output): output_size = output[0] batch_size = output_size.shape[0] output_elements_count = batch_size for val in output_size.shape[1:]: output_elements_count *= val module.__flops__ += int(output_elements_count) def relu_flops_counter_hook(module, input, output): active_elements_count = output.numel() module.__flops__ += int(active_elements_count) def linear_flops_counter_hook(module, input, output): input = input[0] # pytorch checks dimensions, so here we don't care much output_last_dim = output.shape[-1] bias_flops = output_last_dim if module.bias is not None else 0 module.__flops__ += int(np.prod(input.shape) * output_last_dim + bias_flops) def pool_flops_counter_hook(module, input, output): input = input[0] module.__flops__ += int(np.prod(input.shape)) def bn_flops_counter_hook(module, input, output): input = input[0] batch_flops = np.prod(input.shape) if module.affine: batch_flops *= 2 module.__flops__ += int(batch_flops) def conv_flops_counter_hook(conv_module, input, output): # Can have multiple inputs, getting the first one input = input[0] batch_size = input.shape[0] output_dims = list(output.shape[2:]) kernel_dims = list(conv_module.kernel_size) in_channels = conv_module.in_channels out_channels = conv_module.out_channels groups = conv_module.groups filters_per_channel = out_channels // groups conv_per_position_flops = int(np.prod(kernel_dims)) * \ in_channels * filters_per_channel active_elements_count = batch_size * int(np.prod(output_dims)) overall_conv_flops = conv_per_position_flops * active_elements_count bias_flops = 0 if conv_module.bias is not None: bias_flops = out_channels * active_elements_count overall_flops = overall_conv_flops + bias_flops conv_module.__flops__ += int(overall_flops) def rnn_flops(flops, rnn_module, w_ih, w_hh, input_size): # matrix matrix mult ih state and internal state flops += w_ih.shape[0]*w_ih.shape[1] # matrix matrix mult hh state and internal state flops += w_hh.shape[0]*w_hh.shape[1] if isinstance(rnn_module, (nn.RNN, nn.RNNCell)): # add both operations flops += rnn_module.hidden_size elif isinstance(rnn_module, (nn.GRU, nn.GRUCell)): # hadamard of r flops += rnn_module.hidden_size # adding operations from both states flops += rnn_module.hidden_size*3 # last two hadamard product and add flops += rnn_module.hidden_size*3 elif isinstance(rnn_module, (nn.LSTM, nn.LSTMCell)): # adding operations from both states flops += rnn_module.hidden_size*4 # two hadamard product and add for C state flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size # final hadamard flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size return flops def rnn_flops_counter_hook(rnn_module, input, output): """ Takes into account batch goes at first position, contrary to pytorch common rule (but actually it doesn't matter). If sigmoid and tanh are hard, only a comparison FLOPS should be accurate """ flops = 0 # input is a tuple containing a sequence to process and (optionally) hidden state inp = input[0] batch_size = inp[0].shape[0] seq_length = inp[0].shape[1] num_layers = rnn_module.num_layers for i in range(num_layers): w_ih = rnn_module.__getattr__('weight_ih_l' + str(i)) w_hh = rnn_module.__getattr__('weight_hh_l' + str(i)) if i == 0: input_size = rnn_module.input_size else: input_size = rnn_module.hidden_size flops = rnn_flops(flops, rnn_module, w_ih, w_hh, input_size) if rnn_module.bias: b_ih = rnn_module.__getattr__('bias_ih_l' + str(i)) b_hh = rnn_module.__getattr__('bias_hh_l' + str(i)) flops += b_ih.shape[0] + b_hh.shape[0] flops *= batch_size flops *= seq_length if rnn_module.bidirectional: flops *= 2 rnn_module.__flops__ += int(flops) def rnn_cell_flops_counter_hook(rnn_cell_module, input, output): flops = 0 inp = input[0] batch_size = inp.shape[0] w_ih = rnn_cell_module.__getattr__('weight_ih') w_hh = rnn_cell_module.__getattr__('weight_hh') input_size = inp.shape[1] flops = rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size) if rnn_cell_module.bias: b_ih = rnn_cell_module.__getattr__('bias_ih') b_hh = rnn_cell_module.__getattr__('bias_hh') flops += b_ih.shape[0] + b_hh.shape[0] flops *= batch_size rnn_cell_module.__flops__ += int(flops) def multihead_attention_counter_hook(multihead_attention_module, input, output): flops = 0 q, k, v = input batch_first = multihead_attention_module.batch_first \ if hasattr(multihead_attention_module, 'batch_first') else False if batch_first: batch_size = q.shape[0] len_idx = 1 else: batch_size = q.shape[1] len_idx = 0 dim_idx = 2 qdim = q.shape[dim_idx] kdim = k.shape[dim_idx] vdim = v.shape[dim_idx] qlen = q.shape[len_idx] klen = k.shape[len_idx] vlen = v.shape[len_idx] num_heads = multihead_attention_module.num_heads assert qdim == multihead_attention_module.embed_dim if multihead_attention_module.kdim is None: assert kdim == qdim if multihead_attention_module.vdim is None: assert vdim == qdim flops = 0 # Q scaling flops += qlen * qdim # Initial projections flops += ( (qlen * qdim * qdim) # QW + (klen * kdim * kdim) # KW + (vlen * vdim * vdim) # VW ) if multihead_attention_module.in_proj_bias is not None: flops += (qlen + klen + vlen) * qdim # attention heads: scale, matmul, softmax, matmul qk_head_dim = qdim // num_heads v_head_dim = vdim // num_heads head_flops = ( (qlen * klen * qk_head_dim) # QK^T + (qlen * klen) # softmax + (qlen * klen * v_head_dim) # AV ) flops += num_heads * head_flops # final projection, bias is always enabled flops += qlen * vdim * (vdim + 1) flops *= batch_size multihead_attention_module.__flops__ += int(flops) CUSTOM_MODULES_MAPPING = {} MODULES_MAPPING = { # convolutions nn.Conv1d: conv_flops_counter_hook, nn.Conv2d: conv_flops_counter_hook, nn.Conv3d: conv_flops_counter_hook, # activations nn.ReLU: relu_flops_counter_hook, nn.PReLU: relu_flops_counter_hook, nn.ELU: relu_flops_counter_hook, nn.LeakyReLU: relu_flops_counter_hook, nn.ReLU6: relu_flops_counter_hook, # poolings nn.MaxPool1d: pool_flops_counter_hook, nn.AvgPool1d: pool_flops_counter_hook, nn.AvgPool2d: pool_flops_counter_hook, nn.MaxPool2d: pool_flops_counter_hook, nn.MaxPool3d: pool_flops_counter_hook, nn.AvgPool3d: pool_flops_counter_hook, nn.AdaptiveMaxPool1d: pool_flops_counter_hook, nn.AdaptiveAvgPool1d: pool_flops_counter_hook, nn.AdaptiveMaxPool2d: pool_flops_counter_hook, nn.AdaptiveAvgPool2d: pool_flops_counter_hook, nn.AdaptiveMaxPool3d: pool_flops_counter_hook, nn.AdaptiveAvgPool3d: pool_flops_counter_hook, # BNs nn.BatchNorm1d: bn_flops_counter_hook, nn.BatchNorm2d: bn_flops_counter_hook, nn.BatchNorm3d: bn_flops_counter_hook, nn.InstanceNorm1d: bn_flops_counter_hook, nn.InstanceNorm2d: bn_flops_counter_hook, nn.InstanceNorm3d: bn_flops_counter_hook, nn.GroupNorm: bn_flops_counter_hook, # FC nn.Linear: linear_flops_counter_hook, # Upscale nn.Upsample: upsample_flops_counter_hook, # Deconvolution nn.ConvTranspose1d: conv_flops_counter_hook, nn.ConvTranspose2d: conv_flops_counter_hook, nn.ConvTranspose3d: conv_flops_counter_hook, # RNN nn.RNN: rnn_flops_counter_hook, nn.GRU: rnn_flops_counter_hook, nn.LSTM: rnn_flops_counter_hook, nn.RNNCell: rnn_cell_flops_counter_hook, nn.LSTMCell: rnn_cell_flops_counter_hook, nn.GRUCell: rnn_cell_flops_counter_hook, nn.MultiheadAttention: multihead_attention_counter_hook } if hasattr(nn, 'GELU'): MODULES_MAPPING[nn.GELU] = relu_flops_counter_hook import sys from functools import partial import torch.nn as nn import copy def accumulate_flops(self): if is_supported_instance(self): return self.__flops__ else: sum = 0 for m in self.children(): sum += m.accumulate_flops() return sum def get_model_parameters_number(model): params_num = sum(p.numel() for p in model.parameters() if p.requires_grad) return params_num def add_flops_counting_methods(net_main_module): # adding additional methods to the existing module object, # this is done this way so that each function has access to self object net_main_module.start_flops_count = start_flops_count.__get__(net_main_module) net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module) net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module) net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__( net_main_module) net_main_module.reset_flops_count() return net_main_module def compute_average_flops_cost(self): """ A method that will be available after add_flops_counting_methods() is called on a desired net object. Returns current mean flops consumption per image. """ for m in self.modules(): m.accumulate_flops = accumulate_flops.__get__(m) flops_sum = self.accumulate_flops() for m in self.modules(): if hasattr(m, 'accumulate_flops'): del m.accumulate_flops params_sum = get_model_parameters_number(self) return flops_sum / self.__batch_counter__, params_sum def start_flops_count(self, **kwargs): """ A method that will be available after add_flops_counting_methods() is called on a desired net object. Activates the computation of mean flops consumption per image. Call it before you run the network. """ add_batch_counter_hook_function(self) seen_types = set() def add_flops_counter_hook_function(module, ost, verbose, ignore_list): if type(module) in ignore_list: seen_types.add(type(module)) if is_supported_instance(module): module.__params__ = 0 elif is_supported_instance(module): if hasattr(module, '__flops_handle__'): return if type(module) in CUSTOM_MODULES_MAPPING: handle = module.register_forward_hook( CUSTOM_MODULES_MAPPING[type(module)]) else: handle = module.register_forward_hook(MODULES_MAPPING[type(module)]) module.__flops_handle__ = handle seen_types.add(type(module)) else: if verbose and not type(module) in (nn.Sequential, nn.ModuleList) and \ not type(module) in seen_types: print('Warning: module ' + type(module).__name__ + ' is treated as a zero-op.', file=ost) seen_types.add(type(module)) self.apply(partial(add_flops_counter_hook_function, **kwargs)) def stop_flops_count(self): """ A method that will be available after add_flops_counting_methods() is called on a desired net object. Stops computing the mean flops consumption per image. Call whenever you want to pause the computation. """ remove_batch_counter_hook_function(self) self.apply(remove_flops_counter_hook_function) self.apply(remove_flops_counter_variables) def reset_flops_count(self): """ A method that will be available after add_flops_counting_methods() is called on a desired net object. Resets statistics computed so far. """ add_batch_counter_variables_or_reset(self) self.apply(add_flops_counter_variable_or_reset) # ---- Internal functions def batch_counter_hook(module, input, output): batch_size = 1 if len(input) > 0: # Can have multiple inputs, getting the first one input = input[0] batch_size = len(input) else: pass print('Warning! No positional inputs found for a module,' ' assuming batch size is 1.') module.__batch_counter__ += batch_size def add_batch_counter_variables_or_reset(module): module.__batch_counter__ = 0 def add_batch_counter_hook_function(module): if hasattr(module, '__batch_counter_handle__'): return handle = module.register_forward_hook(batch_counter_hook) module.__batch_counter_handle__ = handle def remove_batch_counter_hook_function(module): if hasattr(module, '__batch_counter_handle__'): module.__batch_counter_handle__.remove() del module.__batch_counter_handle__ def add_flops_counter_variable_or_reset(module): if is_supported_instance(module): if hasattr(module, '__flops__') or hasattr(module, '__params__'): print('Warning: variables __flops__ or __params__ are already ' 'defined for the module' + type(module).__name__ + ' ptflops can affect your code!') module.__ptflops_backup_flops__ = module.__flops__ module.__ptflops_backup_params__ = module.__params__ module.__flops__ = 0 module.__params__ = get_model_parameters_number(module) def is_supported_instance(module): if type(module) in MODULES_MAPPING or type(module) in CUSTOM_MODULES_MAPPING: return True return False def remove_flops_counter_hook_function(module): if is_supported_instance(module): if hasattr(module, '__flops_handle__'): module.__flops_handle__.remove() del module.__flops_handle__ def remove_flops_counter_variables(module): if is_supported_instance(module): if hasattr(module, '__flops__'): del module.__flops__ if hasattr(module, '__ptflops_backup_flops__'): module.__flops__ = module.__ptflops_backup_flops__ if hasattr(module, '__params__'): del module.__params__ if hasattr(module, '__ptflops_backup_params__'): module.__params__ = module.__ptflops_backup_params__ ================================================ FILE: ddpm_exp/torch_pruning/utils/utils.py ================================================ from ..ops import TORCH_CONV, TORCH_BATCHNORM, TORCH_PRELU, TORCH_LINEAR from ..ops import module2type import torch from .op_counter import count_ops_and_params import torch.nn as nn @torch.no_grad() def count_params(module): return sum([p.numel() for p in module.parameters()]) def flatten_as_list(obj): if isinstance(obj, torch.Tensor): return [obj] elif isinstance(obj, (list, tuple)): flattened_list = [] for sub_obj in obj: flattened_list.extend(flatten_as_list(sub_obj)) return flattened_list elif isinstance(obj, dict): flattened_list = [] for sub_obj in obj.values(): flattened_list.extend(flatten_as_list(sub_obj)) return flattened_list else: return obj def draw_computational_graph(DG, save_as, title='Computational Graph', figsize=(16, 16), dpi=200, cmap=None): import numpy as np import matplotlib.pyplot as plt plt.style.use('bmh') n_nodes = len(DG.module2node) module2idx = {m: i for (i, m) in enumerate(DG.module2node.keys())} G = np.zeros((n_nodes, n_nodes)) fill_value = 1 for module, node in DG.module2node.items(): for input_node in node.inputs: G[module2idx[input_node.module], module2idx[node.module]] = fill_value G[module2idx[node.module], module2idx[input_node.module]] = fill_value for out_node in node.outputs: G[module2idx[out_node.module], module2idx[node.module]] = fill_value G[module2idx[node.module], module2idx[out_node.module]] = fill_value pruner = DG.get_pruner_of_module(module) fig, ax = plt.subplots(figsize=(figsize)) ax.imshow(G, cmap=cmap if cmap is not None else plt.get_cmap('Blues')) # plt.hlines(y=np.arange(0, n_nodes)+0.5, xmin=np.full(n_nodes, 0)-0.5, xmax=np.full(n_nodes, n_nodes)-0.5, color="#444444", linewidth=0.1) # plt.vlines(x=np.arange(0, n_nodes)+0.5, ymin=np.full(n_nodes, 0)-0.5, ymax=np.full(n_nodes, n_nodes)-0.5, color="#444444", linewidth=0.1) if title is not None: ax.set_title(title) fig.tight_layout() plt.savefig(save_as, dpi=dpi) return fig, ax def draw_groups(DG, save_as, title='Group', figsize=(16, 16), dpi=200, cmap=None): import numpy as np import matplotlib.pyplot as plt plt.style.use('bmh') n_nodes = 2*len(DG.module2node) node2idx = {m: i for (i, m) in enumerate(DG.module2node.values())} G = np.zeros((n_nodes, n_nodes)) fill_value = 10 for i, (module, node) in enumerate(DG.module2node.items()): pruning_fn = DG.get_pruner_of_module(module).prune_out_channels prunable_ch = DG.get_out_channels(module) if prunable_ch is None: continue group = DG.get_pruning_group(module, pruning_fn, list(range(prunable_ch))) grouped_idxs = [] for dep, _ in group: source, target, trigger, handler = dep.source, dep.target, dep.trigger, dep.handler if DG.is_out_channel_pruning_fn(trigger): grouped_idxs.append(node2idx[source]*2+1) else: grouped_idxs.append(node2idx[source]*2) if DG.is_out_channel_pruning_fn(handler): grouped_idxs.append(node2idx[target]*2+1) else: grouped_idxs.append(node2idx[target]*2) grouped_idxs = list(set(grouped_idxs)) for k1 in grouped_idxs: for k2 in grouped_idxs: G[k1, k2] = fill_value fig, ax = plt.subplots(figsize=(figsize)) ax.imshow(G, cmap=cmap if cmap is not None else plt.get_cmap('Blues')) # plt.hlines(y=np.arange(0, n_nodes)+0.5, xmin=np.full(n_nodes, 0)-0.5, xmax=np.full(n_nodes, n_nodes)-0.5, color="#999999", linewidth=0.1) # plt.vlines(x=np.arange(0, n_nodes)+0.5, ymin=np.full(n_nodes, 0)-0.5, ymax=np.full(n_nodes, n_nodes)-0.5, color="#999999", linewidth=0.1) if title is not None: ax.set_title(title) fig.tight_layout() plt.savefig(save_as, dpi=dpi) return fig, ax def draw_dependency_graph(DG, save_as, title='Group', figsize=(16, 16), dpi=200, cmap=None): import numpy as np import matplotlib.pyplot as plt plt.style.use('bmh') n_nodes = len(DG.module2node) node2idx = {node: i for (i, node) in enumerate(DG.module2node.values())} G = np.zeros((2*n_nodes, 2*n_nodes)) fill_value = 10 for module, node in DG.module2node.items(): for dep in node.dependencies: trigger = dep.trigger handler = dep.handler source = dep.source target = dep.target if DG.is_out_channel_pruning_fn(trigger): G[2*node2idx[source]+1, 2*node2idx[target]] = fill_value else: G[2*node2idx[source], 2*node2idx[target]+1] = fill_value pruner = DG.get_pruner_of_module(module) if pruner.prune_out_channels == pruner.prune_in_channels: G[2*node2idx[node], 2*node2idx[node]+1] = fill_value fig, ax = plt.subplots(figsize=(figsize)) ax.imshow(G, cmap=cmap if cmap is not None else plt.get_cmap('Blues')) # plt.hlines(y=np.arange(0, 2*n_nodes)+0.5, xmin=np.full(2*n_nodes, 0)-0.5, xmax=np.full(2*n_nodes, 2*n_nodes)-0.5, color="#999999", linewidth=0.05) # plt.vlines(x=np.arange(0, 2*n_nodes)+0.5, ymin=np.full(2*n_nodes, 0)-0.5, ymax=np.full(2*n_nodes, 2*n_nodes)-0.5, color="#999999", linewidth=0.05) if title is not None: ax.set_title(title) fig.tight_layout() plt.savefig(save_as, dpi=dpi) return fig, ax ================================================ FILE: ddpm_exp/utils.py ================================================ import torch, os from glob import glob from PIL import Image class UnlabeledImageFolder(torch.utils.data.Dataset): def __init__(self, root, transform=None, exts=["*.jpg", "*.png", "*.jpeg", "*.webp"]): self.root = root self.files = [] self.transform = transform for ext in exts: self.files.extend(glob(os.path.join(root, '**/*.{}'.format(ext)), recursive=True)) def __len__(self): return len(self.files) def __getitem__(self, idx): path = self.files[idx] img = Image.open(path).convert("RGB") if self.transform is not None: img = self.transform(img) return img import torch def set_dropout(model, p): for m in model.modules(): if isinstance(m, torch.nn.Dropout): m.p = p ================================================ FILE: ddpm_prune.py ================================================ from diffusers import DiffusionPipeline, DDPMPipeline, DDIMPipeline, DDIMScheduler, DDPMScheduler from diffusers.models import UNet2DModel import torch_pruning as tp import torch import torchvision from torchvision import transforms import torchvision from tqdm import tqdm import os from glob import glob from PIL import Image import accelerate import utils import argparse parser = argparse.ArgumentParser() parser.add_argument("--dataset", type=str, default=None, help="path to an image folder") parser.add_argument("--model_path", type=str, required=True) parser.add_argument("--save_path", type=str, required=True) parser.add_argument("--pruning_ratio", type=float, default=0.3) parser.add_argument("--batch_size", type=int, default=128) parser.add_argument("--device", type=str, default='cpu') parser.add_argument("--pruner", type=str, default='taylor', choices=['taylor', 'random', 'magnitude', 'reinit', 'diff-pruning']) parser.add_argument("--thr", type=float, default=0.05, help="threshold for diff-pruning") args = parser.parse_args() batch_size = args.batch_size dataset = args.dataset if __name__=='__main__': # loading images for gradient-based pruning if args.pruner in ['taylor', 'diff-pruning']: dataset = utils.get_dataset(args.dataset) print(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.batch_size, shuffle=True, num_workers=4, drop_last=True ) import torch_pruning as tp clean_images = next(iter(train_dataloader)) if isinstance(clean_images, (list, tuple)): clean_images = clean_images[0] clean_images = clean_images.to(args.device) noise = torch.randn(clean_images.shape).to(clean_images.device) # Loading pretrained model print("Loading pretrained model from {}".format(args.model_path)) pipeline = DDPMPipeline.from_pretrained(args.model_path).to(args.device) scheduler = pipeline.scheduler model = pipeline.unet.eval() if 'cifar' in args.model_path: example_inputs = {'sample': torch.randn(1, 3, 32, 32).to(args.device), 'timestep': torch.ones((1,)).long().to(args.device)} else: example_inputs = {'sample': torch.randn(1, 3, 256, 256).to(args.device), 'timestep': torch.ones((1,)).long().to(args.device)} if args.pruning_ratio>0: if args.pruner == 'taylor': imp = tp.importance.TaylorImportance(multivariable=True) # standard first-order taylor expansion elif args.pruner == 'random' or args.pruner=='reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() elif args.pruner == 'diff-pruning': imp = tp.importance.TaylorImportance(multivariable=False) # a modified version, estimating the accumulated error of weight removal else: raise NotImplementedError ignored_layers = [model.conv_out] channel_groups = {} #from diffusers.models.attention import #for m in model.modules(): # if isinstance(m, AttentionBlock): # channel_groups[m.query] = m.num_heads # channel_groups[m.key] = m.num_heads # channel_groups[m.value] = m.num_heads pruner = tp.pruner.MagnitudePruner( model, example_inputs, importance=imp, iterative_steps=1, channel_groups=channel_groups, ch_sparsity=args.pruning_ratio, ignored_layers=ignored_layers, ) base_macs, base_params = tp.utils.count_ops_and_params(model, example_inputs) model.zero_grad() model.eval() import random if args.pruner in ['taylor', 'diff-pruning']: loss_max = 0 print("Accumulating gradients for pruning...") for step_k in tqdm(range(1000)): timesteps = (step_k*torch.ones((args.batch_size,), device=clean_images.device)).long() noisy_images = scheduler.add_noise(clean_images, noise, timesteps) model_output = model(noisy_images, timesteps).sample loss = torch.nn.functional.mse_loss(model_output, noise) loss.backward() if args.pruner=='diff-pruning': if loss>loss_max: loss_max = loss if loss thr ) for g in pruner.step(interactive=True): g.prune() # Update static attributes from diffusers.models.resnet import Upsample2D, Downsample2D for m in model.modules(): if isinstance(m, (Upsample2D, Downsample2D)): m.channels = m.conv.in_channels m.out_channels == m.conv.out_channels macs, params = tp.utils.count_ops_and_params(model, example_inputs) print(model) print("#Params: {:.4f} M => {:.4f} M".format(base_params/1e6, params/1e6)) print("#MACS: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) model.zero_grad() del pruner if args.pruner=='reinit': def reset_parameters(model): for m in model.modules(): if hasattr(m, 'reset_parameters'): m.reset_parameters() reset_parameters(model) pipeline.save_pretrained(args.save_path) if args.pruning_ratio>0: os.makedirs(os.path.join(args.save_path, "pruned"), exist_ok=True) torch.save(model, os.path.join(args.save_path, "pruned", "unet_pruned.pth")) # Sampling images from the pruned model pipeline = DDIMPipeline( unet = model, scheduler = DDIMScheduler.from_pretrained(args.save_path, subfolder="scheduler") ) with torch.no_grad(): generator = torch.Generator(device=pipeline.device).manual_seed(0) pipeline.to("cuda") images = pipeline(num_inference_steps=100, batch_size=args.batch_size, generator=generator, output_type="numpy").images os.makedirs(os.path.join(args.save_path, 'vis'), exist_ok=True) torchvision.utils.save_image(torch.from_numpy(images).permute([0, 3, 1, 2]), "{}/vis/after_pruning.png".format(args.save_path)) ================================================ FILE: ddpm_sample.py ================================================ from diffusers import DDIMPipeline, DDIMScheduler, UNet2DModel import argparse, os, torch from tqdm import tqdm import torch_pruning as tp import accelerate parser = argparse.ArgumentParser() parser.add_argument("--total_samples", type=int, default=50000) parser.add_argument("--batch_size", type=int, default=100) parser.add_argument("--output_dir", type=str, default="samples") parser.add_argument("--model_path", type=str, default="samples") parser.add_argument("--ddim_steps", type=int, default=100) parser.add_argument("--pruned_model_ckpt", type=str, default=None) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--skip_type", type=str, default="uniform") args = parser.parse_args() if __name__ == "__main__": os.makedirs(args.output_dir, exist_ok=True) # pruned model accelerator = accelerate.Accelerator() if os.path.isdir(args.model_path): if args.pruned_model_ckpt is not None: print("Loading pruned model from {}".format(args.pruned_model_ckpt)) unet = torch.load(args.pruned_model_ckpt).eval() else: print("Loading model from {}".format(args.model_path)) subfolder = 'unet' if os.path.isdir(os.path.join(args.model_path, 'unet')) else None unet = UNet2DModel.from_pretrained(args.model_path, subfolder=subfolder).eval() pipeline = DDIMPipeline( unet=unet, scheduler=DDIMScheduler.from_pretrained(args.model_path, subfolder="scheduler") ) # standard model else: print("Loading pretrained model from {}".format(args.model_path)) pipeline = DDIMPipeline.from_pretrained( args.model_path, ) pipeline.scheduler.skip_type = args.skip_type # Test Flops pipeline.to(accelerator.device) if accelerator.is_main_process: if 'cifar' in args.model_path: example_inputs = {'sample': torch.randn(1, 3, 32, 32).to(accelerator.device), 'timestep': torch.ones((1,)).long().to(accelerator.device)} else: example_inputs = {'sample': torch.randn(1, 3, 256, 256).to(accelerator.device), 'timestep': torch.ones((1,)).long().to(accelerator.device)} macs, params = tp.utils.count_ops_and_params(pipeline.unet, example_inputs) print(f"MACS: {macs/1e9} G, Params: {params/1e6} M") # Create subfolders for each process save_sub_dir = os.path.join(args.output_dir, 'process_{}'.format(accelerator.process_index)) os.makedirs(save_sub_dir, exist_ok=True) generator = torch.Generator(device=pipeline.device).manual_seed(args.seed+accelerator.process_index) # Set up progress bar if not accelerator.is_main_process: pipeline.set_progress_bar_config(disable=True) # Sampling accelerator.wait_for_everyone() with torch.no_grad(): # num_batches of each process num_batches = (args.total_samples) // (args.batch_size * accelerator.num_processes) if accelerator.is_main_process: print("Samping {}x{}={} images with {} process(es)".format(num_batches*args.batch_size, accelerator.num_processes, num_batches*accelerator.num_processes*args.batch_size, accelerator.num_processes)) for i in tqdm(range(num_batches), disable=not accelerator.is_main_process): images = pipeline(batch_size=args.batch_size, num_inference_steps=args.ddim_steps, generator=generator).images for j, image in enumerate(images): filename = os.path.join(save_sub_dir, f"{i * args.batch_size + j}.png") image.save(filename) # Finished accelerator.wait_for_everyone() if accelerator.is_main_process: accelerator.print(f"Saved {num_batches*accelerator.num_processes*args.batch_size} samples to {args.output_dir}") #accelerator.end_training() ================================================ FILE: ddpm_train.py ================================================ # Modifed from https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation import argparse import inspect import logging import math import os, sys import accelerate import torch import torch.nn.functional as F from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration from packaging import version from torchvision import transforms import torchvision from tqdm.auto import tqdm import diffusers from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel, DDIMPipeline, DDIMScheduler from diffusers.optimization import get_scheduler from diffusers.training_utils import EMAModel from diffusers.utils import is_accelerate_version, is_tensorboard_available, is_wandb_available import utils logger = get_logger(__name__, log_level="INFO") def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument("--pruned_model_ckpt", type=str, default=None) parser.add_argument( "--dataset", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that HF Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--model_path", type=str, default=None, help="The config of the UNet model to train, leave as None to use standard DDPM configuration.", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. Folder contents must follow the structure described in" " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" " must exist to provide the captions for the images. Ignored if `dataset` is specified." ), ) parser.add_argument( "--output_dir", type=str, default="ddpm-model-64", help="The output directory where the checkpoints will be written.", ) parser.add_argument("--overwrite_output_dir", action="store_true") parser.add_argument( "--cache_dir", type=str, default='./cache', help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument( "--resolution", type=int, default=64, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--dropout", type=float, default=0.0, ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument( "--eval_batch_size", type=int, default=16, help="The number of images to generate for evaluation." ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" " process." ), ) parser.add_argument( "--checkpoint_id", type=int, default=None, ) parser.add_argument( "--load_ema", action="store_true", default=False, ) parser.add_argument("--num_iters", type=int, default=10000) parser.add_argument( "--save_model_steps", type=int, default=1000, help="How often to save the model during training." ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument( "--adam_weight_decay", type=float, default=0.0, help="Weight decay magnitude for the Adam optimizer." ) parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.") parser.add_argument( "--use_ema", action="store_true", help="Whether to use Exponential Moving Average for the final model weights.", ) parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.") parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.") parser.add_argument("--ema_max_decay", type=float, default=0.999, help="The maximum decay magnitude for EMA.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--hub_private_repo", action="store_true", help="Whether or not to create a private repository." ) parser.add_argument( "--logger", type=str, default="tensorboard", choices=["tensorboard", "wandb"], help=( "Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)" " for experiment tracking and logging of model metrics and model checkpoints" ), ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU." ), ) parser.add_argument( "--prediction_type", type=str, default="epsilon", choices=["epsilon", "sample"], help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.", ) parser.add_argument("--ddpm_num_steps", type=int, default=1000) parser.add_argument("--ddim_num_inference_steps", type=int, default=100) parser.add_argument("--ddpm_beta_schedule", type=str, default="linear") parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' ), ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.dataset is None and args.train_data_dir is None: raise ValueError("You must specify either a dataset name from the hub or a train data directory.") return args def main(args): logging_dir = os.path.join(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration() accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.logger, project_dir=logging_dir, project_config=accelerator_project_config, ) if args.logger == "tensorboard": if not is_tensorboard_available(): raise ImportError("Make sure to install tensorboard if you want to use it for logging during training.") elif args.logger == "wandb": if not is_wandb_available(): raise ImportError("Make sure to install wandb if you want to use it for logging during training.") import wandb # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: diffusers.utils.logging.set_verbosity_info() else: diffusers.utils.logging.set_verbosity_error() # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) # Loading pruned model if os.path.isdir(args.model_path): if args.pruned_model_ckpt is not None: print("Loading pruned model from {}".format(args.pruned_model_ckpt)) unet = torch.load(args.pruned_model_ckpt, map_location='cpu').eval() else: print("Loading model from {}".format(args.model_path)) subfolder = 'unet' if os.path.isdir(os.path.join(args.model_path, 'unet')) else None unet = UNet2DModel.from_pretrained(args.model_path, subfolder=subfolder).eval() pipeline = DDPMPipeline( unet=unet, scheduler=DDPMScheduler.from_pretrained(args.model_path, subfolder="scheduler") ) # Loading standard model else: print("Loading pretrained model from {}".format(args.model_path)) pipeline = DDPMPipeline.from_pretrained( args.model_path, ) model = pipeline.unet noise_scheduler = pipeline.scheduler # Get the datasets: you can either provide your own training and evaluation files (see below) dataset = utils.get_dataset(args.dataset) logger.info(f"Dataset size: {len(dataset)}") train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers ) num_epochs = math.ceil(args.num_iters / len(train_dataloader)) # Create EMA for the model. if args.use_ema: ema_model = EMAModel( model.parameters(), decay=args.ema_max_decay, use_ema_warmup=False, inv_gamma=args.ema_inv_gamma, power=args.ema_power, model_cls=UNet2DModel, model_config=model.config, ) # Initialize the optimizer optimizer = torch.optim.Adam( model.parameters(), lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # Initialize the learning rate scheduler lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, num_training_steps=(len(train_dataloader) * num_epochs), ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, lr_scheduler ) if args.use_ema: ema_model.to(accelerator.device) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: run = os.path.split(__file__)[-1].split(".")[0] accelerator.init_trackers(run) total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) logger.info("***** Running training *****") logger.info(f" Num examples = {len(dataset)}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Num Epochs = {num_epochs}") logger.info(f" Total optimization steps = {args.num_iters}") global_step = 0 first_epoch = 0 # save the shell command if accelerator.is_main_process: with open(os.path.join(args.output_dir, 'run.sh'), 'w') as f: f.write('python ' + ' '.join(sys.argv)) # setup dropout if args.dropout>0: utils.set_dropout(model, args.dropout) # generate images before training if accelerator.is_main_process: unet = accelerator.unwrap_model(model).eval() if args.use_ema: ema_model.store(unet.parameters()) ema_model.copy_to(unet.parameters()) pipeline = DDIMPipeline( unet=unet, scheduler=DDIMScheduler(num_train_timesteps=args.ddpm_num_steps) ) pipeline.scheduler.set_timesteps(args.ddim_num_inference_steps) images = pipeline( batch_size=args.eval_batch_size, num_inference_steps=args.ddim_num_inference_steps, output_type="numpy", ).images if args.use_ema: ema_model.restore(unet.parameters()) os.makedirs(os.path.join(args.output_dir, 'vis'), exist_ok=True) torchvision.utils.save_image(torch.from_numpy(images).permute([0, 3, 1, 2]), os.path.join(args.output_dir, 'vis', 'before_training.png')) images_processed = (images * 255).round().astype("uint8") if args.logger == "tensorboard": if is_accelerate_version(">=", "0.17.0.dev0"): tracker = accelerator.get_tracker("tensorboard", unwrap=True) else: tracker = accelerator.get_tracker("tensorboard") tracker.add_images("After Pruning", images_processed.transpose(0, 3, 1, 2), 0) elif args.logger == "wandb": # Upcoming `log_images` helper coming in https://github.com/huggingface/accelerate/pull/962/files accelerator.get_tracker("wandb").log( {"After Pruning": [wandb.Image(img) for img in images_processed], "epoch": 0}, step=global_step, ) del unet del pipeline accelerator.wait_for_everyone() # Train! os.makedirs(os.path.join(args.output_dir, 'vis'), exist_ok=True) for epoch in range(first_epoch, num_epochs): progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process) progress_bar.set_description(f"Epoch {epoch}") for step, batch in enumerate(train_dataloader): model.train() # Skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) continue if isinstance(batch, (list, tuple)): clean_images = batch[0] else: clean_images = batch noise = torch.randn(clean_images.shape).to(clean_images.device) bsz = clean_images.shape[0] # The standard training procedure in diffusers #timesteps = torch.randint( # 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device #).long() # Our experiements were conduct on https://github.com/ermongroup/ddim/blob/main/runners/diffusion.py timesteps = torch.randint( low=0, high=noise_scheduler.config.num_train_timesteps, size=(bsz // 2 + 1,) ).to(clean_images.device) timesteps = torch.cat([timesteps, noise_scheduler.config.num_train_timesteps - timesteps - 1], dim=0)[:bsz] # Add noise to the clean images according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps) with accelerator.accumulate(model): optimizer.zero_grad() # Predict the noise residual model_output = model(noisy_images, timesteps).sample loss = (noise - model_output).square().sum(dim=(1, 2, 3)).mean(dim=0) accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() lr_scheduler.step() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: if args.use_ema: ema_model.step(model.parameters()) progress_bar.update(1) global_step += 1 logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step} if args.use_ema: logs["ema_decay"] = ema_model.cur_decay_value progress_bar.set_postfix(**logs) accelerator.log(logs, step=global_step) # Save the model & generate sample images if global_step % args.save_model_steps == 0: accelerator.wait_for_everyone() if accelerator.is_main_process: # save the model unet = accelerator.unwrap_model(model).eval() unet.zero_grad() os.makedirs(os.path.join(args.output_dir, 'pruned'), exist_ok=True) torch.save(unet, os.path.join(args.output_dir, 'pruned', 'unet_pruned.pth'.format(global_step))) torch.save(unet, os.path.join(args.output_dir, 'pruned', 'unet_pruned-{}.pth'.format(global_step))) if args.use_ema: ema_model.store(unet.parameters()) ema_model.copy_to(unet.parameters()) torch.save(unet, os.path.join(args.output_dir, 'pruned', 'unet_ema_pruned.pth'.format(global_step))) torch.save(unet, os.path.join(args.output_dir, 'pruned', 'unet_ema_pruned-{}.pth'.format(global_step))) pipeline = DDPMPipeline( unet=unet, scheduler=noise_scheduler, ) pipeline.save_pretrained(args.output_dir) # generate images logger.info("Sampling images...") pipeline = DDIMPipeline( unet=unet, scheduler=DDIMScheduler(num_train_timesteps=args.ddpm_num_steps) ) pipeline.scheduler.set_timesteps(args.ddim_num_inference_steps) images = pipeline( batch_size=args.eval_batch_size, num_inference_steps=args.ddim_num_inference_steps, output_type="numpy", ).images if args.use_ema: ema_model.restore(unet.parameters()) torchvision.utils.save_image(torch.from_numpy(images).permute([0, 3, 1, 2]), os.path.join(args.output_dir, 'vis', 'iter-{}.png'.format(global_step))) # denormalize the images and save to tensorboard images_processed = (images * 255).round().astype("uint8") if args.logger == "tensorboard": if is_accelerate_version(">=", "0.17.0.dev0"): tracker = accelerator.get_tracker("tensorboard", unwrap=True) else: tracker = accelerator.get_tracker("tensorboard") tracker.add_images("test_samples", images_processed.transpose(0, 3, 1, 2), global_step) elif args.logger == "wandb": # Upcoming `log_images` helper coming in https://github.com/huggingface/accelerate/pull/962/files accelerator.get_tracker("wandb").log( {"test_samples": [wandb.Image(img) for img in images_processed], "steps": global_step}, step=global_step, ) del unet del pipeline if global_step>args.num_iters: progress_bar.close() accelerator.wait_for_everyone() accelerator.end_training() return progress_bar.close() accelerator.wait_for_everyone() accelerator.end_training() if __name__ == "__main__": args = parse_args() main(args) ================================================ FILE: diffusers/__init__.py ================================================ __version__ = "0.17.0.dev0" from .configuration_utils import ConfigMixin from .utils import ( OptionalDependencyNotAvailable, is_flax_available, is_inflect_available, is_k_diffusion_available, is_k_diffusion_version, is_librosa_available, is_note_seq_available, is_onnx_available, is_scipy_available, is_torch_available, is_torchsde_available, is_transformers_available, is_transformers_version, is_unidecode_available, logging, ) try: if not is_onnx_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_onnx_objects import * # noqa F403 else: from .pipelines import OnnxRuntimeModel try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * # noqa F403 else: from .models import ( AutoencoderKL, ControlNetModel, ModelMixin, PriorTransformer, T5FilmDecoder, Transformer2DModel, UNet1DModel, UNet2DConditionModel, UNet2DModel, UNet3DConditionModel, VQModel, ) from .optimization import ( get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pipelines import ( AudioPipelineOutput, DanceDiffusionPipeline, DDIMPipeline, DDPMPipeline, DiffusionPipeline, DiTPipeline, ImagePipelineOutput, KarrasVePipeline, LDMPipeline, LDMSuperResolutionPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline, ) from .schedulers import ( DDIMInverseScheduler, DDIMScheduler, DDPMScheduler, DEISMultistepScheduler, DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, IPNDMScheduler, KarrasVeScheduler, KDPM2AncestralDiscreteScheduler, KDPM2DiscreteScheduler, PNDMScheduler, RePaintScheduler, SchedulerMixin, ScoreSdeVeScheduler, UnCLIPScheduler, UniPCMultistepScheduler, VQDiffusionScheduler, ) from .training_utils import EMAModel try: if not (is_torch_available() and is_scipy_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_scipy_objects import * # noqa F403 else: from .schedulers import LMSDiscreteScheduler try: if not (is_torch_available() and is_torchsde_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_torchsde_objects import * # noqa F403 else: from .schedulers import DPMSolverSDEScheduler try: if not (is_torch_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipelines import ( AltDiffusionImg2ImgPipeline, AltDiffusionPipeline, AudioLDMPipeline, CycleDiffusionPipeline, IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline, LDMTextToImagePipeline, PaintByExamplePipeline, SemanticStableDiffusionPipeline, StableDiffusionAttendAndExcitePipeline, StableDiffusionControlNetImg2ImgPipeline, StableDiffusionControlNetInpaintPipeline, StableDiffusionControlNetPipeline, StableDiffusionDepth2ImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionImageVariationPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionInstructPix2PixPipeline, StableDiffusionLatentUpscalePipeline, StableDiffusionModelEditingPipeline, StableDiffusionPanoramaPipeline, StableDiffusionPipeline, StableDiffusionPipelineSafe, StableDiffusionPix2PixZeroPipeline, StableDiffusionSAGPipeline, StableDiffusionUpscalePipeline, StableUnCLIPImg2ImgPipeline, StableUnCLIPPipeline, TextToVideoSDPipeline, TextToVideoZeroPipeline, UnCLIPImageVariationPipeline, UnCLIPPipeline, VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, VQDiffusionPipeline, ) try: if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .pipelines import StableDiffusionKDiffusionPipeline try: if not (is_torch_available() and is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403 else: from .pipelines import ( OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionInpaintPipeline, OnnxStableDiffusionInpaintPipelineLegacy, OnnxStableDiffusionPipeline, OnnxStableDiffusionUpscalePipeline, StableDiffusionOnnxPipeline, ) try: if not (is_torch_available() and is_librosa_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_librosa_objects import * # noqa F403 else: from .pipelines import AudioDiffusionPipeline, Mel try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .pipelines import SpectrogramDiffusionPipeline try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_objects import * # noqa F403 else: from .models.controlnet_flax import FlaxControlNetModel from .models.modeling_flax_utils import FlaxModelMixin from .models.unet_2d_condition_flax import FlaxUNet2DConditionModel from .models.vae_flax import FlaxAutoencoderKL from .pipelines import FlaxDiffusionPipeline from .schedulers import ( FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxDPMSolverMultistepScheduler, FlaxKarrasVeScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, FlaxSchedulerMixin, FlaxScoreSdeVeScheduler, ) try: if not (is_flax_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_and_transformers_objects import * # noqa F403 else: from .pipelines import ( FlaxStableDiffusionControlNetPipeline, FlaxStableDiffusionImg2ImgPipeline, FlaxStableDiffusionInpaintPipeline, FlaxStableDiffusionPipeline, ) try: if not (is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_note_seq_objects import * # noqa F403 else: from .pipelines import MidiProcessor ================================================ FILE: diffusers/commands/__init__.py ================================================ # Copyright 2023 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 abc import ABC, abstractmethod from argparse import ArgumentParser class BaseDiffusersCLICommand(ABC): @staticmethod @abstractmethod def register_subcommand(parser: ArgumentParser): raise NotImplementedError() @abstractmethod def run(self): raise NotImplementedError() ================================================ FILE: diffusers/commands/diffusers_cli.py ================================================ #!/usr/bin/env python # Copyright 2023 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 argparse import ArgumentParser from .env import EnvironmentCommand def main(): parser = ArgumentParser("Diffusers CLI tool", usage="diffusers-cli []") commands_parser = parser.add_subparsers(help="diffusers-cli command helpers") # Register commands EnvironmentCommand.register_subcommand(commands_parser) # Let's go args = parser.parse_args() if not hasattr(args, "func"): parser.print_help() exit(1) # Run service = args.func(args) service.run() if __name__ == "__main__": main() ================================================ FILE: diffusers/commands/env.py ================================================ # Copyright 2023 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. import platform from argparse import ArgumentParser import huggingface_hub from .. import __version__ as version from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available from . import BaseDiffusersCLICommand def info_command_factory(_): return EnvironmentCommand() class EnvironmentCommand(BaseDiffusersCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): download_parser = parser.add_parser("env") download_parser.set_defaults(func=info_command_factory) def run(self): hub_version = huggingface_hub.__version__ pt_version = "not installed" pt_cuda_available = "NA" if is_torch_available(): import torch pt_version = torch.__version__ pt_cuda_available = torch.cuda.is_available() transformers_version = "not installed" if is_transformers_available(): import transformers transformers_version = transformers.__version__ accelerate_version = "not installed" if is_accelerate_available(): import accelerate accelerate_version = accelerate.__version__ xformers_version = "not installed" if is_xformers_available(): import xformers xformers_version = xformers.__version__ info = { "`diffusers` version": version, "Platform": platform.platform(), "Python version": platform.python_version(), "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})", "Huggingface_hub version": hub_version, "Transformers version": transformers_version, "Accelerate version": accelerate_version, "xFormers version": xformers_version, "Using GPU in script?": "", "Using distributed or parallel set-up in script?": "", } print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n") print(self.format_dict(info)) return info @staticmethod def format_dict(d): return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" ================================================ FILE: diffusers/configuration_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. 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. """ ConfigMixin base class and utilities.""" import dataclasses import functools import importlib import inspect import json import os import re from collections import OrderedDict from pathlib import PosixPath from typing import Any, Dict, Tuple, Union import numpy as np from huggingface_hub import hf_hub_download from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError from requests import HTTPError from . import __version__ from .utils import ( DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, DummyObject, deprecate, extract_commit_hash, http_user_agent, logging, ) logger = logging.get_logger(__name__) _re_configuration_file = re.compile(r"config\.(.*)\.json") class FrozenDict(OrderedDict): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for key, value in self.items(): setattr(self, key, value) self.__frozen = True def __delitem__(self, *args, **kwargs): raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.") def setdefault(self, *args, **kwargs): raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.") def pop(self, *args, **kwargs): raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.") def update(self, *args, **kwargs): raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.") def __setattr__(self, name, value): if hasattr(self, "__frozen") and self.__frozen: raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.") super().__setattr__(name, value) def __setitem__(self, name, value): if hasattr(self, "__frozen") and self.__frozen: raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.") super().__setitem__(name, value) class ConfigMixin: r""" Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with - [`~ConfigMixin.from_config`] - [`~ConfigMixin.save_config`] Class attributes: - **config_name** (`str`) -- A filename under which the config should stored when calling [`~ConfigMixin.save_config`] (should be overridden by parent class). - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be overridden by subclass). - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass). - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the init function should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by subclass). """ config_name = None ignore_for_config = [] has_compatibles = False _deprecated_kwargs = [] def register_to_config(self, **kwargs): if self.config_name is None: raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`") # Special case for `kwargs` used in deprecation warning added to schedulers # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument, # or solve in a more general way. kwargs.pop("kwargs", None) if not hasattr(self, "_internal_dict"): internal_dict = kwargs else: previous_dict = dict(self._internal_dict) internal_dict = {**self._internal_dict, **kwargs} logger.debug(f"Updating config from {previous_dict} to {internal_dict}") self._internal_dict = FrozenDict(internal_dict) def __getattr__(self, name: str) -> Any: """The only reason we overwrite `getattr` here is to gracefully deprecate accessing config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 Tihs funtion is mostly copied from PyTorch's __getattr__ overwrite: https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module """ is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name) is_attribute = name in self.__dict__ if is_in_config and not is_attribute: deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'scheduler.config.{name}'." deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False) return self._internal_dict[name] raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs): """ Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the [`~ConfigMixin.from_config`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the configuration JSON file will be saved (will be created if it does not exist). """ if os.path.isfile(save_directory): raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file") os.makedirs(save_directory, exist_ok=True) # If we save using the predefined names, we can load using `from_config` output_config_file = os.path.join(save_directory, self.config_name) self.to_json_file(output_config_file) logger.info(f"Configuration saved in {output_config_file}") @classmethod def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs): r""" Instantiate a Python class from a config dictionary Parameters: config (`Dict[str, Any]`): A config dictionary from which the Python class will be instantiated. Make sure to only load configuration files of compatible classes. return_unused_kwargs (`bool`, *optional*, defaults to `False`): Whether kwargs that are not consumed by the Python class should be returned or not. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the Python class. `**kwargs` will be directly passed to the underlying scheduler/model's `__init__` method and eventually overwrite same named arguments of `config`. Examples: ```python >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler >>> # Download scheduler from huggingface.co and cache. >>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32") >>> # Instantiate DDIM scheduler class with same config as DDPM >>> scheduler = DDIMScheduler.from_config(scheduler.config) >>> # Instantiate PNDM scheduler class with same config as DDPM >>> scheduler = PNDMScheduler.from_config(scheduler.config) ``` """ # <===== TO BE REMOVED WITH DEPRECATION # TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated if "pretrained_model_name_or_path" in kwargs: config = kwargs.pop("pretrained_model_name_or_path") if config is None: raise ValueError("Please make sure to provide a config as the first positional argument.") # ======> if not isinstance(config, dict): deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`." if "Scheduler" in cls.__name__: deprecation_message += ( f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead." " Otherwise, please make sure to pass a configuration dictionary instead. This functionality will" " be removed in v1.0.0." ) elif "Model" in cls.__name__: deprecation_message += ( f"If you were trying to load a model, please use {cls}.load_config(...) followed by" f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary" " instead. This functionality will be removed in v1.0.0." ) deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False) config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs) init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs) # Allow dtype to be specified on initialization if "dtype" in unused_kwargs: init_dict["dtype"] = unused_kwargs.pop("dtype") # add possible deprecated kwargs for deprecated_kwarg in cls._deprecated_kwargs: if deprecated_kwarg in unused_kwargs: init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg) # Return model and optionally state and/or unused_kwargs model = cls(**init_dict) # make sure to also save config parameters that might be used for compatible classes model.register_to_config(**hidden_dict) # add hidden kwargs of compatible classes to unused_kwargs unused_kwargs = {**unused_kwargs, **hidden_dict} if return_unused_kwargs: return (model, unused_kwargs) else: return model @classmethod def get_config_dict(cls, *args, **kwargs): deprecation_message = ( f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be" " removed in version v1.0.0" ) deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False) return cls.load_config(*args, **kwargs) @classmethod def load_config( cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, return_commit_hash=False, **kwargs, ) -> Tuple[Dict[str, Any], Dict[str, Any]]: r""" Instantiate a Python class from a config dictionary Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~ConfigMixin.save_config`], e.g., `./my_model_directory/`. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. return_unused_kwargs (`bool`, *optional*, defaults to `False): Whether unused keyword arguments of the config shall be returned. return_commit_hash (`bool`, *optional*, defaults to `False): Whether the commit_hash of the loaded configuration shall be returned. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to use this method in a firewalled environment. """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) use_auth_token = kwargs.pop("use_auth_token", None) local_files_only = kwargs.pop("local_files_only", False) revision = kwargs.pop("revision", None) _ = kwargs.pop("mirror", None) subfolder = kwargs.pop("subfolder", None) user_agent = kwargs.pop("user_agent", {}) user_agent = {**user_agent, "file_type": "config"} user_agent = http_user_agent(user_agent) pretrained_model_name_or_path = str(pretrained_model_name_or_path) if cls.config_name is None: raise ValueError( "`self.config_name` is not defined. Note that one should not load a config from " "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`" ) if os.path.isfile(pretrained_model_name_or_path): config_file = pretrained_model_name_or_path elif os.path.isdir(pretrained_model_name_or_path): if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)): # Load from a PyTorch checkpoint config_file = os.path.join(pretrained_model_name_or_path, cls.config_name) elif subfolder is not None and os.path.isfile( os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name) ): config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name) else: raise EnvironmentError( f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}." ) else: try: # Load from URL or cache if already cached config_file = hf_hub_download( pretrained_model_name_or_path, filename=cls.config_name, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, subfolder=subfolder, revision=revision, ) except RepositoryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier" " listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a" " token having permission to this repo with `use_auth_token` or log in with `huggingface-cli" " login`." ) except RevisionNotFoundError: raise EnvironmentError( f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for" " this model name. Check the model page at" f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." ) except EntryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}." ) except HTTPError as err: raise EnvironmentError( "There was a specific connection error when trying to load" f" {pretrained_model_name_or_path}:\n{err}" ) except ValueError: raise EnvironmentError( f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it" f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a" f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to" " run the library in offline mode at" " 'https://huggingface.co/docs/diffusers/installation#offline-mode'." ) except EnvironmentError: raise EnvironmentError( f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from " "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " f"containing a {cls.config_name} file" ) try: # Load config dict config_dict = cls._dict_from_json_file(config_file) commit_hash = extract_commit_hash(config_file) except (json.JSONDecodeError, UnicodeDecodeError): raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.") if not (return_unused_kwargs or return_commit_hash): return config_dict outputs = (config_dict,) if return_unused_kwargs: outputs += (kwargs,) if return_commit_hash: outputs += (commit_hash,) return outputs @staticmethod def _get_init_keys(cls): return set(dict(inspect.signature(cls.__init__).parameters).keys()) @classmethod def extract_init_dict(cls, config_dict, **kwargs): # 0. Copy origin config dict original_dict = dict(config_dict.items()) # 1. Retrieve expected config attributes from __init__ signature expected_keys = cls._get_init_keys(cls) expected_keys.remove("self") # remove general kwargs if present in dict if "kwargs" in expected_keys: expected_keys.remove("kwargs") # remove flax internal keys if hasattr(cls, "_flax_internal_args"): for arg in cls._flax_internal_args: expected_keys.remove(arg) # 2. Remove attributes that cannot be expected from expected config attributes # remove keys to be ignored if len(cls.ignore_for_config) > 0: expected_keys = expected_keys - set(cls.ignore_for_config) # load diffusers library to import compatible and original scheduler diffusers_library = importlib.import_module(__name__.split(".")[0]) if cls.has_compatibles: compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)] else: compatible_classes = [] expected_keys_comp_cls = set() for c in compatible_classes: expected_keys_c = cls._get_init_keys(c) expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c) expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls) config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls} # remove attributes from orig class that cannot be expected orig_cls_name = config_dict.pop("_class_name", cls.__name__) if orig_cls_name != cls.__name__ and hasattr(diffusers_library, orig_cls_name): orig_cls = getattr(diffusers_library, orig_cls_name) unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig} # remove private attributes config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")} # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments init_dict = {} for key in expected_keys: # if config param is passed to kwarg and is present in config dict # it should overwrite existing config dict key if key in kwargs and key in config_dict: config_dict[key] = kwargs.pop(key) if key in kwargs: # overwrite key init_dict[key] = kwargs.pop(key) elif key in config_dict: # use value from config dict init_dict[key] = config_dict.pop(key) # 4. Give nice warning if unexpected values have been passed if len(config_dict) > 0: logger.warning( f"The config attributes {config_dict} were passed to {cls.__name__}, " "but are not expected and will be ignored. Please verify your " f"{cls.config_name} configuration file." ) # 5. Give nice info if config attributes are initiliazed to default because they have not been passed passed_keys = set(init_dict.keys()) if len(expected_keys - passed_keys) > 0: logger.info( f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values." ) # 6. Define unused keyword arguments unused_kwargs = {**config_dict, **kwargs} # 7. Define "hidden" config parameters that were saved for compatible classes hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict} return init_dict, unused_kwargs, hidden_config_dict @classmethod def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]): with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() return json.loads(text) def __repr__(self): return f"{self.__class__.__name__} {self.to_json_string()}" @property def config(self) -> Dict[str, Any]: """ Returns the config of the class as a frozen dictionary Returns: `Dict[str, Any]`: Config of the class. """ return self._internal_dict def to_json_string(self) -> str: """ Serializes this instance to a JSON string. Returns: `str`: String containing all the attributes that make up this configuration instance in JSON format. """ config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {} config_dict["_class_name"] = self.__class__.__name__ config_dict["_diffusers_version"] = __version__ def to_json_saveable(value): if isinstance(value, np.ndarray): value = value.tolist() elif isinstance(value, PosixPath): value = str(value) return value config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()} # Don't save "_ignore_files" config_dict.pop("_ignore_files", None) return json.dumps(config_dict, indent=2, sort_keys=True) + "\n" def to_json_file(self, json_file_path: Union[str, os.PathLike]): """ Save this instance to a JSON file. Args: json_file_path (`str` or `os.PathLike`): Path to the JSON file in which this configuration instance's parameters will be saved. """ with open(json_file_path, "w", encoding="utf-8") as writer: writer.write(self.to_json_string()) def register_to_config(init): r""" Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that shouldn't be registered in the config, use the `ignore_for_config` class variable Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init! """ @functools.wraps(init) def inner_init(self, *args, **kwargs): # Ignore private kwargs in the init. init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")} config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")} if not isinstance(self, ConfigMixin): raise RuntimeError( f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does " "not inherit from `ConfigMixin`." ) ignore = getattr(self, "ignore_for_config", []) # Get positional arguments aligned with kwargs new_kwargs = {} signature = inspect.signature(init) parameters = { name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore } for arg, name in zip(args, parameters.keys()): new_kwargs[name] = arg # Then add all kwargs new_kwargs.update( { k: init_kwargs.get(k, default) for k, default in parameters.items() if k not in ignore and k not in new_kwargs } ) new_kwargs = {**config_init_kwargs, **new_kwargs} getattr(self, "register_to_config")(**new_kwargs) init(self, *args, **init_kwargs) return inner_init def flax_register_to_config(cls): original_init = cls.__init__ @functools.wraps(original_init) def init(self, *args, **kwargs): if not isinstance(self, ConfigMixin): raise RuntimeError( f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does " "not inherit from `ConfigMixin`." ) # Ignore private kwargs in the init. Retrieve all passed attributes init_kwargs = dict(kwargs.items()) # Retrieve default values fields = dataclasses.fields(self) default_kwargs = {} for field in fields: # ignore flax specific attributes if field.name in self._flax_internal_args: continue if type(field.default) == dataclasses._MISSING_TYPE: default_kwargs[field.name] = None else: default_kwargs[field.name] = getattr(self, field.name) # Make sure init_kwargs override default kwargs new_kwargs = {**default_kwargs, **init_kwargs} # dtype should be part of `init_kwargs`, but not `new_kwargs` if "dtype" in new_kwargs: new_kwargs.pop("dtype") # Get positional arguments aligned with kwargs for i, arg in enumerate(args): name = fields[i].name new_kwargs[name] = arg getattr(self, "register_to_config")(**new_kwargs) original_init(self, *args, **kwargs) cls.__init__ = init return cls ================================================ FILE: diffusers/dependency_versions_check.py ================================================ # Copyright 2023 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. import sys from .dependency_versions_table import deps from .utils.versions import require_version, require_version_core # define which module versions we always want to check at run time # (usually the ones defined in `install_requires` in setup.py) # # order specific notes: # - tqdm must be checked before tokenizers pkgs_to_check_at_runtime = "python tqdm regex requests packaging filelock numpy tokenizers".split() if sys.version_info < (3, 7): pkgs_to_check_at_runtime.append("dataclasses") if sys.version_info < (3, 8): pkgs_to_check_at_runtime.append("importlib_metadata") for pkg in pkgs_to_check_at_runtime: if pkg in deps: if pkg == "tokenizers": # must be loaded here, or else tqdm check may fail from .utils import is_tokenizers_available if not is_tokenizers_available(): continue # not required, check version only if installed require_version_core(deps[pkg]) else: raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py") def dep_version_check(pkg, hint=None): require_version(deps[pkg], hint) ================================================ FILE: diffusers/dependency_versions_table.py ================================================ # THIS FILE HAS BEEN AUTOGENERATED. To update: # 1. modify the `_deps` dict in setup.py # 2. run `make deps_table_update`` deps = { "Pillow": "Pillow", "accelerate": "accelerate>=0.11.0", "compel": "compel==0.1.8", "black": "black~=23.1", "datasets": "datasets", "filelock": "filelock", "flax": "flax>=0.4.1", "hf-doc-builder": "hf-doc-builder>=0.3.0", "huggingface-hub": "huggingface-hub>=0.13.2", "requests-mock": "requests-mock==1.10.0", "importlib_metadata": "importlib_metadata", "isort": "isort>=5.5.4", "jax": "jax>=0.2.8,!=0.3.2", "jaxlib": "jaxlib>=0.1.65", "Jinja2": "Jinja2", "k-diffusion": "k-diffusion>=0.0.12", "librosa": "librosa", "numpy": "numpy", "omegaconf": "omegaconf", "parameterized": "parameterized", "protobuf": "protobuf>=3.20.3,<4", "pytest": "pytest", "pytest-timeout": "pytest-timeout", "pytest-xdist": "pytest-xdist", "ruff": "ruff>=0.0.241", "safetensors": "safetensors", "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92", "scipy": "scipy", "regex": "regex!=2019.12.17", "requests": "requests", "tensorboard": "tensorboard", "torch": "torch>=1.4", "torchvision": "torchvision", "transformers": "transformers>=4.25.1", "urllib3": "urllib3<=2.0.0", } ================================================ FILE: diffusers/experimental/README.md ================================================ # 🧨 Diffusers Experimental We are adding experimental code to support novel applications and usages of the Diffusers library. Currently, the following experiments are supported: * Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model. ================================================ FILE: diffusers/experimental/__init__.py ================================================ from .rl import ValueGuidedRLPipeline ================================================ FILE: diffusers/experimental/rl/__init__.py ================================================ from .value_guided_sampling import ValueGuidedRLPipeline ================================================ FILE: diffusers/experimental/rl/value_guided_sampling.py ================================================ # Copyright 2023 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. import numpy as np import torch import tqdm from ...models.unet_1d import UNet1DModel from ...pipelines import DiffusionPipeline from ...utils import randn_tensor from ...utils.dummy_pt_objects import DDPMScheduler class ValueGuidedRLPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Pipeline for sampling actions from a diffusion model trained to predict sequences of states. Original implementation inspired by this repository: https://github.com/jannerm/diffuser. Parameters: value_function ([`UNet1DModel`]): A specialized UNet for fine-tuning trajectories base on reward. unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded trajectories. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this application is [`DDPMScheduler`]. env: An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models. """ def __init__( self, value_function: UNet1DModel, unet: UNet1DModel, scheduler: DDPMScheduler, env, ): super().__init__() self.value_function = value_function self.unet = unet self.scheduler = scheduler self.env = env self.data = env.get_dataset() self.means = {} for key in self.data.keys(): try: self.means[key] = self.data[key].mean() except: # noqa: E722 pass self.stds = {} for key in self.data.keys(): try: self.stds[key] = self.data[key].std() except: # noqa: E722 pass self.state_dim = env.observation_space.shape[0] self.action_dim = env.action_space.shape[0] def normalize(self, x_in, key): return (x_in - self.means[key]) / self.stds[key] def de_normalize(self, x_in, key): return x_in * self.stds[key] + self.means[key] def to_torch(self, x_in): if type(x_in) is dict: return {k: self.to_torch(v) for k, v in x_in.items()} elif torch.is_tensor(x_in): return x_in.to(self.unet.device) return torch.tensor(x_in, device=self.unet.device) def reset_x0(self, x_in, cond, act_dim): for key, val in cond.items(): x_in[:, key, act_dim:] = val.clone() return x_in def run_diffusion(self, x, conditions, n_guide_steps, scale): batch_size = x.shape[0] y = None for i in tqdm.tqdm(self.scheduler.timesteps): # create batch of timesteps to pass into model timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long) for _ in range(n_guide_steps): with torch.enable_grad(): x.requires_grad_() # permute to match dimension for pre-trained models y = self.value_function(x.permute(0, 2, 1), timesteps).sample grad = torch.autograd.grad([y.sum()], [x])[0] posterior_variance = self.scheduler._get_variance(i) model_std = torch.exp(0.5 * posterior_variance) grad = model_std * grad grad[timesteps < 2] = 0 x = x.detach() x = x + scale * grad x = self.reset_x0(x, conditions, self.action_dim) prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1) # TODO: verify deprecation of this kwarg x = self.scheduler.step(prev_x, i, x, predict_epsilon=False)["prev_sample"] # apply conditions to the trajectory (set the initial state) x = self.reset_x0(x, conditions, self.action_dim) x = self.to_torch(x) return x, y def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1): # normalize the observations and create batch dimension obs = self.normalize(obs, "observations") obs = obs[None].repeat(batch_size, axis=0) conditions = {0: self.to_torch(obs)} shape = (batch_size, planning_horizon, self.state_dim + self.action_dim) # generate initial noise and apply our conditions (to make the trajectories start at current state) x1 = randn_tensor(shape, device=self.unet.device) x = self.reset_x0(x1, conditions, self.action_dim) x = self.to_torch(x) # run the diffusion process x, y = self.run_diffusion(x, conditions, n_guide_steps, scale) # sort output trajectories by value sorted_idx = y.argsort(0, descending=True).squeeze() sorted_values = x[sorted_idx] actions = sorted_values[:, :, : self.action_dim] actions = actions.detach().cpu().numpy() denorm_actions = self.de_normalize(actions, key="actions") # select the action with the highest value if y is not None: selected_index = 0 else: # if we didn't run value guiding, select a random action selected_index = np.random.randint(0, batch_size) denorm_actions = denorm_actions[selected_index, 0] return denorm_actions ================================================ FILE: diffusers/image_processor.py ================================================ # Copyright 2023 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. import warnings from typing import List, Optional, Union import numpy as np import PIL import torch from PIL import Image from .configuration_utils import ConfigMixin, register_to_config from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate class VaeImageProcessor(ConfigMixin): """ Image Processor for VAE Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. vae_scale_factor (`int`, *optional*, defaults to `8`): VAE scale factor. If `do_resize` is True, the image will be automatically resized to multiples of this factor. resample (`str`, *optional*, defaults to `lanczos`): Resampling filter to use when resizing the image. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image to [-1,1] """ config_name = CONFIG_NAME @register_to_config def __init__( self, do_resize: bool = True, vae_scale_factor: int = 8, resample: str = "lanczos", do_normalize: bool = True, ): super().__init__() @staticmethod def numpy_to_pil(images): """ Convert a numpy image or a batch of images to a PIL image. """ if images.ndim == 3: images = images[None, ...] images = (images * 255).round().astype("uint8") if images.shape[-1] == 1: # special case for grayscale (single channel) images pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images] else: pil_images = [Image.fromarray(image) for image in images] return pil_images @staticmethod def numpy_to_pt(images): """ Convert a numpy image to a pytorch tensor """ if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) return images @staticmethod def pt_to_numpy(images): """ Convert a pytorch tensor to a numpy image """ images = images.cpu().permute(0, 2, 3, 1).float().numpy() return images @staticmethod def normalize(images): """ Normalize an image array to [-1,1] """ return 2.0 * images - 1.0 @staticmethod def denormalize(images): """ Denormalize an image array to [0,1] """ return (images / 2 + 0.5).clamp(0, 1) def resize(self, images: PIL.Image.Image) -> PIL.Image.Image: """ Resize a PIL image. Both height and width will be downscaled to the next integer multiple of `vae_scale_factor` """ w, h = images.size w, h = (x - x % self.config.vae_scale_factor for x in (w, h)) # resize to integer multiple of vae_scale_factor images = images.resize((w, h), resample=PIL_INTERPOLATION[self.config.resample]) return images def preprocess( self, image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray], ) -> torch.Tensor: """ Preprocess the image input, accepted formats are PIL images, numpy arrays or pytorch tensors" """ supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor) if isinstance(image, supported_formats): image = [image] elif not (isinstance(image, list) and all(isinstance(i, supported_formats) for i in image)): raise ValueError( f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support {', '.join(supported_formats)}" ) if isinstance(image[0], PIL.Image.Image): if self.config.do_resize: image = [self.resize(i) for i in image] image = [np.array(i).astype(np.float32) / 255.0 for i in image] image = np.stack(image, axis=0) # to np image = self.numpy_to_pt(image) # to pt elif isinstance(image[0], np.ndarray): image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0) image = self.numpy_to_pt(image) _, _, height, width = image.shape if self.config.do_resize and ( height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0 ): raise ValueError( f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}" f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor" ) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0) _, _, height, width = image.shape if self.config.do_resize and ( height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0 ): raise ValueError( f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}" f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor" ) # expected range [0,1], normalize to [-1,1] do_normalize = self.config.do_normalize if image.min() < 0: warnings.warn( "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] " f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]", FutureWarning, ) do_normalize = False if do_normalize: image = self.normalize(image) return image def postprocess( self, image: torch.FloatTensor, output_type: str = "pil", do_denormalize: Optional[List[bool]] = None, ): if not isinstance(image, torch.Tensor): raise ValueError( f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor" ) if output_type not in ["latent", "pt", "np", "pil"]: deprecation_message = ( f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: " "`pil`, `np`, `pt`, `latent`" ) deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False) output_type = "np" if output_type == "latent": return image if do_denormalize is None: do_denormalize = [self.config.do_normalize] * image.shape[0] image = torch.stack( [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])] ) if output_type == "pt": return image image = self.pt_to_numpy(image) if output_type == "np": return image if output_type == "pil": return self.numpy_to_pil(image) ================================================ FILE: diffusers/loaders.py ================================================ # Copyright 2023 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. import os import warnings from collections import defaultdict from pathlib import Path from typing import Callable, Dict, List, Optional, Union import torch from huggingface_hub import hf_hub_download from .models.attention_processor import ( AttnAddedKVProcessor, AttnAddedKVProcessor2_0, CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, LoRAAttnAddedKVProcessor, LoRAAttnProcessor, SlicedAttnAddedKVProcessor, ) from .utils import ( DIFFUSERS_CACHE, HF_HUB_OFFLINE, TEXT_ENCODER_TARGET_MODULES, _get_model_file, deprecate, is_safetensors_available, is_transformers_available, logging, ) if is_safetensors_available(): import safetensors if is_transformers_available(): from transformers import PreTrainedModel, PreTrainedTokenizer logger = logging.get_logger(__name__) TEXT_ENCODER_NAME = "text_encoder" UNET_NAME = "unet" LORA_WEIGHT_NAME = "pytorch_lora_weights.bin" LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors" TEXT_INVERSION_NAME = "learned_embeds.bin" TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors" CUSTOM_DIFFUSION_WEIGHT_NAME = "pytorch_custom_diffusion_weights.bin" CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensors" class AttnProcsLayers(torch.nn.Module): def __init__(self, state_dict: Dict[str, torch.Tensor]): super().__init__() self.layers = torch.nn.ModuleList(state_dict.values()) self.mapping = dict(enumerate(state_dict.keys())) self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())} # .processor for unet, .k_proj, ".q_proj", ".v_proj", and ".out_proj" for text encoder self.split_keys = [".processor", ".k_proj", ".q_proj", ".v_proj", ".out_proj"] # we add a hook to state_dict() and load_state_dict() so that the # naming fits with `unet.attn_processors` def map_to(module, state_dict, *args, **kwargs): new_state_dict = {} for key, value in state_dict.items(): num = int(key.split(".")[1]) # 0 is always "layers" new_key = key.replace(f"layers.{num}", module.mapping[num]) new_state_dict[new_key] = value return new_state_dict def remap_key(key, state_dict): for k in self.split_keys: if k in key: return key.split(k)[0] + k raise ValueError( f"There seems to be a problem with the state_dict: {set(state_dict.keys())}. {key} has to have one of {self.split_keys}." ) def map_from(module, state_dict, *args, **kwargs): all_keys = list(state_dict.keys()) for key in all_keys: replace_key = remap_key(key, state_dict) new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}") state_dict[new_key] = state_dict[key] del state_dict[key] self._register_state_dict_hook(map_to) self._register_load_state_dict_pre_hook(map_from, with_module=True) class UNet2DConditionLoadersMixin: text_encoder_name = TEXT_ENCODER_NAME unet_name = UNET_NAME def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs): r""" Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be defined in [`cross_attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py) and be a `torch.nn.Module` class. This function is experimental and might change in the future. Parameters: pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g., `./my_model_directory/`. - A [torch state dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", None) weight_name = kwargs.pop("weight_name", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True user_agent = { "file_type": "attn_procs_weights", "framework": "pytorch", } model_file = None if not isinstance(pretrained_model_name_or_path_or_dict, dict): # Let's first try to load .safetensors weights if (use_safetensors and weight_name is None) or ( weight_name is not None and weight_name.endswith(".safetensors") ): try: model_file = _get_model_file( pretrained_model_name_or_path_or_dict, weights_name=weight_name or LORA_WEIGHT_NAME_SAFE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = safetensors.torch.load_file(model_file, device="cpu") except IOError as e: if not allow_pickle: raise e # try loading non-safetensors weights pass if model_file is None: model_file = _get_model_file( pretrained_model_name_or_path_or_dict, weights_name=weight_name or LORA_WEIGHT_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = torch.load(model_file, map_location="cpu") else: state_dict = pretrained_model_name_or_path_or_dict # fill attn processors attn_processors = {} is_lora = all("lora" in k for k in state_dict.keys()) is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys()) if is_lora: is_new_lora_format = all( key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in state_dict.keys() ) if is_new_lora_format: # Strip the `"unet"` prefix. is_text_encoder_present = any(key.startswith(self.text_encoder_name) for key in state_dict.keys()) if is_text_encoder_present: warn_message = "The state_dict contains LoRA params corresponding to the text encoder which are not being used here. To use both UNet and text encoder related LoRA params, use [`pipe.load_lora_weights()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.load_lora_weights)." warnings.warn(warn_message) unet_keys = [k for k in state_dict.keys() if k.startswith(self.unet_name)] state_dict = {k.replace(f"{self.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys} lora_grouped_dict = defaultdict(dict) for key, value in state_dict.items(): attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:]) lora_grouped_dict[attn_processor_key][sub_key] = value for key, value_dict in lora_grouped_dict.items(): rank = value_dict["to_k_lora.down.weight"].shape[0] hidden_size = value_dict["to_k_lora.up.weight"].shape[0] attn_processor = self for sub_key in key.split("."): attn_processor = getattr(attn_processor, sub_key) if isinstance( attn_processor, (AttnAddedKVProcessor, SlicedAttnAddedKVProcessor, AttnAddedKVProcessor2_0) ): cross_attention_dim = value_dict["add_k_proj_lora.down.weight"].shape[1] attn_processor_class = LoRAAttnAddedKVProcessor else: cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1] attn_processor_class = LoRAAttnProcessor attn_processors[key] = attn_processor_class( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank ) attn_processors[key].load_state_dict(value_dict) elif is_custom_diffusion: custom_diffusion_grouped_dict = defaultdict(dict) for key, value in state_dict.items(): if len(value) == 0: custom_diffusion_grouped_dict[key] = {} else: if "to_out" in key: attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:]) else: attn_processor_key, sub_key = ".".join(key.split(".")[:-2]), ".".join(key.split(".")[-2:]) custom_diffusion_grouped_dict[attn_processor_key][sub_key] = value for key, value_dict in custom_diffusion_grouped_dict.items(): if len(value_dict) == 0: attn_processors[key] = CustomDiffusionAttnProcessor( train_kv=False, train_q_out=False, hidden_size=None, cross_attention_dim=None ) else: cross_attention_dim = value_dict["to_k_custom_diffusion.weight"].shape[1] hidden_size = value_dict["to_k_custom_diffusion.weight"].shape[0] train_q_out = True if "to_q_custom_diffusion.weight" in value_dict else False attn_processors[key] = CustomDiffusionAttnProcessor( train_kv=True, train_q_out=train_q_out, hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, ) attn_processors[key].load_state_dict(value_dict) else: raise ValueError( f"{model_file} does not seem to be in the correct format expected by LoRA or Custom Diffusion training." ) # set correct dtype & device attn_processors = {k: v.to(device=self.device, dtype=self.dtype) for k, v in attn_processors.items()} # set layers self.set_attn_processor(attn_processors) def save_attn_procs( self, save_directory: Union[str, os.PathLike], is_main_process: bool = True, weight_name: str = None, save_function: Callable = None, safe_serialization: bool = False, **kwargs, ): r""" Save an attention processor to a directory, so that it can be re-loaded using the [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method. Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. is_main_process (`bool`, *optional*, defaults to `True`): Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions. save_function (`Callable`): The function to use to save the state dictionary. Useful on distributed training like TPUs when one need to replace `torch.save` by another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`. """ weight_name = weight_name or deprecate( "weights_name", "0.18.0", "`weights_name` is deprecated, please use `weight_name` instead.", take_from=kwargs, ) if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return if save_function is None: if safe_serialization: def save_function(weights, filename): return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"}) else: save_function = torch.save os.makedirs(save_directory, exist_ok=True) is_custom_diffusion = any( isinstance(x, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor)) for (_, x) in self.attn_processors.items() ) if is_custom_diffusion: model_to_save = AttnProcsLayers( { y: x for (y, x) in self.attn_processors.items() if isinstance(x, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor)) } ) state_dict = model_to_save.state_dict() for name, attn in self.attn_processors.items(): if len(attn.state_dict()) == 0: state_dict[name] = {} else: model_to_save = AttnProcsLayers(self.attn_processors) state_dict = model_to_save.state_dict() if weight_name is None: if safe_serialization: weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE if is_custom_diffusion else LORA_WEIGHT_NAME_SAFE else: weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME if is_custom_diffusion else LORA_WEIGHT_NAME # Save the model save_function(state_dict, os.path.join(save_directory, weight_name)) logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}") class TextualInversionLoaderMixin: r""" Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder. """ def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): r""" Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds to a multi-vector textual inversion embedding, this function will process the prompt so that the special token is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual inversion token or a textual inversion token that is a single vector, the input prompt is simply returned. Parameters: prompt (`str` or list of `str`): The prompt or prompts to guide the image generation. tokenizer (`PreTrainedTokenizer`): The tokenizer responsible for encoding the prompt into input tokens. Returns: `str` or list of `str`: The converted prompt """ if not isinstance(prompt, List): prompts = [prompt] else: prompts = prompt prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts] if not isinstance(prompt, List): return prompts[0] return prompts def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"): r""" Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds to a multi-vector textual inversion embedding, this function will process the prompt so that the special token is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual inversion token or a textual inversion token that is a single vector, the input prompt is simply returned. Parameters: prompt (`str`): The prompt to guide the image generation. tokenizer (`PreTrainedTokenizer`): The tokenizer responsible for encoding the prompt into input tokens. Returns: `str`: The converted prompt """ tokens = tokenizer.tokenize(prompt) for token in tokens: if token in tokenizer.added_tokens_encoder: replacement = token i = 1 while f"{token}_{i}" in tokenizer.added_tokens_encoder: replacement += f" {token}_{i}" i += 1 prompt = prompt.replace(token, replacement) return prompt def load_textual_inversion( self, pretrained_model_name_or_path: Union[str, List[str]], token: Optional[Union[str, List[str]]] = None, **kwargs, ): r""" Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both `diffusers` and `Automatic1111` formats are supported (see example below). This function is experimental and might change in the future. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]`): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `"sd-concepts-library/low-poly-hd-logos-icons"`. - A path to a *directory* containing textual inversion weights, e.g. `./my_text_inversion_directory/`. - A path to a *file* containing textual inversion weights, e.g. `./my_text_inversions.pt`. Or a list of those elements. token (`str` or `List[str]`, *optional*): Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a list, then `token` must also be a list of equal length. weight_name (`str`, *optional*): Name of a custom weight file. This should be used in two cases: - The saved textual inversion file is in `diffusers` format, but was saved under a specific weight name, such as `text_inv.bin`. - The saved textual inversion file is in the "Automatic1111" form. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Example: To load a textual inversion embedding vector in `diffusers` format: ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") pipe.load_textual_inversion("sd-concepts-library/cat-toy") prompt = "A backpack" image = pipe(prompt, num_inference_steps=50).images[0] image.save("cat-backpack.png") ``` To load a textual inversion embedding vector in Automatic1111 format, make sure to first download the vector, e.g. from [civitAI](https://civitai.com/models/3036?modelVersionId=9857) and then load the vector locally: ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2") prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details." image = pipe(prompt, num_inference_steps=50).images[0] image.save("character.png") ``` """ if not hasattr(self, "tokenizer") or not isinstance(self.tokenizer, PreTrainedTokenizer): raise ValueError( f"{self.__class__.__name__} requires `self.tokenizer` of type `PreTrainedTokenizer` for calling" f" `{self.load_textual_inversion.__name__}`" ) if not hasattr(self, "text_encoder") or not isinstance(self.text_encoder, PreTrainedModel): raise ValueError( f"{self.__class__.__name__} requires `self.text_encoder` of type `PreTrainedModel` for calling" f" `{self.load_textual_inversion.__name__}`" ) cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", None) weight_name = kwargs.pop("weight_name", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True user_agent = { "file_type": "text_inversion", "framework": "pytorch", } if isinstance(pretrained_model_name_or_path, str): pretrained_model_name_or_paths = [pretrained_model_name_or_path] else: pretrained_model_name_or_paths = pretrained_model_name_or_path if isinstance(token, str): tokens = [token] elif token is None: tokens = [None] * len(pretrained_model_name_or_paths) else: tokens = token if len(pretrained_model_name_or_paths) != len(tokens): raise ValueError( f"You have passed a list of models of length {len(pretrained_model_name_or_paths)}, and list of tokens of length {len(tokens)}" f"Make sure both lists have the same length." ) valid_tokens = [t for t in tokens if t is not None] if len(set(valid_tokens)) < len(valid_tokens): raise ValueError(f"You have passed a list of tokens that contains duplicates: {tokens}") token_ids_and_embeddings = [] for pretrained_model_name_or_path, token in zip(pretrained_model_name_or_paths, tokens): # 1. Load textual inversion file model_file = None # Let's first try to load .safetensors weights if (use_safetensors and weight_name is None) or ( weight_name is not None and weight_name.endswith(".safetensors") ): try: model_file = _get_model_file( pretrained_model_name_or_path, weights_name=weight_name or TEXT_INVERSION_NAME_SAFE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = safetensors.torch.load_file(model_file, device="cpu") except Exception as e: if not allow_pickle: raise e model_file = None if model_file is None: model_file = _get_model_file( pretrained_model_name_or_path, weights_name=weight_name or TEXT_INVERSION_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = torch.load(model_file, map_location="cpu") # 2. Load token and embedding correcly from file if isinstance(state_dict, torch.Tensor): if token is None: raise ValueError( "You are trying to load a textual inversion embedding that has been saved as a PyTorch tensor. Make sure to pass the name of the corresponding token in this case: `token=...`." ) embedding = state_dict elif len(state_dict) == 1: # diffusers loaded_token, embedding = next(iter(state_dict.items())) elif "string_to_param" in state_dict: # A1111 loaded_token = state_dict["name"] embedding = state_dict["string_to_param"]["*"] if token is not None and loaded_token != token: logger.info(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.") else: token = loaded_token embedding = embedding.to(dtype=self.text_encoder.dtype, device=self.text_encoder.device) # 3. Make sure we don't mess up the tokenizer or text encoder vocab = self.tokenizer.get_vocab() if token in vocab: raise ValueError( f"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder." ) elif f"{token}_1" in vocab: multi_vector_tokens = [token] i = 1 while f"{token}_{i}" in self.tokenizer.added_tokens_encoder: multi_vector_tokens.append(f"{token}_{i}") i += 1 raise ValueError( f"Multi-vector Token {multi_vector_tokens} already in tokenizer vocabulary. Please choose a different token name or remove the {multi_vector_tokens} and embedding from the tokenizer and text encoder." ) is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1 if is_multi_vector: tokens = [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])] embeddings = [e for e in embedding] # noqa: C416 else: tokens = [token] embeddings = [embedding[0]] if len(embedding.shape) > 1 else [embedding] # add tokens and get ids self.tokenizer.add_tokens(tokens) token_ids = self.tokenizer.convert_tokens_to_ids(tokens) token_ids_and_embeddings += zip(token_ids, embeddings) logger.info(f"Loaded textual inversion embedding for {token}.") # resize token embeddings and set all new embeddings self.text_encoder.resize_token_embeddings(len(self.tokenizer)) for token_id, embedding in token_ids_and_embeddings: self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding class LoraLoaderMixin: r""" Utility class for handling the loading LoRA layers into UNet (of class [`UNet2DConditionModel`]) and Text Encoder (of class [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)). This function is experimental and might change in the future. """ text_encoder_name = TEXT_ENCODER_NAME unet_name = UNET_NAME def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs): r""" Load pretrained attention processor layers (such as LoRA) into [`UNet2DConditionModel`] and [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)). This function is experimental and might change in the future. Parameters: pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g., `./my_model_directory/`. - A [torch state dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). """ # Load the main state dict first which has the LoRA layers for either of # UNet and text encoder or both. cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", None) weight_name = kwargs.pop("weight_name", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True user_agent = { "file_type": "attn_procs_weights", "framework": "pytorch", } model_file = None if not isinstance(pretrained_model_name_or_path_or_dict, dict): # Let's first try to load .safetensors weights if (use_safetensors and weight_name is None) or ( weight_name is not None and weight_name.endswith(".safetensors") ): try: model_file = _get_model_file( pretrained_model_name_or_path_or_dict, weights_name=weight_name or LORA_WEIGHT_NAME_SAFE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = safetensors.torch.load_file(model_file, device="cpu") except IOError as e: if not allow_pickle: raise e # try loading non-safetensors weights pass if model_file is None: model_file = _get_model_file( pretrained_model_name_or_path_or_dict, weights_name=weight_name or LORA_WEIGHT_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = torch.load(model_file, map_location="cpu") else: state_dict = pretrained_model_name_or_path_or_dict # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918), # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as # their prefixes. keys = list(state_dict.keys()) if all(key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in keys): # Load the layers corresponding to UNet. unet_keys = [k for k in keys if k.startswith(self.unet_name)] logger.info(f"Loading {self.unet_name}.") unet_lora_state_dict = { k.replace(f"{self.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys } self.unet.load_attn_procs(unet_lora_state_dict) # Load the layers corresponding to text encoder and make necessary adjustments. text_encoder_keys = [k for k in keys if k.startswith(self.text_encoder_name)] logger.info(f"Loading {self.text_encoder_name}.") text_encoder_lora_state_dict = { k.replace(f"{self.text_encoder_name}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys } if len(text_encoder_lora_state_dict) > 0: attn_procs_text_encoder = self._load_text_encoder_attn_procs(text_encoder_lora_state_dict) self._modify_text_encoder(attn_procs_text_encoder) # save lora attn procs of text encoder so that it can be easily retrieved self._text_encoder_lora_attn_procs = attn_procs_text_encoder # Otherwise, we're dealing with the old format. This means the `state_dict` should only # contain the module names of the `unet` as its keys WITHOUT any prefix. elif not all( key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in state_dict.keys() ): self.unet.load_attn_procs(state_dict) warn_message = "You have saved the LoRA weights using the old format. To convert the old LoRA weights to the new format, you can first load them in a dictionary and then create a new dictionary like the following: `new_state_dict = {f'unet'.{module_name}: params for module_name, params in old_state_dict.items()}`." warnings.warn(warn_message) @property def text_encoder_lora_attn_procs(self): if hasattr(self, "_text_encoder_lora_attn_procs"): return self._text_encoder_lora_attn_procs return def _modify_text_encoder(self, attn_processors: Dict[str, LoRAAttnProcessor]): r""" Monkey-patches the forward passes of attention modules of the text encoder. Parameters: attn_processors: Dict[str, `LoRAAttnProcessor`]: A dictionary mapping the module names and their corresponding [`~LoRAAttnProcessor`]. """ # Loop over the original attention modules. for name, _ in self.text_encoder.named_modules(): if any(x in name for x in TEXT_ENCODER_TARGET_MODULES): # Retrieve the module and its corresponding LoRA processor. module = self.text_encoder.get_submodule(name) # Construct a new function that performs the LoRA merging. We will monkey patch # this forward pass. lora_layer = getattr(attn_processors[name], self._get_lora_layer_attribute(name)) old_forward = module.forward def new_forward(x): return old_forward(x) + lora_layer(x) # Monkey-patch. module.forward = new_forward def _get_lora_layer_attribute(self, name: str) -> str: if "q_proj" in name: return "to_q_lora" elif "v_proj" in name: return "to_v_lora" elif "k_proj" in name: return "to_k_lora" else: return "to_out_lora" def _load_text_encoder_attn_procs( self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs ): r""" Load pretrained attention processor layers for [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel). This function is experimental and might change in the future. Parameters: pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g., `./my_model_directory/`. - A [torch state dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. Returns: `Dict[name, LoRAAttnProcessor]`: Mapping between the module names and their corresponding [`LoRAAttnProcessor`]. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", None) weight_name = kwargs.pop("weight_name", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True user_agent = { "file_type": "attn_procs_weights", "framework": "pytorch", } model_file = None if not isinstance(pretrained_model_name_or_path_or_dict, dict): # Let's first try to load .safetensors weights if (use_safetensors and weight_name is None) or ( weight_name is not None and weight_name.endswith(".safetensors") ): try: model_file = _get_model_file( pretrained_model_name_or_path_or_dict, weights_name=weight_name or LORA_WEIGHT_NAME_SAFE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = safetensors.torch.load_file(model_file, device="cpu") except IOError as e: if not allow_pickle: raise e # try loading non-safetensors weights pass if model_file is None: model_file = _get_model_file( pretrained_model_name_or_path_or_dict, weights_name=weight_name or LORA_WEIGHT_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) state_dict = torch.load(model_file, map_location="cpu") else: state_dict = pretrained_model_name_or_path_or_dict # fill attn processors attn_processors = {} is_lora = all("lora" in k for k in state_dict.keys()) if is_lora: lora_grouped_dict = defaultdict(dict) for key, value in state_dict.items(): attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:]) lora_grouped_dict[attn_processor_key][sub_key] = value for key, value_dict in lora_grouped_dict.items(): rank = value_dict["to_k_lora.down.weight"].shape[0] cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1] hidden_size = value_dict["to_k_lora.up.weight"].shape[0] attn_processors[key] = LoRAAttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank ) attn_processors[key].load_state_dict(value_dict) else: raise ValueError(f"{model_file} does not seem to be in the correct format expected by LoRA training.") # set correct dtype & device attn_processors = { k: v.to(device=self.device, dtype=self.text_encoder.dtype) for k, v in attn_processors.items() } return attn_processors @classmethod def save_lora_weights( self, save_directory: Union[str, os.PathLike], unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None, text_encoder_lora_layers: Dict[str, torch.nn.Module] = None, is_main_process: bool = True, weight_name: str = None, save_function: Callable = None, safe_serialization: bool = False, ): r""" Save the LoRA parameters corresponding to the UNet and the text encoder. Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`): State dict of the LoRA layers corresponding to the UNet. Specifying this helps to make the serialization process easier and cleaner. Values can be both LoRA torch.nn.Modules layers or torch weights. text_encoder_lora_layers (`Dict[str, torch.nn.Module] or `Dict[str, torch.Tensor]`): State dict of the LoRA layers corresponding to the `text_encoder`. Since the `text_encoder` comes from `transformers`, we cannot rejig it. That is why we have to explicitly pass the text encoder LoRA state dict. Values can be both LoRA torch.nn.Modules layers or torch weights. is_main_process (`bool`, *optional*, defaults to `True`): Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions. save_function (`Callable`): The function to use to save the state dictionary. Useful on distributed training like TPUs when one need to replace `torch.save` by another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`. """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return if save_function is None: if safe_serialization: def save_function(weights, filename): return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"}) else: save_function = torch.save os.makedirs(save_directory, exist_ok=True) # Create a flat dictionary. state_dict = {} if unet_lora_layers is not None: weights = ( unet_lora_layers.state_dict() if isinstance(unet_lora_layers, torch.nn.Module) else unet_lora_layers ) unet_lora_state_dict = {f"{self.unet_name}.{module_name}": param for module_name, param in weights.items()} state_dict.update(unet_lora_state_dict) if text_encoder_lora_layers is not None: weights = ( text_encoder_lora_layers.state_dict() if isinstance(text_encoder_lora_layers, torch.nn.Module) else text_encoder_lora_layers ) text_encoder_lora_state_dict = { f"{self.text_encoder_name}.{module_name}": param for module_name, param in weights.items() } state_dict.update(text_encoder_lora_state_dict) # Save the model if weight_name is None: if safe_serialization: weight_name = LORA_WEIGHT_NAME_SAFE else: weight_name = LORA_WEIGHT_NAME save_function(state_dict, os.path.join(save_directory, weight_name)) logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}") class FromCkptMixin: """This helper class allows to directly load .ckpt stable diffusion file_extension into the respective classes.""" @classmethod def from_ckpt(cls, pretrained_model_link_or_path, **kwargs): r""" Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights saved in the original .ckpt format. The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). Parameters: pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A link to the .ckpt file on the Hub. Should be in the format `"https://huggingface.co//blob/main/"` - A path to a *file* containing all pipeline weights. torch_dtype (`str` or `torch.dtype`, *optional*): Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype will be automatically derived from the model's weights. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only (`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the pipeline will load the `safetensors` weights if they're available **and** if the `safetensors` library is installed. If set to `True`, the pipeline will forcibly load the models from `safetensors` weights. If set to `False` the pipeline will *not* use `safetensors`. extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning. upcast_attention (`bool`, *optional*, defaults to `None`): Whether the attention computation should always be upcasted. This is necessary when running stable image_size (`int`, *optional*, defaults to 512): The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use 768 for Stable Diffusion v2. prediction_type (`str`, *optional*): The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2. num_in_channels (`int`, *optional*, defaults to None): The number of input channels. If `None`, it will be automatically inferred. scheduler_type (`str`, *optional*, defaults to 'pndm'): Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", "ddim"]`. load_safety_checker (`bool`, *optional*, defaults to `True`): Whether to load the safety checker or not. Defaults to `True`. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the specific pipeline class. The overwritten components are then directly passed to the pipelines `__init__` method. See example below for more information. Examples: ```py >>> from diffusers import StableDiffusionPipeline >>> # Download pipeline from huggingface.co and cache. >>> pipeline = StableDiffusionPipeline.from_ckpt( ... "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors" ... ) >>> # Download pipeline from local file >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt >>> pipeline = StableDiffusionPipeline.from_ckpt("./v1-5-pruned-emaonly") >>> # Enable float16 and move to GPU >>> pipeline = StableDiffusionPipeline.from_ckpt( ... "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt", ... torch_dtype=torch.float16, ... ) >>> pipeline.to("cuda") ``` """ # import here to avoid circular dependency from .pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) resume_download = kwargs.pop("resume_download", False) force_download = kwargs.pop("force_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) extract_ema = kwargs.pop("extract_ema", False) image_size = kwargs.pop("image_size", 512) scheduler_type = kwargs.pop("scheduler_type", "pndm") num_in_channels = kwargs.pop("num_in_channels", None) upcast_attention = kwargs.pop("upcast_attention", None) load_safety_checker = kwargs.pop("load_safety_checker", True) prediction_type = kwargs.pop("prediction_type", None) torch_dtype = kwargs.pop("torch_dtype", None) use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False) pipeline_name = cls.__name__ file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1] from_safetensors = file_extension == "safetensors" if from_safetensors and use_safetensors is False: raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.") # TODO: For now we only support stable diffusion stable_unclip = None controlnet = False if pipeline_name == "StableDiffusionControlNetPipeline": model_type = "FrozenCLIPEmbedder" controlnet = True elif "StableDiffusion" in pipeline_name: model_type = "FrozenCLIPEmbedder" elif pipeline_name == "StableUnCLIPPipeline": model_type == "FrozenOpenCLIPEmbedder" stable_unclip = "txt2img" elif pipeline_name == "StableUnCLIPImg2ImgPipeline": model_type == "FrozenOpenCLIPEmbedder" stable_unclip = "img2img" elif pipeline_name == "PaintByExamplePipeline": model_type == "PaintByExample" elif pipeline_name == "LDMTextToImagePipeline": model_type == "LDMTextToImage" else: raise ValueError(f"Unhandled pipeline class: {pipeline_name}") # remove huggingface url for prefix in ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]: if pretrained_model_link_or_path.startswith(prefix): pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :] # Code based on diffusers.pipelines.pipeline_utils.DiffusionPipeline.from_pretrained ckpt_path = Path(pretrained_model_link_or_path) if not ckpt_path.is_file(): # get repo_id and (potentially nested) file path of ckpt in repo repo_id = str(Path().joinpath(*ckpt_path.parts[:2])) file_path = str(Path().joinpath(*ckpt_path.parts[2:])) if file_path.startswith("blob/"): file_path = file_path[len("blob/") :] if file_path.startswith("main/"): file_path = file_path[len("main/") :] pretrained_model_link_or_path = hf_hub_download( repo_id, filename=file_path, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, force_download=force_download, ) pipe = download_from_original_stable_diffusion_ckpt( pretrained_model_link_or_path, pipeline_class=cls, model_type=model_type, stable_unclip=stable_unclip, controlnet=controlnet, from_safetensors=from_safetensors, extract_ema=extract_ema, image_size=image_size, scheduler_type=scheduler_type, num_in_channels=num_in_channels, upcast_attention=upcast_attention, load_safety_checker=load_safety_checker, prediction_type=prediction_type, ) if torch_dtype is not None: pipe.to(torch_dtype=torch_dtype) return pipe ================================================ FILE: diffusers/models/README.md ================================================ # Models For more detail on the models, please refer to the [docs](https://huggingface.co/docs/diffusers/api/models). ================================================ FILE: diffusers/models/__init__.py ================================================ # Copyright 2023 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 ..utils import is_flax_available, is_torch_available if is_torch_available(): from .autoencoder_kl import AutoencoderKL from .controlnet import ControlNetModel from .dual_transformer_2d import DualTransformer2DModel from .modeling_utils import ModelMixin from .prior_transformer import PriorTransformer from .t5_film_transformer import T5FilmDecoder from .transformer_2d import Transformer2DModel from .unet_1d import UNet1DModel from .unet_2d import UNet2DModel from .unet_2d_condition import UNet2DConditionModel from .unet_3d_condition import UNet3DConditionModel from .vq_model import VQModel if is_flax_available(): from .controlnet_flax import FlaxControlNetModel from .unet_2d_condition_flax import FlaxUNet2DConditionModel from .vae_flax import FlaxAutoencoderKL ================================================ FILE: diffusers/models/attention.py ================================================ # Copyright 2023 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 import torch import torch.nn.functional as F from torch import nn from ..utils import maybe_allow_in_graph from .attention_processor import Attention from .embeddings import CombinedTimestepLabelEmbeddings @maybe_allow_in_graph class BasicTransformerBlock(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. cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. only_cross_attention (`bool`, *optional*): Whether to use only cross-attention layers. In this case two cross attention layers are used. double_self_attention (`bool`, *optional*): Whether to use two self-attention layers. In this case no cross attention layers are used. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. num_embeds_ada_norm (: obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. attention_bias (: obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. """ def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, dropout=0.0, cross_attention_dim: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, attention_bias: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_elementwise_affine: bool = True, norm_type: str = "layer_norm", final_dropout: bool = False, ): super().__init__() self.only_cross_attention = only_cross_attention self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: raise ValueError( f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." ) # Define 3 blocks. Each block has its own normalization layer. # 1. Self-Attn if self.use_ada_layer_norm: self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) elif self.use_ada_layer_norm_zero: self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) else: self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) self.attn1 = Attention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, cross_attention_dim=cross_attention_dim if only_cross_attention else None, upcast_attention=upcast_attention, ) # 2. Cross-Attn if cross_attention_dim is not None or double_self_attention: # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during # the second cross attention block. self.norm2 = ( AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) ) self.attn2 = Attention( query_dim=dim, cross_attention_dim=cross_attention_dim if not double_self_attention else None, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, upcast_attention=upcast_attention, ) # is self-attn if encoder_hidden_states is none else: self.norm2 = None self.attn2 = None # 3. Feed-forward self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) def forward( self, hidden_states, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, timestep=None, cross_attention_kwargs=None, class_labels=None, ): # Notice that normalization is always applied before the real computation in the following blocks. # 1. Self-Attention if self.use_ada_layer_norm: norm_hidden_states = self.norm1(hidden_states, timestep) elif self.use_ada_layer_norm_zero: norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype ) else: norm_hidden_states = self.norm1(hidden_states) cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=attention_mask, **cross_attention_kwargs, ) if self.use_ada_layer_norm_zero: attn_output = gate_msa.unsqueeze(1) * attn_output hidden_states = attn_output + hidden_states # 2. Cross-Attention if self.attn2 is not None: norm_hidden_states = ( self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) ) # TODO (Birch-San): Here we should prepare the encoder_attention mask correctly # prepare attention mask here attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, **cross_attention_kwargs, ) hidden_states = attn_output + hidden_states # 3. Feed-forward norm_hidden_states = self.norm3(hidden_states) if self.use_ada_layer_norm_zero: norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] ff_output = self.ff(norm_hidden_states) if self.use_ada_layer_norm_zero: ff_output = gate_mlp.unsqueeze(1) * ff_output hidden_states = ff_output + hidden_states return hidden_states class FeedForward(nn.Module): r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. 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. final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. """ def __init__( self, dim: int, dim_out: Optional[int] = None, mult: int = 4, dropout: float = 0.0, activation_fn: str = "geglu", final_dropout: bool = False, ): super().__init__() inner_dim = int(dim * mult) dim_out = dim_out if dim_out is not None else dim if activation_fn == "gelu": act_fn = GELU(dim, inner_dim) if activation_fn == "gelu-approximate": act_fn = GELU(dim, inner_dim, approximate="tanh") elif activation_fn == "geglu": act_fn = GEGLU(dim, inner_dim) elif activation_fn == "geglu-approximate": act_fn = ApproximateGELU(dim, inner_dim) self.net = nn.ModuleList([]) # project in self.net.append(act_fn) # project dropout self.net.append(nn.Dropout(dropout)) # project out self.net.append(nn.Linear(inner_dim, dim_out)) # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout if final_dropout: self.net.append(nn.Dropout(dropout)) def forward(self, hidden_states): for module in self.net: hidden_states = module(hidden_states) return hidden_states class GELU(nn.Module): r""" GELU activation function with tanh approximation support with `approximate="tanh"`. """ def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"): super().__init__() self.proj = nn.Linear(dim_in, dim_out) self.approximate = approximate def gelu(self, gate): if gate.device.type != "mps": return F.gelu(gate, approximate=self.approximate) # mps: gelu is not implemented for float16 return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype) def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = self.gelu(hidden_states) return hidden_states class GEGLU(nn.Module): r""" A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. """ def __init__(self, dim_in: int, dim_out: int): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def gelu(self, gate): if gate.device.type != "mps": return F.gelu(gate) # mps: gelu is not implemented for float16 return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype) def forward(self, hidden_states): hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1) return hidden_states * self.gelu(gate) class ApproximateGELU(nn.Module): """ The approximate form of Gaussian Error Linear Unit (GELU) For more details, see section 2: https://arxiv.org/abs/1606.08415 """ def __init__(self, dim_in: int, dim_out: int): super().__init__() self.proj = nn.Linear(dim_in, dim_out) def forward(self, x): x = self.proj(x) return x * torch.sigmoid(1.702 * x) class AdaLayerNorm(nn.Module): """ Norm layer modified to incorporate timestep embeddings. """ def __init__(self, embedding_dim, num_embeddings): super().__init__() self.emb = nn.Embedding(num_embeddings, embedding_dim) self.silu = nn.SiLU() self.linear = nn.Linear(embedding_dim, embedding_dim * 2) self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False) def forward(self, x, timestep): emb = self.linear(self.silu(self.emb(timestep))) scale, shift = torch.chunk(emb, 2) x = self.norm(x) * (1 + scale) + shift return x class AdaLayerNormZero(nn.Module): """ Norm layer adaptive layer norm zero (adaLN-Zero). """ def __init__(self, embedding_dim, num_embeddings): super().__init__() self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim) self.silu = nn.SiLU() self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) def forward(self, x, timestep, class_labels, hidden_dtype=None): emb = self.linear(self.silu(self.emb(timestep, class_labels, hidden_dtype=hidden_dtype))) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1) x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] return x, gate_msa, shift_mlp, scale_mlp, gate_mlp class AdaGroupNorm(nn.Module): """ GroupNorm layer modified to incorporate timestep embeddings. """ def __init__( self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5 ): super().__init__() self.num_groups = num_groups self.eps = eps self.act = None if act_fn == "swish": self.act = lambda x: F.silu(x) elif act_fn == "mish": self.act = nn.Mish() elif act_fn == "silu": self.act = nn.SiLU() elif act_fn == "gelu": self.act = nn.GELU() self.linear = nn.Linear(embedding_dim, out_dim * 2) def forward(self, x, emb): if self.act: emb = self.act(emb) emb = self.linear(emb) emb = emb[:, :, None, None] scale, shift = emb.chunk(2, dim=1) x = F.group_norm(x, self.num_groups, eps=self.eps) x = x * (1 + scale) + shift return x ================================================ FILE: diffusers/models/attention_flax.py ================================================ # Copyright 2023 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. import functools import math import flax.linen as nn import jax import jax.numpy as jnp def _query_chunk_attention(query, key, value, precision, key_chunk_size: int = 4096): """Multi-head dot product attention with a limited number of queries.""" num_kv, num_heads, k_features = key.shape[-3:] v_features = value.shape[-1] key_chunk_size = min(key_chunk_size, num_kv) query = query / jnp.sqrt(k_features) @functools.partial(jax.checkpoint, prevent_cse=False) def summarize_chunk(query, key, value): attn_weights = jnp.einsum("...qhd,...khd->...qhk", query, key, precision=precision) max_score = jnp.max(attn_weights, axis=-1, keepdims=True) max_score = jax.lax.stop_gradient(max_score) exp_weights = jnp.exp(attn_weights - max_score) exp_values = jnp.einsum("...vhf,...qhv->...qhf", value, exp_weights, precision=precision) max_score = jnp.einsum("...qhk->...qh", max_score) return (exp_values, exp_weights.sum(axis=-1), max_score) def chunk_scanner(chunk_idx): # julienne key array key_chunk = jax.lax.dynamic_slice( operand=key, start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0], # [...,k,h,d] slice_sizes=list(key.shape[:-3]) + [key_chunk_size, num_heads, k_features], # [...,k,h,d] ) # julienne value array value_chunk = jax.lax.dynamic_slice( operand=value, start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0], # [...,v,h,d] slice_sizes=list(value.shape[:-3]) + [key_chunk_size, num_heads, v_features], # [...,v,h,d] ) return summarize_chunk(query, key_chunk, value_chunk) chunk_values, chunk_weights, chunk_max = jax.lax.map(f=chunk_scanner, xs=jnp.arange(0, num_kv, key_chunk_size)) global_max = jnp.max(chunk_max, axis=0, keepdims=True) max_diffs = jnp.exp(chunk_max - global_max) chunk_values *= jnp.expand_dims(max_diffs, axis=-1) chunk_weights *= max_diffs all_values = chunk_values.sum(axis=0) all_weights = jnp.expand_dims(chunk_weights, -1).sum(axis=0) return all_values / all_weights def jax_memory_efficient_attention( query, key, value, precision=jax.lax.Precision.HIGHEST, query_chunk_size: int = 1024, key_chunk_size: int = 4096 ): r""" Flax Memory-efficient multi-head dot product attention. https://arxiv.org/abs/2112.05682v2 https://github.com/AminRezaei0x443/memory-efficient-attention Args: query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head) key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head) value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head) precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`): numerical precision for computation query_chunk_size (`int`, *optional*, defaults to 1024): chunk size to divide query array value must divide query_length equally without remainder key_chunk_size (`int`, *optional*, defaults to 4096): chunk size to divide key and value array value must divide key_value_length equally without remainder Returns: (`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head) """ num_q, num_heads, q_features = query.shape[-3:] def chunk_scanner(chunk_idx, _): # julienne query array query_chunk = jax.lax.dynamic_slice( operand=query, start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0], # [...,q,h,d] slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features], # [...,q,h,d] ) return ( chunk_idx + query_chunk_size, # unused ignore it _query_chunk_attention( query=query_chunk, key=key, value=value, precision=precision, key_chunk_size=key_chunk_size ), ) _, res = jax.lax.scan( f=chunk_scanner, init=0, xs=None, length=math.ceil(num_q / query_chunk_size) # start counter # stop counter ) return jnp.concatenate(res, axis=-3) # fuse the chunked result back class FlaxAttention(nn.Module): r""" A Flax multi-head attention module as described in: https://arxiv.org/abs/1706.03762 Parameters: query_dim (:obj:`int`): Input hidden states dimension heads (:obj:`int`, *optional*, defaults to 8): Number of heads dim_head (:obj:`int`, *optional*, defaults to 64): Hidden states dimension inside each head dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ query_dim: int heads: int = 8 dim_head: int = 64 dropout: float = 0.0 use_memory_efficient_attention: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): inner_dim = self.dim_head * self.heads self.scale = self.dim_head**-0.5 # Weights were exported with old names {to_q, to_k, to_v, to_out} self.query = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_q") self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_k") self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_v") self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out_0") def reshape_heads_to_batch_dim(self, tensor): batch_size, seq_len, dim = tensor.shape head_size = self.heads tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) tensor = jnp.transpose(tensor, (0, 2, 1, 3)) tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size) return tensor def reshape_batch_dim_to_heads(self, tensor): batch_size, seq_len, dim = tensor.shape head_size = self.heads tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) tensor = jnp.transpose(tensor, (0, 2, 1, 3)) tensor = tensor.reshape(batch_size // head_size, seq_len, dim * head_size) return tensor def __call__(self, hidden_states, context=None, deterministic=True): context = hidden_states if context is None else context query_proj = self.query(hidden_states) key_proj = self.key(context) value_proj = self.value(context) query_states = self.reshape_heads_to_batch_dim(query_proj) key_states = self.reshape_heads_to_batch_dim(key_proj) value_states = self.reshape_heads_to_batch_dim(value_proj) if self.use_memory_efficient_attention: query_states = query_states.transpose(1, 0, 2) key_states = key_states.transpose(1, 0, 2) value_states = value_states.transpose(1, 0, 2) # this if statement create a chunk size for each layer of the unet # the chunk size is equal to the query_length dimension of the deepest layer of the unet flatten_latent_dim = query_states.shape[-3] if flatten_latent_dim % 64 == 0: query_chunk_size = int(flatten_latent_dim / 64) elif flatten_latent_dim % 16 == 0: query_chunk_size = int(flatten_latent_dim / 16) elif flatten_latent_dim % 4 == 0: query_chunk_size = int(flatten_latent_dim / 4) else: query_chunk_size = int(flatten_latent_dim) hidden_states = jax_memory_efficient_attention( query_states, key_states, value_states, query_chunk_size=query_chunk_size, key_chunk_size=4096 * 4 ) hidden_states = hidden_states.transpose(1, 0, 2) else: # compute attentions attention_scores = jnp.einsum("b i d, b j d->b i j", query_states, key_states) attention_scores = attention_scores * self.scale attention_probs = nn.softmax(attention_scores, axis=2) # attend to values hidden_states = jnp.einsum("b i j, b j d -> b i d", attention_probs, value_states) hidden_states = self.reshape_batch_dim_to_heads(hidden_states) hidden_states = self.proj_attn(hidden_states) return hidden_states class FlaxBasicTransformerBlock(nn.Module): r""" A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in: https://arxiv.org/abs/1706.03762 Parameters: dim (:obj:`int`): Inner hidden states dimension n_heads (:obj:`int`): Number of heads d_head (:obj:`int`): Hidden states dimension inside each head dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate only_cross_attention (`bool`, defaults to `False`): Whether to only apply cross attention. dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 """ dim: int n_heads: int d_head: int dropout: float = 0.0 only_cross_attention: bool = False dtype: jnp.dtype = jnp.float32 use_memory_efficient_attention: bool = False def setup(self): # self attention (or cross_attention if only_cross_attention is True) self.attn1 = FlaxAttention( self.dim, self.n_heads, self.d_head, self.dropout, self.use_memory_efficient_attention, dtype=self.dtype ) # cross attention self.attn2 = FlaxAttention( self.dim, self.n_heads, self.d_head, self.dropout, self.use_memory_efficient_attention, dtype=self.dtype ) self.ff = FlaxFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype) self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype) self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype) self.norm3 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype) def __call__(self, hidden_states, context, deterministic=True): # self attention residual = hidden_states if self.only_cross_attention: hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic) else: hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic) hidden_states = hidden_states + residual # cross attention residual = hidden_states hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic) hidden_states = hidden_states + residual # feed forward residual = hidden_states hidden_states = self.ff(self.norm3(hidden_states), deterministic=deterministic) hidden_states = hidden_states + residual return hidden_states class FlaxTransformer2DModel(nn.Module): r""" A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in: https://arxiv.org/pdf/1506.02025.pdf Parameters: in_channels (:obj:`int`): Input number of channels n_heads (:obj:`int`): Number of heads d_head (:obj:`int`): Hidden states dimension inside each head depth (:obj:`int`, *optional*, defaults to 1): Number of transformers block dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate use_linear_projection (`bool`, defaults to `False`): tbd only_cross_attention (`bool`, defaults to `False`): tbd dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 """ in_channels: int n_heads: int d_head: int depth: int = 1 dropout: float = 0.0 use_linear_projection: bool = False only_cross_attention: bool = False dtype: jnp.dtype = jnp.float32 use_memory_efficient_attention: bool = False def setup(self): self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-5) inner_dim = self.n_heads * self.d_head if self.use_linear_projection: self.proj_in = nn.Dense(inner_dim, dtype=self.dtype) else: self.proj_in = nn.Conv( inner_dim, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype, ) self.transformer_blocks = [ FlaxBasicTransformerBlock( inner_dim, self.n_heads, self.d_head, dropout=self.dropout, only_cross_attention=self.only_cross_attention, dtype=self.dtype, use_memory_efficient_attention=self.use_memory_efficient_attention, ) for _ in range(self.depth) ] if self.use_linear_projection: self.proj_out = nn.Dense(inner_dim, dtype=self.dtype) else: self.proj_out = nn.Conv( inner_dim, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype, ) def __call__(self, hidden_states, context, deterministic=True): batch, height, width, channels = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) if self.use_linear_projection: hidden_states = hidden_states.reshape(batch, height * width, channels) hidden_states = self.proj_in(hidden_states) else: hidden_states = self.proj_in(hidden_states) hidden_states = hidden_states.reshape(batch, height * width, channels) for transformer_block in self.transformer_blocks: hidden_states = transformer_block(hidden_states, context, deterministic=deterministic) if self.use_linear_projection: hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, channels) else: hidden_states = hidden_states.reshape(batch, height, width, channels) hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states + residual return hidden_states class FlaxFeedForward(nn.Module): r""" Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's [`FeedForward`] class, with the following simplifications: - The activation function is currently hardcoded to a gated linear unit from: https://arxiv.org/abs/2002.05202 - `dim_out` is equal to `dim`. - The number of hidden dimensions is hardcoded to `dim * 4` in [`FlaxGELU`]. Parameters: dim (:obj:`int`): Inner hidden states dimension dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ dim: int dropout: float = 0.0 dtype: jnp.dtype = jnp.float32 def setup(self): # The second linear layer needs to be called # net_2 for now to match the index of the Sequential layer self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype) self.net_2 = nn.Dense(self.dim, dtype=self.dtype) def __call__(self, hidden_states, deterministic=True): hidden_states = self.net_0(hidden_states) hidden_states = self.net_2(hidden_states) return hidden_states class FlaxGEGLU(nn.Module): r""" Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. Parameters: dim (:obj:`int`): Input hidden states dimension dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ dim: int dropout: float = 0.0 dtype: jnp.dtype = jnp.float32 def setup(self): inner_dim = self.dim * 4 self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype) def __call__(self, hidden_states, deterministic=True): hidden_states = self.proj(hidden_states) hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2) return hidden_linear * nn.gelu(hidden_gelu) ================================================ FILE: diffusers/models/attention_processor.py ================================================ # Copyright 2023 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. import warnings from typing import Callable, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..utils import deprecate, logging, maybe_allow_in_graph from ..utils.import_utils import is_xformers_available logger = logging.get_logger(__name__) # pylint: disable=invalid-name if is_xformers_available(): import xformers import xformers.ops else: xformers = None @maybe_allow_in_graph class Attention(nn.Module): r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention_dim (`int`, *optional*): The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`. heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention. dim_head (`int`, *optional*, defaults to 64): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. bias (`bool`, *optional*, defaults to False): Set to `True` for the query, key, and value linear layers to contain a bias parameter. """ def __init__( self, query_dim: int, cross_attention_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: float = 0.0, bias=False, upcast_attention: bool = False, upcast_softmax: bool = False, cross_attention_norm: Optional[str] = None, cross_attention_norm_num_groups: int = 32, added_kv_proj_dim: Optional[int] = None, norm_num_groups: Optional[int] = None, 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, _from_deprecated_attn_block=False, processor: Optional["AttnProcessor"] = None, ): super().__init__() inner_dim = dim_head * heads cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim self.upcast_attention = upcast_attention self.upcast_softmax = upcast_softmax self.rescale_output_factor = rescale_output_factor self.residual_connection = residual_connection # we make use of this private variable to know whether this class is loaded # with an deprecated state dict so that we can convert it on the fly self._from_deprecated_attn_block = _from_deprecated_attn_block self.scale_qk = scale_qk self.scale = dim_head**-0.5 if self.scale_qk else 1.0 self.heads = 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`." ) if norm_num_groups is not None: self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True) else: self.group_norm = None if cross_attention_norm is None: self.norm_cross = None elif cross_attention_norm == "layer_norm": self.norm_cross = nn.LayerNorm(cross_attention_dim) elif cross_attention_norm == "group_norm": if self.added_kv_proj_dim is not None: # The given `encoder_hidden_states` are initially of shape # (batch_size, seq_len, added_kv_proj_dim) before being projected # to (batch_size, seq_len, cross_attention_dim). The norm is applied # before the projection, so we need to use `added_kv_proj_dim` as # the number of channels for the group norm. norm_cross_num_channels = added_kv_proj_dim else: norm_cross_num_channels = cross_attention_dim self.norm_cross = nn.GroupNorm( num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True ) else: raise ValueError( f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'" ) self.to_q = nn.Linear(query_dim, inner_dim, bias=bias) if not self.only_cross_attention: # only relevant for the `AddedKVProcessor` classes self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias) self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias) else: self.to_k = None self.to_v = None if self.added_kv_proj_dim is not None: self.add_k_proj = nn.Linear(added_kv_proj_dim, inner_dim) self.add_v_proj = nn.Linear(added_kv_proj_dim, inner_dim) self.to_out = nn.ModuleList([]) self.to_out.append(nn.Linear(inner_dim, query_dim, bias=out_bias)) self.to_out.append(nn.Dropout(dropout)) # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 if processor is None: processor = ( AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() ) self.set_processor(processor) def set_use_memory_efficient_attention_xformers( self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None ): is_lora = hasattr(self, "processor") and isinstance( self.processor, (LoRAAttnProcessor, LoRAXFormersAttnProcessor) ) is_custom_diffusion = hasattr(self, "processor") and isinstance( self.processor, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor) ) if use_memory_efficient_attention_xformers: if self.added_kv_proj_dim is not None: # TODO(Anton, Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP # which uses this type of cross attention ONLY because the attention mask of format # [0, ..., -10.000, ..., 0, ...,] is not supported raise NotImplementedError( "Memory efficient attention with `xformers` is currently not supported when" " `self.added_kv_proj_dim` is defined." ) elif not is_xformers_available(): raise ModuleNotFoundError( ( "Refer to https://github.com/facebookresearch/xformers for more information on how to install" " xformers" ), name="xformers", ) elif not torch.cuda.is_available(): raise ValueError( "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" " only available for GPU " ) elif hasattr(F, "scaled_dot_product_attention") and self.scale_qk: warnings.warn( "You have specified using flash attention using xFormers but you have PyTorch 2.0 already installed. " "We will default to PyTorch's native efficient flash attention implementation provided by PyTorch 2.0." ) else: try: # Make sure we can run the memory efficient attention _ = xformers.ops.memory_efficient_attention( torch.randn((1, 2, 40), device="cuda"), torch.randn((1, 2, 40), device="cuda"), torch.randn((1, 2, 40), device="cuda"), ) except Exception as e: raise e if is_lora: processor = LoRAXFormersAttnProcessor( hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, rank=self.processor.rank, attention_op=attention_op, ) processor.load_state_dict(self.processor.state_dict()) processor.to(self.processor.to_q_lora.up.weight.device) elif is_custom_diffusion: processor = CustomDiffusionXFormersAttnProcessor( train_kv=self.processor.train_kv, train_q_out=self.processor.train_q_out, hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, attention_op=attention_op, ) processor.load_state_dict(self.processor.state_dict()) if hasattr(self.processor, "to_k_custom_diffusion"): processor.to(self.processor.to_k_custom_diffusion.weight.device) else: processor = XFormersAttnProcessor(attention_op=attention_op) else: if is_lora: processor = LoRAAttnProcessor( hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, rank=self.processor.rank, ) processor.load_state_dict(self.processor.state_dict()) processor.to(self.processor.to_q_lora.up.weight.device) elif is_custom_diffusion: processor = CustomDiffusionAttnProcessor( train_kv=self.processor.train_kv, train_q_out=self.processor.train_q_out, hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, ) processor.load_state_dict(self.processor.state_dict()) if hasattr(self.processor, "to_k_custom_diffusion"): processor.to(self.processor.to_k_custom_diffusion.weight.device) else: # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 processor = ( AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() ) self.set_processor(processor) def set_attention_slice(self, slice_size): if slice_size is not None and slice_size > self.sliceable_head_dim: raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.") if slice_size is not None and self.added_kv_proj_dim is not None: processor = SlicedAttnAddedKVProcessor(slice_size) elif slice_size is not None: processor = SlicedAttnProcessor(slice_size) elif self.added_kv_proj_dim is not None: processor = AttnAddedKVProcessor() else: # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 processor = ( AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() ) self.set_processor(processor) def set_processor(self, processor: "AttnProcessor"): # if current processor is in `self._modules` and if passed `processor` is not, we need to # pop `processor` from `self._modules` if ( hasattr(self, "processor") and isinstance(self.processor, torch.nn.Module) and not isinstance(processor, torch.nn.Module) ): logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}") self._modules.pop("processor") self.processor = processor def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs): # The `Attention` class can call different attention processors / attention functions # here we simply pass along all tensors to the selected processor class # For standard processors that are defined here, `**cross_attention_kwargs` is empty return self.processor( self, hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) def batch_to_head_dim(self, tensor): head_size = self.heads batch_size, seq_len, dim = tensor.shape tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size) return tensor def head_to_batch_dim(self, tensor, out_dim=3): head_size = self.heads batch_size, seq_len, dim = tensor.shape tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) tensor = tensor.permute(0, 2, 1, 3) if out_dim == 3: tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size) return tensor def get_attention_scores(self, query, key, attention_mask=None): dtype = query.dtype if self.upcast_attention: query = query.float() key = key.float() if attention_mask is None: baddbmm_input = torch.empty( query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device ) beta = 0 else: baddbmm_input = attention_mask beta = 1 attention_scores = torch.baddbmm( baddbmm_input, query, key.transpose(-1, -2), beta=beta, alpha=self.scale, ) del baddbmm_input if self.upcast_softmax: attention_scores = attention_scores.float() attention_probs = attention_scores.softmax(dim=-1) del attention_scores attention_probs = attention_probs.to(dtype) return attention_probs def prepare_attention_mask(self, attention_mask, target_length, batch_size=None, out_dim=3): if batch_size is None: deprecate( "batch_size=None", "0.0.15", ( "Not passing the `batch_size` parameter to `prepare_attention_mask` can lead to incorrect" " attention mask preparation and is deprecated behavior. Please make sure to pass `batch_size` to" " `prepare_attention_mask` when preparing the attention_mask." ), ) batch_size = 1 head_size = self.heads if attention_mask is None: return attention_mask if attention_mask.shape[-1] != target_length: if attention_mask.device.type == "mps": # HACK: MPS: Does not support padding by greater than dimension of input tensor. # Instead, we can manually construct the padding tensor. padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length) padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device) attention_mask = torch.cat([attention_mask, padding], dim=2) else: attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) if out_dim == 3: if attention_mask.shape[0] < batch_size * head_size: attention_mask = attention_mask.repeat_interleave(head_size, dim=0) elif out_dim == 4: attention_mask = attention_mask.unsqueeze(1) attention_mask = attention_mask.repeat_interleave(head_size, dim=1) return attention_mask def norm_encoder_hidden_states(self, encoder_hidden_states): assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states" if isinstance(self.norm_cross, nn.LayerNorm): encoder_hidden_states = self.norm_cross(encoder_hidden_states) elif isinstance(self.norm_cross, nn.GroupNorm): # Group norm norms along the channels dimension and expects # input to be in the shape of (N, C, *). In this case, we want # to norm along the hidden dimension, so we need to move # (batch_size, sequence_length, hidden_size) -> # (batch_size, hidden_size, sequence_length) encoder_hidden_states = encoder_hidden_states.transpose(1, 2) encoder_hidden_states = self.norm_cross(encoder_hidden_states) encoder_hidden_states = encoder_hidden_states.transpose(1, 2) else: assert False return encoder_hidden_states class AttnProcessor: def __call__( self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, ): 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 ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # 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 class LoRALinearLayer(nn.Module): def __init__(self, in_features, out_features, rank=4): super().__init__() if rank > min(in_features, out_features): raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}") self.down = nn.Linear(in_features, rank, bias=False) self.up = nn.Linear(rank, out_features, bias=False) nn.init.normal_(self.down.weight, std=1 / rank) nn.init.zeros_(self.up.weight) def forward(self, hidden_states): orig_dtype = hidden_states.dtype dtype = self.down.weight.dtype down_hidden_states = self.down(hidden_states.to(dtype)) up_hidden_states = self.up(down_hidden_states) return up_hidden_states.to(orig_dtype) class LoRAAttnProcessor(nn.Module): def __init__(self, hidden_size, cross_attention_dim=None, rank=4): super().__init__() self.hidden_size = hidden_size self.cross_attention_dim = cross_attention_dim self.rank = rank self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0): 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 ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) + scale * self.to_q_lora(hidden_states) query = attn.head_to_batch_dim(query) 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) + scale * self.to_k_lora(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(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 class CustomDiffusionAttnProcessor(nn.Module): def __init__( self, train_kv=True, train_q_out=True, hidden_size=None, cross_attention_dim=None, out_bias=True, dropout=0.0, ): super().__init__() self.train_kv = train_kv self.train_q_out = train_q_out self.hidden_size = hidden_size self.cross_attention_dim = cross_attention_dim # `_custom_diffusion` id for easy serialization and loading. if self.train_kv: self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) if self.train_q_out: self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False) self.to_out_custom_diffusion = nn.ModuleList([]) self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias)) self.to_out_custom_diffusion.append(nn.Dropout(dropout)) def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) if self.train_q_out: query = self.to_q_custom_diffusion(hidden_states) else: query = attn.to_q(hidden_states) if encoder_hidden_states is None: crossattn = False encoder_hidden_states = hidden_states else: crossattn = True if attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) if self.train_kv: key = self.to_k_custom_diffusion(encoder_hidden_states) value = self.to_v_custom_diffusion(encoder_hidden_states) else: key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if crossattn: detach = torch.ones_like(key) detach[:, :1, :] = detach[:, :1, :] * 0.0 key = detach * key + (1 - detach) * key.detach() value = detach * value + (1 - detach) * value.detach() query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) if self.train_q_out: # linear proj hidden_states = self.to_out_custom_diffusion[0](hidden_states) # dropout hidden_states = self.to_out_custom_diffusion[1](hidden_states) else: # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) return hidden_states class AttnAddedKVProcessor: def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): residual = hidden_states hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states) query = attn.head_to_batch_dim(query) encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj) encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) if not attn.only_cross_attention: key = attn.to_k(hidden_states) value = attn.to_v(hidden_states) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) key = torch.cat([encoder_hidden_states_key_proj, key], dim=1) value = torch.cat([encoder_hidden_states_value_proj, value], dim=1) else: key = encoder_hidden_states_key_proj value = encoder_hidden_states_value_proj attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) hidden_states = hidden_states + residual return hidden_states class AttnAddedKVProcessor2_0: def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError( "AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." ) def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): residual = hidden_states hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4) 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) hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states) query = attn.head_to_batch_dim(query, out_dim=4) encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4) encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4) if not attn.only_cross_attention: key = attn.to_k(hidden_states) value = attn.to_v(hidden_states) key = attn.head_to_batch_dim(key, out_dim=4) value = attn.head_to_batch_dim(value, out_dim=4) key = torch.cat([encoder_hidden_states_key_proj, key], dim=2) value = torch.cat([encoder_hidden_states_value_proj, value], dim=2) else: key = encoder_hidden_states_key_proj value = encoder_hidden_states_value_proj # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 hidden_states = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False ) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1]) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) hidden_states = hidden_states + residual return hidden_states class LoRAAttnAddedKVProcessor(nn.Module): def __init__(self, hidden_size, cross_attention_dim=None, rank=4): super().__init__() self.hidden_size = hidden_size self.cross_attention_dim = cross_attention_dim self.rank = rank self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank) self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank) self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0): residual = hidden_states hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states) query = attn.head_to_batch_dim(query) encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + scale * self.add_k_proj_lora( encoder_hidden_states ) encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + scale * self.add_v_proj_lora( encoder_hidden_states ) encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj) encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) if not attn.only_cross_attention: key = attn.to_k(hidden_states) + scale * self.to_k_lora(hidden_states) value = attn.to_v(hidden_states) + scale * self.to_v_lora(hidden_states) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) key = torch.cat([encoder_hidden_states_key_proj, key], dim=1) value = torch.cat([encoder_hidden_states_value_proj, value], dim=1) else: key = encoder_hidden_states_key_proj value = encoder_hidden_states_value_proj attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) hidden_states = hidden_states + residual return hidden_states class XFormersAttnProcessor: def __init__(self, attention_op: Optional[Callable] = None): self.attention_op = attention_op def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): 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 ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) query = attn.head_to_batch_dim(query).contiguous() key = attn.head_to_batch_dim(key).contiguous() value = attn.head_to_batch_dim(value).contiguous() hidden_states = xformers.ops.memory_efficient_attention( query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale ) hidden_states = hidden_states.to(query.dtype) hidden_states = attn.batch_to_head_dim(hidden_states) # 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 class AttnProcessor2_0: def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): 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 ) inner_dim = hidden_states.shape[-1] if 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]) 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) 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) # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 hidden_states = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False ) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) hidden_states = hidden_states.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 class LoRAXFormersAttnProcessor(nn.Module): def __init__(self, hidden_size, cross_attention_dim, rank=4, attention_op: Optional[Callable] = None): super().__init__() self.hidden_size = hidden_size self.cross_attention_dim = cross_attention_dim self.rank = rank self.attention_op = attention_op self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0): 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 ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) + scale * self.to_q_lora(hidden_states) query = attn.head_to_batch_dim(query).contiguous() 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) + scale * self.to_k_lora(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states) key = attn.head_to_batch_dim(key).contiguous() value = attn.head_to_batch_dim(value).contiguous() hidden_states = xformers.ops.memory_efficient_attention( query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale ) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(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 class CustomDiffusionXFormersAttnProcessor(nn.Module): def __init__( self, train_kv=True, train_q_out=False, hidden_size=None, cross_attention_dim=None, out_bias=True, dropout=0.0, attention_op: Optional[Callable] = None, ): super().__init__() self.train_kv = train_kv self.train_q_out = train_q_out self.hidden_size = hidden_size self.cross_attention_dim = cross_attention_dim self.attention_op = attention_op # `_custom_diffusion` id for easy serialization and loading. if self.train_kv: self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) if self.train_q_out: self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False) self.to_out_custom_diffusion = nn.ModuleList([]) self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias)) self.to_out_custom_diffusion.append(nn.Dropout(dropout)) def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): batch_size, sequence_length, _ = ( hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) if self.train_q_out: query = self.to_q_custom_diffusion(hidden_states) else: query = attn.to_q(hidden_states) if encoder_hidden_states is None: crossattn = False encoder_hidden_states = hidden_states else: crossattn = True if attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) if self.train_kv: key = self.to_k_custom_diffusion(encoder_hidden_states) value = self.to_v_custom_diffusion(encoder_hidden_states) else: key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if crossattn: detach = torch.ones_like(key) detach[:, :1, :] = detach[:, :1, :] * 0.0 key = detach * key + (1 - detach) * key.detach() value = detach * value + (1 - detach) * value.detach() query = attn.head_to_batch_dim(query).contiguous() key = attn.head_to_batch_dim(key).contiguous() value = attn.head_to_batch_dim(value).contiguous() hidden_states = xformers.ops.memory_efficient_attention( query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale ) hidden_states = hidden_states.to(query.dtype) hidden_states = attn.batch_to_head_dim(hidden_states) if self.train_q_out: # linear proj hidden_states = self.to_out_custom_diffusion[0](hidden_states) # dropout hidden_states = self.to_out_custom_diffusion[1](hidden_states) else: # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) return hidden_states class SlicedAttnProcessor: def __init__(self, slice_size): self.slice_size = slice_size def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): 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 ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) dim = query.shape[-1] query = attn.head_to_batch_dim(query) 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) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) batch_size_attention, query_tokens, _ = query.shape hidden_states = torch.zeros( (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype ) for i in range(batch_size_attention // self.slice_size): start_idx = i * self.slice_size end_idx = (i + 1) * self.slice_size query_slice = query[start_idx:end_idx] key_slice = key[start_idx:end_idx] attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) hidden_states[start_idx:end_idx] = attn_slice hidden_states = attn.batch_to_head_dim(hidden_states) # 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 class SlicedAttnAddedKVProcessor: def __init__(self, slice_size): self.slice_size = slice_size def __call__(self, attn: "Attention", hidden_states, encoder_hidden_states=None, attention_mask=None): residual = hidden_states hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states) dim = query.shape[-1] query = attn.head_to_batch_dim(query) encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj) encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) if not attn.only_cross_attention: key = attn.to_k(hidden_states) value = attn.to_v(hidden_states) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) key = torch.cat([encoder_hidden_states_key_proj, key], dim=1) value = torch.cat([encoder_hidden_states_value_proj, value], dim=1) else: key = encoder_hidden_states_key_proj value = encoder_hidden_states_value_proj batch_size_attention, query_tokens, _ = query.shape hidden_states = torch.zeros( (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype ) for i in range(batch_size_attention // self.slice_size): start_idx = i * self.slice_size end_idx = (i + 1) * self.slice_size query_slice = query[start_idx:end_idx] key_slice = key[start_idx:end_idx] attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) hidden_states[start_idx:end_idx] = attn_slice hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) hidden_states = hidden_states + residual return hidden_states AttentionProcessor = Union[ AttnProcessor, AttnProcessor2_0, XFormersAttnProcessor, SlicedAttnProcessor, AttnAddedKVProcessor, SlicedAttnAddedKVProcessor, AttnAddedKVProcessor2_0, LoRAAttnProcessor, LoRAXFormersAttnProcessor, LoRAAttnAddedKVProcessor, CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, ] ================================================ FILE: diffusers/models/autoencoder_kl.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, apply_forward_hook from .modeling_utils import ModelMixin from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder @dataclass class AutoencoderKLOutput(BaseOutput): """ Output of AutoencoderKL encoding method. Args: latent_dist (`DiagonalGaussianDistribution`): Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. `DiagonalGaussianDistribution` allows for sampling latents from the distribution. """ latent_dist: "DiagonalGaussianDistribution" class AutoencoderKL(ModelMixin, ConfigMixin): r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.) Parameters: in_channels (int, *optional*, defaults to 3): Number of channels in the input image. out_channels (int, *optional*, defaults to 3): Number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types. up_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types. block_out_channels (`Tuple[int]`, *optional*, defaults to : obj:`(64,)`): Tuple of block output channels. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space. sample_size (`int`, *optional*, defaults to `32`): TODO scaling_factor (`float`, *optional*, defaults to 0.18215): The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, in_channels: int = 3, out_channels: int = 3, down_block_types: Tuple[str] = ("DownEncoderBlock2D",), up_block_types: Tuple[str] = ("UpDecoderBlock2D",), block_out_channels: Tuple[int] = (64,), layers_per_block: int = 1, act_fn: str = "silu", latent_channels: int = 4, norm_num_groups: int = 32, sample_size: int = 32, scaling_factor: float = 0.18215, ): super().__init__() # pass init params to Encoder self.encoder = Encoder( in_channels=in_channels, out_channels=latent_channels, down_block_types=down_block_types, block_out_channels=block_out_channels, layers_per_block=layers_per_block, act_fn=act_fn, norm_num_groups=norm_num_groups, double_z=True, ) # pass init params to Decoder self.decoder = Decoder( in_channels=latent_channels, out_channels=out_channels, up_block_types=up_block_types, block_out_channels=block_out_channels, layers_per_block=layers_per_block, norm_num_groups=norm_num_groups, act_fn=act_fn, ) self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1) self.use_slicing = False self.use_tiling = False # only relevant if vae tiling is enabled self.tile_sample_min_size = self.config.sample_size sample_size = ( self.config.sample_size[0] if isinstance(self.config.sample_size, (list, tuple)) else self.config.sample_size ) self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1))) self.tile_overlap_factor = 0.25 def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (Encoder, Decoder)): module.gradient_checkpointing = value def enable_tiling(self, use_tiling: bool = True): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.use_tiling = use_tiling def disable_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.enable_tiling(False) def enable_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.use_slicing = True def disable_slicing(self): r""" Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.use_slicing = False @apply_forward_hook def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput: if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size): return self.tiled_encode(x, return_dict=return_dict) h = self.encoder(x) moments = self.quant_conv(h) posterior = DiagonalGaussianDistribution(moments) if not return_dict: return (posterior,) return AutoencoderKLOutput(latent_dist=posterior) def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size): return self.tiled_decode(z, return_dict=return_dict) z = self.post_quant_conv(z) dec = self.decoder(z) if not return_dict: return (dec,) return DecoderOutput(sample=dec) @apply_forward_hook def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: if self.use_slicing and z.shape[0] > 1: decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] decoded = torch.cat(decoded_slices) else: decoded = self._decode(z).sample if not return_dict: return (decoded,) return DecoderOutput(sample=decoded) def blend_v(self, a, b, blend_extent): blend_extent = min(a.shape[2], b.shape[2], blend_extent) for y in range(blend_extent): b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent) return b def blend_h(self, a, b, blend_extent): blend_extent = min(a.shape[3], b.shape[3], blend_extent) for x in range(blend_extent): b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent) return b def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput: r"""Encode a batch of images using a tiled encoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is: different from non-tiled encoding due to each tile using a different encoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the look of the output, but they should be much less noticeable. x (`torch.FloatTensor`): Input batch of images. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`AutoencoderKLOutput`] instead of a plain tuple. """ overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)) blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor) row_limit = self.tile_latent_min_size - blend_extent # Split the image into 512x512 tiles and encode them separately. rows = [] for i in range(0, x.shape[2], overlap_size): row = [] for j in range(0, x.shape[3], overlap_size): tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size] tile = self.encoder(tile) tile = self.quant_conv(tile) row.append(tile) rows.append(row) result_rows = [] for i, row in enumerate(rows): result_row = [] for j, tile in enumerate(row): # blend the above tile and the left tile # to the current tile and add the current tile to the result row if i > 0: tile = self.blend_v(rows[i - 1][j], tile, blend_extent) if j > 0: tile = self.blend_h(row[j - 1], tile, blend_extent) result_row.append(tile[:, :, :row_limit, :row_limit]) result_rows.append(torch.cat(result_row, dim=3)) moments = torch.cat(result_rows, dim=2) posterior = DiagonalGaussianDistribution(moments) if not return_dict: return (posterior,) return AutoencoderKLOutput(latent_dist=posterior) def tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: r"""Decode a batch of images using a tiled decoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several steps. This is useful to keep memory use constant regardless of image size. The end result of tiled decoding is: different from non-tiled decoding due to each tile using a different decoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the look of the output, but they should be much less noticeable. z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. """ overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)) blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) row_limit = self.tile_sample_min_size - blend_extent # Split z into overlapping 64x64 tiles and decode them separately. # The tiles have an overlap to avoid seams between tiles. rows = [] for i in range(0, z.shape[2], overlap_size): row = [] for j in range(0, z.shape[3], overlap_size): tile = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size] tile = self.post_quant_conv(tile) decoded = self.decoder(tile) row.append(decoded) rows.append(row) result_rows = [] for i, row in enumerate(rows): result_row = [] for j, tile in enumerate(row): # blend the above tile and the left tile # to the current tile and add the current tile to the result row if i > 0: tile = self.blend_v(rows[i - 1][j], tile, blend_extent) if j > 0: tile = self.blend_h(row[j - 1], tile, blend_extent) result_row.append(tile[:, :, :row_limit, :row_limit]) result_rows.append(torch.cat(result_row, dim=3)) dec = torch.cat(result_rows, dim=2) if not return_dict: return (dec,) return DecoderOutput(sample=dec) def forward( self, sample: torch.FloatTensor, sample_posterior: bool = False, return_dict: bool = True, generator: Optional[torch.Generator] = None, ) -> Union[DecoderOutput, torch.FloatTensor]: r""" Args: sample (`torch.FloatTensor`): Input sample. sample_posterior (`bool`, *optional*, defaults to `False`): Whether to sample from the posterior. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. """ x = sample posterior = self.encode(x).latent_dist if sample_posterior: z = posterior.sample(generator=generator) else: z = posterior.mode() dec = self.decode(z).sample if not return_dict: return (dec,) return DecoderOutput(sample=dec) ================================================ FILE: diffusers/models/controlnet.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.nn import functional as F from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, logging from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_2d_blocks import ( CrossAttnDownBlock2D, DownBlock2D, UNetMidBlock2DCrossAttn, get_down_block, ) from .unet_2d_condition import UNet2DConditionModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class ControlNetOutput(BaseOutput): down_block_res_samples: Tuple[torch.Tensor] mid_block_res_sample: torch.Tensor class ControlNetConditioningEmbedding(nn.Module): """ Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full model) to encode image-space conditions ... into feature maps ..." """ def __init__( self, conditioning_embedding_channels: int, conditioning_channels: int = 3, block_out_channels: Tuple[int] = (16, 32, 96, 256), ): super().__init__() self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1) self.blocks = nn.ModuleList([]) for i in range(len(block_out_channels) - 1): channel_in = block_out_channels[i] channel_out = block_out_channels[i + 1] self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) self.conv_out = zero_module( nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) ) def forward(self, conditioning): embedding = self.conv_in(conditioning) embedding = F.silu(embedding) for block in self.blocks: embedding = block(embedding) embedding = F.silu(embedding) embedding = self.conv_out(embedding) return embedding class ControlNetModel(ModelMixin, ConfigMixin): _supports_gradient_checkpointing = True @register_to_config def __init__( self, in_channels: int = 4, flip_sin_to_cos: bool = True, freq_shift: int = 0, down_block_types: Tuple[str] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ), only_cross_attention: Union[bool, Tuple[bool]] = False, block_out_channels: Tuple[int] = (320, 640, 1280, 1280), layers_per_block: int = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = "silu", norm_num_groups: Optional[int] = 32, norm_eps: float = 1e-5, cross_attention_dim: int = 1280, attention_head_dim: Union[int, Tuple[int]] = 8, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", projection_class_embeddings_input_dim: Optional[int] = None, controlnet_conditioning_channel_order: str = "rgb", conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256), global_pool_conditions: bool = False, ): super().__init__() # Check inputs if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): raise ValueError( f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." ) if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." ) # input conv_in_kernel = 3 conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time time_embed_dim = block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, ) # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) elif class_embed_type == "projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" ) # The projection `class_embed_type` is the same as the timestep `class_embed_type` except # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings # 2. it projects from an arbitrary input dimension. # # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. # As a result, `TimestepEmbedding` can be passed arbitrary vectors. self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) else: self.class_embedding = None # control net conditioning embedding self.controlnet_cond_embedding = ControlNetConditioningEmbedding( conditioning_embedding_channels=block_out_channels[0], block_out_channels=conditioning_embedding_out_channels, ) self.down_blocks = nn.ModuleList([]) self.controlnet_down_blocks = nn.ModuleList([]) if isinstance(only_cross_attention, bool): only_cross_attention = [only_cross_attention] * len(down_block_types) if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) # down output_channel = block_out_channels[0] controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_down_blocks.append(controlnet_block) for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[i], downsample_padding=downsample_padding, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) self.down_blocks.append(down_block) for _ in range(layers_per_block): controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_down_blocks.append(controlnet_block) if not is_final_block: controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_down_blocks.append(controlnet_block) # mid mid_block_channel = block_out_channels[-1] controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_mid_block = controlnet_block self.mid_block = UNetMidBlock2DCrossAttn( in_channels=mid_block_channel, temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) @classmethod def from_unet( cls, unet: UNet2DConditionModel, controlnet_conditioning_channel_order: str = "rgb", conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256), load_weights_from_unet: bool = True, ): r""" Instantiate Controlnet class from UNet2DConditionModel. Parameters: unet (`UNet2DConditionModel`): UNet model which weights are copied to the ControlNet. Note that all configuration options are also copied where applicable. """ controlnet = cls( in_channels=unet.config.in_channels, flip_sin_to_cos=unet.config.flip_sin_to_cos, freq_shift=unet.config.freq_shift, down_block_types=unet.config.down_block_types, only_cross_attention=unet.config.only_cross_attention, block_out_channels=unet.config.block_out_channels, layers_per_block=unet.config.layers_per_block, downsample_padding=unet.config.downsample_padding, mid_block_scale_factor=unet.config.mid_block_scale_factor, act_fn=unet.config.act_fn, norm_num_groups=unet.config.norm_num_groups, norm_eps=unet.config.norm_eps, cross_attention_dim=unet.config.cross_attention_dim, attention_head_dim=unet.config.attention_head_dim, use_linear_projection=unet.config.use_linear_projection, class_embed_type=unet.config.class_embed_type, num_class_embeds=unet.config.num_class_embeds, upcast_attention=unet.config.upcast_attention, resnet_time_scale_shift=unet.config.resnet_time_scale_shift, projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, controlnet_conditioning_channel_order=controlnet_conditioning_channel_order, conditioning_embedding_out_channels=conditioning_embedding_out_channels, ) if load_weights_from_unet: controlnet.conv_in.load_state_dict(unet.conv_in.state_dict()) controlnet.time_proj.load_state_dict(unet.time_proj.state_dict()) controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) if controlnet.class_embedding: controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict()) controlnet.mid_block.load_state_dict(unet.mid_block.state_dict()) return controlnet @property # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.processor for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Parameters: `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor of **all** `Attention` layers. In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.: """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ self.set_attn_processor(AttnProcessor()) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_sliceable_layers * [1] slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): module.gradient_checkpointing = value def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, controlnet_cond: torch.FloatTensor, conditioning_scale: float = 1.0, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guess_mode: bool = False, return_dict: bool = True, ) -> Union[ControlNetOutput, Tuple]: # check channel order channel_order = self.config.controlnet_conditioning_channel_order if channel_order == "rgb": # in rgb order by default ... elif channel_order == "bgr": controlnet_cond = torch.flip(controlnet_cond, dims=[1]) else: raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}") # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, 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=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=sample.dtype) emb = self.time_embedding(t_emb, timestep_cond) if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) emb = emb + class_emb # 2. pre-process sample = self.conv_in(sample) controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) sample = sample + controlnet_cond # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples # 4. mid if self.mid_block is not None: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) # 5. Control net blocks controlnet_down_block_res_samples = () for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): down_block_res_sample = controlnet_block(down_block_res_sample) controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = controlnet_down_block_res_samples mid_block_res_sample = self.controlnet_mid_block(sample) # 6. scaling if guess_mode and not self.config.global_pool_conditions: scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0 scales = scales * conditioning_scale down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)] mid_block_res_sample = mid_block_res_sample * scales[-1] # last one else: down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples] mid_block_res_sample = mid_block_res_sample * conditioning_scale if self.config.global_pool_conditions: down_block_res_samples = [ torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples ] mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True) if not return_dict: return (down_block_res_samples, mid_block_res_sample) return ControlNetOutput( down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample ) def zero_module(module): for p in module.parameters(): nn.init.zeros_(p) return module ================================================ FILE: diffusers/models/controlnet_flax.py ================================================ # Copyright 2023 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 import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict from ..configuration_utils import ConfigMixin, flax_register_to_config from ..utils import BaseOutput from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps from .modeling_flax_utils import FlaxModelMixin from .unet_2d_blocks_flax import ( FlaxCrossAttnDownBlock2D, FlaxDownBlock2D, FlaxUNetMidBlock2DCrossAttn, ) @flax.struct.dataclass class FlaxControlNetOutput(BaseOutput): down_block_res_samples: jnp.ndarray mid_block_res_sample: jnp.ndarray class FlaxControlNetConditioningEmbedding(nn.Module): conditioning_embedding_channels: int block_out_channels: Tuple[int] = (16, 32, 96, 256) dtype: jnp.dtype = jnp.float32 def setup(self): self.conv_in = nn.Conv( self.block_out_channels[0], kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, ) blocks = [] for i in range(len(self.block_out_channels) - 1): channel_in = self.block_out_channels[i] channel_out = self.block_out_channels[i + 1] conv1 = nn.Conv( channel_in, kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, ) blocks.append(conv1) conv2 = nn.Conv( channel_out, kernel_size=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)), dtype=self.dtype, ) blocks.append(conv2) self.blocks = blocks self.conv_out = nn.Conv( self.conditioning_embedding_channels, kernel_size=(3, 3), padding=((1, 1), (1, 1)), kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, ) def __call__(self, conditioning): embedding = self.conv_in(conditioning) embedding = nn.silu(embedding) for block in self.blocks: embedding = block(embedding) embedding = nn.silu(embedding) embedding = self.conv_out(embedding) return embedding @flax_register_to_config class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin): r""" Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full model) to encode image-space conditions ... into feature maps ..." This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: sample_size (`int`, *optional*): The size of the input sample. in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D" block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8): The dimension of the attention heads. cross_attention_dim (`int`, *optional*, defaults to 768): The dimension of the cross attention features. dropout (`float`, *optional*, defaults to 0): Dropout probability for down, up and bottleneck blocks. flip_sin_to_cos (`bool`, *optional*, defaults to `True`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. controlnet_conditioning_channel_order (`str`, *optional*, defaults to `rgb`): The channel order of conditional image. Will convert it to `rgb` if it's `bgr` conditioning_embedding_out_channels (`tuple`, *optional*, defaults to `(16, 32, 96, 256)`): The tuple of output channel for each block in conditioning_embedding layer """ sample_size: int = 32 in_channels: int = 4 down_block_types: Tuple[str] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ) only_cross_attention: Union[bool, Tuple[bool]] = False block_out_channels: Tuple[int] = (320, 640, 1280, 1280) layers_per_block: int = 2 attention_head_dim: Union[int, Tuple[int]] = 8 cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: jnp.dtype = jnp.float32 flip_sin_to_cos: bool = True freq_shift: int = 0 controlnet_conditioning_channel_order: str = "rgb" conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256) def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict: # init input tensors sample_shape = (1, self.in_channels, self.sample_size, self.sample_size) sample = jnp.zeros(sample_shape, dtype=jnp.float32) timesteps = jnp.ones((1,), dtype=jnp.int32) encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32) controlnet_cond_shape = (1, 3, self.sample_size * 8, self.sample_size * 8) controlnet_cond = jnp.zeros(controlnet_cond_shape, dtype=jnp.float32) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} return self.init(rngs, sample, timesteps, encoder_hidden_states, controlnet_cond)["params"] def setup(self): block_out_channels = self.block_out_channels time_embed_dim = block_out_channels[0] * 4 # input self.conv_in = nn.Conv( block_out_channels[0], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) # time self.time_proj = FlaxTimesteps( block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift ) self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype) self.controlnet_cond_embedding = FlaxControlNetConditioningEmbedding( conditioning_embedding_channels=block_out_channels[0], block_out_channels=self.conditioning_embedding_out_channels, ) only_cross_attention = self.only_cross_attention if isinstance(only_cross_attention, bool): only_cross_attention = (only_cross_attention,) * len(self.down_block_types) attention_head_dim = self.attention_head_dim if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(self.down_block_types) # down down_blocks = [] controlnet_down_blocks = [] output_channel = block_out_channels[0] controlnet_block = nn.Conv( output_channel, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, ) controlnet_down_blocks.append(controlnet_block) for i, down_block_type in enumerate(self.down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 if down_block_type == "CrossAttnDownBlock2D": down_block = FlaxCrossAttnDownBlock2D( in_channels=input_channel, out_channels=output_channel, dropout=self.dropout, num_layers=self.layers_per_block, attn_num_head_channels=attention_head_dim[i], add_downsample=not is_final_block, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], dtype=self.dtype, ) else: down_block = FlaxDownBlock2D( in_channels=input_channel, out_channels=output_channel, dropout=self.dropout, num_layers=self.layers_per_block, add_downsample=not is_final_block, dtype=self.dtype, ) down_blocks.append(down_block) for _ in range(self.layers_per_block): controlnet_block = nn.Conv( output_channel, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, ) controlnet_down_blocks.append(controlnet_block) if not is_final_block: controlnet_block = nn.Conv( output_channel, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, ) controlnet_down_blocks.append(controlnet_block) self.down_blocks = down_blocks self.controlnet_down_blocks = controlnet_down_blocks # mid mid_block_channel = block_out_channels[-1] self.mid_block = FlaxUNetMidBlock2DCrossAttn( in_channels=mid_block_channel, dropout=self.dropout, attn_num_head_channels=attention_head_dim[-1], use_linear_projection=self.use_linear_projection, dtype=self.dtype, ) self.controlnet_mid_block = nn.Conv( mid_block_channel, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, ) def __call__( self, sample, timesteps, encoder_hidden_states, controlnet_cond, conditioning_scale: float = 1.0, return_dict: bool = True, train: bool = False, ) -> Union[FlaxControlNetOutput, Tuple]: r""" Args: sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor timestep (`jnp.ndarray` or `float` or `int`): timesteps encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states controlnet_cond (`jnp.ndarray`): (batch, channel, height, width) the conditional input tensor conditioning_scale: (`float`) the scale factor for controlnet outputs return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a plain tuple. train (`bool`, *optional*, defaults to `False`): Use deterministic functions and disable dropout when not training. Returns: [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`: [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ channel_order = self.controlnet_conditioning_channel_order if channel_order == "bgr": controlnet_cond = jnp.flip(controlnet_cond, axis=1) # 1. time if not isinstance(timesteps, jnp.ndarray): timesteps = jnp.array([timesteps], dtype=jnp.int32) elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0: timesteps = timesteps.astype(dtype=jnp.float32) timesteps = jnp.expand_dims(timesteps, 0) t_emb = self.time_proj(timesteps) t_emb = self.time_embedding(t_emb) # 2. pre-process sample = jnp.transpose(sample, (0, 2, 3, 1)) sample = self.conv_in(sample) controlnet_cond = jnp.transpose(controlnet_cond, (0, 2, 3, 1)) controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) sample += controlnet_cond # 3. down down_block_res_samples = (sample,) for down_block in self.down_blocks: if isinstance(down_block, FlaxCrossAttnDownBlock2D): sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train) else: sample, res_samples = down_block(sample, t_emb, deterministic=not train) down_block_res_samples += res_samples # 4. mid sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train) # 5. contronet blocks controlnet_down_block_res_samples = () for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): down_block_res_sample = controlnet_block(down_block_res_sample) controlnet_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = controlnet_down_block_res_samples mid_block_res_sample = self.controlnet_mid_block(sample) # 6. scaling down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples] mid_block_res_sample *= conditioning_scale if not return_dict: return (down_block_res_samples, mid_block_res_sample) return FlaxControlNetOutput( down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample ) ================================================ FILE: diffusers/models/cross_attention.py ================================================ # Copyright 2023 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 ..utils import deprecate from .attention_processor import ( # noqa: F401 Attention, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor2_0, LoRAAttnProcessor, LoRALinearLayer, LoRAXFormersAttnProcessor, SlicedAttnAddedKVProcessor, SlicedAttnProcessor, XFormersAttnProcessor, ) from .attention_processor import AttnProcessor as AttnProcessorRename # noqa: F401 deprecate( "cross_attention", "0.18.0", "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.", standard_warn=False, ) AttnProcessor = AttentionProcessor class CrossAttention(Attention): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class CrossAttnProcessor(AttnProcessorRename): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class LoRACrossAttnProcessor(LoRAAttnProcessor): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class CrossAttnAddedKVProcessor(AttnAddedKVProcessor): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class XFormersCrossAttnProcessor(XFormersAttnProcessor): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class LoRAXFormersCrossAttnProcessor(LoRAXFormersAttnProcessor): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class SlicedCrossAttnProcessor(SlicedAttnProcessor): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) class SlicedCrossAttnAddedKVProcessor(SlicedAttnAddedKVProcessor): def __init__(self, *args, **kwargs): deprecation_message = f"{self.__class__.__name__} is deprecated and will be removed in `0.18.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'))} instead." deprecate("cross_attention", "0.18.0", deprecation_message, standard_warn=False) super().__init__(*args, **kwargs) ================================================ FILE: diffusers/models/dual_transformer_2d.py ================================================ # Copyright 2023 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 from torch import nn from .transformer_2d import Transformer2DModel, Transformer2DModelOutput class DualTransformer2DModel(nn.Module): """ Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): Pass if the input is continuous. The number of channels in the input and output. num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.1): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use. sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images. Note that this is fixed at training time as it is used for learning a number of position embeddings. See `ImagePositionalEmbeddings`. num_vector_embeds (`int`, *optional*): Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`. The number of diffusion steps used during training. Note that this is fixed at training time as it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more than steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the TransformerBlocks' attention should contain a bias parameter. """ def __init__( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, num_vector_embeds: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, ): super().__init__() self.transformers = nn.ModuleList( [ Transformer2DModel( num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, in_channels=in_channels, num_layers=num_layers, dropout=dropout, norm_num_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attention_bias=attention_bias, sample_size=sample_size, num_vector_embeds=num_vector_embeds, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, ) for _ in range(2) ] ) # Variables that can be set by a pipeline: # The ratio of transformer1 to transformer2's output states to be combined during inference self.mix_ratio = 0.5 # The shape of `encoder_hidden_states` is expected to be # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)` self.condition_lengths = [77, 257] # Which transformer to use to encode which condition. # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])` self.transformer_index_for_condition = [1, 0] def forward( self, hidden_states, encoder_hidden_states, timestep=None, attention_mask=None, cross_attention_kwargs=None, return_dict: bool = True, ): """ Args: hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`. When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input hidden_states encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.long`, *optional*): Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step. attention_mask (`torch.FloatTensor`, *optional*): Optional attention mask to be applied in Attention return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: [`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`: [`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ input_states = hidden_states encoded_states = [] tokens_start = 0 # attention_mask is not used yet for i in range(2): # for each of the two transformers, pass the corresponding condition tokens condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]] transformer_index = self.transformer_index_for_condition[i] encoded_state = self.transformers[transformer_index]( input_states, encoder_hidden_states=condition_state, timestep=timestep, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] encoded_states.append(encoded_state - input_states) tokens_start += self.condition_lengths[i] output_states = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio) output_states = output_states + input_states if not return_dict: return (output_states,) return Transformer2DModelOutput(sample=output_states) ================================================ FILE: diffusers/models/embeddings.py ================================================ # Copyright 2023 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. import math from typing import Optional import numpy as np import torch from torch import nn 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. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param embedding_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. """ 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 def get_2d_sincos_pos_embed(embed_dim, grid_size, 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_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) 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): if embed_dim % 2 != 0: raise ValueError("embed_dim must be divisible by 2") # 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) """ if embed_dim % 2 != 0: raise ValueError("embed_dim must be divisible by 2") 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 class PatchEmbed(nn.Module): """2D Image to Patch Embedding""" def __init__( self, height=224, width=224, patch_size=16, in_channels=3, embed_dim=768, layer_norm=False, flatten=True, bias=True, ): super().__init__() num_patches = (height // patch_size) * (width // patch_size) self.flatten = flatten self.layer_norm = layer_norm self.proj = nn.Conv2d( in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias ) if layer_norm: self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6) else: self.norm = None pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5)) self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False) def forward(self, latent): latent = self.proj(latent) if self.flatten: latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC if self.layer_norm: latent = self.norm(latent) return latent + self.pos_embed 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, ): super().__init__() self.linear_1 = nn.Linear(in_channels, time_embed_dim) if cond_proj_dim is not None: self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False) else: self.cond_proj = None if act_fn == "silu": self.act = nn.SiLU() elif act_fn == "mish": self.act = nn.Mish() elif act_fn == "gelu": self.act = nn.GELU() else: raise ValueError(f"{act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'") if out_dim is not None: time_embed_dim_out = out_dim else: time_embed_dim_out = time_embed_dim self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out) if post_act_fn is None: self.post_act = None elif post_act_fn == "silu": self.post_act = nn.SiLU() elif post_act_fn == "mish": self.post_act = nn.Mish() elif post_act_fn == "gelu": self.post_act = nn.GELU() else: raise ValueError(f"{post_act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'") 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): super().__init__() self.num_channels = num_channels self.flip_sin_to_cos = flip_sin_to_cos self.downscale_freq_shift = downscale_freq_shift 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, ) return t_emb class GaussianFourierProjection(nn.Module): """Gaussian Fourier embeddings for noise levels.""" def __init__( self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False ): super().__init__() self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False) self.log = log self.flip_sin_to_cos = flip_sin_to_cos if set_W_to_weight: # to delete later self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False) self.weight = self.W def forward(self, x): if self.log: x = torch.log(x) x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi if self.flip_sin_to_cos: out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1) else: out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1) return out class ImagePositionalEmbeddings(nn.Module): """ Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the height and width of the latent space. For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092 For VQ-diffusion: Output vector embeddings are used as input for the transformer. Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE. Args: num_embed (`int`): Number of embeddings for the latent pixels embeddings. height (`int`): Height of the latent image i.e. the number of height embeddings. width (`int`): Width of the latent image i.e. the number of width embeddings. embed_dim (`int`): Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings. """ def __init__( self, num_embed: int, height: int, width: int, embed_dim: int, ): super().__init__() self.height = height self.width = width self.num_embed = num_embed self.embed_dim = embed_dim self.emb = nn.Embedding(self.num_embed, embed_dim) self.height_emb = nn.Embedding(self.height, embed_dim) self.width_emb = nn.Embedding(self.width, embed_dim) def forward(self, index): emb = self.emb(index) height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height)) # 1 x H x D -> 1 x H x 1 x D height_emb = height_emb.unsqueeze(2) width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width)) # 1 x W x D -> 1 x 1 x W x D width_emb = width_emb.unsqueeze(1) pos_emb = height_emb + width_emb # 1 x H x W x D -> 1 x L xD pos_emb = pos_emb.view(1, self.height * self.width, -1) emb = emb + pos_emb[:, : emb.shape[1], :] return emb class LabelEmbedding(nn.Module): """ Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. Args: num_classes (`int`): The number of classes. hidden_size (`int`): The size of the vector embeddings. dropout_prob (`float`): The probability of dropping a label. """ def __init__(self, num_classes, hidden_size, dropout_prob): super().__init__() use_cfg_embedding = dropout_prob > 0 self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size) 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], device=labels.device) < self.dropout_prob else: drop_ids = torch.tensor(force_drop_ids == 1) labels = torch.where(drop_ids, self.num_classes, labels) return labels def forward(self, labels, force_drop_ids=None): use_dropout = self.dropout_prob > 0 if (self.training 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 CombinedTimestepLabelEmbeddings(nn.Module): def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1): super().__init__() self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1) self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob) def forward(self, timestep, class_labels, hidden_dtype=None): timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) class_labels = self.class_embedder(class_labels) # (N, D) conditioning = timesteps_emb + class_labels # (N, D) return conditioning class TextTimeEmbedding(nn.Module): def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64): super().__init__() self.norm1 = nn.LayerNorm(encoder_dim) self.pool = AttentionPooling(num_heads, encoder_dim) self.proj = nn.Linear(encoder_dim, time_embed_dim) self.norm2 = nn.LayerNorm(time_embed_dim) def forward(self, hidden_states): hidden_states = self.norm1(hidden_states) hidden_states = self.pool(hidden_states) hidden_states = self.proj(hidden_states) hidden_states = self.norm2(hidden_states) return hidden_states class AttentionPooling(nn.Module): # Copied from https://github.com/deep-floyd/IF/blob/2f91391f27dd3c468bf174be5805b4cc92980c0b/deepfloyd_if/model/nn.py#L54 def __init__(self, num_heads, embed_dim, dtype=None): super().__init__() self.dtype = dtype self.positional_embedding = nn.Parameter(torch.randn(1, embed_dim) / embed_dim**0.5) self.k_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype) self.q_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype) self.v_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype) self.num_heads = num_heads self.dim_per_head = embed_dim // self.num_heads def forward(self, x): bs, length, width = x.size() def shape(x): # (bs, length, width) --> (bs, length, n_heads, dim_per_head) x = x.view(bs, -1, self.num_heads, self.dim_per_head) # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) x = x.transpose(1, 2) # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) x = x.reshape(bs * self.num_heads, -1, self.dim_per_head) # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length) x = x.transpose(1, 2) return x class_token = x.mean(dim=1, keepdim=True) + self.positional_embedding.to(x.dtype) x = torch.cat([class_token, x], dim=1) # (bs, length+1, width) # (bs*n_heads, class_token_length, dim_per_head) q = shape(self.q_proj(class_token)) # (bs*n_heads, length+class_token_length, dim_per_head) k = shape(self.k_proj(x)) v = shape(self.v_proj(x)) # (bs*n_heads, class_token_length, length+class_token_length): scale = 1 / math.sqrt(math.sqrt(self.dim_per_head)) weight = torch.einsum("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) # (bs*n_heads, dim_per_head, class_token_length) a = torch.einsum("bts,bcs->bct", weight, v) # (bs, length+1, width) a = a.reshape(bs, -1, 1).transpose(1, 2) return a[:, 0, :] # cls_token ================================================ FILE: diffusers/models/embeddings_flax.py ================================================ # Copyright 2023 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. import math import flax.linen as nn import jax.numpy as jnp def get_sinusoidal_embeddings( timesteps: jnp.ndarray, embedding_dim: int, freq_shift: float = 1, min_timescale: float = 1, max_timescale: float = 1.0e4, flip_sin_to_cos: bool = False, scale: float = 1.0, ) -> jnp.ndarray: """Returns the positional encoding (same as Tensor2Tensor). Args: timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. embedding_dim: The number of output channels. min_timescale: The smallest time unit (should probably be 0.0). max_timescale: The largest time unit. Returns: a Tensor of timing signals [N, num_channels] """ assert timesteps.ndim == 1, "Timesteps should be a 1d-array" assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even" num_timescales = float(embedding_dim // 2) log_timescale_increment = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift) inv_timescales = min_timescale * jnp.exp(jnp.arange(num_timescales, dtype=jnp.float32) * -log_timescale_increment) emb = jnp.expand_dims(timesteps, 1) * jnp.expand_dims(inv_timescales, 0) # scale embeddings scaled_time = scale * emb if flip_sin_to_cos: signal = jnp.concatenate([jnp.cos(scaled_time), jnp.sin(scaled_time)], axis=1) else: signal = jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1) signal = jnp.reshape(signal, [jnp.shape(timesteps)[0], embedding_dim]) return signal class FlaxTimestepEmbedding(nn.Module): r""" Time step Embedding Module. Learns embeddings for input time steps. Args: time_embed_dim (`int`, *optional*, defaults to `32`): Time step embedding dimension dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ time_embed_dim: int = 32 dtype: jnp.dtype = jnp.float32 @nn.compact def __call__(self, temb): temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_1")(temb) temb = nn.silu(temb) temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_2")(temb) return temb class FlaxTimesteps(nn.Module): r""" Wrapper Module for sinusoidal Time step Embeddings as described in https://arxiv.org/abs/2006.11239 Args: dim (`int`, *optional*, defaults to `32`): Time step embedding dimension """ dim: int = 32 flip_sin_to_cos: bool = False freq_shift: float = 1 @nn.compact def __call__(self, timesteps): return get_sinusoidal_embeddings( timesteps, embedding_dim=self.dim, flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.freq_shift ) ================================================ FILE: diffusers/models/modeling_flax_pytorch_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ PyTorch - Flax general utilities.""" import re import jax.numpy as jnp from flax.traverse_util import flatten_dict, unflatten_dict from jax.random import PRNGKey from ..utils import logging logger = logging.get_logger(__name__) def rename_key(key): regex = r"\w+[.]\d+" pats = re.findall(regex, key) for pat in pats: key = key.replace(pat, "_".join(pat.split("."))) return key ##################### # PyTorch => Flax # ##################### # Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69 # and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict): """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary""" # conv norm or layer norm renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",) if ( any("norm" in str_ for str_ in pt_tuple_key) and (pt_tuple_key[-1] == "bias") and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict) and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict) ): renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",) return renamed_pt_tuple_key, pt_tensor elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict: renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",) return renamed_pt_tuple_key, pt_tensor # embedding if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict: pt_tuple_key = pt_tuple_key[:-1] + ("embedding",) return renamed_pt_tuple_key, pt_tensor # conv layer renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",) if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4: pt_tensor = pt_tensor.transpose(2, 3, 1, 0) return renamed_pt_tuple_key, pt_tensor # linear layer renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",) if pt_tuple_key[-1] == "weight": pt_tensor = pt_tensor.T return renamed_pt_tuple_key, pt_tensor # old PyTorch layer norm weight renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",) if pt_tuple_key[-1] == "gamma": return renamed_pt_tuple_key, pt_tensor # old PyTorch layer norm bias renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",) if pt_tuple_key[-1] == "beta": return renamed_pt_tuple_key, pt_tensor return pt_tuple_key, pt_tensor def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model, init_key=42): # Step 1: Convert pytorch tensor to numpy pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()} # Step 2: Since the model is stateless, get random Flax params random_flax_params = flax_model.init_weights(PRNGKey(init_key)) random_flax_state_dict = flatten_dict(random_flax_params) flax_state_dict = {} # Need to change some parameters name to match Flax names for pt_key, pt_tensor in pt_state_dict.items(): renamed_pt_key = rename_key(pt_key) pt_tuple_key = tuple(renamed_pt_key.split(".")) # Correctly rename weight parameters flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict) if flax_key in random_flax_state_dict: if flax_tensor.shape != random_flax_state_dict[flax_key].shape: raise ValueError( f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape " f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." ) # also add unexpected weight so that warning is thrown flax_state_dict[flax_key] = jnp.asarray(flax_tensor) return unflatten_dict(flax_state_dict) ================================================ FILE: diffusers/models/modeling_flax_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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 os from pickle import UnpicklingError from typing import Any, Dict, Union import jax import jax.numpy as jnp import msgpack.exceptions from flax.core.frozen_dict import FrozenDict, unfreeze from flax.serialization import from_bytes, to_bytes from flax.traverse_util import flatten_dict, unflatten_dict from huggingface_hub import hf_hub_download from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError from requests import HTTPError from .. import __version__, is_torch_available from ..utils import ( CONFIG_NAME, DIFFUSERS_CACHE, FLAX_WEIGHTS_NAME, HUGGINGFACE_CO_RESOLVE_ENDPOINT, WEIGHTS_NAME, logging, ) from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax logger = logging.get_logger(__name__) class FlaxModelMixin: r""" Base class for all flax models. [`FlaxModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading and saving models. """ config_name = CONFIG_NAME _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"] _flax_internal_args = ["name", "parent", "dtype"] @classmethod def _from_config(cls, config, **kwargs): """ All context managers that the model should be initialized under go here. """ return cls(config, **kwargs) def _cast_floating_to(self, params: Union[Dict, FrozenDict], dtype: jnp.dtype, mask: Any = None) -> Any: """ Helper method to cast floating-point values of given parameter `PyTree` to given `dtype`. """ # taken from https://github.com/deepmind/jmp/blob/3a8318abc3292be38582794dbf7b094e6583b192/jmp/_src/policy.py#L27 def conditional_cast(param): if isinstance(param, jnp.ndarray) and jnp.issubdtype(param.dtype, jnp.floating): param = param.astype(dtype) return param if mask is None: return jax.tree_map(conditional_cast, params) flat_params = flatten_dict(params) flat_mask, _ = jax.tree_flatten(mask) for masked, key in zip(flat_mask, flat_params.keys()): if masked: param = flat_params[key] flat_params[key] = conditional_cast(param) return unflatten_dict(flat_params) def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None): r""" Cast the floating-point `params` to `jax.numpy.bfloat16`. This returns a new `params` tree and does not cast the `params` in place. This method can be used on TPU to explicitly convert the model parameters to bfloat16 precision to do full half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed. Arguments: params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters. mask (`Union[Dict, FrozenDict]`): A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params you want to cast, and should be `False` for those you want to skip. Examples: ```python >>> from diffusers import FlaxUNet2DConditionModel >>> # load model >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision >>> params = model.to_bf16(params) >>> # If you don't want to cast certain parameters (for example layer norm bias and scale) >>> # then pass the mask as follows >>> from flax import traverse_util >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") >>> flat_params = traverse_util.flatten_dict(params) >>> mask = { ... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale")) ... for path in flat_params ... } >>> mask = traverse_util.unflatten_dict(mask) >>> params = model.to_bf16(params, mask) ```""" return self._cast_floating_to(params, jnp.bfloat16, mask) def to_fp32(self, params: Union[Dict, FrozenDict], mask: Any = None): r""" Cast the floating-point `params` to `jax.numpy.float32`. This method can be used to explicitly convert the model parameters to fp32 precision. This returns a new `params` tree and does not cast the `params` in place. Arguments: params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters. mask (`Union[Dict, FrozenDict]`): A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params you want to cast, and should be `False` for those you want to skip Examples: ```python >>> from diffusers import FlaxUNet2DConditionModel >>> # Download model and configuration from huggingface.co >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") >>> # By default, the model params will be in fp32, to illustrate the use of this method, >>> # we'll first cast to fp16 and back to fp32 >>> params = model.to_f16(params) >>> # now cast back to fp32 >>> params = model.to_fp32(params) ```""" return self._cast_floating_to(params, jnp.float32, mask) def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None): r""" Cast the floating-point `params` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the `params` in place. This method can be used on GPU to explicitly convert the model parameters to float16 precision to do full half-precision training or to save weights in float16 for inference in order to save memory and improve speed. Arguments: params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters. mask (`Union[Dict, FrozenDict]`): A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params you want to cast, and should be `False` for those you want to skip Examples: ```python >>> from diffusers import FlaxUNet2DConditionModel >>> # load model >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") >>> # By default, the model params will be in fp32, to cast these to float16 >>> params = model.to_fp16(params) >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale) >>> # then pass the mask as follows >>> from flax import traverse_util >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") >>> flat_params = traverse_util.flatten_dict(params) >>> mask = { ... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale")) ... for path in flat_params ... } >>> mask = traverse_util.unflatten_dict(mask) >>> params = model.to_fp16(params, mask) ```""" return self._cast_floating_to(params, jnp.float16, mask) def init_weights(self, rng: jax.random.KeyArray) -> Dict: raise NotImplementedError(f"init_weights method has to be implemented for {self}") @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], dtype: jnp.dtype = jnp.float32, *model_args, **kwargs, ): r""" Instantiate a pretrained flax model from a pre-trained model configuration. The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task. The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those weights are discarded. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids are namespaced under a user or organization name, like `runwayml/stable-diffusion-v1-5`. - A path to a *directory* containing model weights saved using [`~ModelMixin.save_pretrained`], e.g., `./my_model_directory/`. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~ModelMixin.to_fp16`] and [`~ModelMixin.to_bf16`]. model_args (sequence of positional arguments, *optional*): All remaining positional arguments will be passed to the underlying model's `__init__` method. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. from_pt (`bool`, *optional*, defaults to `False`): Load the model weights from a PyTorch checkpoint save file. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded: - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model's `__init__` method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function ([`~ConfigMixin.from_config`]). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's `__init__` function. Examples: ```python >>> from diffusers import FlaxUNet2DConditionModel >>> # Download model and configuration from huggingface.co and cache. >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable). >>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/") ```""" config = kwargs.pop("config", None) cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) from_pt = kwargs.pop("from_pt", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", None) user_agent = { "diffusers": __version__, "file_type": "model", "framework": "flax", } # Load config if we don't provide a configuration config_path = config if config is not None else pretrained_model_name_or_path model, model_kwargs = cls.from_config( config_path, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, # model args dtype=dtype, **kwargs, ) # Load model pretrained_path_with_subfolder = ( pretrained_model_name_or_path if subfolder is None else os.path.join(pretrained_model_name_or_path, subfolder) ) if os.path.isdir(pretrained_path_with_subfolder): if from_pt: if not os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)): raise EnvironmentError( f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_path_with_subfolder} " ) model_file = os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME) elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)): # Load from a Flax checkpoint model_file = os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME) # Check if pytorch weights exist instead elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)): raise EnvironmentError( f"{WEIGHTS_NAME} file found in directory {pretrained_path_with_subfolder}. Please load the model" " using `from_pt=True`." ) else: raise EnvironmentError( f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory " f"{pretrained_path_with_subfolder}." ) else: try: model_file = hf_hub_download( pretrained_model_name_or_path, filename=FLAX_WEIGHTS_NAME if not from_pt else WEIGHTS_NAME, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, subfolder=subfolder, revision=revision, ) except RepositoryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier " "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a " "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli " "login`." ) except RevisionNotFoundError: raise EnvironmentError( f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for " "this model name. Check the model page at " f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." ) except EntryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} does not appear to have a file named {FLAX_WEIGHTS_NAME}." ) except HTTPError as err: raise EnvironmentError( f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n" f"{err}" ) except ValueError: raise EnvironmentError( f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it" f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a" f" directory containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}.\nCheckout your" " internet connection or see how to run the library in offline mode at" " 'https://huggingface.co/docs/transformers/installation#offline-mode'." ) except EnvironmentError: raise EnvironmentError( f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from " "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " f"containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}." ) if from_pt: if is_torch_available(): from .modeling_utils import load_state_dict else: raise EnvironmentError( "Can't load the model in PyTorch format because PyTorch is not installed. " "Please, install PyTorch or use native Flax weights." ) # Step 1: Get the pytorch file pytorch_model_file = load_state_dict(model_file) # Step 2: Convert the weights state = convert_pytorch_state_dict_to_flax(pytorch_model_file, model) else: try: with open(model_file, "rb") as state_f: state = from_bytes(cls, state_f.read()) except (UnpicklingError, msgpack.exceptions.ExtraData) as e: try: with open(model_file) as f: if f.read().startswith("version"): raise OSError( "You seem to have cloned a repository without having git-lfs installed. Please" " install git-lfs and run `git lfs install` followed by `git lfs pull` in the" " folder you cloned." ) else: raise ValueError from e except (UnicodeDecodeError, ValueError): raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ") # make sure all arrays are stored as jnp.ndarray # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4: # https://github.com/google/flax/issues/1261 state = jax.tree_util.tree_map(lambda x: jax.device_put(x, jax.devices("cpu")[0]), state) # flatten dicts state = flatten_dict(state) params_shape_tree = jax.eval_shape(model.init_weights, rng=jax.random.PRNGKey(0)) required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys()) shape_state = flatten_dict(unfreeze(params_shape_tree)) missing_keys = required_params - set(state.keys()) unexpected_keys = set(state.keys()) - required_params if missing_keys: logger.warning( f"The checkpoint {pretrained_model_name_or_path} is missing required keys: {missing_keys}. " "Make sure to call model.init_weights to initialize the missing weights." ) cls._missing_keys = missing_keys for key in state.keys(): if key in shape_state and state[key].shape != shape_state[key].shape: raise ValueError( f"Trying to load the pretrained weight for {key} failed: checkpoint has shape " f"{state[key].shape} which is incompatible with the model shape {shape_state[key].shape}. " ) # remove unexpected keys to not be saved again for unexpected_key in unexpected_keys: del state[unexpected_key] if len(unexpected_keys) > 0: logger.warning( f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when" f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are" f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or" " with another architecture." ) else: logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n") if len(missing_keys) > 0: logger.warning( f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably" " TRAIN this model on a down-stream task to be able to use it for predictions and inference." ) else: logger.info( f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at" f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint" f" was trained on, you can already use {model.__class__.__name__} for predictions without further" " training." ) return model, unflatten_dict(state) def save_pretrained( self, save_directory: Union[str, os.PathLike], params: Union[Dict, FrozenDict], is_main_process: bool = True, ): """ Save a model and its configuration file to a directory, so that it can be re-loaded using the `[`~FlaxModelMixin.from_pretrained`]` class method Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters. is_main_process (`bool`, *optional*, defaults to `True`): Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions. """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) model_to_save = self # Attach architecture to the config # Save the config if is_main_process: model_to_save.save_config(save_directory) # save model output_model_file = os.path.join(save_directory, FLAX_WEIGHTS_NAME) with open(output_model_file, "wb") as f: model_bytes = to_bytes(params) f.write(model_bytes) logger.info(f"Model weights saved in {output_model_file}") ================================================ FILE: diffusers/models/modeling_pytorch_flax_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ PyTorch - Flax general utilities.""" from pickle import UnpicklingError import jax import jax.numpy as jnp import numpy as np from flax.serialization import from_bytes from flax.traverse_util import flatten_dict from ..utils import logging logger = logging.get_logger(__name__) ##################### # Flax => PyTorch # ##################### # from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_flax_pytorch_utils.py#L224-L352 def load_flax_checkpoint_in_pytorch_model(pt_model, model_file): try: with open(model_file, "rb") as flax_state_f: flax_state = from_bytes(None, flax_state_f.read()) except UnpicklingError as e: try: with open(model_file) as f: if f.read().startswith("version"): raise OSError( "You seem to have cloned a repository without having git-lfs installed. Please" " install git-lfs and run `git lfs install` followed by `git lfs pull` in the" " folder you cloned." ) else: raise ValueError from e except (UnicodeDecodeError, ValueError): raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ") return load_flax_weights_in_pytorch_model(pt_model, flax_state) def load_flax_weights_in_pytorch_model(pt_model, flax_state): """Load flax checkpoints in a PyTorch model""" try: import torch # noqa: F401 except ImportError: logger.error( "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see" " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation" " instructions." ) raise # check if we have bf16 weights is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values() if any(is_type_bf16): # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16 # and bf16 is not fully supported in PT yet. logger.warning( "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` " "before loading those in PyTorch model." ) flax_state = jax.tree_util.tree_map( lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state ) pt_model.base_model_prefix = "" flax_state_dict = flatten_dict(flax_state, sep=".") pt_model_dict = pt_model.state_dict() # keep track of unexpected & missing keys unexpected_keys = [] missing_keys = set(pt_model_dict.keys()) for flax_key_tuple, flax_tensor in flax_state_dict.items(): flax_key_tuple_array = flax_key_tuple.split(".") if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4: flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"] flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1)) elif flax_key_tuple_array[-1] == "kernel": flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"] flax_tensor = flax_tensor.T elif flax_key_tuple_array[-1] == "scale": flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"] if "time_embedding" not in flax_key_tuple_array: for i, flax_key_tuple_string in enumerate(flax_key_tuple_array): flax_key_tuple_array[i] = ( flax_key_tuple_string.replace("_0", ".0") .replace("_1", ".1") .replace("_2", ".2") .replace("_3", ".3") .replace("_4", ".4") .replace("_5", ".5") .replace("_6", ".6") .replace("_7", ".7") .replace("_8", ".8") .replace("_9", ".9") ) flax_key = ".".join(flax_key_tuple_array) if flax_key in pt_model_dict: if flax_tensor.shape != pt_model_dict[flax_key].shape: raise ValueError( f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected " f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." ) else: # add weight to pytorch dict flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor pt_model_dict[flax_key] = torch.from_numpy(flax_tensor) # remove from missing keys missing_keys.remove(flax_key) else: # weight is not expected by PyTorch model unexpected_keys.append(flax_key) pt_model.load_state_dict(pt_model_dict) # re-transform missing_keys to list missing_keys = list(missing_keys) if len(unexpected_keys) > 0: logger.warning( "Some weights of the Flax model were not used when initializing the PyTorch model" f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing" f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture" " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This" f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect" " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a" " FlaxBertForSequenceClassification model)." ) if len(missing_keys) > 0: logger.warning( f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly" f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to" " use it for predictions and inference." ) return pt_model ================================================ FILE: diffusers/models/modeling_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. 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. import inspect import itertools import os from functools import partial from typing import Any, Callable, List, Optional, Tuple, Union import torch from torch import Tensor, device from .. import __version__ from ..utils import ( CONFIG_NAME, DIFFUSERS_CACHE, FLAX_WEIGHTS_NAME, HF_HUB_OFFLINE, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, _add_variant, _get_model_file, deprecate, is_accelerate_available, is_safetensors_available, is_torch_version, logging, ) logger = logging.get_logger(__name__) if is_torch_version(">=", "1.9.0"): _LOW_CPU_MEM_USAGE_DEFAULT = True else: _LOW_CPU_MEM_USAGE_DEFAULT = False if is_accelerate_available(): import accelerate from accelerate.utils import set_module_tensor_to_device from accelerate.utils.versions import is_torch_version if is_safetensors_available(): import safetensors def get_parameter_device(parameter: torch.nn.Module): try: parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers()) return next(parameters_and_buffers).device except StopIteration: # For torch.nn.DataParallel compatibility in PyTorch 1.5 def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]: tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] return tuples gen = parameter._named_members(get_members_fn=find_tensor_attributes) first_tuple = next(gen) return first_tuple[1].device def get_parameter_dtype(parameter: torch.nn.Module): try: params = tuple(parameter.parameters()) if len(params) > 0: return params[0].dtype buffers = tuple(parameter.buffers()) if len(buffers) > 0: return buffers[0].dtype except StopIteration: # For torch.nn.DataParallel compatibility in PyTorch 1.5 def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]: tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] return tuples gen = parameter._named_members(get_members_fn=find_tensor_attributes) first_tuple = next(gen) return first_tuple[1].dtype def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None): """ Reads a checkpoint file, returning properly formatted errors if they arise. """ try: if os.path.basename(checkpoint_file) == _add_variant(WEIGHTS_NAME, variant): return torch.load(checkpoint_file, map_location="cpu") else: return safetensors.torch.load_file(checkpoint_file, device="cpu") except Exception as e: try: with open(checkpoint_file) as f: if f.read().startswith("version"): raise OSError( "You seem to have cloned a repository without having git-lfs installed. Please install " "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder " "you cloned." ) else: raise ValueError( f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained " "model. Make sure you have saved the model properly." ) from e except (UnicodeDecodeError, ValueError): raise OSError( f"Unable to load weights from checkpoint file for '{checkpoint_file}' " f"at '{checkpoint_file}'. " "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True." ) def _load_state_dict_into_model(model_to_load, state_dict): # Convert old format to new format if needed from a PyTorch state_dict # copy state_dict so _load_from_state_dict can modify it state_dict = state_dict.copy() error_msgs = [] # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants # so we need to apply the function recursively. def load(module: torch.nn.Module, prefix=""): args = (state_dict, prefix, {}, True, [], [], error_msgs) module._load_from_state_dict(*args) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + ".") load(model_to_load) return error_msgs class ModelMixin(torch.nn.Module): r""" Base class for all models. [`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading and saving models. - **config_name** ([`str`]) -- A filename under which the model should be stored when calling [`~models.ModelMixin.save_pretrained`]. """ config_name = CONFIG_NAME _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"] _supports_gradient_checkpointing = False def __init__(self): super().__init__() def __getattr__(self, name: str) -> Any: """The only reason we overwrite `getattr` here is to gracefully deprecate accessing config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite __getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__': https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module """ is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name) is_attribute = name in self.__dict__ if is_in_config and not is_attribute: deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'." deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3) return self._internal_dict[name] # call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module return super().__getattr__(name) @property def is_gradient_checkpointing(self) -> bool: """ Whether gradient checkpointing is activated for this model or not. Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint activations". """ return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules()) def enable_gradient_checkpointing(self): """ Activates gradient checkpointing for the current model. Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint activations". """ if not self._supports_gradient_checkpointing: raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.") self.apply(partial(self._set_gradient_checkpointing, value=True)) def disable_gradient_checkpointing(self): """ Deactivates gradient checkpointing for the current model. Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint activations". """ if self._supports_gradient_checkpointing: self.apply(partial(self._set_gradient_checkpointing, value=False)) def set_use_memory_efficient_attention_xformers( self, valid: bool, attention_op: Optional[Callable] = None ) -> None: # Recursively walk through all the children. # Any children which exposes the set_use_memory_efficient_attention_xformers method # gets the message def fn_recursive_set_mem_eff(module: torch.nn.Module): if hasattr(module, "set_use_memory_efficient_attention_xformers"): module.set_use_memory_efficient_attention_xformers(valid, attention_op) for child in module.children(): fn_recursive_set_mem_eff(child) for module in self.children(): if isinstance(module, torch.nn.Module): fn_recursive_set_mem_eff(module) def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None): r""" Enable memory efficient attention as implemented in xformers. When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed. Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used. Parameters: attention_op (`Callable`, *optional*): Override the default `None` operator for use as `op` argument to the [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention) function of xFormers. Examples: ```py >>> import torch >>> from diffusers import UNet2DConditionModel >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp >>> model = UNet2DConditionModel.from_pretrained( ... "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16 ... ) >>> model = model.to("cuda") >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) ``` """ self.set_use_memory_efficient_attention_xformers(True, attention_op) def disable_xformers_memory_efficient_attention(self): r""" Disable memory efficient attention as implemented in xformers. """ self.set_use_memory_efficient_attention_xformers(False) def save_pretrained( self, save_directory: Union[str, os.PathLike], is_main_process: bool = True, save_function: Callable = None, safe_serialization: bool = False, variant: Optional[str] = None, ): """ Save a model and its configuration file to a directory, so that it can be re-loaded using the `[`~models.ModelMixin.from_pretrained`]` class method. Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. is_main_process (`bool`, *optional*, defaults to `True`): Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions. save_function (`Callable`): The function to use to save the state dictionary. Useful on distributed training like TPUs when one need to replace `torch.save` by another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`. safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). variant (`str`, *optional*): If specified, weights are saved in the format pytorch_model..bin. """ if safe_serialization and not is_safetensors_available(): raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.") if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) model_to_save = self # Attach architecture to the config # Save the config if is_main_process: model_to_save.save_config(save_directory) # Save the model state_dict = model_to_save.state_dict() weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME weights_name = _add_variant(weights_name, variant) # Save the model if safe_serialization: safetensors.torch.save_file( state_dict, os.path.join(save_directory, weights_name), metadata={"format": "pt"} ) else: torch.save(state_dict, os.path.join(save_directory, weights_name)) logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}") @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): r""" Instantiate a pretrained pytorch model from a pre-trained model configuration. The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`. The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task. The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those weights are discarded. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g., `./my_model_directory/`. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. torch_dtype (`str` or `torch.dtype`, *optional*): Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype will be automatically derived from the model's weights. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. from_flax (`bool`, *optional*, defaults to `False`): Load the model weights from a Flax checkpoint save file. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading by not initializing the weights and only loading the pre-trained weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch, setting this argument to `True` will raise an error. variant (`str`, *optional*): If specified load weights from `variant` filename, *e.g.* pytorch_model..bin. `variant` is ignored when using `from_flax`. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the `safetensors` library is installed. If set to `True`, the model will be forcibly loaded from `safetensors` weights. If set to `False`, loading will *not* use `safetensors`. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a firewalled environment. """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) force_download = kwargs.pop("force_download", False) from_flax = kwargs.pop("from_flax", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) output_loading_info = kwargs.pop("output_loading_info", False) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) torch_dtype = kwargs.pop("torch_dtype", None) subfolder = kwargs.pop("subfolder", None) device_map = kwargs.pop("device_map", None) low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) variant = kwargs.pop("variant", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True if low_cpu_mem_usage and not is_accelerate_available(): low_cpu_mem_usage = False logger.warning( "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" " install accelerate\n```\n." ) if device_map is not None and not is_accelerate_available(): raise NotImplementedError( "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" " `device_map=None`. You can install accelerate with `pip install accelerate`." ) # Check if we can handle device_map and dispatching the weights if device_map is not None and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" " `device_map=None`." ) if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" " `low_cpu_mem_usage=False`." ) if low_cpu_mem_usage is False and device_map is not None: raise ValueError( f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and" " dispatching. Please make sure to set `low_cpu_mem_usage=True`." ) # Load config if we don't provide a configuration config_path = pretrained_model_name_or_path user_agent = { "diffusers": __version__, "file_type": "model", "framework": "pytorch", } # load config config, unused_kwargs, commit_hash = cls.load_config( config_path, cache_dir=cache_dir, return_unused_kwargs=True, return_commit_hash=True, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, device_map=device_map, user_agent=user_agent, **kwargs, ) # load model model_file = None if from_flax: model_file = _get_model_file( pretrained_model_name_or_path, weights_name=FLAX_WEIGHTS_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, commit_hash=commit_hash, ) model = cls.from_config(config, **unused_kwargs) # Convert the weights from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model model = load_flax_checkpoint_in_pytorch_model(model, model_file) else: if use_safetensors: try: model_file = _get_model_file( pretrained_model_name_or_path, weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, commit_hash=commit_hash, ) except IOError as e: if not allow_pickle: raise e pass if model_file is None: model_file = _get_model_file( pretrained_model_name_or_path, weights_name=_add_variant(WEIGHTS_NAME, variant), cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, commit_hash=commit_hash, ) if low_cpu_mem_usage: # Instantiate model with empty weights with accelerate.init_empty_weights(): model = cls.from_config(config, **unused_kwargs) # if device_map is None, load the state dict and move the params from meta device to the cpu if device_map is None: param_device = "cpu" state_dict = load_state_dict(model_file, variant=variant) model._convert_deprecated_attention_blocks(state_dict) # move the params from meta device to cpu missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) if len(missing_keys) > 0: raise ValueError( f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are" f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass" " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize" " those weights or else make sure your checkpoint file is correct." ) empty_state_dict = model.state_dict() for param_name, param in state_dict.items(): accepts_dtype = "dtype" in set( inspect.signature(set_module_tensor_to_device).parameters.keys() ) if empty_state_dict[param_name].shape != param.shape: raise ValueError( f"Cannot load {pretrained_model_name_or_path} because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example." ) if accepts_dtype: set_module_tensor_to_device( model, param_name, param_device, value=param, dtype=torch_dtype ) else: set_module_tensor_to_device(model, param_name, param_device, value=param) else: # else let accelerate handle loading and dispatching. # Load weights and dispatch according to the device_map # by default the device_map is None and the weights are loaded on the CPU accelerate.load_checkpoint_and_dispatch(model, model_file, device_map, dtype=torch_dtype) loading_info = { "missing_keys": [], "unexpected_keys": [], "mismatched_keys": [], "error_msgs": [], } else: model = cls.from_config(config, **unused_kwargs) state_dict = load_state_dict(model_file, variant=variant) model._convert_deprecated_attention_blocks(state_dict) model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( model, state_dict, model_file, pretrained_model_name_or_path, ignore_mismatched_sizes=ignore_mismatched_sizes, ) loading_info = { "missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "mismatched_keys": mismatched_keys, "error_msgs": error_msgs, } if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): raise ValueError( f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." ) elif torch_dtype is not None: model = model.to(torch_dtype) model.register_to_config(_name_or_path=pretrained_model_name_or_path) # Set model in evaluation mode to deactivate DropOut modules by default model.eval() if output_loading_info: return model, loading_info return model @classmethod def _load_pretrained_model( cls, model, state_dict, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes=False, ): # Retrieve missing & unexpected_keys model_state_dict = model.state_dict() loaded_keys = list(state_dict.keys()) expected_keys = list(model_state_dict.keys()) original_loaded_keys = loaded_keys missing_keys = list(set(expected_keys) - set(loaded_keys)) unexpected_keys = list(set(loaded_keys) - set(expected_keys)) # Make sure we are able to load base models as well as derived models (with heads) model_to_load = model def _find_mismatched_keys( state_dict, model_state_dict, loaded_keys, ignore_mismatched_sizes, ): mismatched_keys = [] if ignore_mismatched_sizes: for checkpoint_key in loaded_keys: model_key = checkpoint_key if ( model_key in model_state_dict and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape ): mismatched_keys.append( (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) ) del state_dict[checkpoint_key] return mismatched_keys if state_dict is not None: # Whole checkpoint mismatched_keys = _find_mismatched_keys( state_dict, model_state_dict, original_loaded_keys, ignore_mismatched_sizes, ) error_msgs = _load_state_dict_into_model(model_to_load, state_dict) if len(error_msgs) > 0: error_msg = "\n\t".join(error_msgs) if "size mismatch" in error_msg: error_msg += ( "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method." ) raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}") if len(unexpected_keys) > 0: logger.warning( f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when" f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are" f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task" " or with another architecture (e.g. initializing a BertForSequenceClassification model from a" " BertForPreTraining model).\n- This IS NOT expected if you are initializing" f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly" " identical (initializing a BertForSequenceClassification model from a" " BertForSequenceClassification model)." ) else: logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n") if len(missing_keys) > 0: logger.warning( f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably" " TRAIN this model on a down-stream task to be able to use it for predictions and inference." ) elif len(mismatched_keys) == 0: logger.info( f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at" f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the" f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions" " without further training." ) if len(mismatched_keys) > 0: mismatched_warning = "\n".join( [ f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" for key, shape1, shape2 in mismatched_keys ] ) logger.warning( f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not" f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be" " able to use it for predictions and inference." ) return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs @property def device(self) -> device: """ `torch.device`: The device on which the module is (assuming that all the module parameters are on the same device). """ return get_parameter_device(self) @property def dtype(self) -> torch.dtype: """ `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype). """ return get_parameter_dtype(self) def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int: """ Get number of (optionally, trainable or non-embeddings) parameters in the module. Args: only_trainable (`bool`, *optional*, defaults to `False`): Whether or not to return only the number of trainable parameters exclude_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to return only the number of non-embeddings parameters Returns: `int`: The number of parameters. """ if exclude_embeddings: embedding_param_names = [ f"{name}.weight" for name, module_type in self.named_modules() if isinstance(module_type, torch.nn.Embedding) ] non_embedding_parameters = [ parameter for name, parameter in self.named_parameters() if name not in embedding_param_names ] return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable) else: return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable) def _convert_deprecated_attention_blocks(self, state_dict): deprecated_attention_block_paths = [] def recursive_find_attn_block(name, module): if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block: deprecated_attention_block_paths.append(name) for sub_name, sub_module in module.named_children(): sub_name = sub_name if name == "" else f"{name}.{sub_name}" recursive_find_attn_block(sub_name, sub_module) recursive_find_attn_block("", self) # NOTE: we have to check if the deprecated parameters are in the state dict # because it is possible we are loading from a state dict that was already # converted for path in deprecated_attention_block_paths: # group_norm path stays the same # query -> to_q if f"{path}.query.weight" in state_dict: state_dict[f"{path}.to_q.weight"] = state_dict.pop(f"{path}.query.weight") if f"{path}.query.bias" in state_dict: state_dict[f"{path}.to_q.bias"] = state_dict.pop(f"{path}.query.bias") # key -> to_k if f"{path}.key.weight" in state_dict: state_dict[f"{path}.to_k.weight"] = state_dict.pop(f"{path}.key.weight") if f"{path}.key.bias" in state_dict: state_dict[f"{path}.to_k.bias"] = state_dict.pop(f"{path}.key.bias") # value -> to_v if f"{path}.value.weight" in state_dict: state_dict[f"{path}.to_v.weight"] = state_dict.pop(f"{path}.value.weight") if f"{path}.value.bias" in state_dict: state_dict[f"{path}.to_v.bias"] = state_dict.pop(f"{path}.value.bias") # proj_attn -> to_out.0 if f"{path}.proj_attn.weight" in state_dict: state_dict[f"{path}.to_out.0.weight"] = state_dict.pop(f"{path}.proj_attn.weight") if f"{path}.proj_attn.bias" in state_dict: state_dict[f"{path}.to_out.0.bias"] = state_dict.pop(f"{path}.proj_attn.bias") ================================================ FILE: diffusers/models/prior_transformer.py ================================================ from dataclasses import dataclass from typing import Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin @dataclass class PriorTransformerOutput(BaseOutput): """ Args: predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`): The predicted CLIP image embedding conditioned on the CLIP text embedding input. """ predicted_image_embedding: torch.FloatTensor class PriorTransformer(ModelMixin, ConfigMixin): """ The prior transformer from unCLIP is used to predict CLIP image embeddings from CLIP text embeddings. Note that the transformer predicts the image embeddings through a denoising diffusion process. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) For more details, see the original paper: https://arxiv.org/abs/2204.06125 Parameters: num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head. num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use. embedding_dim (`int`, *optional*, defaults to 768): The dimension of the CLIP embeddings. Note that CLIP image embeddings and text embeddings are both the same dimension. num_embeddings (`int`, *optional*, defaults to 77): The max number of clip embeddings allowed. I.e. the length of the prompt after it has been tokenized. additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the projected hidden_states. The actual length of the used hidden_states is `num_embeddings + additional_embeddings`. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. """ @register_to_config def __init__( self, num_attention_heads: int = 32, attention_head_dim: int = 64, num_layers: int = 20, embedding_dim: int = 768, num_embeddings=77, additional_embeddings=4, dropout: float = 0.0, ): super().__init__() self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.additional_embeddings = additional_embeddings self.time_proj = Timesteps(inner_dim, True, 0) self.time_embedding = TimestepEmbedding(inner_dim, inner_dim) self.proj_in = nn.Linear(embedding_dim, inner_dim) self.embedding_proj = nn.Linear(embedding_dim, inner_dim) self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim) self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim)) self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim)) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, activation_fn="gelu", attention_bias=True, ) for d in range(num_layers) ] ) self.norm_out = nn.LayerNorm(inner_dim) self.proj_to_clip_embeddings = nn.Linear(inner_dim, embedding_dim) causal_attention_mask = torch.full( [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0 ) causal_attention_mask.triu_(1) causal_attention_mask = causal_attention_mask[None, ...] self.register_buffer("causal_attention_mask", causal_attention_mask, persistent=False) self.clip_mean = nn.Parameter(torch.zeros(1, embedding_dim)) self.clip_std = nn.Parameter(torch.zeros(1, embedding_dim)) def forward( self, hidden_states, timestep: Union[torch.Tensor, float, int], proj_embedding: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor, attention_mask: Optional[torch.BoolTensor] = None, return_dict: bool = True, ): """ Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`): x_t, the currently predicted image embeddings. timestep (`torch.long`): Current denoising step. proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`): Projected embedding vector the denoising process is conditioned on. encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`): Hidden states of the text embeddings the denoising process is conditioned on. attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`): Text mask for the text embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.prior_transformer.PriorTransformerOutput`] instead of a plain tuple. Returns: [`~models.prior_transformer.PriorTransformerOutput`] or `tuple`: [`~models.prior_transformer.PriorTransformerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ batch_size = hidden_states.shape[0] timesteps = timestep if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(hidden_states.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device) timesteps_projected = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might be fp16, so we need to cast here. timesteps_projected = timesteps_projected.to(dtype=self.dtype) time_embeddings = self.time_embedding(timesteps_projected) proj_embeddings = self.embedding_proj(proj_embedding) encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states) hidden_states = self.proj_in(hidden_states) prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1) positional_embeddings = self.positional_embedding.to(hidden_states.dtype) hidden_states = torch.cat( [ encoder_hidden_states, proj_embeddings[:, None, :], time_embeddings[:, None, :], hidden_states[:, None, :], prd_embedding, ], dim=1, ) hidden_states = hidden_states + positional_embeddings if attention_mask is not None: attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0) attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype) attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0) for block in self.transformer_blocks: hidden_states = block(hidden_states, attention_mask=attention_mask) hidden_states = self.norm_out(hidden_states) hidden_states = hidden_states[:, -1] predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states) if not return_dict: return (predicted_image_embedding,) return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding) def post_process_latents(self, prior_latents): prior_latents = (prior_latents * self.clip_std) + self.clip_mean return prior_latents ================================================ FILE: diffusers/models/resnet.py ================================================ # Copyright 2023 The HuggingFace Team. All rights reserved. # `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and 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 functools import partial from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F from .attention import AdaGroupNorm class Upsample1D(nn.Module): """A 1D upsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. use_conv_transpose (`bool`, default `False`): option to use a convolution transpose. out_channels (`int`, optional): number of output channels. Defaults to `channels`. """ def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.use_conv_transpose = use_conv_transpose self.name = name self.conv = None if use_conv_transpose: self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1) elif use_conv: self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1) def forward(self, x): assert x.shape[1] == self.channels if self.use_conv_transpose: return self.conv(x) x = F.interpolate(x, scale_factor=2.0, mode="nearest") if self.use_conv: x = self.conv(x) return x class Downsample1D(nn.Module): """A 1D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. padding (`int`, default `1`): padding for the convolution. """ def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.padding = padding stride = 2 self.name = name if use_conv: self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding) else: assert self.channels == self.out_channels self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.conv(x) class Upsample2D(nn.Module): """A 2D upsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. use_conv_transpose (`bool`, default `False`): option to use a convolution transpose. out_channels (`int`, optional): number of output channels. Defaults to `channels`. """ def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.use_conv_transpose = use_conv_transpose self.name = name conv = None if use_conv_transpose: conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1) elif use_conv: conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed if name == "conv": self.conv = conv else: self.Conv2d_0 = conv def forward(self, hidden_states, output_size=None): assert hidden_states.shape[1] == self.channels if self.use_conv_transpose: return self.conv(hidden_states) # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch # https://github.com/pytorch/pytorch/issues/86679 dtype = hidden_states.dtype if dtype == torch.bfloat16: hidden_states = hidden_states.to(torch.float32) # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 if hidden_states.shape[0] >= 64: hidden_states = hidden_states.contiguous() # if `output_size` is passed we force the interpolation output # size and do not make use of `scale_factor=2` if output_size is None: hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") else: hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") # If the input is bfloat16, we cast back to bfloat16 if dtype == torch.bfloat16: hidden_states = hidden_states.to(dtype) # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed if self.use_conv: if self.name == "conv": hidden_states = self.conv(hidden_states) else: hidden_states = self.Conv2d_0(hidden_states) return hidden_states class Downsample2D(nn.Module): """A 2D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. padding (`int`, default `1`): padding for the convolution. """ def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.padding = padding stride = 2 self.name = name if use_conv: conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) else: assert self.channels == self.out_channels conv = nn.AvgPool2d(kernel_size=stride, stride=stride) # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed if name == "conv": self.Conv2d_0 = conv self.conv = conv elif name == "Conv2d_0": self.conv = conv else: self.conv = conv def forward(self, hidden_states): assert hidden_states.shape[1] == self.channels if self.use_conv and self.padding == 0: pad = (0, 1, 0, 1) hidden_states = F.pad(hidden_states, pad, mode="constant", value=0) assert hidden_states.shape[1] == self.channels hidden_states = self.conv(hidden_states) return hidden_states class FirUpsample2D(nn.Module): """A 2D FIR upsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. fir_kernel (`tuple`, default `(1, 3, 3, 1)`): kernel for the FIR filter. """ def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)): super().__init__() out_channels = out_channels if out_channels else channels if use_conv: self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1) self.use_conv = use_conv self.fir_kernel = fir_kernel self.out_channels = out_channels def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1): """Fused `upsample_2d()` followed by `Conv2d()`. Padding is performed only once at the beginning, not between the operations. The fused op is considerably more efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary order. Args: hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. weight: Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`. kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling. factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0). Returns: output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as `hidden_states`. """ assert isinstance(factor, int) and factor >= 1 # Setup filter kernel. if kernel is None: kernel = [1] * factor # setup kernel kernel = torch.tensor(kernel, dtype=torch.float32) if kernel.ndim == 1: kernel = torch.outer(kernel, kernel) kernel /= torch.sum(kernel) kernel = kernel * (gain * (factor**2)) if self.use_conv: convH = weight.shape[2] convW = weight.shape[3] inC = weight.shape[1] pad_value = (kernel.shape[0] - factor) - (convW - 1) stride = (factor, factor) # Determine data dimensions. output_shape = ( (hidden_states.shape[2] - 1) * factor + convH, (hidden_states.shape[3] - 1) * factor + convW, ) output_padding = ( output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH, output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW, ) assert output_padding[0] >= 0 and output_padding[1] >= 0 num_groups = hidden_states.shape[1] // inC # Transpose weights. weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW)) weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4) weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW)) inverse_conv = F.conv_transpose2d( hidden_states, weight, stride=stride, output_padding=output_padding, padding=0 ) output = upfirdn2d_native( inverse_conv, torch.tensor(kernel, device=inverse_conv.device), pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1), ) else: pad_value = kernel.shape[0] - factor output = upfirdn2d_native( hidden_states, torch.tensor(kernel, device=hidden_states.device), up=factor, pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2), ) return output def forward(self, hidden_states): if self.use_conv: height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel) height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1) else: height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2) return height class FirDownsample2D(nn.Module): """A 2D FIR downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number of output channels. Defaults to `channels`. fir_kernel (`tuple`, default `(1, 3, 3, 1)`): kernel for the FIR filter. """ def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)): super().__init__() out_channels = out_channels if out_channels else channels if use_conv: self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1) self.fir_kernel = fir_kernel self.use_conv = use_conv self.out_channels = out_channels def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1): """Fused `Conv2d()` followed by `downsample_2d()`. Padding is performed only once at the beginning, not between the operations. The fused op is considerably more efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary order. Args: hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. weight: Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`. kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which corresponds to average pooling. factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0). Returns: output: Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same datatype as `x`. """ assert isinstance(factor, int) and factor >= 1 if kernel is None: kernel = [1] * factor # setup kernel kernel = torch.tensor(kernel, dtype=torch.float32) if kernel.ndim == 1: kernel = torch.outer(kernel, kernel) kernel /= torch.sum(kernel) kernel = kernel * gain if self.use_conv: _, _, convH, convW = weight.shape pad_value = (kernel.shape[0] - factor) + (convW - 1) stride_value = [factor, factor] upfirdn_input = upfirdn2d_native( hidden_states, torch.tensor(kernel, device=hidden_states.device), pad=((pad_value + 1) // 2, pad_value // 2), ) output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0) else: pad_value = kernel.shape[0] - factor output = upfirdn2d_native( hidden_states, torch.tensor(kernel, device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2), ) return output def forward(self, hidden_states): if self.use_conv: downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel) hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1) else: hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2) return hidden_states # downsample/upsample layer used in k-upscaler, might be able to use FirDownsample2D/DirUpsample2D instead class KDownsample2D(nn.Module): def __init__(self, pad_mode="reflect"): super().__init__() self.pad_mode = pad_mode kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) self.pad = kernel_1d.shape[1] // 2 - 1 self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False) def forward(self, x): x = F.pad(x, (self.pad,) * 4, self.pad_mode) weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]]) indices = torch.arange(x.shape[1], device=x.device) weight[indices, indices] = self.kernel.to(weight) return F.conv2d(x, weight, stride=2) class KUpsample2D(nn.Module): def __init__(self, pad_mode="reflect"): super().__init__() self.pad_mode = pad_mode kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2 self.pad = kernel_1d.shape[1] // 2 - 1 self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False) def forward(self, x): x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode) weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]]) indices = torch.arange(x.shape[1], device=x.device) weight[indices, indices] = self.kernel.to(weight) return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1) class ResnetBlock2D(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 conv2d layer. If None, same as `in_channels`. dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use. temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding. groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer. groups_out (`int`, *optional*, default to None): The number of groups to use for the second normalization layer. if set to None, same as `groups`. eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization. non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use. time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config. By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or "ada_group" for a stronger conditioning with scale and shift. kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`]. output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output. use_in_shortcut (`bool`, *optional*, default to `True`): If `True`, add a 1x1 nn.conv2d layer for skip-connection. up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer. down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer. conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the `conv_shortcut` output. conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output. If None, same as `out_channels`. """ def __init__( self, *, in_channels, out_channels=None, conv_shortcut=False, dropout=0.0, temb_channels=512, groups=32, groups_out=None, pre_norm=True, eps=1e-6, non_linearity="swish", skip_time_act=False, time_embedding_norm="default", # default, scale_shift, ada_group kernel=None, output_scale_factor=1.0, use_in_shortcut=None, up=False, down=False, conv_shortcut_bias: bool = True, conv_2d_out_channels: Optional[int] = None, ): super().__init__() self.pre_norm = pre_norm self.pre_norm = True 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.up = up self.down = down self.output_scale_factor = output_scale_factor self.time_embedding_norm = time_embedding_norm self.skip_time_act = skip_time_act if groups_out is None: groups_out = groups if self.time_embedding_norm == "ada_group": self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps) else: self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels is not None: if self.time_embedding_norm == "default": self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels) elif self.time_embedding_norm == "scale_shift": self.time_emb_proj = torch.nn.Linear(temb_channels, 2 * out_channels) elif self.time_embedding_norm == "ada_group": self.time_emb_proj = None else: raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") else: self.time_emb_proj = None if self.time_embedding_norm == "ada_group": self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps) else: self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) self.dropout = torch.nn.Dropout(dropout) conv_2d_out_channels = conv_2d_out_channels or out_channels self.conv2 = torch.nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1) if non_linearity == "swish": self.nonlinearity = lambda x: F.silu(x) elif non_linearity == "mish": self.nonlinearity = nn.Mish() elif non_linearity == "silu": self.nonlinearity = nn.SiLU() elif non_linearity == "gelu": self.nonlinearity = nn.GELU() self.upsample = self.downsample = None if self.up: if kernel == "fir": fir_kernel = (1, 3, 3, 1) self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel) elif kernel == "sde_vp": self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest") else: self.upsample = Upsample2D(in_channels, use_conv=False) elif self.down: if kernel == "fir": fir_kernel = (1, 3, 3, 1) self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel) elif kernel == "sde_vp": self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2) else: self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op") self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut self.conv_shortcut = None if self.use_in_shortcut: self.conv_shortcut = torch.nn.Conv2d( in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias ) def forward(self, input_tensor, temb): hidden_states = input_tensor if self.time_embedding_norm == "ada_group": hidden_states = self.norm1(hidden_states, temb) else: hidden_states = self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) if self.upsample is not None: # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 if hidden_states.shape[0] >= 64: input_tensor = input_tensor.contiguous() hidden_states = hidden_states.contiguous() input_tensor = self.upsample(input_tensor) hidden_states = self.upsample(hidden_states) elif self.downsample is not None: input_tensor = self.downsample(input_tensor) hidden_states = self.downsample(hidden_states) hidden_states = self.conv1(hidden_states) if self.time_emb_proj is not None: if not self.skip_time_act: temb = self.nonlinearity(temb) temb = self.time_emb_proj(temb)[:, :, None, None] if temb is not None and self.time_embedding_norm == "default": hidden_states = hidden_states + temb if self.time_embedding_norm == "ada_group": hidden_states = self.norm2(hidden_states, temb) else: hidden_states = self.norm2(hidden_states) if temb is not None and self.time_embedding_norm == "scale_shift": scale, shift = torch.chunk(temb, 2, dim=1) hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.nonlinearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) if self.conv_shortcut is not None: input_tensor = self.conv_shortcut(input_tensor) output_tensor = (input_tensor + hidden_states) / self.output_scale_factor return output_tensor class Mish(torch.nn.Module): def forward(self, hidden_states): return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) # unet_rl.py def rearrange_dims(tensor): if len(tensor.shape) == 2: return tensor[:, :, None] if len(tensor.shape) == 3: return tensor[:, :, None, :] elif len(tensor.shape) == 4: return tensor[:, :, 0, :] else: raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.") class Conv1dBlock(nn.Module): """ Conv1d --> GroupNorm --> Mish """ def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8): super().__init__() self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2) self.group_norm = nn.GroupNorm(n_groups, out_channels) self.mish = nn.Mish() def forward(self, x): x = self.conv1d(x) x = rearrange_dims(x) x = self.group_norm(x) x = rearrange_dims(x) x = self.mish(x) return x # unet_rl.py class ResidualTemporalBlock1D(nn.Module): def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5): super().__init__() self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size) self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size) self.time_emb_act = nn.Mish() self.time_emb = nn.Linear(embed_dim, out_channels) self.residual_conv = ( nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity() ) def forward(self, x, t): """ Args: x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x out_channels x horizon ] """ t = self.time_emb_act(t) t = self.time_emb(t) out = self.conv_in(x) + rearrange_dims(t) out = self.conv_out(out) return out + self.residual_conv(x) def upsample_2d(hidden_states, kernel=None, factor=2, gain=1): r"""Upsample2D a batch of 2D images with the given filter. Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a: multiple of the upsampling factor. Args: hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling. factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0). Returns: output: Tensor of the shape `[N, C, H * factor, W * factor]` """ assert isinstance(factor, int) and factor >= 1 if kernel is None: kernel = [1] * factor kernel = torch.tensor(kernel, dtype=torch.float32) if kernel.ndim == 1: kernel = torch.outer(kernel, kernel) kernel /= torch.sum(kernel) kernel = kernel * (gain * (factor**2)) pad_value = kernel.shape[0] - factor output = upfirdn2d_native( hidden_states, kernel.to(device=hidden_states.device), up=factor, pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2), ) return output def downsample_2d(hidden_states, kernel=None, factor=2, gain=1): r"""Downsample2D a batch of 2D images with the given filter. Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a multiple of the downsampling factor. Args: hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which corresponds to average pooling. factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0). Returns: output: Tensor of the shape `[N, C, H // factor, W // factor]` """ assert isinstance(factor, int) and factor >= 1 if kernel is None: kernel = [1] * factor kernel = torch.tensor(kernel, dtype=torch.float32) if kernel.ndim == 1: kernel = torch.outer(kernel, kernel) kernel /= torch.sum(kernel) kernel = kernel * gain pad_value = kernel.shape[0] - factor output = upfirdn2d_native( hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2) ) return output def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)): up_x = up_y = up down_x = down_y = down pad_x0 = pad_y0 = pad[0] pad_x1 = pad_y1 = pad[1] _, channel, in_h, in_w = tensor.shape tensor = tensor.reshape(-1, in_h, in_w, 1) _, in_h, in_w, minor = tensor.shape kernel_h, kernel_w = kernel.shape out = tensor.view(-1, in_h, 1, in_w, 1, minor) out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) out = out.view(-1, in_h * up_y, in_w * up_x, minor) out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) out = out.to(tensor.device) # Move back to mps if necessary out = out[ :, max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), :, ] out = out.permute(0, 3, 1, 2) 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, ) out = out.permute(0, 2, 3, 1) out = out[:, ::down_y, ::down_x, :] out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 return out.view(-1, channel, out_h, out_w) class TemporalConvLayer(nn.Module): """ Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from: https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016 """ def __init__(self, in_dim, out_dim=None, dropout=0.0): super().__init__() out_dim = out_dim or in_dim self.in_dim = in_dim self.out_dim = out_dim # conv layers self.conv1 = nn.Sequential( nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)) ) self.conv2 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)), ) self.conv3 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)), ) self.conv4 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)), ) # zero out the last layer params,so the conv block is identity nn.init.zeros_(self.conv4[-1].weight) nn.init.zeros_(self.conv4[-1].bias) def forward(self, hidden_states, num_frames=1): hidden_states = ( hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4) ) identity = hidden_states hidden_states = self.conv1(hidden_states) hidden_states = self.conv2(hidden_states) hidden_states = self.conv3(hidden_states) hidden_states = self.conv4(hidden_states) hidden_states = identity + hidden_states hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape( (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:] ) return hidden_states ================================================ FILE: diffusers/models/resnet_flax.py ================================================ # Copyright 2023 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. import flax.linen as nn import jax import jax.numpy as jnp class FlaxUpsample2D(nn.Module): out_channels: int dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( self.out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) def __call__(self, hidden_states): batch, height, width, channels = hidden_states.shape hidden_states = jax.image.resize( hidden_states, shape=(batch, height * 2, width * 2, channels), method="nearest", ) hidden_states = self.conv(hidden_states) return hidden_states class FlaxDownsample2D(nn.Module): out_channels: int dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( self.out_channels, kernel_size=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)), # padding="VALID", dtype=self.dtype, ) def __call__(self, hidden_states): # pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim # hidden_states = jnp.pad(hidden_states, pad_width=pad) hidden_states = self.conv(hidden_states) return hidden_states class FlaxResnetBlock2D(nn.Module): in_channels: int out_channels: int = None dropout_prob: float = 0.0 use_nin_shortcut: bool = None dtype: jnp.dtype = jnp.float32 def setup(self): out_channels = self.in_channels if self.out_channels is None else self.out_channels self.norm1 = nn.GroupNorm(num_groups=32, epsilon=1e-5) self.conv1 = nn.Conv( out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) self.time_emb_proj = nn.Dense(out_channels, dtype=self.dtype) self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-5) self.dropout = nn.Dropout(self.dropout_prob) self.conv2 = nn.Conv( out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut self.conv_shortcut = None if use_nin_shortcut: self.conv_shortcut = nn.Conv( out_channels, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype, ) def __call__(self, hidden_states, temb, deterministic=True): residual = hidden_states hidden_states = self.norm1(hidden_states) hidden_states = nn.swish(hidden_states) hidden_states = self.conv1(hidden_states) temb = self.time_emb_proj(nn.swish(temb)) temb = jnp.expand_dims(jnp.expand_dims(temb, 1), 1) hidden_states = hidden_states + temb hidden_states = self.norm2(hidden_states) hidden_states = nn.swish(hidden_states) hidden_states = self.dropout(hidden_states, deterministic) hidden_states = self.conv2(hidden_states) if self.conv_shortcut is not None: residual = self.conv_shortcut(residual) return hidden_states + residual ================================================ FILE: diffusers/models/t5_film_transformer.py ================================================ # Copyright 2023 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. import math import torch from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from .attention_processor import Attention from .embeddings import get_timestep_embedding from .modeling_utils import ModelMixin class T5FilmDecoder(ModelMixin, ConfigMixin): @register_to_config def __init__( self, input_dims: int = 128, targets_length: int = 256, max_decoder_noise_time: float = 2000.0, d_model: int = 768, num_layers: int = 12, num_heads: int = 12, d_kv: int = 64, d_ff: int = 2048, dropout_rate: float = 0.1, ): super().__init__() self.conditioning_emb = nn.Sequential( nn.Linear(d_model, d_model * 4, bias=False), nn.SiLU(), nn.Linear(d_model * 4, d_model * 4, bias=False), nn.SiLU(), ) self.position_encoding = nn.Embedding(targets_length, d_model) self.position_encoding.weight.requires_grad = False self.continuous_inputs_projection = nn.Linear(input_dims, d_model, bias=False) self.dropout = nn.Dropout(p=dropout_rate) self.decoders = nn.ModuleList() for lyr_num in range(num_layers): # FiLM conditional T5 decoder lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate) self.decoders.append(lyr) self.decoder_norm = T5LayerNorm(d_model) self.post_dropout = nn.Dropout(p=dropout_rate) self.spec_out = nn.Linear(d_model, input_dims, bias=False) def encoder_decoder_mask(self, query_input, key_input): mask = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2)) return mask.unsqueeze(-3) def forward(self, encodings_and_masks, decoder_input_tokens, decoder_noise_time): batch, _, _ = decoder_input_tokens.shape assert decoder_noise_time.shape == (batch,) # decoder_noise_time is in [0, 1), so rescale to expected timing range. time_steps = get_timestep_embedding( decoder_noise_time * self.config.max_decoder_noise_time, embedding_dim=self.config.d_model, max_period=self.config.max_decoder_noise_time, ).to(dtype=self.dtype) conditioning_emb = self.conditioning_emb(time_steps).unsqueeze(1) assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4) seq_length = decoder_input_tokens.shape[1] # If we want to use relative positions for audio context, we can just offset # this sequence by the length of encodings_and_masks. decoder_positions = torch.broadcast_to( torch.arange(seq_length, device=decoder_input_tokens.device), (batch, seq_length), ) position_encodings = self.position_encoding(decoder_positions) inputs = self.continuous_inputs_projection(decoder_input_tokens) inputs += position_encodings y = self.dropout(inputs) # decoder: No padding present. decoder_mask = torch.ones( decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype ) # Translate encoding masks to encoder-decoder masks. encodings_and_encdec_masks = [(x, self.encoder_decoder_mask(decoder_mask, y)) for x, y in encodings_and_masks] # cross attend style: concat encodings encoded = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1) encoder_decoder_mask = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1) for lyr in self.decoders: y = lyr( y, conditioning_emb=conditioning_emb, encoder_hidden_states=encoded, encoder_attention_mask=encoder_decoder_mask, )[0] y = self.decoder_norm(y) y = self.post_dropout(y) spec_out = self.spec_out(y) return spec_out class DecoderLayer(nn.Module): def __init__(self, d_model, d_kv, num_heads, d_ff, dropout_rate, layer_norm_epsilon=1e-6): super().__init__() self.layer = nn.ModuleList() # cond self attention: layer 0 self.layer.append( T5LayerSelfAttentionCond(d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate) ) # cross attention: layer 1 self.layer.append( T5LayerCrossAttention( d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon, ) ) # Film Cond MLP + dropout: last layer self.layer.append( T5LayerFFCond(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon) ) def forward( self, hidden_states, conditioning_emb=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, ): hidden_states = self.layer[0]( hidden_states, conditioning_emb=conditioning_emb, attention_mask=attention_mask, ) if encoder_hidden_states is not None: encoder_extended_attention_mask = torch.where(encoder_attention_mask > 0, 0, -1e10).to( encoder_hidden_states.dtype ) hidden_states = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_extended_attention_mask, ) # Apply Film Conditional Feed Forward layer hidden_states = self.layer[-1](hidden_states, conditioning_emb) return (hidden_states,) class T5LayerSelfAttentionCond(nn.Module): def __init__(self, d_model, d_kv, num_heads, dropout_rate): super().__init__() self.layer_norm = T5LayerNorm(d_model) self.FiLMLayer = T5FiLMLayer(in_features=d_model * 4, out_features=d_model) self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False) self.dropout = nn.Dropout(dropout_rate) def forward( self, hidden_states, conditioning_emb=None, attention_mask=None, ): # pre_self_attention_layer_norm normed_hidden_states = self.layer_norm(hidden_states) if conditioning_emb is not None: normed_hidden_states = self.FiLMLayer(normed_hidden_states, conditioning_emb) # Self-attention block attention_output = self.attention(normed_hidden_states) hidden_states = hidden_states + self.dropout(attention_output) return hidden_states class T5LayerCrossAttention(nn.Module): def __init__(self, d_model, d_kv, num_heads, dropout_rate, layer_norm_epsilon): super().__init__() self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False) self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon) self.dropout = nn.Dropout(dropout_rate) def forward( self, hidden_states, key_value_states=None, attention_mask=None, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.attention( normed_hidden_states, encoder_hidden_states=key_value_states, attention_mask=attention_mask.squeeze(1), ) layer_output = hidden_states + self.dropout(attention_output) return layer_output class T5LayerFFCond(nn.Module): def __init__(self, d_model, d_ff, dropout_rate, layer_norm_epsilon): super().__init__() self.DenseReluDense = T5DenseGatedActDense(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate) self.film = T5FiLMLayer(in_features=d_model * 4, out_features=d_model) self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon) self.dropout = nn.Dropout(dropout_rate) def forward(self, hidden_states, conditioning_emb=None): forwarded_states = self.layer_norm(hidden_states) if conditioning_emb is not None: forwarded_states = self.film(forwarded_states, conditioning_emb) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = hidden_states + self.dropout(forwarded_states) return hidden_states class T5DenseGatedActDense(nn.Module): def __init__(self, d_model, d_ff, dropout_rate): super().__init__() self.wi_0 = nn.Linear(d_model, d_ff, bias=False) self.wi_1 = nn.Linear(d_model, d_ff, bias=False) self.wo = nn.Linear(d_ff, d_model, bias=False) self.dropout = nn.Dropout(dropout_rate) self.act = NewGELUActivation() def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states class T5LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the T5 style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states class NewGELUActivation(nn.Module): """ Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def forward(self, input: torch.Tensor) -> torch.Tensor: return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0)))) class T5FiLMLayer(nn.Module): """ FiLM Layer """ def __init__(self, in_features, out_features): super().__init__() self.scale_bias = nn.Linear(in_features, out_features * 2, bias=False) def forward(self, x, conditioning_emb): emb = self.scale_bias(conditioning_emb) scale, shift = torch.chunk(emb, 2, -1) x = x * (1 + scale) + shift return x ================================================ FILE: diffusers/models/transformer_2d.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..models.embeddings import ImagePositionalEmbeddings from ..utils import BaseOutput, deprecate from .attention import BasicTransformerBlock from .embeddings import PatchEmbed from .modeling_utils import ModelMixin @dataclass class Transformer2DModelOutput(BaseOutput): """ Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels. """ sample: torch.FloatTensor class Transformer2DModel(ModelMixin, ConfigMixin): """ Transformer model for image-like data. Takes either discrete (classes of vector embeddings) or continuous (actual embeddings) inputs. When input is continuous: First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image. When input is discrete: First, input (classes of latent pixels) is converted to embeddings and has positional embeddings applied, see `ImagePositionalEmbeddings`. Then apply standard transformer action. Finally, predict classes of unnoised image. Note that it is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image do not contain a prediction for the masked pixel as the unnoised image cannot be masked. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): Pass if the input is continuous. The number of channels in the input and output. num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use. sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images. Note that this is fixed at training time as it is used for learning a number of position embeddings. See `ImagePositionalEmbeddings`. num_vector_embeds (`int`, *optional*): Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`. The number of diffusion steps used during training. Note that this is fixed at training time as it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more than steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the TransformerBlocks' attention should contain a bias parameter. """ @register_to_config def __init__( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: Optional[int] = None, out_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, num_vector_embeds: Optional[int] = None, patch_size: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, norm_type: str = "layer_norm", norm_elementwise_affine: bool = True, ): super().__init__() self.use_linear_projection = use_linear_projection self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` # Define whether input is continuous or discrete depending on configuration self.is_input_continuous = (in_channels is not None) and (patch_size is None) self.is_input_vectorized = num_vector_embeds is not None self.is_input_patches = in_channels is not None and patch_size is not None if norm_type == "layer_norm" and num_embeds_ada_norm is not None: deprecation_message = ( f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" " would be very nice if you could open a Pull request for the `transformer/config.json` file" ) deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) norm_type = "ada_norm" if self.is_input_continuous and self.is_input_vectorized: raise ValueError( f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" " sure that either `in_channels` or `num_vector_embeds` is None." ) elif self.is_input_vectorized and self.is_input_patches: raise ValueError( f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" " sure that either `num_vector_embeds` or `num_patches` is None." ) elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: raise ValueError( f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." ) # 2. Define input layers if self.is_input_continuous: self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) if use_linear_projection: self.proj_in = nn.Linear(in_channels, inner_dim) else: self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" self.height = sample_size self.width = sample_size self.num_vector_embeds = num_vector_embeds self.num_latent_pixels = self.height * self.width self.latent_image_embedding = ImagePositionalEmbeddings( num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width ) elif self.is_input_patches: assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" self.height = sample_size self.width = sample_size self.patch_size = patch_size self.pos_embed = PatchEmbed( height=sample_size, width=sample_size, patch_size=patch_size, in_channels=in_channels, embed_dim=inner_dim, ) # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, ) for d in range(num_layers) ] ) # 4. Define output layers self.out_channels = in_channels if out_channels is None else out_channels if self.is_input_continuous: # TODO: should use out_channels for continuous projections if use_linear_projection: self.proj_out = nn.Linear(inner_dim, in_channels) else: self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: self.norm_out = nn.LayerNorm(inner_dim) self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) elif self.is_input_patches: self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) def forward( self, hidden_states, encoder_hidden_states=None, timestep=None, class_labels=None, cross_attention_kwargs=None, return_dict: bool = True, ): """ Args: hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`. When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input hidden_states encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.long`, *optional*): Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels conditioning. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: [`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`: [`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # 1. Input if self.is_input_continuous: batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) if not self.use_linear_projection: hidden_states = self.proj_in(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) else: inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) hidden_states = self.proj_in(hidden_states) elif self.is_input_vectorized: hidden_states = self.latent_image_embedding(hidden_states) elif self.is_input_patches: hidden_states = self.pos_embed(hidden_states) # 2. Blocks for block in self.transformer_blocks: hidden_states = block( hidden_states, encoder_hidden_states=encoder_hidden_states, timestep=timestep, cross_attention_kwargs=cross_attention_kwargs, class_labels=class_labels, ) # 3. Output if self.is_input_continuous: if not self.use_linear_projection: hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = self.proj_out(hidden_states) else: hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() output = hidden_states + residual elif self.is_input_vectorized: hidden_states = self.norm_out(hidden_states) logits = self.out(hidden_states) # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) logits = logits.permute(0, 2, 1) # log(p(x_0)) output = F.log_softmax(logits.double(), dim=1).float() elif self.is_input_patches: # TODO: cleanup! conditioning = self.transformer_blocks[0].norm1.emb( timestep, class_labels, hidden_dtype=hidden_states.dtype ) shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] hidden_states = self.proj_out_2(hidden_states) # unpatchify height = width = int(hidden_states.shape[1] ** 0.5) hidden_states = hidden_states.reshape( shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) ) if not return_dict: return (output,) return Transformer2DModelOutput(sample=output) ================================================ FILE: diffusers/models/transformer_temporal.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional import torch from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .modeling_utils import ModelMixin @dataclass class TransformerTemporalModelOutput(BaseOutput): """ Args: sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`) Hidden states conditioned on `encoder_hidden_states` input. """ sample: torch.FloatTensor class TransformerTemporalModel(ModelMixin, ConfigMixin): """ Transformer model for video-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): Pass if the input is continuous. The number of channels in the input and output. num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use. sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images. Note that this is fixed at training time as it is used for learning a number of position embeddings. See `ImagePositionalEmbeddings`. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. attention_bias (`bool`, *optional*): Configure if the TransformerBlocks' attention should contain a bias parameter. double_self_attention (`bool`, *optional*): Configure if each TransformerBlock should contain two self-attention layers """ @register_to_config def __init__( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: Optional[int] = None, out_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, activation_fn: str = "geglu", norm_elementwise_affine: bool = True, double_self_attention: bool = True, ): super().__init__() self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) self.proj_in = nn.Linear(in_channels, inner_dim) # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, attention_bias=attention_bias, double_self_attention=double_self_attention, norm_elementwise_affine=norm_elementwise_affine, ) for d in range(num_layers) ] ) self.proj_out = nn.Linear(inner_dim, in_channels) def forward( self, hidden_states, encoder_hidden_states=None, timestep=None, class_labels=None, num_frames=1, cross_attention_kwargs=None, return_dict: bool = True, ): """ Args: hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`. When continous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input hidden_states encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.long`, *optional*): Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels conditioning. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: [`~models.transformer_2d.TransformerTemporalModelOutput`] or `tuple`: [`~models.transformer_2d.TransformerTemporalModelOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # 1. Input batch_frames, channel, height, width = hidden_states.shape batch_size = batch_frames // num_frames residual = hidden_states hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width) hidden_states = hidden_states.permute(0, 2, 1, 3, 4) hidden_states = self.norm(hidden_states) hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel) hidden_states = self.proj_in(hidden_states) # 2. Blocks for block in self.transformer_blocks: hidden_states = block( hidden_states, encoder_hidden_states=encoder_hidden_states, timestep=timestep, cross_attention_kwargs=cross_attention_kwargs, class_labels=class_labels, ) # 3. Output hidden_states = self.proj_out(hidden_states) hidden_states = ( hidden_states[None, None, :] .reshape(batch_size, height, width, channel, num_frames) .permute(0, 3, 4, 1, 2) .contiguous() ) hidden_states = hidden_states.reshape(batch_frames, channel, height, width) output = hidden_states + residual if not return_dict: return (output,) return TransformerTemporalModelOutput(sample=output) ================================================ FILE: diffusers/models/unet_1d.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_1d_blocks import get_down_block, get_mid_block, get_out_block, get_up_block @dataclass class UNet1DOutput(BaseOutput): """ Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, sample_size)`): Hidden states output. Output of last layer of model. """ sample: torch.FloatTensor class UNet1DModel(ModelMixin, ConfigMixin): r""" UNet1DModel is a 1D UNet model that takes in a noisy sample and a timestep and returns sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.) Parameters: sample_size (`int`, *optional*): Default length of sample. Should be adaptable at runtime. in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 2): Number of channels in the output. extra_in_channels (`int`, *optional*, defaults to 0): Number of additional channels to be added to the input of the first down block. Useful for cases where the input data has more channels than what the model is initially designed for. time_embedding_type (`str`, *optional*, defaults to `"fourier"`): Type of time embedding to use. freq_shift (`float`, *optional*, defaults to 0.0): Frequency shift for fourier time embedding. flip_sin_to_cos (`bool`, *optional*, defaults to : obj:`False`): Whether to flip sin to cos for fourier time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("DownBlock1D", "DownBlock1DNoSkip", "AttnDownBlock1D")`): Tuple of downsample block types. up_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("UpBlock1D", "UpBlock1DNoSkip", "AttnUpBlock1D")`): Tuple of upsample block types. block_out_channels (`Tuple[int]`, *optional*, defaults to : obj:`(32, 32, 64)`): Tuple of block output channels. mid_block_type (`str`, *optional*, defaults to "UNetMidBlock1D"): block type for middle of UNet. out_block_type (`str`, *optional*, defaults to `None`): optional output processing of UNet. act_fn (`str`, *optional*, defaults to None): optional activation function in UNet blocks. norm_num_groups (`int`, *optional*, defaults to 8): group norm member count in UNet blocks. layers_per_block (`int`, *optional*, defaults to 1): added number of layers in a UNet block. downsample_each_block (`int`, *optional*, defaults to False: experimental feature for using a UNet without upsampling. """ @register_to_config def __init__( self, sample_size: int = 65536, sample_rate: Optional[int] = None, in_channels: int = 2, out_channels: int = 2, extra_in_channels: int = 0, time_embedding_type: str = "fourier", flip_sin_to_cos: bool = True, use_timestep_embedding: bool = False, freq_shift: float = 0.0, down_block_types: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"), up_block_types: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"), mid_block_type: Tuple[str] = "UNetMidBlock1D", out_block_type: str = None, block_out_channels: Tuple[int] = (32, 32, 64), act_fn: str = None, norm_num_groups: int = 8, layers_per_block: int = 1, downsample_each_block: bool = False, ): super().__init__() self.sample_size = sample_size # time if time_embedding_type == "fourier": self.time_proj = GaussianFourierProjection( embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos ) timestep_input_dim = 2 * block_out_channels[0] elif time_embedding_type == "positional": self.time_proj = Timesteps( block_out_channels[0], flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=freq_shift ) timestep_input_dim = block_out_channels[0] if use_timestep_embedding: time_embed_dim = block_out_channels[0] * 4 self.time_mlp = TimestepEmbedding( in_channels=timestep_input_dim, time_embed_dim=time_embed_dim, act_fn=act_fn, out_dim=block_out_channels[0], ) self.down_blocks = nn.ModuleList([]) self.mid_block = None self.up_blocks = nn.ModuleList([]) self.out_block = None # down output_channel = in_channels for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] if i == 0: input_channel += extra_in_channels is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=block_out_channels[0], add_downsample=not is_final_block or downsample_each_block, ) self.down_blocks.append(down_block) # mid self.mid_block = get_mid_block( mid_block_type, in_channels=block_out_channels[-1], mid_channels=block_out_channels[-1], out_channels=block_out_channels[-1], embed_dim=block_out_channels[0], num_layers=layers_per_block, add_downsample=downsample_each_block, ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] if out_block_type is None: final_upsample_channels = out_channels else: final_upsample_channels = block_out_channels[0] for i, up_block_type in enumerate(up_block_types): prev_output_channel = output_channel output_channel = ( reversed_block_out_channels[i + 1] if i < len(up_block_types) - 1 else final_upsample_channels ) is_final_block = i == len(block_out_channels) - 1 up_block = get_up_block( up_block_type, num_layers=layers_per_block, in_channels=prev_output_channel, out_channels=output_channel, temb_channels=block_out_channels[0], add_upsample=not is_final_block, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32) self.out_block = get_out_block( out_block_type=out_block_type, num_groups_out=num_groups_out, embed_dim=block_out_channels[0], out_channels=out_channels, act_fn=act_fn, fc_dim=block_out_channels[-1] // 4, ) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], return_dict: bool = True, ) -> Union[UNet1DOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): `(batch_size, num_channels, sample_size)` noisy inputs tensor timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_1d.UNet1DOutput`] instead of a plain tuple. Returns: [`~models.unet_1d.UNet1DOutput`] or `tuple`: [`~models.unet_1d.UNet1DOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # 1. time timesteps = timestep if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) timestep_embed = self.time_proj(timesteps) if self.config.use_timestep_embedding: timestep_embed = self.time_mlp(timestep_embed) else: timestep_embed = timestep_embed[..., None] timestep_embed = timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype) timestep_embed = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])) # 2. down down_block_res_samples = () for downsample_block in self.down_blocks: sample, res_samples = downsample_block(hidden_states=sample, temb=timestep_embed) down_block_res_samples += res_samples # 3. mid if self.mid_block: sample = self.mid_block(sample, timestep_embed) # 4. up for i, upsample_block in enumerate(self.up_blocks): res_samples = down_block_res_samples[-1:] down_block_res_samples = down_block_res_samples[:-1] sample = upsample_block(sample, res_hidden_states_tuple=res_samples, temb=timestep_embed) # 5. post-process if self.out_block: sample = self.out_block(sample, timestep_embed) if not return_dict: return (sample,) return UNet1DOutput(sample=sample) ================================================ FILE: diffusers/models/unet_1d_blocks.py ================================================ # Copyright 2023 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. import math import torch import torch.nn.functional as F from torch import nn from .resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims class DownResnetBlock1D(nn.Module): def __init__( self, in_channels, out_channels=None, num_layers=1, conv_shortcut=False, temb_channels=32, groups=32, groups_out=None, non_linearity=None, time_embedding_norm="default", output_scale_factor=1.0, add_downsample=True, ): 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.time_embedding_norm = time_embedding_norm self.add_downsample = add_downsample self.output_scale_factor = output_scale_factor if groups_out is None: groups_out = groups # there will always be at least one resnet resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)] for _ in range(num_layers): resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels)) self.resnets = nn.ModuleList(resnets) if non_linearity == "swish": self.nonlinearity = lambda x: F.silu(x) elif non_linearity == "mish": self.nonlinearity = nn.Mish() elif non_linearity == "silu": self.nonlinearity = nn.SiLU() else: self.nonlinearity = None self.downsample = None if add_downsample: self.downsample = Downsample1D(out_channels, use_conv=True, padding=1) def forward(self, hidden_states, temb=None): output_states = () hidden_states = self.resnets[0](hidden_states, temb) for resnet in self.resnets[1:]: hidden_states = resnet(hidden_states, temb) output_states += (hidden_states,) if self.nonlinearity is not None: hidden_states = self.nonlinearity(hidden_states) if self.downsample is not None: hidden_states = self.downsample(hidden_states) return hidden_states, output_states class UpResnetBlock1D(nn.Module): def __init__( self, in_channels, out_channels=None, num_layers=1, temb_channels=32, groups=32, groups_out=None, non_linearity=None, time_embedding_norm="default", output_scale_factor=1.0, add_upsample=True, ): 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.time_embedding_norm = time_embedding_norm self.add_upsample = add_upsample self.output_scale_factor = output_scale_factor if groups_out is None: groups_out = groups # there will always be at least one resnet resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)] for _ in range(num_layers): resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels)) self.resnets = nn.ModuleList(resnets) if non_linearity == "swish": self.nonlinearity = lambda x: F.silu(x) elif non_linearity == "mish": self.nonlinearity = nn.Mish() elif non_linearity == "silu": self.nonlinearity = nn.SiLU() else: self.nonlinearity = None self.upsample = None if add_upsample: self.upsample = Upsample1D(out_channels, use_conv_transpose=True) def forward(self, hidden_states, res_hidden_states_tuple=None, temb=None): if res_hidden_states_tuple is not None: res_hidden_states = res_hidden_states_tuple[-1] hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1) hidden_states = self.resnets[0](hidden_states, temb) for resnet in self.resnets[1:]: hidden_states = resnet(hidden_states, temb) if self.nonlinearity is not None: hidden_states = self.nonlinearity(hidden_states) if self.upsample is not None: hidden_states = self.upsample(hidden_states) return hidden_states class ValueFunctionMidBlock1D(nn.Module): def __init__(self, in_channels, out_channels, embed_dim): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.embed_dim = embed_dim self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim) self.down1 = Downsample1D(out_channels // 2, use_conv=True) self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim) self.down2 = Downsample1D(out_channels // 4, use_conv=True) def forward(self, x, temb=None): x = self.res1(x, temb) x = self.down1(x) x = self.res2(x, temb) x = self.down2(x) return x class MidResTemporalBlock1D(nn.Module): def __init__( self, in_channels, out_channels, embed_dim, num_layers: int = 1, add_downsample: bool = False, add_upsample: bool = False, non_linearity=None, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.add_downsample = add_downsample # there will always be at least one resnet resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)] for _ in range(num_layers): resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim)) self.resnets = nn.ModuleList(resnets) if non_linearity == "swish": self.nonlinearity = lambda x: F.silu(x) elif non_linearity == "mish": self.nonlinearity = nn.Mish() elif non_linearity == "silu": self.nonlinearity = nn.SiLU() else: self.nonlinearity = None self.upsample = None if add_upsample: self.upsample = Downsample1D(out_channels, use_conv=True) self.downsample = None if add_downsample: self.downsample = Downsample1D(out_channels, use_conv=True) if self.upsample and self.downsample: raise ValueError("Block cannot downsample and upsample") def forward(self, hidden_states, temb): hidden_states = self.resnets[0](hidden_states, temb) for resnet in self.resnets[1:]: hidden_states = resnet(hidden_states, temb) if self.upsample: hidden_states = self.upsample(hidden_states) if self.downsample: self.downsample = self.downsample(hidden_states) return hidden_states class OutConv1DBlock(nn.Module): def __init__(self, num_groups_out, out_channels, embed_dim, act_fn): super().__init__() self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2) self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim) if act_fn == "silu": self.final_conv1d_act = nn.SiLU() if act_fn == "mish": self.final_conv1d_act = nn.Mish() self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1) def forward(self, hidden_states, temb=None): hidden_states = self.final_conv1d_1(hidden_states) hidden_states = rearrange_dims(hidden_states) hidden_states = self.final_conv1d_gn(hidden_states) hidden_states = rearrange_dims(hidden_states) hidden_states = self.final_conv1d_act(hidden_states) hidden_states = self.final_conv1d_2(hidden_states) return hidden_states class OutValueFunctionBlock(nn.Module): def __init__(self, fc_dim, embed_dim): super().__init__() self.final_block = nn.ModuleList( [ nn.Linear(fc_dim + embed_dim, fc_dim // 2), nn.Mish(), nn.Linear(fc_dim // 2, 1), ] ) def forward(self, hidden_states, temb): hidden_states = hidden_states.view(hidden_states.shape[0], -1) hidden_states = torch.cat((hidden_states, temb), dim=-1) for layer in self.final_block: hidden_states = layer(hidden_states) return hidden_states _kernels = { "linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8], "cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875], "lanczos3": [ 0.003689131001010537, 0.015056144446134567, -0.03399861603975296, -0.066637322306633, 0.13550527393817902, 0.44638532400131226, 0.44638532400131226, 0.13550527393817902, -0.066637322306633, -0.03399861603975296, 0.015056144446134567, 0.003689131001010537, ], } class Downsample1d(nn.Module): def __init__(self, kernel="linear", pad_mode="reflect"): super().__init__() self.pad_mode = pad_mode kernel_1d = torch.tensor(_kernels[kernel]) self.pad = kernel_1d.shape[0] // 2 - 1 self.register_buffer("kernel", kernel_1d) def forward(self, hidden_states): hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode) weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]]) indices = torch.arange(hidden_states.shape[1], device=hidden_states.device) weight[indices, indices] = self.kernel.to(weight) return F.conv1d(hidden_states, weight, stride=2) class Upsample1d(nn.Module): def __init__(self, kernel="linear", pad_mode="reflect"): super().__init__() self.pad_mode = pad_mode kernel_1d = torch.tensor(_kernels[kernel]) * 2 self.pad = kernel_1d.shape[0] // 2 - 1 self.register_buffer("kernel", kernel_1d) def forward(self, hidden_states, temb=None): hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode) weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]]) indices = torch.arange(hidden_states.shape[1], device=hidden_states.device) weight[indices, indices] = self.kernel.to(weight) return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1) class SelfAttention1d(nn.Module): def __init__(self, in_channels, n_head=1, dropout_rate=0.0): super().__init__() self.channels = in_channels self.group_norm = nn.GroupNorm(1, num_channels=in_channels) self.num_heads = n_head self.query = nn.Linear(self.channels, self.channels) self.key = nn.Linear(self.channels, self.channels) self.value = nn.Linear(self.channels, self.channels) self.proj_attn = nn.Linear(self.channels, self.channels, bias=True) self.dropout = nn.Dropout(dropout_rate, inplace=True) def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor: new_projection_shape = projection.size()[:-1] + (self.num_heads, -1) # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D) new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3) return new_projection def forward(self, hidden_states): residual = hidden_states batch, channel_dim, seq = hidden_states.shape hidden_states = self.group_norm(hidden_states) hidden_states = hidden_states.transpose(1, 2) query_proj = self.query(hidden_states) key_proj = self.key(hidden_states) value_proj = self.value(hidden_states) query_states = self.transpose_for_scores(query_proj) key_states = self.transpose_for_scores(key_proj) value_states = self.transpose_for_scores(value_proj) scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1])) attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale) attention_probs = torch.softmax(attention_scores, dim=-1) # compute attention output hidden_states = torch.matmul(attention_probs, value_states) hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous() new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,) hidden_states = hidden_states.view(new_hidden_states_shape) # compute next hidden_states hidden_states = self.proj_attn(hidden_states) hidden_states = hidden_states.transpose(1, 2) hidden_states = self.dropout(hidden_states) output = hidden_states + residual return output class ResConvBlock(nn.Module): def __init__(self, in_channels, mid_channels, out_channels, is_last=False): super().__init__() self.is_last = is_last self.has_conv_skip = in_channels != out_channels if self.has_conv_skip: self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False) self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2) self.group_norm_1 = nn.GroupNorm(1, mid_channels) self.gelu_1 = nn.GELU() self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2) if not self.is_last: self.group_norm_2 = nn.GroupNorm(1, out_channels) self.gelu_2 = nn.GELU() def forward(self, hidden_states): residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states hidden_states = self.conv_1(hidden_states) hidden_states = self.group_norm_1(hidden_states) hidden_states = self.gelu_1(hidden_states) hidden_states = self.conv_2(hidden_states) if not self.is_last: hidden_states = self.group_norm_2(hidden_states) hidden_states = self.gelu_2(hidden_states) output = hidden_states + residual return output class UNetMidBlock1D(nn.Module): def __init__(self, mid_channels, in_channels, out_channels=None): super().__init__() out_channels = in_channels if out_channels is None else out_channels # there is always at least one resnet self.down = Downsample1d("cubic") resnets = [ ResConvBlock(in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels), ] attentions = [ SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(out_channels, out_channels // 32), ] self.up = Upsample1d(kernel="cubic") self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states, temb=None): hidden_states = self.down(hidden_states) for attn, resnet in zip(self.attentions, self.resnets): hidden_states = resnet(hidden_states) hidden_states = attn(hidden_states) hidden_states = self.up(hidden_states) return hidden_states class AttnDownBlock1D(nn.Module): def __init__(self, out_channels, in_channels, mid_channels=None): super().__init__() mid_channels = out_channels if mid_channels is None else mid_channels self.down = Downsample1d("cubic") resnets = [ ResConvBlock(in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels), ] attentions = [ SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(out_channels, out_channels // 32), ] self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states, temb=None): hidden_states = self.down(hidden_states) for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states) hidden_states = attn(hidden_states) return hidden_states, (hidden_states,) class DownBlock1D(nn.Module): def __init__(self, out_channels, in_channels, mid_channels=None): super().__init__() mid_channels = out_channels if mid_channels is None else mid_channels self.down = Downsample1d("cubic") resnets = [ ResConvBlock(in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels), ] self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states, temb=None): hidden_states = self.down(hidden_states) for resnet in self.resnets: hidden_states = resnet(hidden_states) return hidden_states, (hidden_states,) class DownBlock1DNoSkip(nn.Module): def __init__(self, out_channels, in_channels, mid_channels=None): super().__init__() mid_channels = out_channels if mid_channels is None else mid_channels resnets = [ ResConvBlock(in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels), ] self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states, temb=None): hidden_states = torch.cat([hidden_states, temb], dim=1) for resnet in self.resnets: hidden_states = resnet(hidden_states) return hidden_states, (hidden_states,) class AttnUpBlock1D(nn.Module): def __init__(self, in_channels, out_channels, mid_channels=None): super().__init__() mid_channels = out_channels if mid_channels is None else mid_channels resnets = [ ResConvBlock(2 * in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels), ] attentions = [ SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(mid_channels, mid_channels // 32), SelfAttention1d(out_channels, out_channels // 32), ] self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.up = Upsample1d(kernel="cubic") def forward(self, hidden_states, res_hidden_states_tuple, temb=None): res_hidden_states = res_hidden_states_tuple[-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states) hidden_states = attn(hidden_states) hidden_states = self.up(hidden_states) return hidden_states class UpBlock1D(nn.Module): def __init__(self, in_channels, out_channels, mid_channels=None): super().__init__() mid_channels = in_channels if mid_channels is None else mid_channels resnets = [ ResConvBlock(2 * in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels), ] self.resnets = nn.ModuleList(resnets) self.up = Upsample1d(kernel="cubic") def forward(self, hidden_states, res_hidden_states_tuple, temb=None): res_hidden_states = res_hidden_states_tuple[-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) for resnet in self.resnets: hidden_states = resnet(hidden_states) hidden_states = self.up(hidden_states) return hidden_states class UpBlock1DNoSkip(nn.Module): def __init__(self, in_channels, out_channels, mid_channels=None): super().__init__() mid_channels = in_channels if mid_channels is None else mid_channels resnets = [ ResConvBlock(2 * in_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, mid_channels), ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True), ] self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states, res_hidden_states_tuple, temb=None): res_hidden_states = res_hidden_states_tuple[-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) for resnet in self.resnets: hidden_states = resnet(hidden_states) return hidden_states def get_down_block(down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample): if down_block_type == "DownResnetBlock1D": return DownResnetBlock1D( in_channels=in_channels, num_layers=num_layers, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, ) elif down_block_type == "DownBlock1D": return DownBlock1D(out_channels=out_channels, in_channels=in_channels) elif down_block_type == "AttnDownBlock1D": return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels) elif down_block_type == "DownBlock1DNoSkip": return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels) raise ValueError(f"{down_block_type} does not exist.") def get_up_block(up_block_type, num_layers, in_channels, out_channels, temb_channels, add_upsample): if up_block_type == "UpResnetBlock1D": return UpResnetBlock1D( in_channels=in_channels, num_layers=num_layers, out_channels=out_channels, temb_channels=temb_channels, add_upsample=add_upsample, ) elif up_block_type == "UpBlock1D": return UpBlock1D(in_channels=in_channels, out_channels=out_channels) elif up_block_type == "AttnUpBlock1D": return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels) elif up_block_type == "UpBlock1DNoSkip": return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels) raise ValueError(f"{up_block_type} does not exist.") def get_mid_block(mid_block_type, num_layers, in_channels, mid_channels, out_channels, embed_dim, add_downsample): if mid_block_type == "MidResTemporalBlock1D": return MidResTemporalBlock1D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim, add_downsample=add_downsample, ) elif mid_block_type == "ValueFunctionMidBlock1D": return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim) elif mid_block_type == "UNetMidBlock1D": return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels) raise ValueError(f"{mid_block_type} does not exist.") def get_out_block(*, out_block_type, num_groups_out, embed_dim, out_channels, act_fn, fc_dim): if out_block_type == "OutConv1DBlock": return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn) elif out_block_type == "ValueFunction": return OutValueFunctionBlock(fc_dim, embed_dim) return None ================================================ FILE: diffusers/models/unet_2d.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block @dataclass class UNet2DOutput(BaseOutput): """ Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Hidden states output. Output of last layer of model. """ sample: torch.FloatTensor class UNet2DModel(ModelMixin, ConfigMixin): r""" UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.) Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) - 1)`. in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image. out_channels (`int`, *optional*, defaults to 3): Number of channels in the output. center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use. freq_shift (`int`, *optional*, defaults to 0): Frequency shift for fourier time embedding. flip_sin_to_cos (`bool`, *optional*, defaults to : obj:`True`): Whether to flip sin to cos for fourier time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block types. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`): The mid block type. Choose from `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`. up_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types. block_out_channels (`Tuple[int]`, *optional*, defaults to : obj:`(224, 448, 672, 896)`): Tuple of block output channels. layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block. mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block. downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension. norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for the normalization. norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for the normalization. resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for resnet blocks, see [`~models.resnet.ResnetBlock2D`]. Choose from `default` or `scale_shift`. class_embed_type (`str`, *optional*, defaults to None): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, or `"identity"`. num_class_embeds (`int`, *optional*, defaults to None): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`. """ @register_to_config def __init__( self, sample_size: Optional[Union[int, Tuple[int, int]]] = None, in_channels: int = 3, out_channels: int = 3, center_input_sample: bool = False, time_embedding_type: str = "positional", freq_shift: int = 0, flip_sin_to_cos: bool = True, down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), block_out_channels: Tuple[int] = (224, 448, 672, 896), layers_per_block: int = 2, mid_block_scale_factor: float = 1, downsample_padding: int = 1, act_fn: str = "silu", attention_head_dim: Optional[int] = 8, norm_num_groups: int = 32, norm_eps: float = 1e-5, resnet_time_scale_shift: str = "default", add_attention: bool = True, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) # input self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) # time if time_embedding_type == "fourier": self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16) timestep_input_dim = 2 * block_out_channels[0] elif time_embedding_type == "positional": self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) else: self.class_embedding = None self.down_blocks = nn.ModuleList([]) self.mid_block = None self.up_blocks = nn.ModuleList([]) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attn_num_head_channels=attention_head_dim, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, attn_num_head_channels=attention_head_dim, resnet_groups=norm_num_groups, add_attention=add_attention, ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] is_final_block = i == len(block_out_channels) - 1 up_block = get_up_block( up_block_type, num_layers=layers_per_block + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attn_num_head_channels=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32) self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps) self.conv_act = nn.SiLU() self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], class_labels: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet2DOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps class_labels (`torch.FloatTensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple. Returns: [`~models.unet_2d.UNet2DOutput`] or `tuple`: [`~models.unet_2d.UNet2DOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=self.dtype) emb = self.time_embedding(t_emb) if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when doing class conditioning") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) emb = emb + class_emb # 2. pre-process skip_sample = sample sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "skip_conv"): sample, res_samples, skip_sample = downsample_block( hidden_states=sample, temb=emb, skip_sample=skip_sample ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples # 4. mid sample = self.mid_block(sample, emb) # 5. up skip_sample = None for upsample_block in self.up_blocks: res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] if hasattr(upsample_block, "skip_conv"): sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample) else: sample = upsample_block(sample, res_samples, emb) # 6. post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if skip_sample is not None: sample += skip_sample if self.config.time_embedding_type == "fourier": timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:])))) sample = sample / timesteps if not return_dict: return (sample,) return UNet2DOutput(sample=sample) ================================================ FILE: diffusers/models/unet_2d_blocks.py ================================================ # Copyright 2023 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 import numpy as np import torch import torch.nn.functional as F from torch import nn from ..utils import is_torch_version from .attention import AdaGroupNorm from .attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 from .dual_transformer_2d import DualTransformer2DModel from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D from .transformer_2d import Transformer2DModel def get_down_block( down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, downsample_padding=None, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", resnet_skip_time_act=False, resnet_out_scale_factor=1.0, cross_attention_norm=None, ): down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type if down_block_type == "DownBlock2D": return DownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "ResnetDownsampleBlock2D": return ResnetDownsampleBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, ) elif down_block_type == "AttnDownBlock2D": return AttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "CrossAttnDownBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") return CrossAttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "SimpleCrossAttnDownBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") return SimpleCrossAttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif down_block_type == "SkipDownBlock2D": return SkipDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "AttnSkipDownBlock2D": return AttnSkipDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, downsample_padding=downsample_padding, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "DownEncoderBlock2D": return DownEncoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "AttnDownEncoderBlock2D": return AttnDownEncoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "KDownBlock2D": return KDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, ) elif down_block_type == "KCrossAttnDownBlock2D": return KCrossAttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, add_self_attention=True if not add_downsample else False, ) raise ValueError(f"{down_block_type} does not exist.") def get_up_block( up_block_type, num_layers, in_channels, out_channels, prev_output_channel, temb_channels, add_upsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", resnet_skip_time_act=False, resnet_out_scale_factor=1.0, cross_attention_norm=None, ): up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type if up_block_type == "UpBlock2D": return UpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "ResnetUpsampleBlock2D": return ResnetUpsampleBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, ) elif up_block_type == "CrossAttnUpBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") return CrossAttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "SimpleCrossAttnUpBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") return SimpleCrossAttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif up_block_type == "AttnUpBlock2D": return AttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "SkipUpBlock2D": return SkipUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "AttnSkipUpBlock2D": return AttnSkipUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "UpDecoderBlock2D": return UpDecoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "AttnUpDecoderBlock2D": return AttnUpDecoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, attn_num_head_channels=attn_num_head_channels, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "KUpBlock2D": return KUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, ) elif up_block_type == "KCrossAttnUpBlock2D": return KCrossAttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, ) raise ValueError(f"{up_block_type} does not exist.") class UNetMidBlock2D(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, add_attention: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, ): super().__init__() resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) self.add_attention = add_attention # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] attentions = [] for _ in range(num_layers): if self.add_attention: attentions.append( Attention( in_channels, heads=in_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else in_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) else: attentions.append(None) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states, temb=None): hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): if attn is not None: hidden_states = attn(hidden_states) hidden_states = resnet(hidden_states, temb) return hidden_states class UNetMidBlock2DCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, dual_cross_attention=False, use_linear_projection=False, upcast_attention=False, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] attentions = [] for _ in range(num_layers): if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, in_channels // attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, in_channels // attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] hidden_states = resnet(hidden_states, temb) return hidden_states class UNetMidBlock2DSimpleCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, skip_time_act=False, only_cross_attention=False, cross_attention_norm=None, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) self.num_heads = in_channels // self.attn_num_head_channels # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ] attentions = [] for _ in range(num_layers): processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=in_channels, cross_attention_dim=in_channels, heads=self.num_heads, dim_head=attn_num_head_channels, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): # attn hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) # resnet hidden_states = resnet(hidden_states, temb) return hidden_states class AttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, downsample_padding=1, add_downsample=True, ): super().__init__() resnets = [] attentions = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else out_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None def forward(self, hidden_states, temb=None, upsample_size=None): output_states = () for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb) hidden_states = attn(hidden_states) output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class CrossAttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, downsample_padding=1, add_downsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): # TODO(Patrick, William) - attention mask is not used output_states = () for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, use_reentrant=False, )[0] else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states class DownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_downsample=True, downsample_padding=1, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, temb=None): output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states class DownEncoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_downsample=True, downsample_padding=1, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None def forward(self, hidden_states): for resnet in self.resnets: hidden_states = resnet(hidden_states, temb=None) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states class AttnDownEncoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, add_downsample=True, downsample_padding=1, ): super().__init__() resnets = [] attentions = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else out_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None def forward(self, hidden_states): for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb=None) hidden_states = attn(hidden_states) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states class AttnSkipDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=np.sqrt(2.0), downsample_padding=1, add_downsample=True, ): super().__init__() self.attentions = nn.ModuleList([]) self.resnets = nn.ModuleList([]) for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(in_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions.append( Attention( out_channels, heads=out_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else out_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=32, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) if add_downsample: self.resnet_down = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, down=True, kernel="fir", ) self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) else: self.resnet_down = None self.downsamplers = None self.skip_conv = None def forward(self, hidden_states, temb=None, skip_sample=None): output_states = () for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb) hidden_states = attn(hidden_states) output_states += (hidden_states,) if self.downsamplers is not None: hidden_states = self.resnet_down(hidden_states, temb) for downsampler in self.downsamplers: skip_sample = downsampler(skip_sample) hidden_states = self.skip_conv(skip_sample) + hidden_states output_states += (hidden_states,) return hidden_states, output_states, skip_sample class SkipDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, output_scale_factor=np.sqrt(2.0), add_downsample=True, downsample_padding=1, ): super().__init__() self.resnets = nn.ModuleList([]) for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(in_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if add_downsample: self.resnet_down = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, down=True, kernel="fir", ) self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) else: self.resnet_down = None self.downsamplers = None self.skip_conv = None def forward(self, hidden_states, temb=None, skip_sample=None): output_states = () for resnet in self.resnets: hidden_states = resnet(hidden_states, temb) output_states += (hidden_states,) if self.downsamplers is not None: hidden_states = self.resnet_down(hidden_states, temb) for downsampler in self.downsamplers: skip_sample = downsampler(skip_sample) hidden_states = self.skip_conv(skip_sample) + hidden_states output_states += (hidden_states,) return hidden_states, output_states, skip_sample class ResnetDownsampleBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_downsample=True, skip_time_act=False, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, down=True, ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, temb=None): output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, temb) output_states = output_states + (hidden_states,) return hidden_states, output_states class SimpleCrossAttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, add_downsample=True, skip_time_act=False, only_cross_attention=False, cross_attention_norm=None, ): super().__init__() self.has_cross_attention = True resnets = [] attentions = [] self.attn_num_head_channels = attn_num_head_channels self.num_heads = out_channels // self.attn_num_head_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=out_channels, cross_attention_dim=out_channels, heads=self.num_heads, dim_head=attn_num_head_channels, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, down=True, ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): output_states = () cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, temb) output_states = output_states + (hidden_states,) return hidden_states, output_states class KDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 4, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", resnet_group_size: int = 32, add_downsample=False, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, dropout=dropout, temb_channels=temb_channels, groups=groups, groups_out=groups_out, eps=resnet_eps, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: # YiYi's comments- might be able to use FirDownsample2D, look into details later self.downsamplers = nn.ModuleList([KDownsample2D()]) else: self.downsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, temb=None): output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states, output_states class KCrossAttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, cross_attention_dim: int, dropout: float = 0.0, num_layers: int = 4, resnet_group_size: int = 32, add_downsample=True, attn_num_head_channels: int = 64, add_self_attention: bool = False, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, dropout=dropout, temb_channels=temb_channels, groups=groups, groups_out=groups_out, eps=resnet_eps, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) attentions.append( KAttentionBlock( out_channels, out_channels // attn_num_head_channels, attn_num_head_channels, cross_attention_dim=cross_attention_dim, temb_channels=temb_channels, attention_bias=True, add_self_attention=add_self_attention, cross_attention_norm="layer_norm", group_size=resnet_group_size, ) ) self.resnets = nn.ModuleList(resnets) self.attentions = nn.ModuleList(attentions) if add_downsample: self.downsamplers = nn.ModuleList([KDownsample2D()]) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): output_states = () for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, attention_mask, cross_attention_kwargs, use_reentrant=False, ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, attention_mask, cross_attention_kwargs, ) else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, emb=temb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) if self.downsamplers is None: output_states += (None,) else: output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states, output_states class AttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] attentions = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else out_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) hidden_states = attn(hidden_states) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class CrossAttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, add_upsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None, cross_attention_kwargs=None, upsample_size=None, attention_mask=None, ): # TODO(Patrick, William) - attention mask is not used for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, use_reentrant=False, )[0] else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states class UpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states class UpDecoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] for i in range(num_layers): input_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=input_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None def forward(self, hidden_states): for resnet in self.resnets: hidden_states = resnet(hidden_states, temb=None) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class AttnUpDecoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] attentions = [] for i in range(num_layers): input_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=input_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else out_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None def forward(self, hidden_states): for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb=None) hidden_states = attn(hidden_states) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class AttnSkipUpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=np.sqrt(2.0), upsample_padding=1, add_upsample=True, ): super().__init__() self.attentions = nn.ModuleList([]) self.resnets = nn.ModuleList([]) for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(resnet_in_channels + res_skip_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions.append( Attention( out_channels, heads=out_channels // attn_num_head_channels if attn_num_head_channels is not None else 1, dim_head=attn_num_head_channels if attn_num_head_channels is not None else out_channels, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=32, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) if add_upsample: self.resnet_up = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, up=True, kernel="fir", ) self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.skip_norm = torch.nn.GroupNorm( num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True ) self.act = nn.SiLU() else: self.resnet_up = None self.skip_conv = None self.skip_norm = None self.act = None def forward(self, hidden_states, res_hidden_states_tuple, temb=None, skip_sample=None): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) hidden_states = self.attentions[0](hidden_states) if skip_sample is not None: skip_sample = self.upsampler(skip_sample) else: skip_sample = 0 if self.resnet_up is not None: skip_sample_states = self.skip_norm(hidden_states) skip_sample_states = self.act(skip_sample_states) skip_sample_states = self.skip_conv(skip_sample_states) skip_sample = skip_sample + skip_sample_states hidden_states = self.resnet_up(hidden_states, temb) return hidden_states, skip_sample class SkipUpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, output_scale_factor=np.sqrt(2.0), add_upsample=True, upsample_padding=1, ): super().__init__() self.resnets = nn.ModuleList([]) for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min((resnet_in_channels + res_skip_channels) // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) if add_upsample: self.resnet_up = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, up=True, kernel="fir", ) self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.skip_norm = torch.nn.GroupNorm( num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True ) self.act = nn.SiLU() else: self.resnet_up = None self.skip_conv = None self.skip_norm = None self.act = None def forward(self, hidden_states, res_hidden_states_tuple, temb=None, skip_sample=None): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) if skip_sample is not None: skip_sample = self.upsampler(skip_sample) else: skip_sample = 0 if self.resnet_up is not None: skip_sample_states = self.skip_norm(hidden_states) skip_sample_states = self.act(skip_sample_states) skip_sample_states = self.skip_conv(skip_sample_states) skip_sample = skip_sample + skip_sample_states hidden_states = self.resnet_up(hidden_states, temb) return hidden_states, skip_sample class ResnetUpsampleBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_upsample=True, skip_time_act=False, ): super().__init__() resnets = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, up=True, ) ] ) else: self.upsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, temb) return hidden_states class SimpleCrossAttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, add_upsample=True, skip_time_act=False, only_cross_attention=False, cross_attention_norm=None, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels self.num_heads = out_channels // self.attn_num_head_channels for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=out_channels, cross_attention_dim=out_channels, heads=self.num_heads, dim_head=attn_num_head_channels, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, up=True, ) ] ) else: self.upsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None, upsample_size=None, attention_mask=None, cross_attention_kwargs=None, ): cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} for resnet, attn in zip(self.resnets, self.attentions): # resnet # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, temb) return hidden_states class KUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 5, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", resnet_group_size: Optional[int] = 32, add_upsample=True, ): super().__init__() resnets = [] k_in_channels = 2 * out_channels k_out_channels = in_channels num_layers = num_layers - 1 for i in range(num_layers): in_channels = k_in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=k_out_channels if (i == num_layers - 1) else out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=groups, groups_out=groups_out, dropout=dropout, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([KUpsample2D()]) else: self.upsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): res_hidden_states_tuple = res_hidden_states_tuple[-1] if res_hidden_states_tuple is not None: hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class KCrossAttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 4, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", resnet_group_size: int = 32, attn_num_head_channels=1, # attention dim_head cross_attention_dim: int = 768, add_upsample: bool = True, upcast_attention: bool = False, ): super().__init__() resnets = [] attentions = [] is_first_block = in_channels == out_channels == temb_channels is_middle_block = in_channels != out_channels add_self_attention = True if is_first_block else False self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels # in_channels, and out_channels for the block (k-unet) k_in_channels = out_channels if is_first_block else 2 * out_channels k_out_channels = in_channels num_layers = num_layers - 1 for i in range(num_layers): in_channels = k_in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size if is_middle_block and (i == num_layers - 1): conv_2d_out_channels = k_out_channels else: conv_2d_out_channels = None resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, conv_2d_out_channels=conv_2d_out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=groups, groups_out=groups_out, dropout=dropout, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) attentions.append( KAttentionBlock( k_out_channels if (i == num_layers - 1) else out_channels, k_out_channels // attn_num_head_channels if (i == num_layers - 1) else out_channels // attn_num_head_channels, attn_num_head_channels, cross_attention_dim=cross_attention_dim, temb_channels=temb_channels, attention_bias=True, add_self_attention=add_self_attention, cross_attention_norm="layer_norm", upcast_attention=upcast_attention, ) ) self.resnets = nn.ModuleList(resnets) self.attentions = nn.ModuleList(attentions) if add_upsample: self.upsamplers = nn.ModuleList([KUpsample2D()]) else: self.upsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None, cross_attention_kwargs=None, upsample_size=None, attention_mask=None, ): res_hidden_states_tuple = res_hidden_states_tuple[-1] if res_hidden_states_tuple is not None: hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, attention_mask, cross_attention_kwargs, use_reentrant=False, )[0] else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, attention_mask, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, emb=temb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states # can potentially later be renamed to `No-feed-forward` attention class KAttentionBlock(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. cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. num_embeds_ada_norm (: obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. attention_bias (: obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. """ def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, dropout: float = 0.0, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, upcast_attention: bool = False, temb_channels: int = 768, # for ada_group_norm add_self_attention: bool = False, cross_attention_norm: Optional[str] = None, group_size: int = 32, ): super().__init__() self.add_self_attention = add_self_attention # 1. Self-Attn if add_self_attention: self.norm1 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) self.attn1 = Attention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, cross_attention_dim=None, cross_attention_norm=None, ) # 2. Cross-Attn self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) self.attn2 = Attention( query_dim=dim, cross_attention_dim=cross_attention_dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, upcast_attention=upcast_attention, cross_attention_norm=cross_attention_norm, ) def _to_3d(self, hidden_states, height, weight): return hidden_states.permute(0, 2, 3, 1).reshape(hidden_states.shape[0], height * weight, -1) def _to_4d(self, hidden_states, height, weight): return hidden_states.permute(0, 2, 1).reshape(hidden_states.shape[0], -1, height, weight) def forward( self, hidden_states, encoder_hidden_states=None, emb=None, attention_mask=None, cross_attention_kwargs=None, ): cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} # 1. Self-Attention if self.add_self_attention: norm_hidden_states = self.norm1(hidden_states, emb) height, weight = norm_hidden_states.shape[2:] norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=None, **cross_attention_kwargs, ) attn_output = self._to_4d(attn_output, height, weight) hidden_states = attn_output + hidden_states # 2. Cross-Attention/None norm_hidden_states = self.norm2(hidden_states, emb) height, weight = norm_hidden_states.shape[2:] norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, **cross_attention_kwargs, ) attn_output = self._to_4d(attn_output, height, weight) hidden_states = attn_output + hidden_states return hidden_states ================================================ FILE: diffusers/models/unet_2d_blocks_flax.py ================================================ # Copyright 2023 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. import flax.linen as nn import jax.numpy as jnp from .attention_flax import FlaxTransformer2DModel from .resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D class FlaxCrossAttnDownBlock2D(nn.Module): r""" Cross Attention 2D Downsizing block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104 Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of attention blocks layers attn_num_head_channels (:obj:`int`, *optional*, defaults to 1): Number of attention heads of each spatial transformer block add_downsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add downsampling layer before each final output use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int dropout: float = 0.0 num_layers: int = 1 attn_num_head_channels: int = 1 add_downsample: bool = True use_linear_projection: bool = False only_cross_attention: bool = False use_memory_efficient_attention: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): resnets = [] attentions = [] for i in range(self.num_layers): in_channels = self.in_channels if i == 0 else self.out_channels res_block = FlaxResnetBlock2D( in_channels=in_channels, out_channels=self.out_channels, dropout_prob=self.dropout, dtype=self.dtype, ) resnets.append(res_block) attn_block = FlaxTransformer2DModel( in_channels=self.out_channels, n_heads=self.attn_num_head_channels, d_head=self.out_channels // self.attn_num_head_channels, depth=1, use_linear_projection=self.use_linear_projection, only_cross_attention=self.only_cross_attention, use_memory_efficient_attention=self.use_memory_efficient_attention, dtype=self.dtype, ) attentions.append(attn_block) self.resnets = resnets self.attentions = attentions if self.add_downsample: self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype) def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True): output_states = () for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb, deterministic=deterministic) hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic) output_states += (hidden_states,) if self.add_downsample: hidden_states = self.downsamplers_0(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class FlaxDownBlock2D(nn.Module): r""" Flax 2D downsizing block Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of attention blocks layers add_downsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add downsampling layer before each final output dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int dropout: float = 0.0 num_layers: int = 1 add_downsample: bool = True dtype: jnp.dtype = jnp.float32 def setup(self): resnets = [] for i in range(self.num_layers): in_channels = self.in_channels if i == 0 else self.out_channels res_block = FlaxResnetBlock2D( in_channels=in_channels, out_channels=self.out_channels, dropout_prob=self.dropout, dtype=self.dtype, ) resnets.append(res_block) self.resnets = resnets if self.add_downsample: self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype) def __call__(self, hidden_states, temb, deterministic=True): output_states = () for resnet in self.resnets: hidden_states = resnet(hidden_states, temb, deterministic=deterministic) output_states += (hidden_states,) if self.add_downsample: hidden_states = self.downsamplers_0(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class FlaxCrossAttnUpBlock2D(nn.Module): r""" Cross Attention 2D Upsampling block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104 Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of attention blocks layers attn_num_head_channels (:obj:`int`, *optional*, defaults to 1): Number of attention heads of each spatial transformer block add_upsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add upsampling layer before each final output use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int prev_output_channel: int dropout: float = 0.0 num_layers: int = 1 attn_num_head_channels: int = 1 add_upsample: bool = True use_linear_projection: bool = False only_cross_attention: bool = False use_memory_efficient_attention: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): resnets = [] attentions = [] for i in range(self.num_layers): res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels res_block = FlaxResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=self.out_channels, dropout_prob=self.dropout, dtype=self.dtype, ) resnets.append(res_block) attn_block = FlaxTransformer2DModel( in_channels=self.out_channels, n_heads=self.attn_num_head_channels, d_head=self.out_channels // self.attn_num_head_channels, depth=1, use_linear_projection=self.use_linear_projection, only_cross_attention=self.only_cross_attention, use_memory_efficient_attention=self.use_memory_efficient_attention, dtype=self.dtype, ) attentions.append(attn_block) self.resnets = resnets self.attentions = attentions if self.add_upsample: self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True): for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1) hidden_states = resnet(hidden_states, temb, deterministic=deterministic) hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic) if self.add_upsample: hidden_states = self.upsamplers_0(hidden_states) return hidden_states class FlaxUpBlock2D(nn.Module): r""" Flax 2D upsampling block Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels prev_output_channel (:obj:`int`): Output channels from the previous block dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of attention blocks layers add_downsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add downsampling layer before each final output dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int prev_output_channel: int dropout: float = 0.0 num_layers: int = 1 add_upsample: bool = True dtype: jnp.dtype = jnp.float32 def setup(self): resnets = [] for i in range(self.num_layers): res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels res_block = FlaxResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=self.out_channels, dropout_prob=self.dropout, dtype=self.dtype, ) resnets.append(res_block) self.resnets = resnets if self.add_upsample: self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1) hidden_states = resnet(hidden_states, temb, deterministic=deterministic) if self.add_upsample: hidden_states = self.upsamplers_0(hidden_states) return hidden_states class FlaxUNetMidBlock2DCrossAttn(nn.Module): r""" Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104 Parameters: in_channels (:obj:`int`): Input channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of attention blocks layers attn_num_head_channels (:obj:`int`, *optional*, defaults to 1): Number of attention heads of each spatial transformer block use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int dropout: float = 0.0 num_layers: int = 1 attn_num_head_channels: int = 1 use_linear_projection: bool = False use_memory_efficient_attention: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): # there is always at least one resnet resnets = [ FlaxResnetBlock2D( in_channels=self.in_channels, out_channels=self.in_channels, dropout_prob=self.dropout, dtype=self.dtype, ) ] attentions = [] for _ in range(self.num_layers): attn_block = FlaxTransformer2DModel( in_channels=self.in_channels, n_heads=self.attn_num_head_channels, d_head=self.in_channels // self.attn_num_head_channels, depth=1, use_linear_projection=self.use_linear_projection, use_memory_efficient_attention=self.use_memory_efficient_attention, dtype=self.dtype, ) attentions.append(attn_block) res_block = FlaxResnetBlock2D( in_channels=self.in_channels, out_channels=self.in_channels, dropout_prob=self.dropout, dtype=self.dtype, ) resnets.append(res_block) self.resnets = resnets self.attentions = attentions def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True): hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic) hidden_states = resnet(hidden_states, temb, deterministic=deterministic) return hidden_states ================================================ FILE: diffusers/models/unet_2d_condition.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint from ..configuration_utils import ConfigMixin, register_to_config from ..loaders import UNet2DConditionLoadersMixin from ..utils import BaseOutput, logging from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import GaussianFourierProjection, TextTimeEmbedding, TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_2d_blocks import ( CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UNetMidBlock2DCrossAttn, UNetMidBlock2DSimpleCrossAttn, UpBlock2D, get_down_block, get_up_block, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class UNet2DConditionOutput(BaseOutput): """ Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model. """ sample: torch.FloatTensor class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): r""" UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): The number of channels in the output. center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. flip_sin_to_cos (`bool`, *optional*, defaults to `False`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): The mid block type. Choose from `UNetMidBlock2DCrossAttn` or `UNetMidBlock2DSimpleCrossAttn`, will skip the mid block layer if `None`. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`): The tuple of upsample blocks to use. only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): Whether to include self-attention in the basic transformer blocks, see [`~models.attention.BasicTransformerBlock`]. block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. If `None`, it will skip the normalization and activation layers in post-processing norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): The dimension of the cross attention features. encoder_hid_dim (`int`, *optional*, defaults to None): If given, `encoder_hidden_states` will be projected from this dimension to `cross_attention_dim`. attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for resnet blocks, see [`~models.resnet.ResnetBlock2D`]. Choose from `default` or `scale_shift`. class_embed_type (`str`, *optional*, defaults to None): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. addition_embed_type (`str`, *optional*, defaults to None): Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or "text". "text" will use the `TextTimeEmbedding` layer. num_class_embeds (`int`, *optional*, defaults to None): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`. time_embedding_type (`str`, *optional*, default to `positional`): The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. time_embedding_dim (`int`, *optional*, default to `None`): An optional override for the dimension of the projected time embedding. time_embedding_act_fn (`str`, *optional*, default to `None`): Optional activation function to use on the time embeddings only one time before they as passed to the rest of the unet. Choose from `silu`, `mish`, `gelu`, and `swish`. timestep_post_act (`str, *optional*, default to `None`): The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. time_cond_proj_dim (`int`, *optional*, default to `None`): The dimension of `cond_proj` layer in timestep embedding. conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when using the "projection" `class_embed_type`. Required when using the "projection" `class_embed_type`. class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time embeddings with the class embeddings. mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is None, the `only_cross_attention` value will be used as the value for `mid_block_only_cross_attention`. Else, it will default to `False`. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels: int = 4, center_input_sample: bool = False, flip_sin_to_cos: bool = True, freq_shift: int = 0, down_block_types: Tuple[str] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ), mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), only_cross_attention: Union[bool, Tuple[bool]] = False, block_out_channels: Tuple[int] = (320, 640, 1280, 1280), layers_per_block: Union[int, Tuple[int]] = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = "silu", norm_num_groups: Optional[int] = 32, norm_eps: float = 1e-5, cross_attention_dim: Union[int, Tuple[int]] = 1280, encoder_hid_dim: Optional[int] = None, attention_head_dim: Union[int, Tuple[int]] = 8, dual_cross_attention: bool = False, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, addition_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: int = 1.0, time_embedding_type: str = "positional", time_embedding_dim: Optional[int] = None, time_embedding_act_fn: Optional[str] = None, timestep_post_act: Optional[str] = None, time_cond_proj_dim: Optional[int] = None, conv_in_kernel: int = 3, conv_out_kernel: int = 3, projection_class_embeddings_input_dim: Optional[int] = None, class_embeddings_concat: bool = False, mid_block_only_cross_attention: Optional[bool] = None, cross_attention_norm: Optional[str] = None, addition_embed_type_num_heads=64, ): super().__init__() self.sample_size = sample_size # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): raise ValueError( f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." ) if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." ) if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." ) if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): raise ValueError( f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." ) # input conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time if time_embedding_type == "fourier": time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 if time_embed_dim % 2 != 0: raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") self.time_proj = GaussianFourierProjection( time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos ) timestep_input_dim = time_embed_dim elif time_embedding_type == "positional": time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] else: raise ValueError( f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." ) self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, post_act_fn=timestep_post_act, cond_proj_dim=time_cond_proj_dim, ) if encoder_hid_dim is not None: self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) else: self.encoder_hid_proj = None # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) elif class_embed_type == "projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" ) # The projection `class_embed_type` is the same as the timestep `class_embed_type` except # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings # 2. it projects from an arbitrary input dimension. # # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. # As a result, `TimestepEmbedding` can be passed arbitrary vectors. self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) elif class_embed_type == "simple_projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" ) self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) else: self.class_embedding = None if addition_embed_type == "text": if encoder_hid_dim is not None: text_time_embedding_from_dim = encoder_hid_dim else: text_time_embedding_from_dim = cross_attention_dim self.add_embedding = TextTimeEmbedding( text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads ) elif addition_embed_type is not None: raise ValueError(f"addition_embed_type: {addition_embed_type} must be None or 'text'.") if time_embedding_act_fn is None: self.time_embed_act = None elif time_embedding_act_fn == "swish": self.time_embed_act = lambda x: F.silu(x) elif time_embedding_act_fn == "mish": self.time_embed_act = nn.Mish() elif time_embedding_act_fn == "silu": self.time_embed_act = nn.SiLU() elif time_embedding_act_fn == "gelu": self.time_embed_act = nn.GELU() else: raise ValueError(f"Unsupported activation function: {time_embedding_act_fn}") self.down_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([]) if isinstance(only_cross_attention, bool): if mid_block_only_cross_attention is None: mid_block_only_cross_attention = only_cross_attention only_cross_attention = [only_cross_attention] * len(down_block_types) if mid_block_only_cross_attention is None: mid_block_only_cross_attention = False if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) if isinstance(cross_attention_dim, int): cross_attention_dim = (cross_attention_dim,) * len(down_block_types) if isinstance(layers_per_block, int): layers_per_block = [layers_per_block] * len(down_block_types) if class_embeddings_concat: # The time embeddings are concatenated with the class embeddings. The dimension of the # time embeddings passed to the down, middle, and up blocks is twice the dimension of the # regular time embeddings blocks_time_embed_dim = time_embed_dim * 2 else: blocks_time_embed_dim = time_embed_dim # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block[i], in_channels=input_channel, out_channels=output_channel, temb_channels=blocks_time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim[i], attn_num_head_channels=attention_head_dim[i], downsample_padding=downsample_padding, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, cross_attention_norm=cross_attention_norm, ) self.down_blocks.append(down_block) # mid if mid_block_type == "UNetMidBlock2DCrossAttn": self.mid_block = UNetMidBlock2DCrossAttn( in_channels=block_out_channels[-1], temb_channels=blocks_time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim[-1], attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": self.mid_block = UNetMidBlock2DSimpleCrossAttn( in_channels=block_out_channels[-1], temb_channels=blocks_time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, cross_attention_dim=cross_attention_dim[-1], attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, only_cross_attention=mid_block_only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif mid_block_type is None: self.mid_block = None else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_attention_head_dim = list(reversed(attention_head_dim)) reversed_layers_per_block = list(reversed(layers_per_block)) reversed_cross_attention_dim = list(reversed(cross_attention_dim)) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=reversed_layers_per_block[i] + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=blocks_time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=reversed_cross_attention_dim[i], attn_num_head_channels=reversed_attention_head_dim[i], dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, cross_attention_norm=cross_attention_norm, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if norm_num_groups is not None: self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps ) if act_fn == "swish": self.conv_act = lambda x: F.silu(x) elif act_fn == "mish": self.conv_act = nn.Mish() elif act_fn == "silu": self.conv_act = nn.SiLU() elif act_fn == "gelu": self.conv_act = nn.GELU() else: raise ValueError(f"Unsupported activation function: {act_fn}") else: self.conv_norm_out = None self.conv_act = None conv_out_padding = (conv_out_kernel - 1) // 2 self.conv_out = nn.Conv2d( block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding ) @property def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.processor for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Parameters: `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor of **all** `Attention` layers. In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.: """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ self.set_attn_processor(AttnProcessor()) def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_sliceable_layers * [1] slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D)): module.gradient_checkpointing = value def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet2DConditionOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Returns: [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): logger.info("Forward upsample size to force interpolation output size.") forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, 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=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # `Timesteps` does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=sample.dtype) emb = self.time_embedding(t_emb, timestep_cond) if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) # `Timesteps` does not contain any weights and will always return f32 tensors # there might be better ways to encapsulate this. class_labels = class_labels.to(dtype=sample.dtype) class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) if self.config.class_embeddings_concat: emb = torch.cat([emb, class_emb], dim=-1) else: emb = emb + class_emb if self.config.addition_embed_type == "text": aug_emb = self.add_embedding(encoder_hidden_states) emb = emb + aug_emb if self.time_embed_act is not None: emb = self.time_embed_act(emb) if self.encoder_hid_proj is not None: encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) # 2. pre-process sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples if down_block_additional_residuals is not None: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) if mid_block_additional_residual is not None: sample = sample + mid_block_additional_residual # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, upsample_size=upsample_size, attention_mask=attention_mask, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size ) # 6. post-process if self.conv_norm_out: sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if not return_dict: return (sample,) return UNet2DConditionOutput(sample=sample) ================================================ FILE: diffusers/models/unet_2d_condition_flax.py ================================================ # Copyright 2023 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 import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict from ..configuration_utils import ConfigMixin, flax_register_to_config from ..utils import BaseOutput from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps from .modeling_flax_utils import FlaxModelMixin from .unet_2d_blocks_flax import ( FlaxCrossAttnDownBlock2D, FlaxCrossAttnUpBlock2D, FlaxDownBlock2D, FlaxUNetMidBlock2DCrossAttn, FlaxUpBlock2D, ) @flax.struct.dataclass class FlaxUNet2DConditionOutput(BaseOutput): """ Args: sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model. """ sample: jnp.ndarray @flax_register_to_config class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin): r""" FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output. This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: sample_size (`int`, *optional*): The size of the input sample. in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): The number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D" up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`): The tuple of upsample blocks to use. The corresponding class names will be: "FlaxUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D" block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8): The dimension of the attention heads. cross_attention_dim (`int`, *optional*, defaults to 768): The dimension of the cross attention features. dropout (`float`, *optional*, defaults to 0): Dropout probability for down, up and bottleneck blocks. flip_sin_to_cos (`bool`, *optional*, defaults to `True`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): enable memory efficient attention https://arxiv.org/abs/2112.05682 """ sample_size: int = 32 in_channels: int = 4 out_channels: int = 4 down_block_types: Tuple[str] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ) up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D") only_cross_attention: Union[bool, Tuple[bool]] = False block_out_channels: Tuple[int] = (320, 640, 1280, 1280) layers_per_block: int = 2 attention_head_dim: Union[int, Tuple[int]] = 8 cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: jnp.dtype = jnp.float32 flip_sin_to_cos: bool = True freq_shift: int = 0 use_memory_efficient_attention: bool = False def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict: # init input tensors sample_shape = (1, self.in_channels, self.sample_size, self.sample_size) sample = jnp.zeros(sample_shape, dtype=jnp.float32) timesteps = jnp.ones((1,), dtype=jnp.int32) encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"] def setup(self): block_out_channels = self.block_out_channels time_embed_dim = block_out_channels[0] * 4 # input self.conv_in = nn.Conv( block_out_channels[0], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) # time self.time_proj = FlaxTimesteps( block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift ) self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype) only_cross_attention = self.only_cross_attention if isinstance(only_cross_attention, bool): only_cross_attention = (only_cross_attention,) * len(self.down_block_types) attention_head_dim = self.attention_head_dim if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(self.down_block_types) # down down_blocks = [] output_channel = block_out_channels[0] for i, down_block_type in enumerate(self.down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 if down_block_type == "CrossAttnDownBlock2D": down_block = FlaxCrossAttnDownBlock2D( in_channels=input_channel, out_channels=output_channel, dropout=self.dropout, num_layers=self.layers_per_block, attn_num_head_channels=attention_head_dim[i], add_downsample=not is_final_block, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], use_memory_efficient_attention=self.use_memory_efficient_attention, dtype=self.dtype, ) else: down_block = FlaxDownBlock2D( in_channels=input_channel, out_channels=output_channel, dropout=self.dropout, num_layers=self.layers_per_block, add_downsample=not is_final_block, dtype=self.dtype, ) down_blocks.append(down_block) self.down_blocks = down_blocks # mid self.mid_block = FlaxUNetMidBlock2DCrossAttn( in_channels=block_out_channels[-1], dropout=self.dropout, attn_num_head_channels=attention_head_dim[-1], use_linear_projection=self.use_linear_projection, use_memory_efficient_attention=self.use_memory_efficient_attention, dtype=self.dtype, ) # up up_blocks = [] reversed_block_out_channels = list(reversed(block_out_channels)) reversed_attention_head_dim = list(reversed(attention_head_dim)) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(self.up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] is_final_block = i == len(block_out_channels) - 1 if up_block_type == "CrossAttnUpBlock2D": up_block = FlaxCrossAttnUpBlock2D( in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, num_layers=self.layers_per_block + 1, attn_num_head_channels=reversed_attention_head_dim[i], add_upsample=not is_final_block, dropout=self.dropout, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], use_memory_efficient_attention=self.use_memory_efficient_attention, dtype=self.dtype, ) else: up_block = FlaxUpBlock2D( in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, num_layers=self.layers_per_block + 1, add_upsample=not is_final_block, dropout=self.dropout, dtype=self.dtype, ) up_blocks.append(up_block) prev_output_channel = output_channel self.up_blocks = up_blocks # out self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5) self.conv_out = nn.Conv( self.out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) def __call__( self, sample, timesteps, encoder_hidden_states, down_block_additional_residuals=None, mid_block_additional_residual=None, return_dict: bool = True, train: bool = False, ) -> Union[FlaxUNet2DConditionOutput, Tuple]: r""" Args: sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor timestep (`jnp.ndarray` or `float` or `int`): timesteps encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a plain tuple. train (`bool`, *optional*, defaults to `False`): Use deterministic functions and disable dropout when not training. Returns: [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`: [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # 1. time if not isinstance(timesteps, jnp.ndarray): timesteps = jnp.array([timesteps], dtype=jnp.int32) elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0: timesteps = timesteps.astype(dtype=jnp.float32) timesteps = jnp.expand_dims(timesteps, 0) t_emb = self.time_proj(timesteps) t_emb = self.time_embedding(t_emb) # 2. pre-process sample = jnp.transpose(sample, (0, 2, 3, 1)) sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for down_block in self.down_blocks: if isinstance(down_block, FlaxCrossAttnDownBlock2D): sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train) else: sample, res_samples = down_block(sample, t_emb, deterministic=not train) down_block_res_samples += res_samples if down_block_additional_residuals is not None: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample += down_block_additional_residual new_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train) if mid_block_additional_residual is not None: sample += mid_block_additional_residual # 5. up for up_block in self.up_blocks: res_samples = down_block_res_samples[-(self.layers_per_block + 1) :] down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)] if isinstance(up_block, FlaxCrossAttnUpBlock2D): sample = up_block( sample, temb=t_emb, encoder_hidden_states=encoder_hidden_states, res_hidden_states_tuple=res_samples, deterministic=not train, ) else: sample = up_block(sample, temb=t_emb, res_hidden_states_tuple=res_samples, deterministic=not train) # 6. post-process sample = self.conv_norm_out(sample) sample = nn.silu(sample) sample = self.conv_out(sample) sample = jnp.transpose(sample, (0, 3, 1, 2)) if not return_dict: return (sample,) return FlaxUNet2DConditionOutput(sample=sample) ================================================ FILE: diffusers/models/unet_3d_blocks.py ================================================ # Copyright 2023 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. import torch from torch import nn from .resnet import Downsample2D, ResnetBlock2D, TemporalConvLayer, Upsample2D from .transformer_2d import Transformer2DModel from .transformer_temporal import TransformerTemporalModel def get_down_block( down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, downsample_padding=None, dual_cross_attention=False, use_linear_projection=True, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", ): if down_block_type == "DownBlock3D": return DownBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "CrossAttnDownBlock3D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") return CrossAttnDownBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) raise ValueError(f"{down_block_type} does not exist.") def get_up_block( up_block_type, num_layers, in_channels, out_channels, prev_output_channel, temb_channels, add_upsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, dual_cross_attention=False, use_linear_projection=True, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", ): if up_block_type == "UpBlock3D": return UpBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "CrossAttnUpBlock3D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") return CrossAttnUpBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) raise ValueError(f"{up_block_type} does not exist.") class UNetMidBlock3DCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, dual_cross_attention=False, use_linear_projection=True, upcast_attention=False, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] temp_convs = [ TemporalConvLayer( in_channels, in_channels, dropout=0.1, ) ] attentions = [] temp_attentions = [] for _ in range(num_layers): attentions.append( Transformer2DModel( in_channels // attn_num_head_channels, attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) ) temp_attentions.append( TransformerTemporalModel( in_channels // attn_num_head_channels, attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( in_channels, in_channels, dropout=0.1, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) self.attentions = nn.ModuleList(attentions) self.temp_attentions = nn.ModuleList(temp_attentions) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, num_frames=1, cross_attention_kwargs=None, ): hidden_states = self.resnets[0](hidden_states, temb) hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames) for attn, temp_attn, resnet, temp_conv in zip( self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:] ): hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, ).sample hidden_states = temp_attn( hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs ).sample hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) return hidden_states class CrossAttnDownBlock3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, downsample_padding=1, add_downsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] temp_attentions = [] temp_convs = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, ) ) attentions.append( Transformer2DModel( out_channels // attn_num_head_channels, attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) temp_attentions.append( TransformerTemporalModel( out_channels // attn_num_head_channels, attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) self.attentions = nn.ModuleList(attentions) self.temp_attentions = nn.ModuleList(temp_attentions) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, num_frames=1, cross_attention_kwargs=None, ): # TODO(Patrick, William) - attention mask is not used output_states = () for resnet, temp_conv, attn, temp_attn in zip( self.resnets, self.temp_convs, self.attentions, self.temp_attentions ): hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, ).sample hidden_states = temp_attn( hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs ).sample output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class DownBlock3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_downsample=True, downsample_padding=1, ): super().__init__() resnets = [] temp_convs = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, temb=None, num_frames=1): output_states = () for resnet, temp_conv in zip(self.resnets, self.temp_convs): hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class CrossAttnUpBlock3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, add_upsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] temp_convs = [] attentions = [] temp_attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, ) ) attentions.append( Transformer2DModel( out_channels // attn_num_head_channels, attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) temp_attentions.append( TransformerTemporalModel( out_channels // attn_num_head_channels, attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) self.attentions = nn.ModuleList(attentions) self.temp_attentions = nn.ModuleList(temp_attentions) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None, upsample_size=None, attention_mask=None, num_frames=1, cross_attention_kwargs=None, ): # TODO(Patrick, William) - attention mask is not used for resnet, temp_conv, attn, temp_attn in zip( self.resnets, self.temp_convs, self.attentions, self.temp_attentions ): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, ).sample hidden_states = temp_attn( hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs ).sample if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states class UpBlock3D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] temp_convs = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, num_frames=1): for resnet, temp_conv in zip(self.resnets, self.temp_convs): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states ================================================ FILE: diffusers/models/unet_3d_condition.py ================================================ # Copyright 2023 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved. # Copyright 2023 The ModelScope Team. # # 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 dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from ..configuration_utils import ConfigMixin, register_to_config from ..loaders import UNet2DConditionLoadersMixin from ..utils import BaseOutput, logging from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .transformer_temporal import TransformerTemporalModel from .unet_3d_blocks import ( CrossAttnDownBlock3D, CrossAttnUpBlock3D, DownBlock3D, UNetMidBlock3DCrossAttn, UpBlock3D, get_down_block, get_up_block, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class UNet3DConditionOutput(BaseOutput): """ Args: sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model. """ sample: torch.FloatTensor class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): r""" UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): The number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`): The tuple of upsample blocks to use. block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. If `None`, it will skip the normalization and activation layers in post-processing norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features. attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. """ _supports_gradient_checkpointing = False @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels: int = 4, down_block_types: Tuple[str] = ( "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D", ), up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"), block_out_channels: Tuple[int] = (320, 640, 1280, 1280), layers_per_block: int = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = "silu", norm_num_groups: Optional[int] = 32, norm_eps: float = 1e-5, cross_attention_dim: int = 1024, attention_head_dim: Union[int, Tuple[int]] = 64, ): super().__init__() self.sample_size = sample_size # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." ) # input conv_in_kernel = 3 conv_out_kernel = 3 conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time time_embed_dim = block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], True, 0) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, ) self.transformer_in = TransformerTemporalModel( num_attention_heads=8, attention_head_dim=attention_head_dim, in_channels=block_out_channels[0], num_layers=1, ) # class embedding self.down_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([]) if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[i], downsample_padding=downsample_padding, dual_cross_attention=False, ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlock3DCrossAttn( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, dual_cross_attention=False, ) # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_attention_head_dim = list(reversed(attention_head_dim)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=layers_per_block + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=reversed_attention_head_dim[i], dual_cross_attention=False, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if norm_num_groups is not None: self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps ) self.conv_act = nn.SiLU() else: self.conv_norm_out = None self.conv_act = None conv_out_padding = (conv_out_kernel - 1) // 2 self.conv_out = nn.Conv2d( block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding ) @property # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.processor for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_sliceable_layers * [1] slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Parameters: `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor of **all** `Attention` layers. In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.: """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ self.set_attn_processor(AttnProcessor()) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): module.gradient_checkpointing = value def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet3DConditionOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): (batch, num_frames, channel, height, width) noisy inputs tensor timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet3DConditionOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Returns: [`~models.unet_2d_condition.UNet3DConditionOutput`] or `tuple`: [`~models.unet_2d_condition.UNet3DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): logger.info("Forward upsample size to force interpolation output size.") forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, 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=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML num_frames = sample.shape[2] timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=self.dtype) emb = self.time_embedding(t_emb, timestep_cond) emb = emb.repeat_interleave(repeats=num_frames, dim=0) encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0) # 2. pre-process sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:]) sample = self.conv_in(sample) sample = self.transformer_in( sample, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs ).sample # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames) down_block_res_samples += res_samples if down_block_additional_residuals is not None: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, ) if mid_block_additional_residual is not None: sample = sample + mid_block_additional_residual # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, upsample_size=upsample_size, attention_mask=attention_mask, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, num_frames=num_frames, ) # 6. post-process if self.conv_norm_out: sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) # reshape to (batch, channel, framerate, width, height) sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4) if not return_dict: return (sample,) return UNet3DConditionOutput(sample=sample) ================================================ FILE: diffusers/models/vae.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional import numpy as np import torch import torch.nn as nn from ..utils import BaseOutput, is_torch_version, randn_tensor from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block @dataclass class DecoderOutput(BaseOutput): """ Output of decoding method. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Decoded output sample of the model. Output of the last layer of the model. """ sample: torch.FloatTensor class Encoder(nn.Module): def __init__( self, in_channels=3, out_channels=3, down_block_types=("DownEncoderBlock2D",), block_out_channels=(64,), layers_per_block=2, norm_num_groups=32, act_fn="silu", double_z=True, ): super().__init__() self.layers_per_block = layers_per_block self.conv_in = torch.nn.Conv2d( in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1, ) self.mid_block = None self.down_blocks = nn.ModuleList([]) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=self.layers_per_block, in_channels=input_channel, out_channels=output_channel, add_downsample=not is_final_block, resnet_eps=1e-6, downsample_padding=0, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attn_num_head_channels=None, temb_channels=None, ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], resnet_eps=1e-6, resnet_act_fn=act_fn, output_scale_factor=1, resnet_time_scale_shift="default", attn_num_head_channels=None, resnet_groups=norm_num_groups, temb_channels=None, ) # out self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) self.conv_act = nn.SiLU() conv_out_channels = 2 * out_channels if double_z else out_channels self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1) self.gradient_checkpointing = False def forward(self, x): sample = x sample = self.conv_in(sample) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward # down if is_torch_version(">=", "1.11.0"): for down_block in self.down_blocks: sample = torch.utils.checkpoint.checkpoint( create_custom_forward(down_block), sample, use_reentrant=False ) # middle sample = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block), sample, use_reentrant=False ) else: for down_block in self.down_blocks: sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample) # middle sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample) else: # down for down_block in self.down_blocks: sample = down_block(sample) # middle sample = self.mid_block(sample) # post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) return sample class Decoder(nn.Module): def __init__( self, in_channels=3, out_channels=3, up_block_types=("UpDecoderBlock2D",), block_out_channels=(64,), layers_per_block=2, norm_num_groups=32, act_fn="silu", ): super().__init__() self.layers_per_block = layers_per_block self.conv_in = nn.Conv2d( in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1, ) self.mid_block = None self.up_blocks = nn.ModuleList([]) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], resnet_eps=1e-6, resnet_act_fn=act_fn, output_scale_factor=1, resnet_time_scale_shift="default", attn_num_head_channels=None, resnet_groups=norm_num_groups, temb_channels=None, ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 up_block = get_up_block( up_block_type, num_layers=self.layers_per_block + 1, in_channels=prev_output_channel, out_channels=output_channel, prev_output_channel=None, add_upsample=not is_final_block, resnet_eps=1e-6, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attn_num_head_channels=None, temb_channels=None, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) self.conv_act = nn.SiLU() self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) self.gradient_checkpointing = False def forward(self, z): sample = z sample = self.conv_in(sample) upscale_dtype = next(iter(self.up_blocks.parameters())).dtype if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): # middle sample = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block), sample, use_reentrant=False ) sample = sample.to(upscale_dtype) # up for up_block in self.up_blocks: sample = torch.utils.checkpoint.checkpoint( create_custom_forward(up_block), sample, use_reentrant=False ) else: # middle sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample) sample = sample.to(upscale_dtype) # up for up_block in self.up_blocks: sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample) else: # middle sample = self.mid_block(sample) sample = sample.to(upscale_dtype) # up for up_block in self.up_blocks: sample = up_block(sample) # post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) return sample class VectorQuantizer(nn.Module): """ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix multiplications and allows for post-hoc remapping of indices. """ # NOTE: due to a bug the beta term was applied to the wrong term. for # backwards compatibility we use the buggy version by default, but you can # specify legacy=False to fix it. def __init__( self, n_e, vq_embed_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True ): super().__init__() self.n_e = n_e self.vq_embed_dim = vq_embed_dim self.beta = beta self.legacy = legacy self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim) self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) self.remap = remap if self.remap is not None: self.register_buffer("used", torch.tensor(np.load(self.remap))) self.re_embed = self.used.shape[0] self.unknown_index = unknown_index # "random" or "extra" or integer if self.unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed + 1 print( f"Remapping {self.n_e} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices." ) else: self.re_embed = n_e self.sane_index_shape = sane_index_shape def remap_to_used(self, inds): ishape = inds.shape assert len(ishape) > 1 inds = inds.reshape(ishape[0], -1) used = self.used.to(inds) match = (inds[:, :, None] == used[None, None, ...]).long() new = match.argmax(-1) unknown = match.sum(2) < 1 if self.unknown_index == "random": new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device) else: new[unknown] = self.unknown_index return new.reshape(ishape) def unmap_to_all(self, inds): ishape = inds.shape assert len(ishape) > 1 inds = inds.reshape(ishape[0], -1) used = self.used.to(inds) if self.re_embed > self.used.shape[0]: # extra token inds[inds >= self.used.shape[0]] = 0 # simply set to zero back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) return back.reshape(ishape) def forward(self, z): # reshape z -> (batch, height, width, channel) and flatten z = z.permute(0, 2, 3, 1).contiguous() z_flattened = z.view(-1, self.vq_embed_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1) z_q = self.embedding(min_encoding_indices).view(z.shape) perplexity = None min_encodings = None # compute loss for embedding if not self.legacy: loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2) else: loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2) # preserve gradients z_q = z + (z_q - z).detach() # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() if self.remap is not None: min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis min_encoding_indices = self.remap_to_used(min_encoding_indices) min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten if self.sane_index_shape: min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3]) return z_q, loss, (perplexity, min_encodings, min_encoding_indices) def get_codebook_entry(self, indices, shape): # shape specifying (batch, height, width, channel) if self.remap is not None: indices = indices.reshape(shape[0], -1) # add batch axis indices = self.unmap_to_all(indices) indices = indices.reshape(-1) # flatten again # get quantized latent vectors z_q = self.embedding(indices) if shape is not None: z_q = z_q.view(shape) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q 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, dtype=self.parameters.dtype ) def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor: # make sure sample is on the same device as the parameters and has same dtype sample = randn_tensor( self.mean.shape, generator=generator, device=self.parameters.device, dtype=self.parameters.dtype ) x = self.mean + self.std * sample return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.0]) 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([0.0]) 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 ================================================ FILE: diffusers/models/vae_flax.py ================================================ # Copyright 2023 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. # JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers import math from functools import partial from typing import Tuple import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict from ..configuration_utils import ConfigMixin, flax_register_to_config from ..utils import BaseOutput from .modeling_flax_utils import FlaxModelMixin @flax.struct.dataclass class FlaxDecoderOutput(BaseOutput): """ Output of decoding method. Args: sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Decoded output sample of the model. Output of the last layer of the model. dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ sample: jnp.ndarray @flax.struct.dataclass class FlaxAutoencoderKLOutput(BaseOutput): """ Output of AutoencoderKL encoding method. Args: latent_dist (`FlaxDiagonalGaussianDistribution`): Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`. `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution. """ latent_dist: "FlaxDiagonalGaussianDistribution" class FlaxUpsample2D(nn.Module): """ Flax implementation of 2D Upsample layer Args: in_channels (`int`): Input channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( self.in_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) def __call__(self, hidden_states): batch, height, width, channels = hidden_states.shape hidden_states = jax.image.resize( hidden_states, shape=(batch, height * 2, width * 2, channels), method="nearest", ) hidden_states = self.conv(hidden_states) return hidden_states class FlaxDownsample2D(nn.Module): """ Flax implementation of 2D Downsample layer Args: in_channels (`int`): Input channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( self.in_channels, kernel_size=(3, 3), strides=(2, 2), padding="VALID", dtype=self.dtype, ) def __call__(self, hidden_states): pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim hidden_states = jnp.pad(hidden_states, pad_width=pad) hidden_states = self.conv(hidden_states) return hidden_states class FlaxResnetBlock2D(nn.Module): """ Flax implementation of 2D Resnet Block. Args: in_channels (`int`): Input channels out_channels (`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for group norm. use_nin_shortcut (:obj:`bool`, *optional*, defaults to `None`): Whether to use `nin_shortcut`. This activates a new layer inside ResNet block dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int = None dropout: float = 0.0 groups: int = 32 use_nin_shortcut: bool = None dtype: jnp.dtype = jnp.float32 def setup(self): out_channels = self.in_channels if self.out_channels is None else self.out_channels self.norm1 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6) self.conv1 = nn.Conv( out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) self.norm2 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6) self.dropout_layer = nn.Dropout(self.dropout) self.conv2 = nn.Conv( out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut self.conv_shortcut = None if use_nin_shortcut: self.conv_shortcut = nn.Conv( out_channels, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype, ) def __call__(self, hidden_states, deterministic=True): residual = hidden_states hidden_states = self.norm1(hidden_states) hidden_states = nn.swish(hidden_states) hidden_states = self.conv1(hidden_states) hidden_states = self.norm2(hidden_states) hidden_states = nn.swish(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic) hidden_states = self.conv2(hidden_states) if self.conv_shortcut is not None: residual = self.conv_shortcut(residual) return hidden_states + residual class FlaxAttentionBlock(nn.Module): r""" Flax Convolutional based multi-head attention block for diffusion-based VAE. Parameters: channels (:obj:`int`): Input channels num_head_channels (:obj:`int`, *optional*, defaults to `None`): Number of attention heads num_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for group norm dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ channels: int num_head_channels: int = None num_groups: int = 32 dtype: jnp.dtype = jnp.float32 def setup(self): self.num_heads = self.channels // self.num_head_channels if self.num_head_channels is not None else 1 dense = partial(nn.Dense, self.channels, dtype=self.dtype) self.group_norm = nn.GroupNorm(num_groups=self.num_groups, epsilon=1e-6) self.query, self.key, self.value = dense(), dense(), dense() self.proj_attn = dense() def transpose_for_scores(self, projection): new_projection_shape = projection.shape[:-1] + (self.num_heads, -1) # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) new_projection = projection.reshape(new_projection_shape) # (B, T, H, D) -> (B, H, T, D) new_projection = jnp.transpose(new_projection, (0, 2, 1, 3)) return new_projection def __call__(self, hidden_states): residual = hidden_states batch, height, width, channels = hidden_states.shape hidden_states = self.group_norm(hidden_states) hidden_states = hidden_states.reshape((batch, height * width, channels)) query = self.query(hidden_states) key = self.key(hidden_states) value = self.value(hidden_states) # transpose query = self.transpose_for_scores(query) key = self.transpose_for_scores(key) value = self.transpose_for_scores(value) # compute attentions scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads)) attn_weights = jnp.einsum("...qc,...kc->...qk", query * scale, key * scale) attn_weights = nn.softmax(attn_weights, axis=-1) # attend to values hidden_states = jnp.einsum("...kc,...qk->...qc", value, attn_weights) hidden_states = jnp.transpose(hidden_states, (0, 2, 1, 3)) new_hidden_states_shape = hidden_states.shape[:-2] + (self.channels,) hidden_states = hidden_states.reshape(new_hidden_states_shape) hidden_states = self.proj_attn(hidden_states) hidden_states = hidden_states.reshape((batch, height, width, channels)) hidden_states = hidden_states + residual return hidden_states class FlaxDownEncoderBlock2D(nn.Module): r""" Flax Resnet blocks-based Encoder block for diffusion-based VAE. Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of Resnet layer block resnet_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for the Resnet block group norm add_downsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add downsample layer dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int dropout: float = 0.0 num_layers: int = 1 resnet_groups: int = 32 add_downsample: bool = True dtype: jnp.dtype = jnp.float32 def setup(self): resnets = [] for i in range(self.num_layers): in_channels = self.in_channels if i == 0 else self.out_channels res_block = FlaxResnetBlock2D( in_channels=in_channels, out_channels=self.out_channels, dropout=self.dropout, groups=self.resnet_groups, dtype=self.dtype, ) resnets.append(res_block) self.resnets = resnets if self.add_downsample: self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype) def __call__(self, hidden_states, deterministic=True): for resnet in self.resnets: hidden_states = resnet(hidden_states, deterministic=deterministic) if self.add_downsample: hidden_states = self.downsamplers_0(hidden_states) return hidden_states class FlaxUpDecoderBlock2D(nn.Module): r""" Flax Resnet blocks-based Decoder block for diffusion-based VAE. Parameters: in_channels (:obj:`int`): Input channels out_channels (:obj:`int`): Output channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of Resnet layer block resnet_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for the Resnet block group norm add_upsample (:obj:`bool`, *optional*, defaults to `True`): Whether to add upsample layer dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int out_channels: int dropout: float = 0.0 num_layers: int = 1 resnet_groups: int = 32 add_upsample: bool = True dtype: jnp.dtype = jnp.float32 def setup(self): resnets = [] for i in range(self.num_layers): in_channels = self.in_channels if i == 0 else self.out_channels res_block = FlaxResnetBlock2D( in_channels=in_channels, out_channels=self.out_channels, dropout=self.dropout, groups=self.resnet_groups, dtype=self.dtype, ) resnets.append(res_block) self.resnets = resnets if self.add_upsample: self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) def __call__(self, hidden_states, deterministic=True): for resnet in self.resnets: hidden_states = resnet(hidden_states, deterministic=deterministic) if self.add_upsample: hidden_states = self.upsamplers_0(hidden_states) return hidden_states class FlaxUNetMidBlock2D(nn.Module): r""" Flax Unet Mid-Block module. Parameters: in_channels (:obj:`int`): Input channels dropout (:obj:`float`, *optional*, defaults to 0.0): Dropout rate num_layers (:obj:`int`, *optional*, defaults to 1): Number of Resnet layer block resnet_groups (:obj:`int`, *optional*, defaults to `32`): The number of groups to use for the Resnet and Attention block group norm attn_num_head_channels (:obj:`int`, *optional*, defaults to `1`): Number of attention heads for each attention block dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int dropout: float = 0.0 num_layers: int = 1 resnet_groups: int = 32 attn_num_head_channels: int = 1 dtype: jnp.dtype = jnp.float32 def setup(self): resnet_groups = self.resnet_groups if self.resnet_groups is not None else min(self.in_channels // 4, 32) # there is always at least one resnet resnets = [ FlaxResnetBlock2D( in_channels=self.in_channels, out_channels=self.in_channels, dropout=self.dropout, groups=resnet_groups, dtype=self.dtype, ) ] attentions = [] for _ in range(self.num_layers): attn_block = FlaxAttentionBlock( channels=self.in_channels, num_head_channels=self.attn_num_head_channels, num_groups=resnet_groups, dtype=self.dtype, ) attentions.append(attn_block) res_block = FlaxResnetBlock2D( in_channels=self.in_channels, out_channels=self.in_channels, dropout=self.dropout, groups=resnet_groups, dtype=self.dtype, ) resnets.append(res_block) self.resnets = resnets self.attentions = attentions def __call__(self, hidden_states, deterministic=True): hidden_states = self.resnets[0](hidden_states, deterministic=deterministic) for attn, resnet in zip(self.attentions, self.resnets[1:]): hidden_states = attn(hidden_states) hidden_states = resnet(hidden_states, deterministic=deterministic) return hidden_states class FlaxEncoder(nn.Module): r""" Flax Implementation of VAE Encoder. This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: in_channels (:obj:`int`, *optional*, defaults to 3): Input channels out_channels (:obj:`int`, *optional*, defaults to 3): Output channels down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`): DownEncoder block type block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`): Tuple containing the number of output channels for each block layers_per_block (:obj:`int`, *optional*, defaults to `2`): Number of Resnet layer for each block norm_num_groups (:obj:`int`, *optional*, defaults to `32`): norm num group act_fn (:obj:`str`, *optional*, defaults to `silu`): Activation function double_z (:obj:`bool`, *optional*, defaults to `False`): Whether to double the last output channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): Parameters `dtype` """ in_channels: int = 3 out_channels: int = 3 down_block_types: Tuple[str] = ("DownEncoderBlock2D",) block_out_channels: Tuple[int] = (64,) layers_per_block: int = 2 norm_num_groups: int = 32 act_fn: str = "silu" double_z: bool = False dtype: jnp.dtype = jnp.float32 def setup(self): block_out_channels = self.block_out_channels # in self.conv_in = nn.Conv( block_out_channels[0], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) # downsampling down_blocks = [] output_channel = block_out_channels[0] for i, _ in enumerate(self.down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = FlaxDownEncoderBlock2D( in_channels=input_channel, out_channels=output_channel, num_layers=self.layers_per_block, resnet_groups=self.norm_num_groups, add_downsample=not is_final_block, dtype=self.dtype, ) down_blocks.append(down_block) self.down_blocks = down_blocks # middle self.mid_block = FlaxUNetMidBlock2D( in_channels=block_out_channels[-1], resnet_groups=self.norm_num_groups, attn_num_head_channels=None, dtype=self.dtype, ) # end conv_out_channels = 2 * self.out_channels if self.double_z else self.out_channels self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6) self.conv_out = nn.Conv( conv_out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) def __call__(self, sample, deterministic: bool = True): # in sample = self.conv_in(sample) # downsampling for block in self.down_blocks: sample = block(sample, deterministic=deterministic) # middle sample = self.mid_block(sample, deterministic=deterministic) # end sample = self.conv_norm_out(sample) sample = nn.swish(sample) sample = self.conv_out(sample) return sample class FlaxDecoder(nn.Module): r""" Flax Implementation of VAE Decoder. This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: in_channels (:obj:`int`, *optional*, defaults to 3): Input channels out_channels (:obj:`int`, *optional*, defaults to 3): Output channels up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`): UpDecoder block type block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`): Tuple containing the number of output channels for each block layers_per_block (:obj:`int`, *optional*, defaults to `2`): Number of Resnet layer for each block norm_num_groups (:obj:`int`, *optional*, defaults to `32`): norm num group act_fn (:obj:`str`, *optional*, defaults to `silu`): Activation function double_z (:obj:`bool`, *optional*, defaults to `False`): Whether to double the last output channels dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): parameters `dtype` """ in_channels: int = 3 out_channels: int = 3 up_block_types: Tuple[str] = ("UpDecoderBlock2D",) block_out_channels: int = (64,) layers_per_block: int = 2 norm_num_groups: int = 32 act_fn: str = "silu" dtype: jnp.dtype = jnp.float32 def setup(self): block_out_channels = self.block_out_channels # z to block_in self.conv_in = nn.Conv( block_out_channels[-1], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) # middle self.mid_block = FlaxUNetMidBlock2D( in_channels=block_out_channels[-1], resnet_groups=self.norm_num_groups, attn_num_head_channels=None, dtype=self.dtype, ) # upsampling reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] up_blocks = [] for i, _ in enumerate(self.up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 up_block = FlaxUpDecoderBlock2D( in_channels=prev_output_channel, out_channels=output_channel, num_layers=self.layers_per_block + 1, resnet_groups=self.norm_num_groups, add_upsample=not is_final_block, dtype=self.dtype, ) up_blocks.append(up_block) prev_output_channel = output_channel self.up_blocks = up_blocks # end self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6) self.conv_out = nn.Conv( self.out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) def __call__(self, sample, deterministic: bool = True): # z to block_in sample = self.conv_in(sample) # middle sample = self.mid_block(sample, deterministic=deterministic) # upsampling for block in self.up_blocks: sample = block(sample, deterministic=deterministic) sample = self.conv_norm_out(sample) sample = nn.swish(sample) sample = self.conv_out(sample) return sample class FlaxDiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): # Last axis to account for channels-last self.mean, self.logvar = jnp.split(parameters, 2, axis=-1) self.logvar = jnp.clip(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = jnp.exp(0.5 * self.logvar) self.var = jnp.exp(self.logvar) if self.deterministic: self.var = self.std = jnp.zeros_like(self.mean) def sample(self, key): return self.mean + self.std * jax.random.normal(key, self.mean.shape) def kl(self, other=None): if self.deterministic: return jnp.array([0.0]) if other is None: return 0.5 * jnp.sum(self.mean**2 + self.var - 1.0 - self.logvar, axis=[1, 2, 3]) return 0.5 * jnp.sum( jnp.square(self.mean - other.mean) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, axis=[1, 2, 3], ) def nll(self, sample, axis=[1, 2, 3]): if self.deterministic: return jnp.array([0.0]) logtwopi = jnp.log(2.0 * jnp.pi) return 0.5 * jnp.sum(logtwopi + self.logvar + jnp.square(sample - self.mean) / self.var, axis=axis) def mode(self): return self.mean @flax_register_to_config class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin): r""" Flax Implementation of Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling. This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: in_channels (:obj:`int`, *optional*, defaults to 3): Input channels out_channels (:obj:`int`, *optional*, defaults to 3): Output channels down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`): DownEncoder block type up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`): UpDecoder block type block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`): Tuple containing the number of output channels for each block layers_per_block (:obj:`int`, *optional*, defaults to `2`): Number of Resnet layer for each block act_fn (:obj:`str`, *optional*, defaults to `silu`): Activation function latent_channels (:obj:`int`, *optional*, defaults to `4`): Latent space channels norm_num_groups (:obj:`int`, *optional*, defaults to `32`): Norm num group sample_size (:obj:`int`, *optional*, defaults to 32): Sample input size scaling_factor (`float`, *optional*, defaults to 0.18215): The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): parameters `dtype` """ in_channels: int = 3 out_channels: int = 3 down_block_types: Tuple[str] = ("DownEncoderBlock2D",) up_block_types: Tuple[str] = ("UpDecoderBlock2D",) block_out_channels: Tuple[int] = (64,) layers_per_block: int = 1 act_fn: str = "silu" latent_channels: int = 4 norm_num_groups: int = 32 sample_size: int = 32 scaling_factor: float = 0.18215 dtype: jnp.dtype = jnp.float32 def setup(self): self.encoder = FlaxEncoder( in_channels=self.config.in_channels, out_channels=self.config.latent_channels, down_block_types=self.config.down_block_types, block_out_channels=self.config.block_out_channels, layers_per_block=self.config.layers_per_block, act_fn=self.config.act_fn, norm_num_groups=self.config.norm_num_groups, double_z=True, dtype=self.dtype, ) self.decoder = FlaxDecoder( in_channels=self.config.latent_channels, out_channels=self.config.out_channels, up_block_types=self.config.up_block_types, block_out_channels=self.config.block_out_channels, layers_per_block=self.config.layers_per_block, norm_num_groups=self.config.norm_num_groups, act_fn=self.config.act_fn, dtype=self.dtype, ) self.quant_conv = nn.Conv( 2 * self.config.latent_channels, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype, ) self.post_quant_conv = nn.Conv( self.config.latent_channels, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype, ) def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict: # init input tensors sample_shape = (1, self.in_channels, self.sample_size, self.sample_size) sample = jnp.zeros(sample_shape, dtype=jnp.float32) params_rng, dropout_rng, gaussian_rng = jax.random.split(rng, 3) rngs = {"params": params_rng, "dropout": dropout_rng, "gaussian": gaussian_rng} return self.init(rngs, sample)["params"] def encode(self, sample, deterministic: bool = True, return_dict: bool = True): sample = jnp.transpose(sample, (0, 2, 3, 1)) hidden_states = self.encoder(sample, deterministic=deterministic) moments = self.quant_conv(hidden_states) posterior = FlaxDiagonalGaussianDistribution(moments) if not return_dict: return (posterior,) return FlaxAutoencoderKLOutput(latent_dist=posterior) def decode(self, latents, deterministic: bool = True, return_dict: bool = True): if latents.shape[-1] != self.config.latent_channels: latents = jnp.transpose(latents, (0, 2, 3, 1)) hidden_states = self.post_quant_conv(latents) hidden_states = self.decoder(hidden_states, deterministic=deterministic) hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2)) if not return_dict: return (hidden_states,) return FlaxDecoderOutput(sample=hidden_states) def __call__(self, sample, sample_posterior=False, deterministic: bool = True, return_dict: bool = True): posterior = self.encode(sample, deterministic=deterministic, return_dict=return_dict) if sample_posterior: rng = self.make_rng("gaussian") hidden_states = posterior.latent_dist.sample(rng) else: hidden_states = posterior.latent_dist.mode() sample = self.decode(hidden_states, return_dict=return_dict).sample if not return_dict: return (sample,) return FlaxDecoderOutput(sample=sample) ================================================ FILE: diffusers/models/vq_model.py ================================================ # Copyright 2023 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 dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .modeling_utils import ModelMixin from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer @dataclass class VQEncoderOutput(BaseOutput): """ Output of VQModel encoding method. Args: latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Encoded output sample of the model. Output of the last layer of the model. """ latents: torch.FloatTensor class VQModel(ModelMixin, ConfigMixin): r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.) Parameters: in_channels (int, *optional*, defaults to 3): Number of channels in the input image. out_channels (int, *optional*, defaults to 3): Number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types. up_block_types (`Tuple[str]`, *optional*, defaults to : obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types. block_out_channels (`Tuple[int]`, *optional*, defaults to : obj:`(64,)`): Tuple of block output channels. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space. sample_size (`int`, *optional*, defaults to `32`): TODO num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE. vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE. scaling_factor (`float`, *optional*, defaults to `0.18215`): The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. """ @register_to_config def __init__( self, in_channels: int = 3, out_channels: int = 3, down_block_types: Tuple[str] = ("DownEncoderBlock2D",), up_block_types: Tuple[str] = ("UpDecoderBlock2D",), block_out_channels: Tuple[int] = (64,), layers_per_block: int = 1, act_fn: str = "silu", latent_channels: int = 3, sample_size: int = 32, num_vq_embeddings: int = 256, norm_num_groups: int = 32, vq_embed_dim: Optional[int] = None, scaling_factor: float = 0.18215, ): super().__init__() # pass init params to Encoder self.encoder = Encoder( in_channels=in_channels, out_channels=latent_channels, down_block_types=down_block_types, block_out_channels=block_out_channels, layers_per_block=layers_per_block, act_fn=act_fn, norm_num_groups=norm_num_groups, double_z=False, ) vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels self.quant_conv = nn.Conv2d(latent_channels, vq_embed_dim, 1) self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False) self.post_quant_conv = nn.Conv2d(vq_embed_dim, latent_channels, 1) # pass init params to Decoder self.decoder = Decoder( in_channels=latent_channels, out_channels=out_channels, up_block_types=up_block_types, block_out_channels=block_out_channels, layers_per_block=layers_per_block, act_fn=act_fn, norm_num_groups=norm_num_groups, ) def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput: h = self.encoder(x) h = self.quant_conv(h) if not return_dict: return (h,) return VQEncoderOutput(latents=h) def decode( self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True ) -> Union[DecoderOutput, torch.FloatTensor]: # also go through quantization layer if not force_not_quantize: quant, emb_loss, info = self.quantize(h) else: quant = h quant = self.post_quant_conv(quant) dec = self.decoder(quant) if not return_dict: return (dec,) return DecoderOutput(sample=dec) def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: r""" Args: sample (`torch.FloatTensor`): Input sample. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. """ x = sample h = self.encode(x).latents dec = self.decode(h).sample if not return_dict: return (dec,) return DecoderOutput(sample=dec) ================================================ FILE: diffusers/optimization.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """PyTorch optimization for diffusion models.""" import math from enum import Enum from typing import Optional, Union from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR from .utils import logging logger = logging.get_logger(__name__) class SchedulerType(Enum): LINEAR = "linear" COSINE = "cosine" COSINE_WITH_RESTARTS = "cosine_with_restarts" POLYNOMIAL = "polynomial" CONSTANT = "constant" CONSTANT_WITH_WARMUP = "constant_with_warmup" PIECEWISE_CONSTANT = "piecewise_constant" def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1): """ Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch) def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1): """ Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ def lr_lambda(current_step: int): if current_step < num_warmup_steps: return float(current_step) / float(max(1.0, num_warmup_steps)) return 1.0 return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch) def get_piecewise_constant_schedule(optimizer: Optimizer, step_rules: str, last_epoch: int = -1): """ Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. step_rules (`string`): The rules for the learning rate. ex: rule_steps="1:10,0.1:20,0.01:30,0.005" it means that the learning rate if multiple 1 for the first 10 steps, mutiple 0.1 for the next 20 steps, multiple 0.01 for the next 30 steps and multiple 0.005 for the other steps. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ rules_dict = {} rule_list = step_rules.split(",") for rule_str in rule_list[:-1]: value_str, steps_str = rule_str.split(":") steps = int(steps_str) value = float(value_str) rules_dict[steps] = value last_lr_multiple = float(rule_list[-1]) def create_rules_function(rules_dict, last_lr_multiple): def rule_func(steps: int) -> float: sorted_steps = sorted(rules_dict.keys()) for i, sorted_step in enumerate(sorted_steps): if steps < sorted_step: return rules_dict[sorted_steps[i]] return last_lr_multiple return rule_func rules_func = create_rules_function(rules_dict, last_lr_multiple) return LambdaLR(optimizer, rules_func, last_epoch=last_epoch) def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1): """ Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ def lr_lambda(current_step: int): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) return max( 0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps)) ) return LambdaLR(optimizer, lr_lambda, last_epoch) def get_cosine_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1 ): """ Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_periods (`float`, *optional*, defaults to 0.5): The number of periods of the cosine function in a schedule (the default is to just decrease from the max value to 0 following a half-cosine). last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))) return LambdaLR(optimizer, lr_lambda, last_epoch) def get_cosine_with_hard_restarts_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1 ): """ Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`int`, *optional*, defaults to 1): The number of hard restarts to use. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) if progress >= 1.0: return 0.0 return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0)))) return LambdaLR(optimizer, lr_lambda, last_epoch) def get_polynomial_decay_schedule_with_warmup( optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1 ): """ Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. lr_end (`float`, *optional*, defaults to 1e-7): The end LR. power (`float`, *optional*, defaults to 1.0): Power factor. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37 Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ lr_init = optimizer.defaults["lr"] if not (lr_init > lr_end): raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})") def lr_lambda(current_step: int): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) elif current_step > num_training_steps: return lr_end / lr_init # as LambdaLR multiplies by lr_init else: lr_range = lr_init - lr_end decay_steps = num_training_steps - num_warmup_steps pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps decay = lr_range * pct_remaining**power + lr_end return decay / lr_init # as LambdaLR multiplies by lr_init return LambdaLR(optimizer, lr_lambda, last_epoch) TYPE_TO_SCHEDULER_FUNCTION = { SchedulerType.LINEAR: get_linear_schedule_with_warmup, SchedulerType.COSINE: get_cosine_schedule_with_warmup, SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup, SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup, SchedulerType.CONSTANT: get_constant_schedule, SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup, SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule, } def get_scheduler( name: Union[str, SchedulerType], optimizer: Optimizer, step_rules: Optional[str] = None, num_warmup_steps: Optional[int] = None, num_training_steps: Optional[int] = None, num_cycles: int = 1, power: float = 1.0, last_epoch: int = -1, ): """ Unified API to get any scheduler from its name. Args: name (`str` or `SchedulerType`): The name of the scheduler to use. optimizer (`torch.optim.Optimizer`): The optimizer that will be used during training. step_rules (`str`, *optional*): A string representing the step rules to use. This is only used by the `PIECEWISE_CONSTANT` scheduler. num_warmup_steps (`int`, *optional*): The number of warmup steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. num_training_steps (`int``, *optional*): The number of training steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. num_cycles (`int`, *optional*): The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler. power (`float`, *optional*, defaults to 1.0): Power factor. See `POLYNOMIAL` scheduler last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. """ name = SchedulerType(name) schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] if name == SchedulerType.CONSTANT: return schedule_func(optimizer, last_epoch=last_epoch) if name == SchedulerType.PIECEWISE_CONSTANT: return schedule_func(optimizer, rules=step_rules, last_epoch=last_epoch) # All other schedulers require `num_warmup_steps` if num_warmup_steps is None: raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.") if name == SchedulerType.CONSTANT_WITH_WARMUP: return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, last_epoch=last_epoch) # All other schedulers require `num_training_steps` if num_training_steps is None: raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.") if name == SchedulerType.COSINE_WITH_RESTARTS: return schedule_func( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles, last_epoch=last_epoch, ) if name == SchedulerType.POLYNOMIAL: return schedule_func( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power, last_epoch=last_epoch, ) return schedule_func( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, last_epoch=last_epoch ) ================================================ FILE: diffusers/pipeline_utils.py ================================================ # Copyright 2023 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. # NOTE: This file is deprecated and will be removed in a future version. # It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401 from .utils import deprecate deprecate( "pipelines_utils", "0.22.0", "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.", standard_warn=False, stacklevel=3, ) ================================================ FILE: diffusers/pipelines/README.md ================================================ # 🧨 Diffusers Pipelines Pipelines provide a simple way to run state-of-the-art diffusion models in inference. Most diffusion systems consist of multiple independently-trained models and highly adaptable scheduler components - all of which are needed to have a functioning end-to-end diffusion system. As an example, [Stable Diffusion](https://huggingface.co/blog/stable_diffusion) has three independently trained models: - [Autoencoder](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/models/vae.py#L392) - [Conditional Unet](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/models/unet_2d_condition.py#L12) - [CLIP text encoder](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel) - a scheduler component, [scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py), - a [CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor), - as well as a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py). All of these components are necessary to run stable diffusion in inference even though they were trained or created independently from each other. To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API. More specifically, we strive to provide pipelines that - 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LDMTextToImagePipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)), - 2. have a simple user interface to run the model in inference (see the [Pipelines API](#pipelines-api) section), - 3. are easy to understand with code that is self-explanatory and can be read along-side the official paper (see [Pipelines summary](#pipelines-summary)), - 4. can easily be contributed by the community (see the [Contribution](#contribution) section). **Note** that pipelines do not (and should not) offer any training functionality. If you are looking for *official* training examples, please have a look at [examples](https://github.com/huggingface/diffusers/tree/main/examples). ## Pipelines Summary The following table summarizes all officially supported pipelines, their corresponding paper, and if available a colab notebook to directly try them out. | Pipeline | Source | Tasks | Colab |-------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|:---:|:---:| | [dance diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/Harmonai-org/sample-generator) | *Unconditional Audio Generation* | | [ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | *Unconditional Image Generation* | | [ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | *Unconditional Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | [latent_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | *Text-to-Image Generation* | | [latent_diffusion_uncond](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | *Unconditional Image Generation* | | [pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | *Unconditional Image Generation* | | [score_sde_ve](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | *Unconditional Image Generation* | | [score_sde_vp](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | *Unconditional Image Generation* | | [stable_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [stable_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [stable_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) | [stochastic_karras_ve](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | *Unconditional Image Generation* | **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. However, most of them can be adapted to use different scheduler components or even different model components. Some pipeline examples are shown in the [Examples](#examples) below. ## Pipelines API Diffusion models often consist of multiple independently-trained models or other previously existing components. Each model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one. During inference, we however want to be able to easily load all components and use them in inference - even if one component, *e.g.* CLIP's text encoder, originates from a different library, such as [Transformers](https://github.com/huggingface/transformers). To that end, all pipelines provide the following functionality: - [`from_pretrained` method](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L139) that accepts a Hugging Face Hub repository id, *e.g.* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) or a path to a local directory, *e.g.* "./stable-diffusion". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [runwayml/stable-diffusion-v1-5/model_index.json](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), which defines all components that should be loaded into the pipelines. More specifically, for each model/component one needs to define the format `: ["", ""]`. `` is the attribute name given to the loaded instance of `` which can be found in the library or pipeline folder called `""`. - [`save_pretrained`](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L90) that accepts a local path, *e.g.* `./stable-diffusion` under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, *e.g.* `./stable_diffusion/unet`. In addition, a `model_index.json` file is created at the root of the local path, *e.g.* `./stable_diffusion/model_index.json` so that the complete pipeline can again be instantiated from the local path. - [`to`](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L118) which accepts a `string` or `torch.device` to move all models that are of type `torch.nn.Module` to the passed device. The behavior is fully analogous to [PyTorch's `to` method](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to). - [`__call__`] method to use the pipeline in inference. `__call__` defines inference logic of the pipeline and should ideally encompass all aspects of it, from pre-processing to forwarding tensors to the different models and schedulers, as well as post-processing. The API of the `__call__` method can strongly vary from pipeline to pipeline. *E.g.* a text-to-image pipeline, such as [`StableDiffusionPipeline`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) should accept among other things the text prompt to generate the image. A pure image generation pipeline, such as [DDPMPipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/ddpm) on the other hand can be run without providing any inputs. To better understand what inputs can be adapted for each pipeline, one should look directly into the respective pipeline. **Note**: All pipelines have PyTorch's autograd disabled by decorating the `__call__` method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community) ## Contribution We are more than happy about any contribution to the officially supported pipelines 🤗. We aspire all of our pipelines to be **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**. - **Self-contained**: A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file itself, should be inherited from (and only from) the [`DiffusionPipeline` class](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L56) or be directly attached to the model and scheduler components of the pipeline. - **Easy-to-use**: Pipelines should be extremely easy to use - one should be able to load the pipeline and use it for its designated task, *e.g.* text-to-image generation, in just a couple of lines of code. Most logic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `__call__` method. - **Easy-to-tweak**: Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs. We try to make the pipeline code as readable as possible so that each part –from pre-processing to diffusing to post-processing– can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community). If you feel that an important pipeline should be part of the official pipelines but isn't, a contribution to the [official pipelines](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines) would be even better. - **One-purpose-only**: Pipelines should be used for one task and one task only. Even if two tasks are very similar from a modeling point of view, *e.g.* image2image translation and in-painting, pipelines shall be used for one task only to keep them *easy-to-tweak* and *readable*. ## Examples ### Text-to-Image generation with Stable Diffusion ```python # make sure you're logged in with `huggingface-cli login` from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### Image-to-Image text-guided generation with Stable Diffusion The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images. ```python import requests from PIL import Image from io import BytesIO from diffusers import StableDiffusionImg2ImgPipeline # load the pipeline device = "cuda" pipe = StableDiffusionImg2ImgPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, ).to(device) # let's download an initial image url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((768, 512)) prompt = "A fantasy landscape, trending on artstation" images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images images[0].save("fantasy_landscape.png") ``` You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ### Tweak prompts reusing seeds and latents You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb). ### In-painting using Stable Diffusion The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and text prompt. ```python import PIL import requests import torch from io import BytesIO from diffusers import StableDiffusionInpaintPipeline def download_image(url): response = requests.get(url) return PIL.Image.open(BytesIO(response.content)).convert("RGB") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" init_image = download_image(img_url).resize((512, 512)) mask_image = download_image(mask_url).resize((512, 512)) pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, ) pipe = pipe.to("cuda") prompt = "Face of a yellow cat, high resolution, sitting on a park bench" image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] ``` You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) ================================================ FILE: diffusers/pipelines/__init__.py ================================================ from ..utils import ( OptionalDependencyNotAvailable, is_flax_available, is_k_diffusion_available, is_librosa_available, is_note_seq_available, is_onnx_available, is_torch_available, is_transformers_available, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_pt_objects import * # noqa F403 else: from .dance_diffusion import DanceDiffusionPipeline from .ddim import DDIMPipeline from .ddpm import DDPMPipeline from .dit import DiTPipeline from .latent_diffusion import LDMSuperResolutionPipeline from .latent_diffusion_uncond import LDMPipeline from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput from .pndm import PNDMPipeline from .repaint import RePaintPipeline from .score_sde_ve import ScoreSdeVePipeline from .stochastic_karras_ve import KarrasVePipeline try: if not (is_torch_available() and is_librosa_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_librosa_objects import * # noqa F403 else: from .audio_diffusion import AudioDiffusionPipeline, Mel try: if not (is_torch_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .alt_diffusion import AltDiffusionImg2ImgPipeline, AltDiffusionPipeline from .audioldm import AudioLDMPipeline from .controlnet import ( StableDiffusionControlNetImg2ImgPipeline, StableDiffusionControlNetInpaintPipeline, StableDiffusionControlNetPipeline, ) from .deepfloyd_if import ( IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline, ) from .latent_diffusion import LDMTextToImagePipeline from .paint_by_example import PaintByExamplePipeline from .semantic_stable_diffusion import SemanticStableDiffusionPipeline from .stable_diffusion import ( CycleDiffusionPipeline, StableDiffusionAttendAndExcitePipeline, StableDiffusionDepth2ImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionImageVariationPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionInstructPix2PixPipeline, StableDiffusionLatentUpscalePipeline, StableDiffusionModelEditingPipeline, StableDiffusionPanoramaPipeline, StableDiffusionPipeline, StableDiffusionPix2PixZeroPipeline, StableDiffusionSAGPipeline, StableDiffusionUpscalePipeline, StableUnCLIPImg2ImgPipeline, StableUnCLIPPipeline, ) from .stable_diffusion_safe import StableDiffusionPipelineSafe from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline from .versatile_diffusion import ( VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, ) from .vq_diffusion import VQDiffusionPipeline try: if not is_onnx_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_onnx_objects import * # noqa F403 else: from .onnx_utils import OnnxRuntimeModel try: if not (is_torch_available() and is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403 else: from .stable_diffusion import ( OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionInpaintPipeline, OnnxStableDiffusionInpaintPipelineLegacy, OnnxStableDiffusionPipeline, OnnxStableDiffusionUpscalePipeline, StableDiffusionOnnxPipeline, ) try: if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .stable_diffusion import StableDiffusionKDiffusionPipeline try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_flax_objects import * # noqa F403 else: from .pipeline_flax_utils import FlaxDiffusionPipeline try: if not (is_flax_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_flax_and_transformers_objects import * # noqa F403 else: from .controlnet import FlaxStableDiffusionControlNetPipeline from .stable_diffusion import ( FlaxStableDiffusionImg2ImgPipeline, FlaxStableDiffusionInpaintPipeline, FlaxStableDiffusionPipeline, ) try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline ================================================ FILE: diffusers/pipelines/alt_diffusion/__init__.py ================================================ from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import BaseOutput, is_torch_available, is_transformers_available @dataclass # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt class AltDiffusionPipelineOutput(BaseOutput): """ Output class for Alt Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. nsfw_content_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, or `None` if safety checking could not be performed. """ images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] if is_transformers_available() and is_torch_available(): from .modeling_roberta_series import RobertaSeriesModelWithTransformation from .pipeline_alt_diffusion import AltDiffusionPipeline from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline ================================================ FILE: diffusers/pipelines/alt_diffusion/modeling_roberta_series.py ================================================ from dataclasses import dataclass from typing import Optional, Tuple import torch from torch import nn from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel from transformers.utils import ModelOutput @dataclass class TransformationModelOutput(ModelOutput): """ Base class for text model's outputs that also contains a pooling of the last hidden states. Args: text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The text embeddings obtained by applying the projection layer to the pooler_output. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ projection_state: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None class RobertaSeriesConfig(XLMRobertaConfig): def __init__( self, pad_token_id=1, bos_token_id=0, eos_token_id=2, project_dim=512, pooler_fn="cls", learn_encoder=False, use_attention_mask=True, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.project_dim = project_dim self.pooler_fn = pooler_fn self.learn_encoder = learn_encoder self.use_attention_mask = use_attention_mask class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler", r"logit_scale"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] base_model_prefix = "roberta" config_class = RobertaSeriesConfig def __init__(self, config): super().__init__(config) self.roberta = XLMRobertaModel(config) self.transformation = nn.Linear(config.hidden_size, config.project_dim) self.has_pre_transformation = getattr(config, "has_pre_transformation", False) if self.has_pre_transformation: self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim) self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ): r""" """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.base_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=True if self.has_pre_transformation else output_hidden_states, return_dict=return_dict, ) if self.has_pre_transformation: sequence_output2 = outputs["hidden_states"][-2] sequence_output2 = self.pre_LN(sequence_output2) projection_state2 = self.transformation_pre(sequence_output2) return TransformationModelOutput( projection_state=projection_state2, last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: projection_state = self.transformation(outputs.last_hidden_state) return TransformationModelOutput( projection_state=projection_state, last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) ================================================ FILE: diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from packaging import version from transformers import CLIPImageProcessor, XLMRobertaTokenizer from diffusers.utils import is_accelerate_available, is_accelerate_version from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import deprecate, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import AltDiffusionPipeline >>> pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> # "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap" >>> prompt = "黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图" >>> image = pipe(prompt).images[0] ``` """ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Alt Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`RobertaSeriesModelWithTransformation`]): Frozen text-encoder. Alt Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.RobertaSeriesModelWithTransformation), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`XLMRobertaTokenizer`): Tokenizer of class [XLMRobertaTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.XLMRobertaTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: RobertaSeriesModelWithTransformation, tokenizer: XLMRobertaTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept def decode_latents(self, latents): warnings.warn( ( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead" ), FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPImageProcessor, XLMRobertaTokenizer from diffusers.utils import is_accelerate_available, is_accelerate_version from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import requests >>> import torch >>> from PIL import Image >>> from io import BytesIO >>> from diffusers import AltDiffusionImg2ImgPipeline >>> device = "cuda" >>> model_id_or_path = "BAAI/AltDiffusion-m9" >>> pipe = AltDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) >>> pipe = pipe.to(device) >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> init_image = Image.open(BytesIO(response.content)).convert("RGB") >>> init_image = init_image.resize((768, 512)) >>> # "A fantasy landscape, trending on artstation" >>> prompt = "幻想风景, artstation" >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images >>> images[0].save("幻想风景.png") ``` """ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker class AltDiffusionImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image to image generation using Alt Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`RobertaSeriesModelWithTransformation`]): Frozen text-encoder. Alt Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.RobertaSeriesModelWithTransformation), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`XLMRobertaTokenizer`): Tokenizer of class [XLMRobertaTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.XLMRobertaTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: RobertaSeriesModelWithTransformation, tokenizer: XLMRobertaTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept def decode_latents(self, latents): warnings.warn( ( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead" ), FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Preprocess image image = self.image_processor.preprocess(image) # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/audio_diffusion/__init__.py ================================================ from .mel import Mel from .pipeline_audio_diffusion import AudioDiffusionPipeline ================================================ FILE: diffusers/pipelines/audio_diffusion/mel.py ================================================ # Copyright 2023 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. import numpy as np # noqa: E402 from ...configuration_utils import ConfigMixin, register_to_config from ...schedulers.scheduling_utils import SchedulerMixin try: import librosa # noqa: E402 _librosa_can_be_imported = True _import_error = "" except Exception as e: _librosa_can_be_imported = False _import_error = ( f"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it." ) from PIL import Image # noqa: E402 class Mel(ConfigMixin, SchedulerMixin): """ Parameters: x_res (`int`): x resolution of spectrogram (time) y_res (`int`): y resolution of spectrogram (frequency bins) sample_rate (`int`): sample rate of audio n_fft (`int`): number of Fast Fourier Transforms hop_length (`int`): hop length (a higher number is recommended for lower than 256 y_res) top_db (`int`): loudest in decibels n_iter (`int`): number of iterations for Griffin Linn mel inversion """ config_name = "mel_config.json" @register_to_config def __init__( self, x_res: int = 256, y_res: int = 256, sample_rate: int = 22050, n_fft: int = 2048, hop_length: int = 512, top_db: int = 80, n_iter: int = 32, ): self.hop_length = hop_length self.sr = sample_rate self.n_fft = n_fft self.top_db = top_db self.n_iter = n_iter self.set_resolution(x_res, y_res) self.audio = None if not _librosa_can_be_imported: raise ValueError(_import_error) def set_resolution(self, x_res: int, y_res: int): """Set resolution. Args: x_res (`int`): x resolution of spectrogram (time) y_res (`int`): y resolution of spectrogram (frequency bins) """ self.x_res = x_res self.y_res = y_res self.n_mels = self.y_res self.slice_size = self.x_res * self.hop_length - 1 def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None): """Load audio. Args: audio_file (`str`): must be a file on disk due to Librosa limitation or raw_audio (`np.ndarray`): audio as numpy array """ if audio_file is not None: self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr) else: self.audio = raw_audio # Pad with silence if necessary. if len(self.audio) < self.x_res * self.hop_length: self.audio = np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))]) def get_number_of_slices(self) -> int: """Get number of slices in audio. Returns: `int`: number of spectograms audio can be sliced into """ return len(self.audio) // self.slice_size def get_audio_slice(self, slice: int = 0) -> np.ndarray: """Get slice of audio. Args: slice (`int`): slice number of audio (out of get_number_of_slices()) Returns: `np.ndarray`: audio as numpy array """ return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)] def get_sample_rate(self) -> int: """Get sample rate: Returns: `int`: sample rate of audio """ return self.sr def audio_slice_to_image(self, slice: int) -> Image.Image: """Convert slice of audio to spectrogram. Args: slice (`int`): slice number of audio to convert (out of get_number_of_slices()) Returns: `PIL Image`: grayscale image of x_res x y_res """ S = librosa.feature.melspectrogram( y=self.get_audio_slice(slice), sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels ) log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db) bytedata = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8) image = Image.fromarray(bytedata) return image def image_to_audio(self, image: Image.Image) -> np.ndarray: """Converts spectrogram to audio. Args: image (`PIL Image`): x_res x y_res grayscale image Returns: audio (`np.ndarray`): raw audio """ bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape((image.height, image.width)) log_S = bytedata.astype("float") * self.top_db / 255 - self.top_db S = librosa.db_to_power(log_S) audio = librosa.feature.inverse.mel_to_audio( S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter ) return audio ================================================ FILE: diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py ================================================ # Copyright 2023 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 math import acos, sin from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import randn_tensor from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput from .mel import Mel class AudioDiffusionPipeline(DiffusionPipeline): """ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqae ([`AutoencoderKL`]): Variational AutoEncoder for Latent Audio Diffusion or None unet ([`UNet2DConditionModel`]): UNET model mel ([`Mel`]): transform audio <-> spectrogram scheduler ([`DDIMScheduler` or `DDPMScheduler`]): de-noising scheduler """ _optional_components = ["vqvae"] def __init__( self, vqvae: AutoencoderKL, unet: UNet2DConditionModel, mel: Mel, scheduler: Union[DDIMScheduler, DDPMScheduler], ): super().__init__() self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae) def get_default_steps(self) -> int: """Returns default number of steps recommended for inference Returns: `int`: number of steps """ return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000 @torch.no_grad() def __call__( self, batch_size: int = 1, audio_file: str = None, raw_audio: np.ndarray = None, slice: int = 0, start_step: int = 0, steps: int = None, generator: torch.Generator = None, mask_start_secs: float = 0, mask_end_secs: float = 0, step_generator: torch.Generator = None, eta: float = 0, noise: torch.Tensor = None, encoding: torch.Tensor = None, return_dict=True, ) -> Union[ Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]], ]: """Generate random mel spectrogram from audio input and convert to audio. Args: batch_size (`int`): number of samples to generate audio_file (`str`): must be a file on disk due to Librosa limitation or raw_audio (`np.ndarray`): audio as numpy array slice (`int`): slice number of audio to convert start_step (int): step to start from steps (`int`): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM) generator (`torch.Generator`): random number generator or None mask_start_secs (`float`): number of seconds of audio to mask (not generate) at start mask_end_secs (`float`): number of seconds of audio to mask (not generate) at end step_generator (`torch.Generator`): random number generator used to de-noise or None eta (`float`): parameter between 0 and 1 used with DDIM scheduler noise (`torch.Tensor`): noise tensor of shape (batch_size, 1, height, width) or None encoding (`torch.Tensor`): for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim) return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple Returns: `List[PIL Image]`: mel spectrograms (`float`, `List[np.ndarray]`): sample rate and raw audios """ steps = steps or self.get_default_steps() self.scheduler.set_timesteps(steps) step_generator = step_generator or generator # For backwards compatibility if type(self.unet.config.sample_size) == int: self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: noise = randn_tensor( ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1], ), generator=generator, device=self.device, ) images = noise mask = None if audio_file is not None or raw_audio is not None: self.mel.load_audio(audio_file, raw_audio) input_image = self.mel.audio_slice_to_image(slice) input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape( (input_image.height, input_image.width) ) input_image = (input_image / 255) * 2 - 1 input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device) if self.vqvae is not None: input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample( generator=generator )[0] input_images = self.vqvae.config.scaling_factor * input_images if start_step > 0: images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1]) pixels_per_second = ( self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length ) mask_start = int(mask_start_secs * pixels_per_second) mask_end = int(mask_end_secs * pixels_per_second) mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:])) for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): if isinstance(self.unet, UNet2DConditionModel): model_output = self.unet(images, t, encoding)["sample"] else: model_output = self.unet(images, t)["sample"] if isinstance(self.scheduler, DDIMScheduler): images = self.scheduler.step( model_output=model_output, timestep=t, sample=images, eta=eta, generator=step_generator, )["prev_sample"] else: images = self.scheduler.step( model_output=model_output, timestep=t, sample=images, generator=step_generator, )["prev_sample"] if mask is not None: if mask_start > 0: images[:, :, :, :mask_start] = mask[:, step, :, :mask_start] if mask_end > 0: images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:] if self.vqvae is not None: # 0.18215 was scaling factor used in training to ensure unit variance images = 1 / self.vqvae.config.scaling_factor * images images = self.vqvae.decode(images)["sample"] images = (images / 2 + 0.5).clamp(0, 1) images = images.cpu().permute(0, 2, 3, 1).numpy() images = (images * 255).round().astype("uint8") images = list( (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 else (Image.fromarray(_, mode="RGB").convert("L") for _ in images) ) audios = [self.mel.image_to_audio(_) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images)) @torch.no_grad() def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray: """Reverse step process: recover noisy image from generated image. Args: images (`List[PIL Image]`): list of images to encode steps (`int`): number of encoding steps to perform (defaults to 50) Returns: `np.ndarray`: noise tensor of shape (batch_size, 1, height, width) """ # Only works with DDIM as this method is deterministic assert isinstance(self.scheduler, DDIMScheduler) self.scheduler.set_timesteps(steps) sample = np.array( [np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images] ) sample = (sample / 255) * 2 - 1 sample = torch.Tensor(sample).to(self.device) for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))): prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps alpha_prod_t = self.scheduler.alphas_cumprod[t] alpha_prod_t_prev = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) beta_prod_t = 1 - alpha_prod_t model_output = self.unet(sample, t)["sample"] pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output return sample @staticmethod def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor: """Spherical Linear intERPolation Args: x0 (`torch.Tensor`): first tensor to interpolate between x1 (`torch.Tensor`): seconds tensor to interpolate between alpha (`float`): interpolation between 0 and 1 Returns: `torch.Tensor`: interpolated tensor """ theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1)) return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta) ================================================ FILE: diffusers/pipelines/audioldm/__init__.py ================================================ from ...utils import ( OptionalDependencyNotAvailable, is_torch_available, is_transformers_available, is_transformers_version, ) try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import ( AudioLDMPipeline, ) else: from .pipeline_audioldm import AudioLDMPipeline ================================================ FILE: diffusers/pipelines/audioldm/pipeline_audioldm.py ================================================ # Copyright 2023 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. import inspect from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import torch import torch.nn.functional as F from transformers import ClapTextModelWithProjection, RobertaTokenizer, RobertaTokenizerFast, SpeechT5HifiGan from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import AudioLDMPipeline >>> pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "A hammer hitting a wooden surface" >>> audio = pipe(prompt).audio[0] ``` """ class AudioLDMPipeline(DiffusionPipeline): r""" Pipeline for text-to-audio generation using AudioLDM. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode audios to and from latent representations. text_encoder ([`ClapTextModelWithProjection`]): Frozen text-encoder. AudioLDM uses the text portion of [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap#transformers.ClapTextModelWithProjection), specifically the [RoBERTa HSTAT-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant. tokenizer ([`PreTrainedTokenizer`]): Tokenizer of class [RobertaTokenizer](https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaTokenizer). unet ([`UNet2DConditionModel`]): U-Net architecture to denoise the encoded audio latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. vocoder ([`SpeechT5HifiGan`]): Vocoder of class [SpeechT5HifiGan](https://huggingface.co/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5HifiGan). """ def __init__( self, vae: AutoencoderKL, text_encoder: ClapTextModelWithProjection, tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast], unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, vocoder: SpeechT5HifiGan, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, vocoder=vocoder, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and vocoder have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.vocoder]: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt( self, prompt, device, num_waveforms_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device (`torch.device`): torch device num_waveforms_per_prompt (`int`): number of waveforms that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the audio generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids attention_mask = text_inputs.attention_mask untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLAP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask.to(device), ) prompt_embeds = prompt_embeds.text_embeds # additional L_2 normalization over each hidden-state prompt_embeds = F.normalize(prompt_embeds, dim=-1) prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) ( bs_embed, seq_len, ) = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt) prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_input.input_ids.to(device) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input_ids, attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds.text_embeds # additional L_2 normalization over each hidden-state negative_prompt_embeds = F.normalize(negative_prompt_embeds, dim=-1) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def decode_latents(self, latents): latents = 1 / self.vae.config.scaling_factor * latents mel_spectrogram = self.vae.decode(latents).sample return mel_spectrogram def mel_spectrogram_to_waveform(self, mel_spectrogram): if mel_spectrogram.dim() == 4: mel_spectrogram = mel_spectrogram.squeeze(1) waveform = self.vocoder(mel_spectrogram) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 waveform = waveform.cpu().float() return waveform # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, audio_length_in_s, vocoder_upsample_factor, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor if audio_length_in_s < min_audio_length_in_s: raise ValueError( f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but " f"is {audio_length_in_s}." ) if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0: raise ValueError( f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the " f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of " f"{self.vae_scale_factor}." ) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None): shape = ( batch_size, num_channels_latents, height // self.vae_scale_factor, self.vocoder.config.model_in_dim // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, audio_length_in_s: Optional[float] = None, num_inference_steps: int = 10, guidance_scale: float = 2.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_waveforms_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, output_type: Optional[str] = "np", ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the audio generation. If not defined, one has to pass `prompt_embeds`. instead. audio_length_in_s (`int`, *optional*, defaults to 5.12): The length of the generated audio sample in seconds. num_inference_steps (`int`, *optional*, defaults to 10): The number of denoising steps. More denoising steps usually lead to a higher quality audio at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 2.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate audios that are closely linked to the text `prompt`, usually at the expense of lower sound quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the audio generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_waveforms_per_prompt (`int`, *optional*, defaults to 1): The number of waveforms to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for audio generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). output_type (`str`, *optional*, defaults to `"np"`): The output format of the generate image. Choose between: - `"np"`: Return Numpy `np.ndarray` objects. - `"pt"`: Return PyTorch `torch.Tensor` objects. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated audios. """ # 0. Convert audio input length from seconds to spectrogram height vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate if audio_length_in_s is None: audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor height = int(audio_length_in_s / vocoder_upsample_factor) original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate) if height % self.vae_scale_factor != 0: height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor logger.info( f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} " f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the " f"denoising process." ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, audio_length_in_s, vocoder_upsample_factor, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_waveforms_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_waveforms_per_prompt, num_channels_latents, height, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=None, class_labels=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 8. Post-processing mel_spectrogram = self.decode_latents(latents) audio = self.mel_spectrogram_to_waveform(mel_spectrogram) audio = audio[:, :original_waveform_length] if output_type == "np": audio = audio.numpy() if not return_dict: return (audio,) return AudioPipelineOutput(audios=audio) ================================================ FILE: diffusers/pipelines/controlnet/__init__.py ================================================ from ...utils import ( OptionalDependencyNotAvailable, is_flax_available, is_torch_available, is_transformers_available, ) try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .multicontrolnet import MultiControlNetModel from .pipeline_controlnet import StableDiffusionControlNetPipeline from .pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline if is_transformers_available() and is_flax_available(): from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline ================================================ FILE: diffusers/pipelines/controlnet/multicontrolnet.py ================================================ from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch import nn from ...models.controlnet import ControlNetModel, ControlNetOutput from ...models.modeling_utils import ModelMixin class MultiControlNetModel(ModelMixin): r""" Multiple `ControlNetModel` wrapper class for Multi-ControlNet This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be compatible with `ControlNetModel`. Args: controlnets (`List[ControlNetModel]`): Provides additional conditioning to the unet during the denoising process. You must set multiple `ControlNetModel` as a list. """ def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]): super().__init__() self.nets = nn.ModuleList(controlnets) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, controlnet_cond: List[torch.tensor], conditioning_scale: List[float], class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guess_mode: bool = False, return_dict: bool = True, ) -> Union[ControlNetOutput, Tuple]: for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)): down_samples, mid_sample = controlnet( sample, timestep, encoder_hidden_states, image, scale, class_labels, timestep_cond, attention_mask, cross_attention_kwargs, guess_mode, return_dict, ) # merge samples if i == 0: down_block_res_samples, mid_block_res_sample = down_samples, mid_sample else: down_block_res_samples = [ samples_prev + samples_curr for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) ] mid_block_res_sample += mid_sample return down_block_res_samples, mid_block_res_sample ================================================ FILE: diffusers/pipelines/controlnet/pipeline_controlnet.py ================================================ # Copyright 2023 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. import inspect import os import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, is_compiled_module, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from ..stable_diffusion import StableDiffusionPipelineOutput from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker from .multicontrolnet import MultiControlNetModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> import numpy as np >>> import torch >>> import cv2 >>> from PIL import Image >>> # download an image >>> image = load_image( ... "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" ... ) >>> image = np.array(image) >>> # get canny image >>> image = cv2.Canny(image, 100, 200) >>> image = image[:, :, None] >>> image = np.concatenate([image, image, image], axis=2) >>> canny_image = Image.fromarray(image) >>> # load control net and stable diffusion v1-5 >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> # speed up diffusion process with faster scheduler and memory optimization >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) >>> # remove following line if xformers is not installed >>> pipe.enable_xformers_memory_efficient_attention() >>> pipe.enable_model_cpu_offload() >>> # generate image >>> generator = torch.manual_seed(0) >>> image = pipe( ... "futuristic-looking woman", num_inference_steps=20, generator=generator, image=canny_image ... ).images[0] ``` """ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets as a list, the outputs from each ControlNet are added together to create one combined additional conditioning. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: # the safety checker can offload the vae again _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # control net hook has be manually offloaded as it alternates with unet cpu_offload_with_hook(self.controlnet, device) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, controlnet_conditioning_scale=1.0, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # `prompt` needs more sophisticated handling when there are multiple # conditionings. if isinstance(self.controlnet, MultiControlNetModel): if isinstance(prompt, list): logger.warning( f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" " prompts. The conditionings will be fixed across the prompts." ) # Check `image` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): self.check_image(image, prompt, prompt_embeds) elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if not isinstance(image, list): raise TypeError("For multiple controlnets: `image` must be type `list`") # When `image` is a nested list: # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]]) elif any(isinstance(i, list) for i in image): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif len(image) != len(self.controlnet.nets): raise ValueError( "For multiple controlnets: `image` must have the same length as the number of controlnets." ) for image_ in image: self.check_image(image_, prompt, prompt_embeds) else: assert False # Check `controlnet_conditioning_scale` if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if isinstance(controlnet_conditioning_scale, list): if any(isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" " the same length as the number of controlnets" ) else: assert False def check_image(self, image, prompt, prompt_embeds): image_is_pil = isinstance(image, PIL.Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list: raise TypeError( "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors" ) if image_is_pil: image_batch_size = 1 elif image_is_tensor: image_batch_size = image.shape[0] elif image_is_pil_list: image_batch_size = len(image) elif image_is_tensor_list: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) def prepare_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): if not isinstance(image, torch.Tensor): if isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): images = [] for image_ in image: image_ = image_.convert("RGB") image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]) image_ = np.array(image_) image_ = image_[None, :] images.append(image_) image = images image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance and not guess_mode: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def _default_height_width(self, height, width, image): # NOTE: It is possible that a list of images have different # dimensions for each image, so just checking the first image # is not _exactly_ correct, but it is simple. while isinstance(image, list): image = image[0] if height is None: if isinstance(image, PIL.Image.Image): height = image.height elif isinstance(image, torch.Tensor): height = image.shape[2] height = (height // 8) * 8 # round down to nearest multiple of 8 if width is None: if isinstance(image, PIL.Image.Image): width = image.width elif isinstance(image, torch.Tensor): width = image.shape[3] width = (width // 8) * 8 # round down to nearest multiple of 8 return height, width # override DiffusionPipeline def save_pretrained( self, save_directory: Union[str, os.PathLike], safe_serialization: bool = False, variant: Optional[str] = None, ): if isinstance(self.controlnet, ControlNetModel): super().save_pretrained(save_directory, safe_serialization, variant) else: raise NotImplementedError("Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.") @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, guess_mode: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`): The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, `image` is resized according to them. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single controlnet. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height, width = self._default_height_width(height, width, image) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, controlnet_conditioning_scale, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare image if isinstance(controlnet, ControlNetModel): image = self.prepare_image( image=image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) elif isinstance(controlnet, MultiControlNetModel): images = [] for image_ in image: image_ = self.prepare_image( image=image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) images.append(image_) image = images else: assert False # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # controlnet(s) inference if guess_mode and do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. controlnet_latent_model_input = latents controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: controlnet_latent_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds down_block_res_samples, mid_block_res_sample = self.controlnet( controlnet_latent_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=image, conditioning_scale=controlnet_conditioning_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # If we do sequential model offloading, let's offload unet and controlnet # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") self.controlnet.to("cpu") torch.cuda.empty_cache() if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py ================================================ # Copyright 2023 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. import inspect import os import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, is_compiled_module, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from ..stable_diffusion import StableDiffusionPipelineOutput from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker from .multicontrolnet import MultiControlNetModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> import numpy as np >>> import torch >>> import cv2 >>> from PIL import Image >>> # download an image >>> image = load_image( ... "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" ... ) >>> np_image = np.array(image) >>> # get canny image >>> np_image = cv2.Canny(np_image, 100, 200) >>> np_image = np_image[:, :, None] >>> np_image = np.concatenate([np_image, np_image, np_image], axis=2) >>> canny_image = Image.fromarray(np_image) >>> # load control net and stable diffusion v1-5 >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> # speed up diffusion process with faster scheduler and memory optimization >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) >>> pipe.enable_model_cpu_offload() >>> # generate image >>> generator = torch.manual_seed(0) >>> image = pipe( ... "futuristic-looking woman", ... num_inference_steps=20, ... generator=generator, ... image=image, ... control_image=canny_image, ... ).images[0] ``` """ def prepare_image(image): if isinstance(image, torch.Tensor): # Batch single image if image.ndim == 3: image = image.unsqueeze(0) image = image.to(dtype=torch.float32) else: # preprocess image if isinstance(image, (PIL.Image.Image, np.ndarray)): image = [image] if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): image = [np.array(i.convert("RGB"))[None, :] for i in image] image = np.concatenate(image, axis=0) elif isinstance(image, list) and isinstance(image[0], np.ndarray): image = np.concatenate([i[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 return image class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets as a list, the outputs from each ControlNet are added together to create one combined additional conditioning. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: # the safety checker can offload the vae again _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # control net hook has be manually offloaded as it alternates with unet cpu_offload_with_hook(self.controlnet, device) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, controlnet_conditioning_scale=1.0, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # `prompt` needs more sophisticated handling when there are multiple # conditionings. if isinstance(self.controlnet, MultiControlNetModel): if isinstance(prompt, list): logger.warning( f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" " prompts. The conditionings will be fixed across the prompts." ) # Check `image` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): self.check_image(image, prompt, prompt_embeds) elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if not isinstance(image, list): raise TypeError("For multiple controlnets: `image` must be type `list`") # When `image` is a nested list: # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]]) elif any(isinstance(i, list) for i in image): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif len(image) != len(self.controlnet.nets): raise ValueError( "For multiple controlnets: `image` must have the same length as the number of controlnets." ) for image_ in image: self.check_image(image_, prompt, prompt_embeds) else: assert False # Check `controlnet_conditioning_scale` if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if isinstance(controlnet_conditioning_scale, list): if any(isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" " the same length as the number of controlnets" ) else: assert False def check_image(self, image, prompt, prompt_embeds): image_is_pil = isinstance(image, PIL.Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list: raise TypeError( "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors" ) if image_is_pil: image_batch_size = 1 elif image_is_tensor: image_batch_size = image.shape[0] elif image_is_pil_list: image_batch_size = len(image) elif image_is_tensor_list: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image def prepare_control_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): if not isinstance(image, torch.Tensor): if isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): images = [] for image_ in image: image_ = image_.convert("RGB") image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]) image_ = np.array(image_) image_ = image_[None, :] images.append(image_) image = images image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance and not guess_mode: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents def _default_height_width(self, height, width, image): # NOTE: It is possible that a list of images have different # dimensions for each image, so just checking the first image # is not _exactly_ correct, but it is simple. while isinstance(image, list): image = image[0] if height is None: if isinstance(image, PIL.Image.Image): height = image.height elif isinstance(image, torch.Tensor): height = image.shape[2] height = (height // 8) * 8 # round down to nearest multiple of 8 if width is None: if isinstance(image, PIL.Image.Image): width = image.width elif isinstance(image, torch.Tensor): width = image.shape[3] width = (width // 8) * 8 # round down to nearest multiple of 8 return height, width # override DiffusionPipeline def save_pretrained( self, save_directory: Union[str, os.PathLike], safe_serialization: bool = False, variant: Optional[str] = None, ): if isinstance(self.controlnet, ControlNetModel): super().save_pretrained(save_directory, safe_serialization, variant) else: raise NotImplementedError("Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.") @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None, control_image: Union[ torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image] ] = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 0.8, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 0.8, guess_mode: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`): The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, `image` is resized according to them. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single controlnet. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting than for [`~StableDiffusionControlNetPipeline.__call__`]. guess_mode (`bool`, *optional*, defaults to `False`): In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height, width = self._default_height_width(height, width, image) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, control_image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, controlnet_conditioning_scale, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare image, and controlnet_conditioning_image image = prepare_image(image) # 5. Prepare image if isinstance(controlnet, ControlNetModel): control_image = self.prepare_control_image( image=control_image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) elif isinstance(controlnet, MultiControlNetModel): control_images = [] for control_image_ in control_image: control_image_ = self.prepare_control_image( image=control_image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) control_images.append(control_image_) control_image = control_images else: assert False # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # controlnet(s) inference if guess_mode and do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. controlnet_latent_model_input = latents controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: controlnet_latent_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds down_block_res_samples, mid_block_res_sample = self.controlnet( controlnet_latent_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=control_image, conditioning_scale=controlnet_conditioning_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # If we do sequential model offloading, let's offload unet and controlnet # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") self.controlnet.to("cpu") torch.cuda.empty_cache() if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py ================================================ # Copyright 2023 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. # This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/ import inspect import os import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, is_compiled_module, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from ..stable_diffusion import StableDiffusionPipelineOutput from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker from .multicontrolnet import MultiControlNetModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler >>> from diffusers.utils import load_image >>> import numpy as np >>> import torch >>> import cv2 >>> from PIL import Image >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" >>> init_image = load_image(img_url).resize((512, 512)) >>> mask_image = load_image(mask_url).resize((512, 512)) >>> image = np.array(init_image) >>> # get canny image >>> image = cv2.Canny(image, 100, 200) >>> image = image[:, :, None] >>> image = np.concatenate([image, image, image], axis=2) >>> canny_image = Image.fromarray(image) >>> # load control net and stable diffusion inpainting >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( ... "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> # speed up diffusion process with faster scheduler and memory optimization >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) >>> pipe.enable_model_cpu_offload() >>> # generate image >>> generator = torch.manual_seed(0) >>> image = pipe( ... "spiderman", ... num_inference_steps=30, ... generator=generator, ... image=init_image, ... mask_image=mask_image, ... control_image=canny_image, ... ).images[0] ``` """ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image def prepare_mask_and_masked_image(image, mask, height, width, return_image=False): """ Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the ``image`` and ``1`` for the ``mask``. The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be binarized (``mask > 0.5``) and cast to ``torch.float32`` too. Args: image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint. It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``. mask (_type_): The mask to apply to the image, i.e. regions to inpaint. It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``. Raises: ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not (ot the other way around). Returns: tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4 dimensions: ``batch x channels x height x width``. """ if image is None: raise ValueError("`image` input cannot be undefined.") if mask is None: raise ValueError("`mask_image` input cannot be undefined.") if isinstance(image, torch.Tensor): if not isinstance(mask, torch.Tensor): raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not") # Batch single image if image.ndim == 3: assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)" image = image.unsqueeze(0) # Batch and add channel dim for single mask if mask.ndim == 2: mask = mask.unsqueeze(0).unsqueeze(0) # Batch single mask or add channel dim if mask.ndim == 3: # Single batched mask, no channel dim or single mask not batched but channel dim if mask.shape[0] == 1: mask = mask.unsqueeze(0) # Batched masks no channel dim else: mask = mask.unsqueeze(1) assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" # Check image is in [-1, 1] if image.min() < -1 or image.max() > 1: raise ValueError("Image should be in [-1, 1] range") # Check mask is in [0, 1] if mask.min() < 0 or mask.max() > 1: raise ValueError("Mask should be in [0, 1] range") # Binarize mask mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 # Image as float32 image = image.to(dtype=torch.float32) elif isinstance(mask, torch.Tensor): raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not") else: # preprocess image if isinstance(image, (PIL.Image.Image, np.ndarray)): image = [image] if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): # resize all images w.r.t passed height an width image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image] image = [np.array(i.convert("RGB"))[None, :] for i in image] image = np.concatenate(image, axis=0) elif isinstance(image, list) and isinstance(image[0], np.ndarray): image = np.concatenate([i[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 # preprocess mask if isinstance(mask, (PIL.Image.Image, np.ndarray)): mask = [mask] if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image): mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask] mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) mask = mask.astype(np.float32) / 255.0 elif isinstance(mask, list) and isinstance(mask[0], np.ndarray): mask = np.concatenate([m[None, None, :] for m in mask], axis=0) mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask = torch.from_numpy(mask) masked_image = image * (mask < 0.5) # n.b. ensure backwards compatibility as old function does not return image if return_image: return mask, masked_image, image return mask, masked_image class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. controlnet ([`ControlNetModel`] or `List[ControlNetModel]`): Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets as a list, the outputs from each ControlNet are added together to create one combined additional conditioning. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: # the safety checker can offload the vae again _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # control net hook has be manually offloaded as it alternates with unet cpu_offload_with_hook(self.controlnet, device) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, controlnet_conditioning_scale=1.0, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # `prompt` needs more sophisticated handling when there are multiple # conditionings. if isinstance(self.controlnet, MultiControlNetModel): if isinstance(prompt, list): logger.warning( f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" " prompts. The conditionings will be fixed across the prompts." ) # Check `image` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): self.check_image(image, prompt, prompt_embeds) elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if not isinstance(image, list): raise TypeError("For multiple controlnets: `image` must be type `list`") # When `image` is a nested list: # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]]) elif any(isinstance(i, list) for i in image): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif len(image) != len(self.controlnet.nets): raise ValueError( "For multiple controlnets: `image` must have the same length as the number of controlnets." ) for image_ in image: self.check_image(image_, prompt, prompt_embeds) else: assert False # Check `controlnet_conditioning_scale` if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if isinstance(controlnet_conditioning_scale, list): if any(isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" " the same length as the number of controlnets" ) else: assert False def check_image(self, image, prompt, prompt_embeds): image_is_pil = isinstance(image, PIL.Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list: raise TypeError( "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors" ) if image_is_pil: image_batch_size = 1 elif image_is_tensor: image_batch_size = image.shape[0] elif image_is_pil_list: image_batch_size = len(image) elif image_is_tensor_list: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image def prepare_control_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): if not isinstance(image, torch.Tensor): if isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): images = [] for image_ in image: image_ = image_.convert("RGB") image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]) image_ = np.array(image_) image_ = image_[None, :] images.append(image_) image = images image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance and not guess_mode: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_latents def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, image=None, timestep=None, is_strength_max=True, ): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if (image is None or timestep is None) and not is_strength_max: raise ValueError( "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise." "However, either the image or the noise timestep has not been provided." ) if latents is None: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) if is_strength_max: # if strength is 100% then simply initialise the latents to noise latents = noise else: # otherwise initialise latents as init image + noise image = image.to(device=device, dtype=dtype) if isinstance(generator, list): image_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(batch_size) ] else: image_latents = self.vae.encode(image).latent_dist.sample(generator=generator) image_latents = self.vae.config.scaling_factor * image_latents latents = self.scheduler.add_noise(image_latents, noise, timestep) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def _default_height_width(self, height, width, image): # NOTE: It is possible that a list of images have different # dimensions for each image, so just checking the first image # is not _exactly_ correct, but it is simple. while isinstance(image, list): image = image[0] if height is None: if isinstance(image, PIL.Image.Image): height = image.height elif isinstance(image, torch.Tensor): height = image.shape[2] height = (height // 8) * 8 # round down to nearest multiple of 8 if width is None: if isinstance(image, PIL.Image.Image): width = image.width elif isinstance(image, torch.Tensor): width = image.shape[3] width = (width // 8) * 8 # round down to nearest multiple of 8 return height, width # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask = torch.nn.functional.interpolate( mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) ) mask = mask.to(device=device, dtype=dtype) masked_image = masked_image.to(device=device, dtype=dtype) # encode the mask image into latents space so we can concatenate it to the latents if isinstance(generator, list): masked_image_latents = [ self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(batch_size) ] masked_image_latents = torch.cat(masked_image_latents, dim=0) else: masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) masked_image_latents = self.vae.config.scaling_factor * masked_image_latents # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if mask.shape[0] < batch_size: if not batch_size % mask.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1) mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask masked_image_latents = ( torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return mask, masked_image_latents # override DiffusionPipeline def save_pretrained( self, save_directory: Union[str, os.PathLike], safe_serialization: bool = False, variant: Optional[str] = None, ): if isinstance(self.controlnet, ControlNetModel): super().save_pretrained(save_directory, safe_serialization, variant) else: raise NotImplementedError("Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.") @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.Tensor, PIL.Image.Image] = None, mask_image: Union[torch.Tensor, PIL.Image.Image] = None, control_image: Union[ torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image] ] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 0.5, guess_mode: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`): The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, `image` is resized according to them. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single controlnet. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 0.5): The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting than for [`~StableDiffusionControlNetPipeline.__call__`]. guess_mode (`bool`, *optional*, defaults to `False`): In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height, width = self._default_height_width(height, width, image) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, control_image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, controlnet_conditioning_scale, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare image if isinstance(controlnet, ControlNetModel): control_image = self.prepare_control_image( image=control_image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) elif isinstance(controlnet, MultiControlNetModel): control_images = [] for control_image_ in control_image: control_image_ = self.prepare_control_image( image=control_image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) control_images.append(control_image_) control_image = control_images else: assert False # 4. Preprocess mask and image - resizes image and mask w.r.t height and width # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 6. Prepare latent variables num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 7. Prepare mask latent variables mask, masked_image = prepare_mask_and_masked_image(image, mask_image, height, width) mask, masked_image_latents = self.prepare_mask_latents( mask, masked_image, batch_size * num_images_per_prompt, height, width, prompt_embeds.dtype, device, generator, do_classifier_free_guidance, ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) if guess_mode and do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. controlnet_latent_model_input = latents controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: controlnet_latent_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds down_block_res_samples, mid_block_res_sample = self.controlnet( controlnet_latent_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=control_image, conditioning_scale=controlnet_conditioning_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) # predict the noise residual latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # If we do sequential model offloading, let's offload unet and controlnet # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") self.controlnet.to("cpu") torch.cuda.empty_cache() if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/controlnet/pipeline_flax_controlnet.py ================================================ # Copyright 2023 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. import warnings from functools import partial from typing import Dict, List, Optional, Union import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict from flax.jax_utils import unreplicate from flax.training.common_utils import shard from PIL import Image from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel from ...models import FlaxAutoencoderKL, FlaxControlNetModel, FlaxUNet2DConditionModel from ...schedulers import ( FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, ) from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring from ..pipeline_flax_utils import FlaxDiffusionPipeline from ..stable_diffusion import FlaxStableDiffusionPipelineOutput from ..stable_diffusion.safety_checker_flax import FlaxStableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Set to True to use python for loop instead of jax.fori_loop for easier debugging DEBUG = False EXAMPLE_DOC_STRING = """ Examples: ```py >>> import jax >>> import numpy as np >>> import jax.numpy as jnp >>> from flax.jax_utils import replicate >>> from flax.training.common_utils import shard >>> from diffusers.utils import load_image >>> from PIL import Image >>> from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel >>> def image_grid(imgs, rows, cols): ... w, h = imgs[0].size ... grid = Image.new("RGB", size=(cols * w, rows * h)) ... for i, img in enumerate(imgs): ... grid.paste(img, box=(i % cols * w, i // cols * h)) ... return grid >>> def create_key(seed=0): ... return jax.random.PRNGKey(seed) >>> rng = create_key(0) >>> # get canny image >>> canny_image = load_image( ... "https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/blog_post_cell_10_output_0.jpeg" ... ) >>> prompts = "best quality, extremely detailed" >>> negative_prompts = "monochrome, lowres, bad anatomy, worst quality, low quality" >>> # load control net and stable diffusion v1-5 >>> controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( ... "lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.float32 ... ) >>> pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32 ... ) >>> params["controlnet"] = controlnet_params >>> num_samples = jax.device_count() >>> rng = jax.random.split(rng, jax.device_count()) >>> prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples) >>> negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples) >>> processed_image = pipe.prepare_image_inputs([canny_image] * num_samples) >>> p_params = replicate(params) >>> prompt_ids = shard(prompt_ids) >>> negative_prompt_ids = shard(negative_prompt_ids) >>> processed_image = shard(processed_image) >>> output = pipe( ... prompt_ids=prompt_ids, ... image=processed_image, ... params=p_params, ... prng_seed=rng, ... num_inference_steps=50, ... neg_prompt_ids=negative_prompt_ids, ... jit=True, ... ).images >>> output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:]))) >>> output_images = image_grid(output_images, num_samples // 4, 4) >>> output_images.save("generated_image.png") ``` """ class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet Guidance. This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`FlaxAutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`FlaxCLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. controlnet ([`FlaxControlNetModel`]: Provides additional conditioning to the unet during the denoising process. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or [`FlaxDPMSolverMultistepScheduler`]. safety_checker ([`FlaxStableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: FlaxAutoencoderKL, text_encoder: FlaxCLIPTextModel, tokenizer: CLIPTokenizer, unet: FlaxUNet2DConditionModel, controlnet: FlaxControlNetModel, scheduler: Union[ FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler ], safety_checker: FlaxStableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, dtype: jnp.dtype = jnp.float32, ): super().__init__() self.dtype = dtype if safety_checker is None: logger.warn( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) def prepare_text_inputs(self, prompt: Union[str, List[str]]): if not isinstance(prompt, (str, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) return text_input.input_ids def prepare_image_inputs(self, image: Union[Image.Image, List[Image.Image]]): if not isinstance(image, (Image.Image, list)): raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}") if isinstance(image, Image.Image): image = [image] processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image]) return processed_images def _get_has_nsfw_concepts(self, features, params): has_nsfw_concepts = self.safety_checker(features, params) return has_nsfw_concepts def _run_safety_checker(self, images, safety_model_params, jit=False): # safety_model_params should already be replicated when jit is True pil_images = [Image.fromarray(image) for image in images] features = self.feature_extractor(pil_images, return_tensors="np").pixel_values if jit: features = shard(features) has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params) has_nsfw_concepts = unshard(has_nsfw_concepts) safety_model_params = unreplicate(safety_model_params) else: has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params) images_was_copied = False for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: if not images_was_copied: images_was_copied = True images = images.copy() images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image if any(has_nsfw_concepts): warnings.warn( "Potential NSFW content was detected in one or more images. A black image will be returned" " instead. Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts def _generate( self, prompt_ids: jnp.array, image: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, num_inference_steps: int, guidance_scale: float, latents: Optional[jnp.array] = None, neg_prompt_ids: Optional[jnp.array] = None, controlnet_conditioning_scale: float = 1.0, ): height, width = image.shape[-2:] if height % 64 != 0 or width % 64 != 0: raise ValueError(f"`height` and `width` have to be divisible by 64 but are {height} and {width}.") # get prompt text embeddings prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0] # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0` # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0` batch_size = prompt_ids.shape[0] max_length = prompt_ids.shape[-1] if neg_prompt_ids is None: uncond_input = self.tokenizer( [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np" ).input_ids else: uncond_input = neg_prompt_ids negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0] context = jnp.concatenate([negative_prompt_embeds, prompt_embeds]) image = jnp.concatenate([image] * 2) latents_shape = ( batch_size, self.unet.config.in_channels, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if latents is None: latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") def loop_body(step, args): latents, scheduler_state = args # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes latents_input = jnp.concatenate([latents] * 2) t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step] timestep = jnp.broadcast_to(t, latents_input.shape[0]) latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t) down_block_res_samples, mid_block_res_sample = self.controlnet.apply( {"params": params["controlnet"]}, jnp.array(latents_input), jnp.array(timestep, dtype=jnp.int32), encoder_hidden_states=context, controlnet_cond=image, conditioning_scale=controlnet_conditioning_scale, return_dict=False, ) # predict the noise residual noise_pred = self.unet.apply( {"params": params["unet"]}, jnp.array(latents_input), jnp.array(timestep, dtype=jnp.int32), encoder_hidden_states=context, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, ).sample # perform guidance noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple() return latents, scheduler_state scheduler_state = self.scheduler.set_timesteps( params["scheduler"], num_inference_steps=num_inference_steps, shape=latents_shape ) # scale the initial noise by the standard deviation required by the scheduler latents = latents * params["scheduler"].init_noise_sigma if DEBUG: # run with python for loop for i in range(num_inference_steps): latents, scheduler_state = loop_body(i, (latents, scheduler_state)) else: latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state)) # scale and decode the image latents with vae latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1) return image @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt_ids: jnp.array, image: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, num_inference_steps: int = 50, guidance_scale: Union[float, jnp.array] = 7.5, latents: jnp.array = None, neg_prompt_ids: jnp.array = None, controlnet_conditioning_scale: Union[float, jnp.array] = 1.0, return_dict: bool = True, jit: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt_ids (`jnp.array`): The prompt or prompts to guide the image generation. image (`jnp.array`): Array representing the ControlNet input condition. ControlNet use this input condition to generate guidance to Unet. params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. latents (`jnp.array`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. controlnet_conditioning_scale (`float` or `jnp.array`, *optional*, defaults to 1.0): The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of a plain tuple. jit (`bool`, defaults to `False`): Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release. Examples: Returns: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ height, width = image.shape[-2:] if isinstance(guidance_scale, float): # Convert to a tensor so each device gets a copy. Follow the prompt_ids for # shape information, as they may be sharded (when `jit` is `True`), or not. guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0]) if len(prompt_ids.shape) > 2: # Assume sharded guidance_scale = guidance_scale[:, None] if isinstance(controlnet_conditioning_scale, float): # Convert to a tensor so each device gets a copy. Follow the prompt_ids for # shape information, as they may be sharded (when `jit` is `True`), or not. controlnet_conditioning_scale = jnp.array([controlnet_conditioning_scale] * prompt_ids.shape[0]) if len(prompt_ids.shape) > 2: # Assume sharded controlnet_conditioning_scale = controlnet_conditioning_scale[:, None] if jit: images = _p_generate( self, prompt_ids, image, params, prng_seed, num_inference_steps, guidance_scale, latents, neg_prompt_ids, controlnet_conditioning_scale, ) else: images = self._generate( prompt_ids, image, params, prng_seed, num_inference_steps, guidance_scale, latents, neg_prompt_ids, controlnet_conditioning_scale, ) if self.safety_checker is not None: safety_params = params["safety_checker"] images_uint8_casted = (images * 255).round().astype("uint8") num_devices, batch_size = images.shape[:2] images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3) images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit) images = np.asarray(images) # block images if any(has_nsfw_concept): for i, is_nsfw in enumerate(has_nsfw_concept): if is_nsfw: images[i] = np.asarray(images_uint8_casted[i]) images = images.reshape(num_devices, batch_size, height, width, 3) else: images = np.asarray(images) has_nsfw_concept = False if not return_dict: return (images, has_nsfw_concept) return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept) # Static argnums are pipe, num_inference_steps. A change would trigger recompilation. # Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`). @partial( jax.pmap, in_axes=(None, 0, 0, 0, 0, None, 0, 0, 0, 0), static_broadcasted_argnums=(0, 5), ) def _p_generate( pipe, prompt_ids, image, params, prng_seed, num_inference_steps, guidance_scale, latents, neg_prompt_ids, controlnet_conditioning_scale, ): return pipe._generate( prompt_ids, image, params, prng_seed, num_inference_steps, guidance_scale, latents, neg_prompt_ids, controlnet_conditioning_scale, ) @partial(jax.pmap, static_broadcasted_argnums=(0,)) def _p_get_has_nsfw_concepts(pipe, features, params): return pipe._get_has_nsfw_concepts(features, params) def unshard(x: jnp.ndarray): # einops.rearrange(x, 'd b ... -> (d b) ...') num_devices, batch_size = x.shape[:2] rest = x.shape[2:] return x.reshape(num_devices * batch_size, *rest) def preprocess(image, dtype): image = image.convert("RGB") w, h = image.size w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = jnp.array(image).astype(dtype) / 255.0 image = image[None].transpose(0, 3, 1, 2) return image ================================================ FILE: diffusers/pipelines/dance_diffusion/__init__.py ================================================ from .pipeline_dance_diffusion import DanceDiffusionPipeline ================================================ FILE: diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py ================================================ # Copyright 2023 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 List, Optional, Tuple, Union import torch from ...utils import logging, randn_tensor from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline logger = logging.get_logger(__name__) # pylint: disable=invalid-name class DanceDiffusionPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of [`IPNDMScheduler`]. """ def __init__(self, unet, scheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, num_inference_steps: int = 100, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, audio_length_in_s: Optional[float] = None, return_dict: bool = True, ) -> Union[AudioPipelineOutput, Tuple]: r""" Args: batch_size (`int`, *optional*, defaults to 1): The number of audio samples to generate. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality audio sample at the expense of slower inference. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`): The length of the generated audio sample in seconds. Note that the output of the pipeline, *i.e.* `sample_size`, will be `audio_length_in_s` * `self.unet.config.sample_rate`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.AudioPipelineOutput`] or `tuple`: [`~pipelines.utils.AudioPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ if audio_length_in_s is None: audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate sample_size = audio_length_in_s * self.unet.config.sample_rate down_scale_factor = 2 ** len(self.unet.up_blocks) if sample_size < 3 * down_scale_factor: raise ValueError( f"{audio_length_in_s} is too small. Make sure it's bigger or equal to" f" {3 * down_scale_factor / self.unet.config.sample_rate}." ) original_sample_size = int(sample_size) if sample_size % down_scale_factor != 0: sample_size = ( (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1 ) * down_scale_factor logger.info( f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled" f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising" " process." ) sample_size = int(sample_size) dtype = next(iter(self.unet.parameters())).dtype shape = (batch_size, self.unet.config.in_channels, sample_size) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) audio = randn_tensor(shape, generator=generator, device=self.device, dtype=dtype) # set step values self.scheduler.set_timesteps(num_inference_steps, device=audio.device) self.scheduler.timesteps = self.scheduler.timesteps.to(dtype) for t in self.progress_bar(self.scheduler.timesteps): # 1. predict noise model_output model_output = self.unet(audio, t).sample # 2. compute previous image: x_t -> t_t-1 audio = self.scheduler.step(model_output, t, audio).prev_sample audio = audio.clamp(-1, 1).float().cpu().numpy() audio = audio[:, :, :original_sample_size] if not return_dict: return (audio,) return AudioPipelineOutput(audios=audio) ================================================ FILE: diffusers/pipelines/ddim/__init__.py ================================================ from .pipeline_ddim import DDIMPipeline ================================================ FILE: diffusers/pipelines/ddim/pipeline_ddim.py ================================================ # Copyright 2023 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 List, Optional, Tuple, Union import torch from ...schedulers import DDIMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class DDIMPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of [`DDPMScheduler`], or [`DDIMScheduler`]. """ def __init__(self, unet, scheduler): super().__init__() # make sure scheduler can always be converted to DDIM scheduler = DDIMScheduler.from_config(scheduler.config) self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, eta: float = 0.0, num_inference_steps: int = 50, use_clipped_model_output: Optional[bool] = None, output_type: Optional[str] = "pil", return_dict: bool = True, ) -> Union[ImagePipelineOutput, Tuple]: r""" Args: batch_size (`int`, *optional*, defaults to 1): The number of images to generate. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. eta (`float`, *optional*, defaults to 0.0): The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM). num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. use_clipped_model_output (`bool`, *optional*, defaults to `None`): if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed downstream to the scheduler. So use `None` for schedulers which don't support this argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ # Sample gaussian noise to begin loop if isinstance(self.unet.config.sample_size, int): image_shape = ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size, ) else: image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) image = randn_tensor(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype) # set step values self.scheduler.set_timesteps(num_inference_steps) for t in self.progress_bar(self.scheduler.timesteps): # 1. predict noise model_output model_output = self.unet(image, t).sample # 2. predict previous mean of image x_t-1 and add variance depending on eta # eta corresponds to η in paper and should be between [0, 1] # do x_t -> x_t-1 image = self.scheduler.step( model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator ).prev_sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/ddpm/__init__.py ================================================ from .pipeline_ddpm import DDPMPipeline ================================================ FILE: diffusers/pipelines/ddpm/pipeline_ddpm.py ================================================ # Copyright 2023 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 List, Optional, Tuple, Union import torch from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class DDPMPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of [`DDPMScheduler`], or [`DDIMScheduler`]. """ def __init__(self, unet, scheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, num_inference_steps: int = 1000, output_type: Optional[str] = "pil", return_dict: bool = True, ) -> Union[ImagePipelineOutput, Tuple]: r""" Args: batch_size (`int`, *optional*, defaults to 1): The number of images to generate. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. num_inference_steps (`int`, *optional*, defaults to 1000): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ # Sample gaussian noise to begin loop if isinstance(self.unet.config.sample_size, int): image_shape = ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size, ) else: image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size) if self.device.type == "mps": # randn does not work reproducibly on mps image = randn_tensor(image_shape, generator=generator) image = image.to(self.device) else: image = randn_tensor(image_shape, generator=generator, device=self.device) # set step values self.scheduler.set_timesteps(num_inference_steps) for t in self.progress_bar(self.scheduler.timesteps): # 1. predict noise model_output model_output = self.unet(image, t).sample # 2. compute previous image: x_t -> x_t-1 image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/deepfloyd_if/__init__.py ================================================ from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available from .timesteps import ( fast27_timesteps, smart27_timesteps, smart50_timesteps, smart100_timesteps, smart185_timesteps, super27_timesteps, super40_timesteps, super100_timesteps, ) @dataclass class IFPipelineOutput(BaseOutput): """ Args: Output class for Stable Diffusion pipelines. images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. nsfw_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content or a watermark. `None` if safety checking could not be performed. watermark_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely has a watermark. `None` if safety checking could not be performed. """ images: Union[List[PIL.Image.Image], np.ndarray] nsfw_detected: Optional[List[bool]] watermark_detected: Optional[List[bool]] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_if import IFPipeline from .pipeline_if_img2img import IFImg2ImgPipeline from .pipeline_if_img2img_superresolution import IFImg2ImgSuperResolutionPipeline from .pipeline_if_inpainting import IFInpaintingPipeline from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline from .pipeline_if_superresolution import IFSuperResolutionPipeline from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker ================================================ FILE: diffusers/pipelines/deepfloyd_if/pipeline_if.py ================================================ import html import inspect import re import urllib.parse as ul from typing import Any, Callable, Dict, List, Optional, Union import torch from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer from ...loaders import LoraLoaderMixin from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, is_accelerate_available, is_accelerate_version, is_bs4_available, is_ftfy_available, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker logger = logging.get_logger(__name__) # pylint: disable=invalid-name if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16) >>> pipe.enable_model_cpu_offload() >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt" ... ).images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> safety_modules = { ... "feature_extractor": pipe.feature_extractor, ... "safety_checker": pipe.safety_checker, ... "watermarker": pipe.watermarker, ... } >>> super_res_2_pipe = DiffusionPipeline.from_pretrained( ... "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16 ... ) >>> super_res_2_pipe.enable_model_cpu_offload() >>> image = super_res_2_pipe( ... prompt=prompt, ... image=image, ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFPipeline(DiffusionPipeline, LoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the IF license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.text_encoder, self.unet, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None if self.text_encoder is not None: _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) # Accelerate will move the next model to the device _before_ calling the offload hook of the # previous model. This will cause both models to be present on the device at the same time. # IF uses T5 for its text encoder which is really large. We can manually call the offload # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to # the GPU. self.text_encoder_offload_hook = hook _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet self.unet_offload_hook = hook if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook def remove_all_hooks(self): if is_accelerate_available(): from accelerate.hooks import remove_hook_from_module else: raise ImportError("Please install accelerate via `pip install accelerate`") for model in [self.text_encoder, self.unet, self.safety_checker]: if model is not None: remove_hook_from_module(model, recurse=True) self.unet_offload_hook = None self.text_encoder_offload_hook = None self.final_offload_hook = None @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if device is None: device = self._execution_device if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator): shape = (batch_size, num_channels, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler intermediate_images = intermediate_images * self.scheduler.init_noise_sigma return intermediate_images def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, num_inference_steps: int = 100, timesteps: List[int] = None, guidance_scale: float = 7.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, height: Optional[int] = None, width: Optional[int] = None, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, clean_caption: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. height (`int`, *optional*, defaults to self.unet.config.sample_size): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size): The width in pixels of the generated image. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # 2. Define call parameters height = height or self.unet.config.sample_size width = width or self.unet.config.sample_size if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare intermediate images intermediate_images = self.prepare_intermediate_images( batch_size * num_images_per_prompt, self.unet.config.in_channels, height, width, prompt_embeds.dtype, device, generator, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): model_input = ( torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images ) model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if self.scheduler.config.variance_type not in ["learned", "learned_range"]: noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1) # compute the previous noisy sample x_t -> x_t-1 intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, intermediate_images) image = intermediate_images if output_type == "pil": # 8. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 9. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 10. Convert to PIL image = self.numpy_to_pil(image) # 11. Apply watermark if self.watermarker is not None: image = self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() else: # 8. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 9. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) ================================================ FILE: diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py ================================================ import html import inspect import re import urllib.parse as ul from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer from ...loaders import LoraLoaderMixin from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, is_bs4_available, is_ftfy_available, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker logger = logging.get_logger(__name__) # pylint: disable=invalid-name if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image: w, h = images.size coef = w / h w, h = img_size, img_size if coef >= 1: w = int(round(img_size / 8 * coef) * 8) else: h = int(round(img_size / 8 / coef) * 8) images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None) return images EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> from PIL import Image >>> import requests >>> from io import BytesIO >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> original_image = Image.open(BytesIO(response.content)).convert("RGB") >>> original_image = original_image.resize((768, 512)) >>> pipe = IFImg2ImgPipeline.from_pretrained( ... "DeepFloyd/IF-I-XL-v1.0", ... variant="fp16", ... torch_dtype=torch.float16, ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "A fantasy landscape in style minecraft" >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe( ... image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... output_type="pt", ... ).images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", ... text_encoder=None, ... variant="fp16", ... torch_dtype=torch.float16, ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, ... original_image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the IF license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.text_encoder, self.unet, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None if self.text_encoder is not None: _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) # Accelerate will move the next model to the device _before_ calling the offload hook of the # previous model. This will cause both models to be present on the device at the same time. # IF uses T5 for its text encoder which is really large. We can manually call the offload # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to # the GPU. self.text_encoder_offload_hook = hook _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet self.unet_offload_hook = hook if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks def remove_all_hooks(self): if is_accelerate_available(): from accelerate.hooks import remove_hook_from_module else: raise ImportError("Please install accelerate via `pip install accelerate`") for model in [self.text_encoder, self.unet, self.safety_checker]: if model is not None: remove_hook_from_module(model, recurse=True) self.unet_offload_hook = None self.text_encoder_offload_hook = None self.final_offload_hook = None @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if device is None: device = self._execution_device if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, batch_size, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if isinstance(image, list): check_image_type = image[0] else: check_image_type = image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(image, list): image_batch_size = len(image) elif isinstance(image, torch.Tensor): image_batch_size = image.shape[0] elif isinstance(image, PIL.Image.Image): image_batch_size = 1 elif isinstance(image, np.ndarray): image_batch_size = image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}") # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor: if not isinstance(image, list): image = [image] def numpy_to_pt(images): if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) return images if isinstance(image[0], PIL.Image.Image): new_image = [] for image_ in image: image_ = image_.convert("RGB") image_ = resize(image_, self.unet.sample_size) image_ = np.array(image_) image_ = image_.astype(np.float32) image_ = image_ / 127.5 - 1 new_image.append(image_) image = new_image image = np.stack(image, axis=0) # to np image = numpy_to_pt(image) # to pt elif isinstance(image[0], np.ndarray): image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0) image = numpy_to_pt(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0) return image def get_timesteps(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start def prepare_intermediate_images( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None ): _, channels, height, width = image.shape batch_size = batch_size * num_images_per_prompt shape = (batch_size, channels, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) image = self.scheduler.add_noise(image, noise, timestep) return image @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, strength: float = 0.7, num_inference_steps: int = 80, timesteps: List[int] = None, guidance_scale: float = 10.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, clean_caption: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] self.check_inputs( prompt, image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) dtype = prompt_embeds.dtype # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength) # 5. Prepare intermediate images image = self.preprocess_image(image) image = image.to(device=device, dtype=dtype) noise_timestep = timesteps[0:1] noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt) intermediate_images = self.prepare_intermediate_images( image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): model_input = ( torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images ) model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if self.scheduler.config.variance_type not in ["learned", "learned_range"]: noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1) # compute the previous noisy sample x_t -> x_t-1 intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, intermediate_images) image = intermediate_images if output_type == "pil": # 8. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 9. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 10. Convert to PIL image = self.numpy_to_pil(image) # 11. Apply watermark if self.watermarker is not None: self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() else: # 8. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 9. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) ================================================ FILE: diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py ================================================ import html import inspect import re import urllib.parse as ul from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, is_bs4_available, is_ftfy_available, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image: w, h = images.size coef = w / h w, h = img_size, img_size if coef >= 1: w = int(round(img_size / 8 * coef) * 8) else: h = int(round(img_size / 8 / coef) * 8) images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None) return images EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> from PIL import Image >>> import requests >>> from io import BytesIO >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> original_image = Image.open(BytesIO(response.content)).convert("RGB") >>> original_image = original_image.resize((768, 512)) >>> pipe = IFImg2ImgPipeline.from_pretrained( ... "DeepFloyd/IF-I-XL-v1.0", ... variant="fp16", ... torch_dtype=torch.float16, ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "A fantasy landscape in style minecraft" >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe( ... image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... output_type="pt", ... ).images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", ... text_encoder=None, ... variant="fp16", ... torch_dtype=torch.float16, ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, ... original_image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, image_noising_scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the IF license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if unet.config.in_channels != 6: logger.warn( "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, image_noising_scheduler=image_noising_scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.text_encoder, self.unet, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None if self.text_encoder is not None: _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) # Accelerate will move the next model to the device _before_ calling the offload hook of the # previous model. This will cause both models to be present on the device at the same time. # IF uses T5 for its text encoder which is really large. We can manually call the offload # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to # the GPU. self.text_encoder_offload_hook = hook _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet self.unet_offload_hook = hook if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks def remove_all_hooks(self): if is_accelerate_available(): from accelerate.hooks import remove_hook_from_module else: raise ImportError("Please install accelerate via `pip install accelerate`") for model in [self.text_encoder, self.unet, self.safety_checker]: if model is not None: remove_hook_from_module(model, recurse=True) self.unet_offload_hook = None self.text_encoder_offload_hook = None self.final_offload_hook = None # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if device is None: device = self._execution_device if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, original_image, batch_size, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # image if isinstance(image, list): check_image_type = image[0] else: check_image_type = image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(image, list): image_batch_size = len(image) elif isinstance(image, torch.Tensor): image_batch_size = image.shape[0] elif isinstance(image, PIL.Image.Image): image_batch_size = 1 elif isinstance(image, np.ndarray): image_batch_size = image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}") # original_image if isinstance(original_image, list): check_image_type = original_image[0] else: check_image_type = original_image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`original_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(original_image, list): image_batch_size = len(original_image) elif isinstance(original_image, torch.Tensor): image_batch_size = original_image.shape[0] elif isinstance(original_image, PIL.Image.Image): image_batch_size = 1 elif isinstance(original_image, np.ndarray): image_batch_size = original_image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError( f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}" ) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor: if not isinstance(image, list): image = [image] def numpy_to_pt(images): if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) return images if isinstance(image[0], PIL.Image.Image): new_image = [] for image_ in image: image_ = image_.convert("RGB") image_ = resize(image_, self.unet.sample_size) image_ = np.array(image_) image_ = image_.astype(np.float32) image_ = image_ / 127.5 - 1 new_image.append(image_) image = new_image image = np.stack(image, axis=0) # to np image = numpy_to_pt(image) # to pt elif isinstance(image[0], np.ndarray): image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0) image = numpy_to_pt(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0) return image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor: if not isinstance(image, torch.Tensor) and not isinstance(image, list): image = [image] if isinstance(image[0], PIL.Image.Image): image = [np.array(i).astype(np.float32) / 255.0 for i in image] image = np.stack(image, axis=0) # to np image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image[0], np.ndarray): image = np.stack(image, axis=0) # to np if image.ndim == 5: image = image[0] image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image, list) and isinstance(image[0], torch.Tensor): dims = image[0].ndim if dims == 3: image = torch.stack(image, dim=0) elif dims == 4: image = torch.concat(image, dim=0) else: raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}") image = image.to(device=device, dtype=self.unet.dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) return image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.prepare_intermediate_images def prepare_intermediate_images( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None ): _, channels, height, width = image.shape batch_size = batch_size * num_images_per_prompt shape = (batch_size, channels, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) image = self.scheduler.add_noise(image, noise, timestep) return image @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor], original_image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, strength: float = 0.8, prompt: Union[str, List[str]] = None, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 4.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, noise_level: int = 250, clean_caption: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. original_image (`torch.FloatTensor` or `PIL.Image.Image`): The original image that `image` was varied from. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). noise_level (`int`, *optional*, defaults to 250): The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)` clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] self.check_inputs( prompt, image, original_image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 device = self._execution_device # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) dtype = prompt_embeds.dtype # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength) # 5. prepare original image original_image = self.preprocess_original_image(original_image) original_image = original_image.to(device=device, dtype=dtype) # 6. Prepare intermediate images noise_timestep = timesteps[0:1] noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt) intermediate_images = self.prepare_intermediate_images( original_image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator, ) # 7. Prepare upscaled image and noise level _, _, height, width = original_image.shape image = self.preprocess_image(image, num_images_per_prompt, device) upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True) noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device) noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype) upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level) if do_classifier_free_guidance: noise_level = torch.cat([noise_level] * 2) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): model_input = torch.cat([intermediate_images, upscaled], dim=1) model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, class_labels=noise_level, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) # compute the previous noisy sample x_t -> x_t-1 intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, intermediate_images) image = intermediate_images if output_type == "pil": # 10. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 11. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 12. Convert to PIL image = self.numpy_to_pil(image) # 13. Apply watermark if self.watermarker is not None: self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() else: # 10. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 11. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) ================================================ FILE: diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py ================================================ import html import inspect import re import urllib.parse as ul from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer from ...loaders import LoraLoaderMixin from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, is_bs4_available, is_ftfy_available, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker logger = logging.get_logger(__name__) # pylint: disable=invalid-name if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image: w, h = images.size coef = w / h w, h = img_size, img_size if coef >= 1: w = int(round(img_size / 8 * coef) * 8) else: h = int(round(img_size / 8 / coef) * 8) images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None) return images EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> from PIL import Image >>> import requests >>> from io import BytesIO >>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png" >>> response = requests.get(url) >>> original_image = Image.open(BytesIO(response.content)).convert("RGB") >>> original_image = original_image >>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png" >>> response = requests.get(url) >>> mask_image = Image.open(BytesIO(response.content)) >>> mask_image = mask_image >>> pipe = IFInpaintingPipeline.from_pretrained( ... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16 ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "blue sunglasses" >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe( ... image=original_image, ... mask_image=mask_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... output_type="pt", ... ).images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, ... mask_image=mask_image, ... original_image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the IF license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.text_encoder, self.unet, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None if self.text_encoder is not None: _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) # Accelerate will move the next model to the device _before_ calling the offload hook of the # previous model. This will cause both models to be present on the device at the same time. # IF uses T5 for its text encoder which is really large. We can manually call the offload # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to # the GPU. self.text_encoder_offload_hook = hook _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet self.unet_offload_hook = hook if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks def remove_all_hooks(self): if is_accelerate_available(): from accelerate.hooks import remove_hook_from_module else: raise ImportError("Please install accelerate via `pip install accelerate`") for model in [self.text_encoder, self.unet, self.safety_checker]: if model is not None: remove_hook_from_module(model, recurse=True) self.unet_offload_hook = None self.text_encoder_offload_hook = None self.final_offload_hook = None @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if device is None: device = self._execution_device if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, mask_image, batch_size, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # image if isinstance(image, list): check_image_type = image[0] else: check_image_type = image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(image, list): image_batch_size = len(image) elif isinstance(image, torch.Tensor): image_batch_size = image.shape[0] elif isinstance(image, PIL.Image.Image): image_batch_size = 1 elif isinstance(image, np.ndarray): image_batch_size = image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}") # mask_image if isinstance(mask_image, list): check_image_type = mask_image[0] else: check_image_type = mask_image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`mask_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(mask_image, list): image_batch_size = len(mask_image) elif isinstance(mask_image, torch.Tensor): image_batch_size = mask_image.shape[0] elif isinstance(mask_image, PIL.Image.Image): image_batch_size = 1 elif isinstance(mask_image, np.ndarray): image_batch_size = mask_image.shape[0] else: assert False if image_batch_size != 1 and batch_size != image_batch_size: raise ValueError( f"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}" ) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor: if not isinstance(image, list): image = [image] def numpy_to_pt(images): if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) return images if isinstance(image[0], PIL.Image.Image): new_image = [] for image_ in image: image_ = image_.convert("RGB") image_ = resize(image_, self.unet.sample_size) image_ = np.array(image_) image_ = image_.astype(np.float32) image_ = image_ / 127.5 - 1 new_image.append(image_) image = new_image image = np.stack(image, axis=0) # to np image = numpy_to_pt(image) # to pt elif isinstance(image[0], np.ndarray): image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0) image = numpy_to_pt(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0) return image def preprocess_mask_image(self, mask_image) -> torch.Tensor: if not isinstance(mask_image, list): mask_image = [mask_image] if isinstance(mask_image[0], torch.Tensor): mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0) if mask_image.ndim == 2: # Batch and add channel dim for single mask mask_image = mask_image.unsqueeze(0).unsqueeze(0) elif mask_image.ndim == 3 and mask_image.shape[0] == 1: # Single mask, the 0'th dimension is considered to be # the existing batch size of 1 mask_image = mask_image.unsqueeze(0) elif mask_image.ndim == 3 and mask_image.shape[0] != 1: # Batch of mask, the 0'th dimension is considered to be # the batching dimension mask_image = mask_image.unsqueeze(1) mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 elif isinstance(mask_image[0], PIL.Image.Image): new_mask_image = [] for mask_image_ in mask_image: mask_image_ = mask_image_.convert("L") mask_image_ = resize(mask_image_, self.unet.sample_size) mask_image_ = np.array(mask_image_) mask_image_ = mask_image_[None, None, :] new_mask_image.append(mask_image_) mask_image = new_mask_image mask_image = np.concatenate(mask_image, axis=0) mask_image = mask_image.astype(np.float32) / 255.0 mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 mask_image = torch.from_numpy(mask_image) elif isinstance(mask_image[0], np.ndarray): mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0) mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 mask_image = torch.from_numpy(mask_image) return mask_image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start def prepare_intermediate_images( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None ): image_batch_size, channels, height, width = image.shape batch_size = batch_size * num_images_per_prompt shape = (batch_size, channels, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) noised_image = self.scheduler.add_noise(image, noise, timestep) image = (1 - mask_image) * image + mask_image * noised_image return image @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, mask_image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, strength: float = 1.0, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 7.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, clean_caption: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] self.check_inputs( prompt, image, mask_image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) dtype = prompt_embeds.dtype # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength) # 5. Prepare intermediate images image = self.preprocess_image(image) image = image.to(device=device, dtype=dtype) mask_image = self.preprocess_mask_image(mask_image) mask_image = mask_image.to(device=device, dtype=dtype) if mask_image.shape[0] == 1: mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0) else: mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0) noise_timestep = timesteps[0:1] noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt) intermediate_images = self.prepare_intermediate_images( image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): model_input = ( torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images ) model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if self.scheduler.config.variance_type not in ["learned", "learned_range"]: noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1) # compute the previous noisy sample x_t -> x_t-1 prev_intermediate_images = intermediate_images intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, intermediate_images) image = intermediate_images if output_type == "pil": # 8. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 9. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 10. Convert to PIL image = self.numpy_to_pil(image) # 11. Apply watermark if self.watermarker is not None: self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() else: # 8. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 9. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) ================================================ FILE: diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py ================================================ import html import inspect import re import urllib.parse as ul from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, is_bs4_available, is_ftfy_available, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image: w, h = images.size coef = w / h w, h = img_size, img_size if coef >= 1: w = int(round(img_size / 8 * coef) * 8) else: h = int(round(img_size / 8 / coef) * 8) images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None) return images EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> from PIL import Image >>> import requests >>> from io import BytesIO >>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png" >>> response = requests.get(url) >>> original_image = Image.open(BytesIO(response.content)).convert("RGB") >>> original_image = original_image >>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png" >>> response = requests.get(url) >>> mask_image = Image.open(BytesIO(response.content)) >>> mask_image = mask_image >>> pipe = IFInpaintingPipeline.from_pretrained( ... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16 ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "blue sunglasses" >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe( ... image=original_image, ... mask_image=mask_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... output_type="pt", ... ).images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, ... mask_image=mask_image, ... original_image=original_image, ... prompt_embeds=prompt_embeds, ... negative_prompt_embeds=negative_embeds, ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, image_noising_scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the IF license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if unet.config.in_channels != 6: logger.warn( "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, image_noising_scheduler=image_noising_scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.text_encoder, self.unet, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None if self.text_encoder is not None: _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) # Accelerate will move the next model to the device _before_ calling the offload hook of the # previous model. This will cause both models to be present on the device at the same time. # IF uses T5 for its text encoder which is really large. We can manually call the offload # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to # the GPU. self.text_encoder_offload_hook = hook _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet self.unet_offload_hook = hook if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks def remove_all_hooks(self): if is_accelerate_available(): from accelerate.hooks import remove_hook_from_module else: raise ImportError("Please install accelerate via `pip install accelerate`") for model in [self.text_encoder, self.unet, self.safety_checker]: if model is not None: remove_hook_from_module(model, recurse=True) self.unet_offload_hook = None self.text_encoder_offload_hook = None self.final_offload_hook = None # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if device is None: device = self._execution_device if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, original_image, mask_image, batch_size, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # image if isinstance(image, list): check_image_type = image[0] else: check_image_type = image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(image, list): image_batch_size = len(image) elif isinstance(image, torch.Tensor): image_batch_size = image.shape[0] elif isinstance(image, PIL.Image.Image): image_batch_size = 1 elif isinstance(image, np.ndarray): image_batch_size = image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}") # original_image if isinstance(original_image, list): check_image_type = original_image[0] else: check_image_type = original_image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`original_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(original_image, list): image_batch_size = len(original_image) elif isinstance(original_image, torch.Tensor): image_batch_size = original_image.shape[0] elif isinstance(original_image, PIL.Image.Image): image_batch_size = 1 elif isinstance(original_image, np.ndarray): image_batch_size = original_image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError( f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}" ) # mask_image if isinstance(mask_image, list): check_image_type = mask_image[0] else: check_image_type = mask_image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`mask_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(mask_image, list): image_batch_size = len(mask_image) elif isinstance(mask_image, torch.Tensor): image_batch_size = mask_image.shape[0] elif isinstance(mask_image, PIL.Image.Image): image_batch_size = 1 elif isinstance(mask_image, np.ndarray): image_batch_size = mask_image.shape[0] else: assert False if image_batch_size != 1 and batch_size != image_batch_size: raise ValueError( f"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}" ) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor: if not isinstance(image, list): image = [image] def numpy_to_pt(images): if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) return images if isinstance(image[0], PIL.Image.Image): new_image = [] for image_ in image: image_ = image_.convert("RGB") image_ = resize(image_, self.unet.sample_size) image_ = np.array(image_) image_ = image_.astype(np.float32) image_ = image_ / 127.5 - 1 new_image.append(image_) image = new_image image = np.stack(image, axis=0) # to np image = numpy_to_pt(image) # to pt elif isinstance(image[0], np.ndarray): image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0) image = numpy_to_pt(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0) return image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor: if not isinstance(image, torch.Tensor) and not isinstance(image, list): image = [image] if isinstance(image[0], PIL.Image.Image): image = [np.array(i).astype(np.float32) / 255.0 for i in image] image = np.stack(image, axis=0) # to np image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image[0], np.ndarray): image = np.stack(image, axis=0) # to np if image.ndim == 5: image = image[0] image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image, list) and isinstance(image[0], torch.Tensor): dims = image[0].ndim if dims == 3: image = torch.stack(image, dim=0) elif dims == 4: image = torch.concat(image, dim=0) else: raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}") image = image.to(device=device, dtype=self.unet.dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) return image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.preprocess_mask_image def preprocess_mask_image(self, mask_image) -> torch.Tensor: if not isinstance(mask_image, list): mask_image = [mask_image] if isinstance(mask_image[0], torch.Tensor): mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0) if mask_image.ndim == 2: # Batch and add channel dim for single mask mask_image = mask_image.unsqueeze(0).unsqueeze(0) elif mask_image.ndim == 3 and mask_image.shape[0] == 1: # Single mask, the 0'th dimension is considered to be # the existing batch size of 1 mask_image = mask_image.unsqueeze(0) elif mask_image.ndim == 3 and mask_image.shape[0] != 1: # Batch of mask, the 0'th dimension is considered to be # the batching dimension mask_image = mask_image.unsqueeze(1) mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 elif isinstance(mask_image[0], PIL.Image.Image): new_mask_image = [] for mask_image_ in mask_image: mask_image_ = mask_image_.convert("L") mask_image_ = resize(mask_image_, self.unet.sample_size) mask_image_ = np.array(mask_image_) mask_image_ = mask_image_[None, None, :] new_mask_image.append(mask_image_) mask_image = new_mask_image mask_image = np.concatenate(mask_image, axis=0) mask_image = mask_image.astype(np.float32) / 255.0 mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 mask_image = torch.from_numpy(mask_image) elif isinstance(mask_image[0], np.ndarray): mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0) mask_image[mask_image < 0.5] = 0 mask_image[mask_image >= 0.5] = 1 mask_image = torch.from_numpy(mask_image) return mask_image # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.prepare_intermediate_images def prepare_intermediate_images( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None ): image_batch_size, channels, height, width = image.shape batch_size = batch_size * num_images_per_prompt shape = (batch_size, channels, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) noised_image = self.scheduler.add_noise(image, noise, timestep) image = (1 - mask_image) * image + mask_image * noised_image return image @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor], original_image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, mask_image: Union[ PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray] ] = None, strength: float = 0.8, prompt: Union[str, List[str]] = None, num_inference_steps: int = 100, timesteps: List[int] = None, guidance_scale: float = 4.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, noise_level: int = 0, clean_caption: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. original_image (`torch.FloatTensor` or `PIL.Image.Image`): The original image that `image` was varied from. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). noise_level (`int`, *optional*, defaults to 0): The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)` clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] self.check_inputs( prompt, image, original_image, mask_image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 device = self._execution_device # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) dtype = prompt_embeds.dtype # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength) # 5. prepare original image original_image = self.preprocess_original_image(original_image) original_image = original_image.to(device=device, dtype=dtype) # 6. prepare mask image mask_image = self.preprocess_mask_image(mask_image) mask_image = mask_image.to(device=device, dtype=dtype) if mask_image.shape[0] == 1: mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0) else: mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0) # 6. Prepare intermediate images noise_timestep = timesteps[0:1] noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt) intermediate_images = self.prepare_intermediate_images( original_image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator, ) # 7. Prepare upscaled image and noise level _, _, height, width = original_image.shape image = self.preprocess_image(image, num_images_per_prompt, device) upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True) noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device) noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype) upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level) if do_classifier_free_guidance: noise_level = torch.cat([noise_level] * 2) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): model_input = torch.cat([intermediate_images, upscaled], dim=1) model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, class_labels=noise_level, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) # compute the previous noisy sample x_t -> x_t-1 prev_intermediate_images = intermediate_images intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, intermediate_images) image = intermediate_images if output_type == "pil": # 10. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 11. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 12. Convert to PIL image = self.numpy_to_pil(image) # 13. Apply watermark if self.watermarker is not None: self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() else: # 10. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 11. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) ================================================ FILE: diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py ================================================ import html import inspect import re import urllib.parse as ul from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer from ...models import UNet2DConditionModel from ...schedulers import DDPMScheduler from ...utils import ( BACKENDS_MAPPING, is_accelerate_available, is_accelerate_version, is_bs4_available, is_ftfy_available, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import IFPipelineOutput from .safety_checker import IFSafetyChecker from .watermark import IFWatermarker if is_bs4_available(): from bs4 import BeautifulSoup if is_ftfy_available(): import ftfy logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline >>> from diffusers.utils import pt_to_pil >>> import torch >>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16) >>> pipe.enable_model_cpu_offload() >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) >>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images >>> # save intermediate image >>> pil_image = pt_to_pil(image) >>> pil_image[0].save("./if_stage_I.png") >>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained( ... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 ... ) >>> super_res_1_pipe.enable_model_cpu_offload() >>> image = super_res_1_pipe( ... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds ... ).images >>> image[0].save("./if_stage_II.png") ``` """ class IFSuperResolutionPipeline(DiffusionPipeline): tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler feature_extractor: Optional[CLIPImageProcessor] safety_checker: Optional[IFSafetyChecker] watermarker: Optional[IFWatermarker] bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: UNet2DConditionModel, scheduler: DDPMScheduler, image_noising_scheduler: DDPMScheduler, safety_checker: Optional[IFSafetyChecker], feature_extractor: Optional[CLIPImageProcessor], watermarker: Optional[IFWatermarker], requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the IF license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) if unet.config.in_channels != 6: logger.warn( "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`." ) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, image_noising_scheduler=image_noising_scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, watermarker=watermarker, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.text_encoder, self.unet, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None if self.text_encoder is not None: _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook) # Accelerate will move the next model to the device _before_ calling the offload hook of the # previous model. This will cause both models to be present on the device at the same time. # IF uses T5 for its text encoder which is really large. We can manually call the offload # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to # the GPU. self.text_encoder_offload_hook = hook _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook) # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet self.unet_offload_hook = hook if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks def remove_all_hooks(self): if is_accelerate_available(): from accelerate.hooks import remove_hook_from_module else: raise ImportError("Please install accelerate via `pip install accelerate`") for model in [self.text_encoder, self.unet, self.safety_checker]: if model is not None: remove_hook_from_module(model, recurse=True) self.unet_offload_hook = None self.text_encoder_offload_hook = None self.final_offload_hook = None # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warn("Setting `clean_caption` to False...") clean_caption = False if not isinstance(text, (tuple, list)): text = [text] def process(text: str): if clean_caption: text = self._clean_caption(text) text = self._clean_caption(text) else: text = text.lower().strip() return text return [process(t) for t in text] # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption def _clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption)) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, clean_caption: bool = False, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if device is None: device = self._execution_device if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF max_length = 77 if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.unet is not None: dtype = self.unet.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, batch_size, noise_level, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps: raise ValueError( f"`noise_level`: {noise_level} must be a valid timestep in `self.noising_scheduler`, [0, {self.image_noising_scheduler.config.num_train_timesteps})" ) if isinstance(image, list): check_image_type = image[0] else: check_image_type = image if ( not isinstance(check_image_type, torch.Tensor) and not isinstance(check_image_type, PIL.Image.Image) and not isinstance(check_image_type, np.ndarray) ): raise ValueError( "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is" f" {type(check_image_type)}" ) if isinstance(image, list): image_batch_size = len(image) elif isinstance(image, torch.Tensor): image_batch_size = image.shape[0] elif isinstance(image, PIL.Image.Image): image_batch_size = 1 elif isinstance(image, np.ndarray): image_batch_size = image.shape[0] else: assert False if batch_size != image_batch_size: raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}") # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_intermediate_images def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator): shape = (batch_size, num_channels, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler intermediate_images = intermediate_images * self.scheduler.init_noise_sigma return intermediate_images def preprocess_image(self, image, num_images_per_prompt, device): if not isinstance(image, torch.Tensor) and not isinstance(image, list): image = [image] if isinstance(image[0], PIL.Image.Image): image = [np.array(i).astype(np.float32) / 255.0 for i in image] image = np.stack(image, axis=0) # to np image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image[0], np.ndarray): image = np.stack(image, axis=0) # to np if image.ndim == 5: image = image[0] image = torch.from_numpy(image.transpose(0, 3, 1, 2)) elif isinstance(image, list) and isinstance(image[0], torch.Tensor): dims = image[0].ndim if dims == 3: image = torch.stack(image, dim=0) elif dims == 4: image = torch.concat(image, dim=0) else: raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}") image = image.to(device=device, dtype=self.unet.dtype) image = image.repeat_interleave(num_images_per_prompt, dim=0) return image @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: int = None, width: int = None, image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor] = None, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 4.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, noise_level: int = 250, clean_caption: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size): The width in pixels of the generated image. image (`PIL.Image.Image`, `np.ndarray`, `torch.FloatTensor`): The image to be upscaled. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). noise_level (`int`, *optional*, defaults to 250): The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)` clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. Examples: Returns: [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] self.check_inputs( prompt, image, batch_size, noise_level, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters height = height or self.unet.config.sample_size width = width or self.unet.config.sample_size device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, clean_caption=clean_caption, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) # 4. Prepare timesteps if timesteps is not None: self.scheduler.set_timesteps(timesteps=timesteps, device=device) timesteps = self.scheduler.timesteps num_inference_steps = len(timesteps) else: self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare intermediate images num_channels = self.unet.config.in_channels // 2 intermediate_images = self.prepare_intermediate_images( batch_size * num_images_per_prompt, num_channels, height, width, prompt_embeds.dtype, device, generator, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Prepare upscaled image and noise level image = self.preprocess_image(image, num_images_per_prompt, device) upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True) noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device) noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype) upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level) if do_classifier_free_guidance: noise_level = torch.cat([noise_level] * 2) # HACK: see comment in `enable_model_cpu_offload` if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): model_input = torch.cat([intermediate_images, upscaled], dim=1) model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet( model_input, t, encoder_hidden_states=prompt_embeds, class_labels=noise_level, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) # compute the previous noisy sample x_t -> x_t-1 intermediate_images = self.scheduler.step( noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False )[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, intermediate_images) image = intermediate_images if output_type == "pil": # 9. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 10. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # 11. Convert to PIL image = self.numpy_to_pil(image) # 12. Apply watermark if self.watermarker is not None: self.watermarker.apply_watermark(image, self.unet.config.sample_size) elif output_type == "pt": nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() else: # 9. Post-processing image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() # 10. Run safety checker image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, nsfw_detected, watermark_detected) return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected) ================================================ FILE: diffusers/pipelines/deepfloyd_if/safety_checker.py ================================================ import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel from ...utils import logging logger = logging.get_logger(__name__) class IFSafetyChecker(PreTrainedModel): config_class = CLIPConfig _no_split_modules = ["CLIPEncoderLayer"] def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModelWithProjection(config.vision_config) self.p_head = nn.Linear(config.vision_config.projection_dim, 1) self.w_head = nn.Linear(config.vision_config.projection_dim, 1) @torch.no_grad() def forward(self, clip_input, images, p_threshold=0.5, w_threshold=0.5): image_embeds = self.vision_model(clip_input)[0] nsfw_detected = self.p_head(image_embeds) nsfw_detected = nsfw_detected.flatten() nsfw_detected = nsfw_detected > p_threshold nsfw_detected = nsfw_detected.tolist() if any(nsfw_detected): logger.warning( "Potential NSFW content was detected in one or more images. A black image will be returned instead." " Try again with a different prompt and/or seed." ) for idx, nsfw_detected_ in enumerate(nsfw_detected): if nsfw_detected_: images[idx] = np.zeros(images[idx].shape) watermark_detected = self.w_head(image_embeds) watermark_detected = watermark_detected.flatten() watermark_detected = watermark_detected > w_threshold watermark_detected = watermark_detected.tolist() if any(watermark_detected): logger.warning( "Potential watermarked content was detected in one or more images. A black image will be returned instead." " Try again with a different prompt and/or seed." ) for idx, watermark_detected_ in enumerate(watermark_detected): if watermark_detected_: images[idx] = np.zeros(images[idx].shape) return images, nsfw_detected, watermark_detected ================================================ FILE: diffusers/pipelines/deepfloyd_if/timesteps.py ================================================ fast27_timesteps = [ 999, 800, 799, 600, 599, 500, 400, 399, 377, 355, 333, 311, 288, 266, 244, 222, 200, 199, 177, 155, 133, 111, 88, 66, 44, 22, 0, ] smart27_timesteps = [ 999, 976, 952, 928, 905, 882, 858, 857, 810, 762, 715, 714, 572, 429, 428, 286, 285, 238, 190, 143, 142, 118, 95, 71, 47, 24, 0, ] smart50_timesteps = [ 999, 988, 977, 966, 955, 944, 933, 922, 911, 900, 899, 879, 859, 840, 820, 800, 799, 766, 733, 700, 699, 650, 600, 599, 500, 499, 400, 399, 350, 300, 299, 266, 233, 200, 199, 179, 159, 140, 120, 100, 99, 88, 77, 66, 55, 44, 33, 22, 11, 0, ] smart100_timesteps = [ 999, 995, 992, 989, 985, 981, 978, 975, 971, 967, 964, 961, 957, 956, 951, 947, 942, 937, 933, 928, 923, 919, 914, 913, 908, 903, 897, 892, 887, 881, 876, 871, 870, 864, 858, 852, 846, 840, 834, 828, 827, 820, 813, 806, 799, 792, 785, 784, 777, 770, 763, 756, 749, 742, 741, 733, 724, 716, 707, 699, 698, 688, 677, 666, 656, 655, 645, 634, 623, 613, 612, 598, 584, 570, 569, 555, 541, 527, 526, 505, 484, 483, 462, 440, 439, 396, 395, 352, 351, 308, 307, 264, 263, 220, 219, 176, 132, 88, 44, 0, ] smart185_timesteps = [ 999, 997, 995, 992, 990, 988, 986, 984, 981, 979, 977, 975, 972, 970, 968, 966, 964, 961, 959, 957, 956, 954, 951, 949, 946, 944, 941, 939, 936, 934, 931, 929, 926, 924, 921, 919, 916, 914, 913, 910, 907, 905, 902, 899, 896, 893, 891, 888, 885, 882, 879, 877, 874, 871, 870, 867, 864, 861, 858, 855, 852, 849, 846, 843, 840, 837, 834, 831, 828, 827, 824, 821, 817, 814, 811, 808, 804, 801, 798, 795, 791, 788, 785, 784, 780, 777, 774, 770, 766, 763, 760, 756, 752, 749, 746, 742, 741, 737, 733, 730, 726, 722, 718, 714, 710, 707, 703, 699, 698, 694, 690, 685, 681, 677, 673, 669, 664, 660, 656, 655, 650, 646, 641, 636, 632, 627, 622, 618, 613, 612, 607, 602, 596, 591, 586, 580, 575, 570, 569, 563, 557, 551, 545, 539, 533, 527, 526, 519, 512, 505, 498, 491, 484, 483, 474, 466, 457, 449, 440, 439, 428, 418, 407, 396, 395, 381, 366, 352, 351, 330, 308, 307, 286, 264, 263, 242, 220, 219, 176, 175, 132, 131, 88, 44, 0, ] super27_timesteps = [ 999, 991, 982, 974, 966, 958, 950, 941, 933, 925, 916, 908, 900, 899, 874, 850, 825, 800, 799, 700, 600, 500, 400, 300, 200, 100, 0, ] super40_timesteps = [ 999, 992, 985, 978, 971, 964, 957, 949, 942, 935, 928, 921, 914, 907, 900, 899, 879, 859, 840, 820, 800, 799, 766, 733, 700, 699, 650, 600, 599, 500, 499, 400, 399, 300, 299, 200, 199, 100, 99, 0, ] super100_timesteps = [ 999, 996, 992, 989, 985, 982, 979, 975, 972, 968, 965, 961, 958, 955, 951, 948, 944, 941, 938, 934, 931, 927, 924, 920, 917, 914, 910, 907, 903, 900, 899, 891, 884, 876, 869, 861, 853, 846, 838, 830, 823, 815, 808, 800, 799, 788, 777, 766, 755, 744, 733, 722, 711, 700, 699, 688, 677, 666, 655, 644, 633, 622, 611, 600, 599, 585, 571, 557, 542, 528, 514, 500, 499, 485, 471, 457, 442, 428, 414, 400, 399, 379, 359, 340, 320, 300, 299, 279, 259, 240, 220, 200, 199, 166, 133, 100, 99, 66, 33, 0, ] ================================================ FILE: diffusers/pipelines/deepfloyd_if/watermark.py ================================================ from typing import List import PIL import torch from PIL import Image from ...configuration_utils import ConfigMixin from ...models.modeling_utils import ModelMixin from ...utils import PIL_INTERPOLATION class IFWatermarker(ModelMixin, ConfigMixin): def __init__(self): super().__init__() self.register_buffer("watermark_image", torch.zeros((62, 62, 4))) self.watermark_image_as_pil = None def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None): # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287 h = images[0].height w = images[0].width sample_size = sample_size or h coef = min(h / sample_size, w / sample_size) img_h, img_w = (int(h / coef), int(w / coef)) if coef < 1 else (h, w) S1, S2 = 1024**2, img_w * img_h K = (S2 / S1) ** 0.5 wm_size, wm_x, wm_y = int(K * 62), img_w - int(14 * K), img_h - int(14 * K) if self.watermark_image_as_pil is None: watermark_image = self.watermark_image.to(torch.uint8).cpu().numpy() watermark_image = Image.fromarray(watermark_image, mode="RGBA") self.watermark_image_as_pil = watermark_image wm_img = self.watermark_image_as_pil.resize( (wm_size, wm_size), PIL_INTERPOLATION["bicubic"], reducing_gap=None ) for pil_img in images: pil_img.paste(wm_img, box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y), mask=wm_img.split()[-1]) return images ================================================ FILE: diffusers/pipelines/dit/__init__.py ================================================ from .pipeline_dit import DiTPipeline ================================================ FILE: diffusers/pipelines/dit/pipeline_dit.py ================================================ # Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) # William Peebles and Saining Xie # # Copyright (c) 2021 OpenAI # MIT License # # Copyright 2023 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 Dict, List, Optional, Tuple, Union import torch from ...models import AutoencoderKL, Transformer2DModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class DiTPipeline(DiffusionPipeline): r""" This pipeline inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: transformer ([`Transformer2DModel`]): Class conditioned Transformer in Diffusion model to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. scheduler ([`DDIMScheduler`]): A scheduler to be used in combination with `dit` to denoise the encoded image latents. """ def __init__( self, transformer: Transformer2DModel, vae: AutoencoderKL, scheduler: KarrasDiffusionSchedulers, id2label: Optional[Dict[int, str]] = None, ): super().__init__() self.register_modules(transformer=transformer, vae=vae, scheduler=scheduler) # create a imagenet -> id dictionary for easier use self.labels = {} if id2label is not None: for key, value in id2label.items(): for label in value.split(","): self.labels[label.lstrip().rstrip()] = int(key) self.labels = dict(sorted(self.labels.items())) def get_label_ids(self, label: Union[str, List[str]]) -> List[int]: r""" Map label strings, *e.g.* from ImageNet, to corresponding class ids. Parameters: label (`str` or `dict` of `str`): label strings to be mapped to class ids. Returns: `list` of `int`: Class ids to be processed by pipeline. """ if not isinstance(label, list): label = list(label) for l in label: if l not in self.labels: raise ValueError( f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." ) return [self.labels[l] for l in label] @torch.no_grad() def __call__( self, class_labels: List[int], guidance_scale: float = 4.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, num_inference_steps: int = 50, output_type: Optional[str] = "pil", return_dict: bool = True, ) -> Union[ImagePipelineOutput, Tuple]: r""" Function invoked when calling the pipeline for generation. Args: class_labels (List[int]): List of imagenet class labels for the images to be generated. guidance_scale (`float`, *optional*, defaults to 4.0): Scale of the guidance signal. generator (`torch.Generator`, *optional*): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. num_inference_steps (`int`, *optional*, defaults to 250): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple. """ batch_size = len(class_labels) latent_size = self.transformer.config.sample_size latent_channels = self.transformer.config.in_channels latents = randn_tensor( shape=(batch_size, latent_channels, latent_size, latent_size), generator=generator, device=self.device, dtype=self.transformer.dtype, ) latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1 else latents class_labels = torch.tensor(class_labels, device=self.device).reshape(-1) class_null = torch.tensor([1000] * batch_size, device=self.device) class_labels_input = torch.cat([class_labels, class_null], 0) if guidance_scale > 1 else class_labels # set step values self.scheduler.set_timesteps(num_inference_steps) for t in self.progress_bar(self.scheduler.timesteps): if guidance_scale > 1: half = latent_model_input[: len(latent_model_input) // 2] latent_model_input = torch.cat([half, half], dim=0) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) timesteps = t if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = latent_model_input.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=latent_model_input.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(latent_model_input.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(latent_model_input.shape[0]) # predict noise model_output noise_pred = self.transformer( latent_model_input, timestep=timesteps, class_labels=class_labels_input ).sample # perform guidance if guidance_scale > 1: eps, rest = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:] cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps) eps = torch.cat([half_eps, half_eps], dim=0) noise_pred = torch.cat([eps, rest], dim=1) # learned sigma if self.transformer.config.out_channels // 2 == latent_channels: model_output, _ = torch.split(noise_pred, latent_channels, dim=1) else: model_output = noise_pred # compute previous image: x_t -> x_t-1 latent_model_input = self.scheduler.step(model_output, t, latent_model_input).prev_sample if guidance_scale > 1: latents, _ = latent_model_input.chunk(2, dim=0) else: latents = latent_model_input latents = 1 / self.vae.config.scaling_factor * latents samples = self.vae.decode(latents).sample samples = (samples / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 samples = samples.cpu().permute(0, 2, 3, 1).float().numpy() if output_type == "pil": samples = self.numpy_to_pil(samples) if not return_dict: return (samples,) return ImagePipelineOutput(images=samples) ================================================ FILE: diffusers/pipelines/latent_diffusion/__init__.py ================================================ from ...utils import is_transformers_available from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline if is_transformers_available(): from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline ================================================ FILE: diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py ================================================ # Copyright 2023 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. import inspect from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer from transformers.activations import ACT2FN from transformers.modeling_outputs import BaseModelOutput from transformers.utils import logging from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class LDMTextToImagePipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqvae ([`VQModel`]): Vector-quantized (VQ) Model to encode and decode images to and from latent representations. bert ([`LDMBertModel`]): Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture. tokenizer (`transformers.BertTokenizer`): Tokenizer of class [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ def __init__( self, vqvae: Union[VQModel, AutoencoderKL], bert: PreTrainedModel, tokenizer: PreTrainedTokenizer, unet: Union[UNet2DModel, UNet2DConditionModel], scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], ): super().__init__() self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler) self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 1.0, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[Tuple, ImagePipelineOutput]: r""" Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 1.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt` at the, usually at the expense of lower image quality. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") # get unconditional embeddings for classifier free guidance if guidance_scale != 1.0: uncond_input = self.tokenizer( [""] * batch_size, padding="max_length", max_length=77, truncation=True, return_tensors="pt" ) negative_prompt_embeds = self.bert(uncond_input.input_ids.to(self.device))[0] # get prompt text embeddings text_input = self.tokenizer(prompt, padding="max_length", max_length=77, truncation=True, return_tensors="pt") prompt_embeds = self.bert(text_input.input_ids.to(self.device))[0] # get the initial random noise unless the user supplied it latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(latents_shape, generator=generator, device=self.device, dtype=prompt_embeds.dtype) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) self.scheduler.set_timesteps(num_inference_steps) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_kwargs = {} if accepts_eta: extra_kwargs["eta"] = eta for t in self.progress_bar(self.scheduler.timesteps): if guidance_scale == 1.0: # guidance_scale of 1 means no guidance latents_input = latents context = prompt_embeds else: # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes latents_input = torch.cat([latents] * 2) context = torch.cat([negative_prompt_embeds, prompt_embeds]) # predict the noise residual noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample # perform guidance if guidance_scale != 1.0: noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / self.vqvae.config.scaling_factor * latents image = self.vqvae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ################################################################################ # Code for the text transformer model ################################################################################ """ PyTorch LDMBERT model.""" logger = logging.get_logger(__name__) LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "ldm-bert", # See all LDMBert models at https://huggingface.co/models?filter=ldmbert ] LDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "ldm-bert": "https://huggingface.co/valhalla/ldm-bert/blob/main/config.json", } """ LDMBERT model configuration""" class LDMBertConfig(PretrainedConfig): model_type = "ldmbert" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=30522, max_position_embeddings=77, encoder_layers=32, encoder_ffn_dim=5120, encoder_attention_heads=8, head_dim=64, encoder_layerdrop=0.0, activation_function="gelu", d_model=1280, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, use_cache=True, pad_token_id=0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.head_dim = head_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True super().__init__(pad_token_id=pad_token_id, **kwargs) def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert class LDMBertAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, head_dim: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = False, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = head_dim self.inner_dim = head_dim * num_heads self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias) self.out_proj = nn.Linear(self.inner_dim, embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class LDMBertEncoderLayer(nn.Module): def __init__(self, config: LDMBertConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = LDMBertAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, head_dim=config.head_dim, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert class LDMBertPreTrainedModel(PreTrainedModel): config_class = LDMBertConfig base_model_prefix = "model" _supports_gradient_checkpointing = True _keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"] def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (LDMBertEncoder,)): module.gradient_checkpointing = value @property def dummy_inputs(self): pad_token = self.config.pad_token_id input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) dummy_inputs = { "attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, } return dummy_inputs class LDMBertEncoder(LDMBertPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`LDMBertEncoderLayer`]. Args: config: LDMBertConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: LDMBertConfig): super().__init__(config) self.dropout = config.dropout embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim) self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim) self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layer_norm = nn.LayerNorm(embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) seq_len = input_shape[1] if position_ids is None: position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1)) embed_pos = self.embed_positions(position_ids) hidden_states = inputs_embeds + embed_pos hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) hidden_states = self.layer_norm(hidden_states) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class LDMBertModel(LDMBertPreTrainedModel): _no_split_modules = [] def __init__(self, config: LDMBertConfig): super().__init__(config) self.model = LDMBertEncoder(config) self.to_logits = nn.Linear(config.hidden_size, config.vocab_size) def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return outputs ================================================ FILE: diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py ================================================ import inspect from typing import List, Optional, Tuple, Union import numpy as np import PIL import torch import torch.utils.checkpoint from ...models import UNet2DModel, VQModel from ...schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, ) from ...utils import PIL_INTERPOLATION, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput def preprocess(image): w, h = image.size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) image = torch.from_numpy(image) return 2.0 * image - 1.0 class LDMSuperResolutionPipeline(DiffusionPipeline): r""" A pipeline for image super-resolution using Latent This class inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqvae ([`VQModel`]): Vector-quantized (VQ) VAE Model to encode and decode images to and from latent representations. unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`]. """ def __init__( self, vqvae: VQModel, unet: UNet2DModel, scheduler: Union[ DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, ], ): super().__init__() self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, image: Union[torch.Tensor, PIL.Image.Image] = None, batch_size: Optional[int] = 1, num_inference_steps: Optional[int] = 100, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, ) -> Union[Tuple, ImagePipelineOutput]: r""" Args: image (`torch.Tensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. batch_size (`int`, *optional*, defaults to 1): Number of images to generate. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ if isinstance(image, PIL.Image.Image): batch_size = 1 elif isinstance(image, torch.Tensor): batch_size = image.shape[0] else: raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(image)}") if isinstance(image, PIL.Image.Image): image = preprocess(image) height, width = image.shape[-2:] # in_channels should be 6: 3 for latents, 3 for low resolution image latents_shape = (batch_size, self.unet.config.in_channels // 2, height, width) latents_dtype = next(self.unet.parameters()).dtype latents = randn_tensor(latents_shape, generator=generator, device=self.device, dtype=latents_dtype) image = image.to(device=self.device, dtype=latents_dtype) # set timesteps and move to the correct device self.scheduler.set_timesteps(num_inference_steps, device=self.device) timesteps_tensor = self.scheduler.timesteps # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature. # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_kwargs = {} if accepts_eta: extra_kwargs["eta"] = eta for t in self.progress_bar(timesteps_tensor): # concat latents and low resolution image in the channel dimension. latents_input = torch.cat([latents, image], dim=1) latents_input = self.scheduler.scale_model_input(latents_input, t) # predict the noise residual noise_pred = self.unet(latents_input, t).sample # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample # decode the image latents with the VQVAE image = self.vqvae.decode(latents).sample image = torch.clamp(image, -1.0, 1.0) image = image / 2 + 0.5 image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/latent_diffusion_uncond/__init__.py ================================================ from .pipeline_latent_diffusion_uncond import LDMPipeline ================================================ FILE: diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py ================================================ # Copyright 2023 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. import inspect from typing import List, Optional, Tuple, Union import torch from ...models import UNet2DModel, VQModel from ...schedulers import DDIMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class LDMPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqvae ([`VQModel`]): Vector-quantized (VQ) Model to encode and decode images to and from latent representations. unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): [`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latents. """ def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler): super().__init__() self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, eta: float = 0.0, num_inference_steps: int = 50, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[Tuple, ImagePipelineOutput]: r""" Args: batch_size (`int`, *optional*, defaults to 1): Number of images to generate. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ latents = randn_tensor( (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), generator=generator, ) latents = latents.to(self.device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(num_inference_steps) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_kwargs = {} if accepts_eta: extra_kwargs["eta"] = eta for t in self.progress_bar(self.scheduler.timesteps): latent_model_input = self.scheduler.scale_model_input(latents, t) # predict the noise residual noise_prediction = self.unet(latent_model_input, t).sample # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample # decode the image latents with the VAE image = self.vqvae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/onnx_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. 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. import os import shutil from pathlib import Path from typing import Optional, Union import numpy as np from huggingface_hub import hf_hub_download from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging if is_onnx_available(): import onnxruntime as ort logger = logging.get_logger(__name__) ORT_TO_NP_TYPE = { "tensor(bool)": np.bool_, "tensor(int8)": np.int8, "tensor(uint8)": np.uint8, "tensor(int16)": np.int16, "tensor(uint16)": np.uint16, "tensor(int32)": np.int32, "tensor(uint32)": np.uint32, "tensor(int64)": np.int64, "tensor(uint64)": np.uint64, "tensor(float16)": np.float16, "tensor(float)": np.float32, "tensor(double)": np.float64, } class OnnxRuntimeModel: def __init__(self, model=None, **kwargs): logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.") self.model = model self.model_save_dir = kwargs.get("model_save_dir", None) self.latest_model_name = kwargs.get("latest_model_name", ONNX_WEIGHTS_NAME) def __call__(self, **kwargs): inputs = {k: np.array(v) for k, v in kwargs.items()} return self.model.run(None, inputs) @staticmethod def load_model(path: Union[str, Path], provider=None, sess_options=None): """ Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider` Arguments: path (`str` or `Path`): Directory from which to load provider(`str`, *optional*): Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider` """ if provider is None: logger.info("No onnxruntime provider specified, using CPUExecutionProvider") provider = "CPUExecutionProvider" return ort.InferenceSession(path, providers=[provider], sess_options=sess_options) def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs): """ Save a model and its configuration file to a directory, so that it can be re-loaded using the [`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the latest_model_name. Arguments: save_directory (`str` or `Path`): Directory where to save the model file. file_name(`str`, *optional*): Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the model with a different name. """ model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME src_path = self.model_save_dir.joinpath(self.latest_model_name) dst_path = Path(save_directory).joinpath(model_file_name) try: shutil.copyfile(src_path, dst_path) except shutil.SameFileError: pass # copy external weights (for models >2GB) src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME) if src_path.exists(): dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME) try: shutil.copyfile(src_path, dst_path) except shutil.SameFileError: pass def save_pretrained( self, save_directory: Union[str, os.PathLike], **kwargs, ): """ Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class method.: Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) # saving model weights/files self._save_pretrained(save_directory, **kwargs) @classmethod def _from_pretrained( cls, model_id: Union[str, Path], use_auth_token: Optional[Union[bool, str, None]] = None, revision: Optional[Union[str, None]] = None, force_download: bool = False, cache_dir: Optional[str] = None, file_name: Optional[str] = None, provider: Optional[str] = None, sess_options: Optional["ort.SessionOptions"] = None, **kwargs, ): """ Load a model from a directory or the HF Hub. Arguments: model_id (`str` or `Path`): Directory from which to load use_auth_token (`str` or `bool`): Is needed to load models from a private or gated repository revision (`str`): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id cache_dir (`Union[str, Path]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. file_name(`str`): Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load different model files from the same repository or directory. provider(`str`): The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`. kwargs (`Dict`, *optional*): kwargs will be passed to the model during initialization """ model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME # load model from local directory if os.path.isdir(model_id): model = OnnxRuntimeModel.load_model( os.path.join(model_id, model_file_name), provider=provider, sess_options=sess_options ) kwargs["model_save_dir"] = Path(model_id) # load model from hub else: # download model model_cache_path = hf_hub_download( repo_id=model_id, filename=model_file_name, use_auth_token=use_auth_token, revision=revision, cache_dir=cache_dir, force_download=force_download, ) kwargs["model_save_dir"] = Path(model_cache_path).parent kwargs["latest_model_name"] = Path(model_cache_path).name model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options) return cls(model=model, **kwargs) @classmethod def from_pretrained( cls, model_id: Union[str, Path], force_download: bool = True, use_auth_token: Optional[str] = None, cache_dir: Optional[str] = None, **model_kwargs, ): revision = None if len(str(model_id).split("@")) == 2: model_id, revision = model_id.split("@") return cls._from_pretrained( model_id=model_id, revision=revision, cache_dir=cache_dir, force_download=force_download, use_auth_token=use_auth_token, **model_kwargs, ) ================================================ FILE: diffusers/pipelines/paint_by_example/__init__.py ================================================ from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import is_torch_available, is_transformers_available if is_transformers_available() and is_torch_available(): from .image_encoder import PaintByExampleImageEncoder from .pipeline_paint_by_example import PaintByExamplePipeline ================================================ FILE: diffusers/pipelines/paint_by_example/image_encoder.py ================================================ # Copyright 2023 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. import torch from torch import nn from transformers import CLIPPreTrainedModel, CLIPVisionModel from ...models.attention import BasicTransformerBlock from ...utils import logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name class PaintByExampleImageEncoder(CLIPPreTrainedModel): def __init__(self, config, proj_size=768): super().__init__(config) self.proj_size = proj_size self.model = CLIPVisionModel(config) self.mapper = PaintByExampleMapper(config) self.final_layer_norm = nn.LayerNorm(config.hidden_size) self.proj_out = nn.Linear(config.hidden_size, self.proj_size) # uncondition for scaling self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size))) def forward(self, pixel_values, return_uncond_vector=False): clip_output = self.model(pixel_values=pixel_values) latent_states = clip_output.pooler_output latent_states = self.mapper(latent_states[:, None]) latent_states = self.final_layer_norm(latent_states) latent_states = self.proj_out(latent_states) if return_uncond_vector: return latent_states, self.uncond_vector return latent_states class PaintByExampleMapper(nn.Module): def __init__(self, config): super().__init__() num_layers = (config.num_hidden_layers + 1) // 5 hid_size = config.hidden_size num_heads = 1 self.blocks = nn.ModuleList( [ BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True) for _ in range(num_layers) ] ) def forward(self, hidden_states): for block in self.blocks: hidden_states = block(hidden_states) return hidden_states ================================================ FILE: diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor from diffusers.utils import is_accelerate_available from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from ...utils import logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from ..stable_diffusion import StableDiffusionPipelineOutput from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker from .image_encoder import PaintByExampleImageEncoder logger = logging.get_logger(__name__) # pylint: disable=invalid-name def prepare_mask_and_masked_image(image, mask): """ Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the ``image`` and ``1`` for the ``mask``. The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be binarized (``mask > 0.5``) and cast to ``torch.float32`` too. Args: image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint. It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``. mask (_type_): The mask to apply to the image, i.e. regions to inpaint. It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``. Raises: ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not (ot the other way around). Returns: tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4 dimensions: ``batch x channels x height x width``. """ if isinstance(image, torch.Tensor): if not isinstance(mask, torch.Tensor): raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not") # Batch single image if image.ndim == 3: assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)" image = image.unsqueeze(0) # Batch and add channel dim for single mask if mask.ndim == 2: mask = mask.unsqueeze(0).unsqueeze(0) # Batch single mask or add channel dim if mask.ndim == 3: # Batched mask if mask.shape[0] == image.shape[0]: mask = mask.unsqueeze(1) else: mask = mask.unsqueeze(0) assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" assert mask.shape[1] == 1, "Mask image must have a single channel" # Check image is in [-1, 1] if image.min() < -1 or image.max() > 1: raise ValueError("Image should be in [-1, 1] range") # Check mask is in [0, 1] if mask.min() < 0 or mask.max() > 1: raise ValueError("Mask should be in [0, 1] range") # paint-by-example inverses the mask mask = 1 - mask # Binarize mask mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 # Image as float32 image = image.to(dtype=torch.float32) elif isinstance(mask, torch.Tensor): raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not") else: if isinstance(image, PIL.Image.Image): image = [image] image = np.concatenate([np.array(i.convert("RGB"))[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 # preprocess mask if isinstance(mask, PIL.Image.Image): mask = [mask] mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) mask = mask.astype(np.float32) / 255.0 # paint-by-example inverses the mask mask = 1 - mask mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask = torch.from_numpy(mask) masked_image = image * mask return mask, masked_image class PaintByExamplePipeline(DiffusionPipeline): r""" Pipeline for image-guided image inpainting using Stable Diffusion. *This is an experimental feature*. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. image_encoder ([`PaintByExampleImageEncoder`]): Encodes the example input image. The unet is conditioned on the example image instead of a text prompt. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ # TODO: feature_extractor is required to encode initial images (if they are in PIL format), # we should give a descriptive message if the pipeline doesn't have one. _optional_components = ["safety_checker"] def __init__( self, vae: AutoencoderKL, image_encoder: PaintByExampleImageEncoder, unet: UNet2DConditionModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = False, ): super().__init__() self.register_modules( vae=vae, image_encoder=image_encoder, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.vae, self.image_encoder]: cpu_offload(cpu_offloaded_model, execution_device=device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs def check_inputs(self, image, height, width, callback_steps): if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask = torch.nn.functional.interpolate( mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) ) mask = mask.to(device=device, dtype=dtype) masked_image = masked_image.to(device=device, dtype=dtype) # encode the mask image into latents space so we can concatenate it to the latents if isinstance(generator, list): masked_image_latents = [ self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(batch_size) ] masked_image_latents = torch.cat(masked_image_latents, dim=0) else: masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) masked_image_latents = self.vae.config.scaling_factor * masked_image_latents # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if mask.shape[0] < batch_size: if not batch_size % mask.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1) mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask masked_image_latents = ( torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return mask, masked_image_latents def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeddings, negative_prompt_embeds = self.image_encoder(image, return_uncond_vector=True) # duplicate image embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = image_embeddings.shape image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1) image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) if do_classifier_free_guidance: negative_prompt_embeds = negative_prompt_embeds.repeat(1, image_embeddings.shape[0], 1) negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, 1, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings]) return image_embeddings @torch.no_grad() def __call__( self, example_image: Union[torch.FloatTensor, PIL.Image.Image], image: Union[torch.FloatTensor, PIL.Image.Image], mask_image: Union[torch.FloatTensor, PIL.Image.Image], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 5.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: example_image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): The exemplar image to guide the image generation. image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will be masked out with `mask_image` and repainted according to `prompt`. mask_image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Define call parameters if isinstance(image, PIL.Image.Image): batch_size = 1 elif isinstance(image, list): batch_size = len(image) else: batch_size = image.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 2. Preprocess mask and image mask, masked_image = prepare_mask_and_masked_image(image, mask_image) height, width = masked_image.shape[-2:] # 3. Check inputs self.check_inputs(example_image, height, width, callback_steps) # 4. Encode input image image_embeddings = self._encode_image( example_image, device, num_images_per_prompt, do_classifier_free_guidance ) # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 6. Prepare latent variables num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, image_embeddings.dtype, device, generator, latents, ) # 7. Prepare mask latent variables mask, masked_image_latents = self.prepare_mask_latents( mask, masked_image, batch_size * num_images_per_prompt, height, width, image_embeddings.dtype, device, generator, do_classifier_free_guidance, ) # 8. Check that sizes of mask, masked image and latents match num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.unet` or your `mask_image` or `image` input." ) # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 10. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = torch.cat([latent_model_input, masked_image_latents, mask], dim=1) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/pipeline_flax_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. 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. import importlib import inspect import os from typing import Any, Dict, List, Optional, Union import flax import numpy as np import PIL from flax.core.frozen_dict import FrozenDict from huggingface_hub import snapshot_download from PIL import Image from tqdm.auto import tqdm from ..configuration_utils import ConfigMixin from ..models.modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin from ..schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin from ..utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, http_user_agent, is_transformers_available, logging if is_transformers_available(): from transformers import FlaxPreTrainedModel INDEX_FILE = "diffusion_flax_model.bin" logger = logging.get_logger(__name__) LOADABLE_CLASSES = { "diffusers": { "FlaxModelMixin": ["save_pretrained", "from_pretrained"], "FlaxSchedulerMixin": ["save_pretrained", "from_pretrained"], "FlaxDiffusionPipeline": ["save_pretrained", "from_pretrained"], }, "transformers": { "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"], "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"], "FlaxPreTrainedModel": ["save_pretrained", "from_pretrained"], "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"], "ProcessorMixin": ["save_pretrained", "from_pretrained"], "ImageProcessingMixin": ["save_pretrained", "from_pretrained"], }, } ALL_IMPORTABLE_CLASSES = {} for library in LOADABLE_CLASSES: ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library]) def import_flax_or_no_model(module, class_name): try: # 1. First make sure that if a Flax object is present, import this one class_obj = getattr(module, "Flax" + class_name) except AttributeError: # 2. If this doesn't work, it's not a model and we don't append "Flax" class_obj = getattr(module, class_name) except AttributeError: raise ValueError(f"Neither Flax{class_name} nor {class_name} exist in {module}") return class_obj @flax.struct.dataclass class FlaxImagePipelineOutput(BaseOutput): """ Output class for image pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. """ images: Union[List[PIL.Image.Image], np.ndarray] class FlaxDiffusionPipeline(ConfigMixin): r""" Base class for all models. [`FlaxDiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion pipelines and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines to: - enabling/disabling the progress bar for the denoising iteration Class attributes: - **config_name** ([`str`]) -- name of the config file that will store the class and module names of all components of the diffusion pipeline. """ config_name = "model_index.json" def register_modules(self, **kwargs): # import it here to avoid circular import from diffusers import pipelines for name, module in kwargs.items(): if module is None: register_dict = {name: (None, None)} else: # retrieve library library = module.__module__.split(".")[0] # check if the module is a pipeline module pipeline_dir = module.__module__.split(".")[-2] path = module.__module__.split(".") is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir) # if library is not in LOADABLE_CLASSES, then it is a custom module. # Or if it's a pipeline module, then the module is inside the pipeline # folder so we set the library to module name. if library not in LOADABLE_CLASSES or is_pipeline_module: library = pipeline_dir # retrieve class_name class_name = module.__class__.__name__ register_dict = {name: (library, class_name)} # save model index config self.register_to_config(**register_dict) # set models setattr(self, name, module) def save_pretrained(self, save_directory: Union[str, os.PathLike], params: Union[Dict, FrozenDict]): # TODO: handle inference_state """ Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading method. The pipeline can easily be re-loaded using the `[`~FlaxDiffusionPipeline.from_pretrained`]` class method. Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. """ self.save_config(save_directory) model_index_dict = dict(self.config) model_index_dict.pop("_class_name") model_index_dict.pop("_diffusers_version") model_index_dict.pop("_module", None) for pipeline_component_name in model_index_dict.keys(): sub_model = getattr(self, pipeline_component_name) if sub_model is None: # edge case for saving a pipeline with safety_checker=None continue model_cls = sub_model.__class__ save_method_name = None # search for the model's base class in LOADABLE_CLASSES for library_name, library_classes in LOADABLE_CLASSES.items(): library = importlib.import_module(library_name) for base_class, save_load_methods in library_classes.items(): class_candidate = getattr(library, base_class, None) if class_candidate is not None and issubclass(model_cls, class_candidate): # if we found a suitable base class in LOADABLE_CLASSES then grab its save method save_method_name = save_load_methods[0] break if save_method_name is not None: break save_method = getattr(sub_model, save_method_name) expects_params = "params" in set(inspect.signature(save_method).parameters.keys()) if expects_params: save_method( os.path.join(save_directory, pipeline_component_name), params=params[pipeline_component_name] ) else: save_method(os.path.join(save_directory, pipeline_component_name)) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): r""" Instantiate a Flax diffusion pipeline from pre-trained pipeline weights. The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task. The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those weights are discarded. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* of a pretrained pipeline hosted inside a model repo on https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like `CompVis/ldm-text2im-large-256`. - A path to a *directory* containing pipeline weights saved using [`~FlaxDiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`. dtype (`str` or `jnp.dtype`, *optional*): Override the default `jnp.dtype` and load the model under this dtype. If `"auto"` is passed the dtype will be automatically derived from the model's weights. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. specify the folder name here. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the specific pipeline class. The overwritten components are then directly passed to the pipelines `__init__` method. See example below for more information. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"` Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a firewalled environment. Examples: ```py >>> from diffusers import FlaxDiffusionPipeline >>> # Download pipeline from huggingface.co and cache. >>> # Requires to be logged in to Hugging Face hub, >>> # see more in [the documentation](https://huggingface.co/docs/hub/security-tokens) >>> pipeline, params = FlaxDiffusionPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", ... revision="bf16", ... dtype=jnp.bfloat16, ... ) >>> # Download pipeline, but use a different scheduler >>> from diffusers import FlaxDPMSolverMultistepScheduler >>> model_id = "runwayml/stable-diffusion-v1-5" >>> dpmpp, dpmpp_state = FlaxDPMSolverMultistepScheduler.from_pretrained( ... model_id, ... subfolder="scheduler", ... ) >>> dpm_pipe, dpm_params = FlaxStableDiffusionPipeline.from_pretrained( ... model_id, revision="bf16", dtype=jnp.bfloat16, scheduler=dpmpp ... ) >>> dpm_params["scheduler"] = dpmpp_state ``` """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) from_pt = kwargs.pop("from_pt", False) use_memory_efficient_attention = kwargs.pop("use_memory_efficient_attention", False) dtype = kwargs.pop("dtype", None) # 1. Download the checkpoints and configs # use snapshot download here to get it working from from_pretrained if not os.path.isdir(pretrained_model_name_or_path): config_dict = cls.load_config( pretrained_model_name_or_path, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, ) # make sure we only download sub-folders and `diffusers` filenames folder_names = [k for k in config_dict.keys() if not k.startswith("_")] allow_patterns = [os.path.join(k, "*") for k in folder_names] allow_patterns += [FLAX_WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, cls.config_name] # make sure we don't download PyTorch weights, unless when using from_pt ignore_patterns = "*.bin" if not from_pt else [] if cls != FlaxDiffusionPipeline: requested_pipeline_class = cls.__name__ else: requested_pipeline_class = config_dict.get("_class_name", cls.__name__) requested_pipeline_class = ( requested_pipeline_class if requested_pipeline_class.startswith("Flax") else "Flax" + requested_pipeline_class ) user_agent = {"pipeline_class": requested_pipeline_class} user_agent = http_user_agent(user_agent) # download all allow_patterns cached_folder = snapshot_download( pretrained_model_name_or_path, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, allow_patterns=allow_patterns, ignore_patterns=ignore_patterns, user_agent=user_agent, ) else: cached_folder = pretrained_model_name_or_path config_dict = cls.load_config(cached_folder) # 2. Load the pipeline class, if using custom module then load it from the hub # if we load from explicit class, let's use it if cls != FlaxDiffusionPipeline: pipeline_class = cls else: diffusers_module = importlib.import_module(cls.__module__.split(".")[0]) class_name = ( config_dict["_class_name"] if config_dict["_class_name"].startswith("Flax") else "Flax" + config_dict["_class_name"] ) pipeline_class = getattr(diffusers_module, class_name) # some modules can be passed directly to the init # in this case they are already instantiated in `kwargs` # extract them here expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class) passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs} init_dict, _, _ = pipeline_class.extract_init_dict(config_dict, **kwargs) init_kwargs = {} # inference_params params = {} # import it here to avoid circular import from diffusers import pipelines # 3. Load each module in the pipeline for name, (library_name, class_name) in init_dict.items(): if class_name is None: # edge case for when the pipeline was saved with safety_checker=None init_kwargs[name] = None continue is_pipeline_module = hasattr(pipelines, library_name) loaded_sub_model = None sub_model_should_be_defined = True # if the model is in a pipeline module, then we load it from the pipeline if name in passed_class_obj: # 1. check that passed_class_obj has correct parent class if not is_pipeline_module: library = importlib.import_module(library_name) class_obj = getattr(library, class_name) importable_classes = LOADABLE_CLASSES[library_name] class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()} expected_class_obj = None for class_name, class_candidate in class_candidates.items(): if class_candidate is not None and issubclass(class_obj, class_candidate): expected_class_obj = class_candidate if not issubclass(passed_class_obj[name].__class__, expected_class_obj): raise ValueError( f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be" f" {expected_class_obj}" ) elif passed_class_obj[name] is None: logger.warning( f"You have passed `None` for {name} to disable its functionality in {pipeline_class}. Note" f" that this might lead to problems when using {pipeline_class} and is not recommended." ) sub_model_should_be_defined = False else: logger.warning( f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it" " has the correct type" ) # set passed class object loaded_sub_model = passed_class_obj[name] elif is_pipeline_module: pipeline_module = getattr(pipelines, library_name) class_obj = import_flax_or_no_model(pipeline_module, class_name) importable_classes = ALL_IMPORTABLE_CLASSES class_candidates = {c: class_obj for c in importable_classes.keys()} else: # else we just import it from the library. library = importlib.import_module(library_name) class_obj = import_flax_or_no_model(library, class_name) importable_classes = LOADABLE_CLASSES[library_name] class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()} if loaded_sub_model is None and sub_model_should_be_defined: load_method_name = None for class_name, class_candidate in class_candidates.items(): if class_candidate is not None and issubclass(class_obj, class_candidate): load_method_name = importable_classes[class_name][1] load_method = getattr(class_obj, load_method_name) # check if the module is in a subdirectory if os.path.isdir(os.path.join(cached_folder, name)): loadable_folder = os.path.join(cached_folder, name) else: loaded_sub_model = cached_folder if issubclass(class_obj, FlaxModelMixin): loaded_sub_model, loaded_params = load_method( loadable_folder, from_pt=from_pt, use_memory_efficient_attention=use_memory_efficient_attention, dtype=dtype, ) params[name] = loaded_params elif is_transformers_available() and issubclass(class_obj, FlaxPreTrainedModel): if from_pt: # TODO(Suraj): Fix this in Transformers. We should be able to use `_do_init=False` here loaded_sub_model = load_method(loadable_folder, from_pt=from_pt) loaded_params = loaded_sub_model.params del loaded_sub_model._params else: loaded_sub_model, loaded_params = load_method(loadable_folder, _do_init=False) params[name] = loaded_params elif issubclass(class_obj, FlaxSchedulerMixin): loaded_sub_model, scheduler_state = load_method(loadable_folder) params[name] = scheduler_state else: loaded_sub_model = load_method(loadable_folder) init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...) # 4. Potentially add passed objects if expected missing_modules = set(expected_modules) - set(init_kwargs.keys()) passed_modules = list(passed_class_obj.keys()) if len(missing_modules) > 0 and missing_modules <= set(passed_modules): for module in missing_modules: init_kwargs[module] = passed_class_obj.get(module, None) elif len(missing_modules) > 0: passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs raise ValueError( f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed." ) model = pipeline_class(**init_kwargs, dtype=dtype) return model, params @staticmethod def _get_signature_keys(obj): parameters = inspect.signature(obj.__init__).parameters required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) expected_modules = set(required_parameters.keys()) - {"self"} return expected_modules, optional_parameters @property def components(self) -> Dict[str, Any]: r""" The `self.components` property can be useful to run different pipelines with the same weights and configurations to not have to re-allocate memory. Examples: ```py >>> from diffusers import ( ... FlaxStableDiffusionPipeline, ... FlaxStableDiffusionImg2ImgPipeline, ... ) >>> text2img = FlaxStableDiffusionPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jnp.bfloat16 ... ) >>> img2img = FlaxStableDiffusionImg2ImgPipeline(**text2img.components) ``` Returns: A dictionary containing all the modules needed to initialize the pipeline. """ expected_modules, optional_parameters = self._get_signature_keys(self) components = { k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters } if set(components.keys()) != expected_modules: raise ValueError( f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected" f" {expected_modules} to be defined, but {components} are defined." ) return components @staticmethod def numpy_to_pil(images): """ Convert a numpy image or a batch of images to a PIL image. """ if images.ndim == 3: images = images[None, ...] images = (images * 255).round().astype("uint8") if images.shape[-1] == 1: # special case for grayscale (single channel) images pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images] else: pil_images = [Image.fromarray(image) for image in images] return pil_images # TODO: make it compatible with jax.lax def progress_bar(self, iterable): if not hasattr(self, "_progress_bar_config"): self._progress_bar_config = {} elif not isinstance(self._progress_bar_config, dict): raise ValueError( f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}." ) return tqdm(iterable, **self._progress_bar_config) def set_progress_bar_config(self, **kwargs): self._progress_bar_config = kwargs ================================================ FILE: diffusers/pipelines/pipeline_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # Copyright (c) 2022, NVIDIA CORPORATION. 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. import fnmatch import importlib import inspect import os import re import sys import warnings from dataclasses import dataclass from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from huggingface_hub import hf_hub_download, model_info, snapshot_download from packaging import version from tqdm.auto import tqdm import diffusers from .. import __version__ from ..configuration_utils import ConfigMixin from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT from ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME from ..utils import ( CONFIG_NAME, DEPRECATED_REVISION_ARGS, DIFFUSERS_CACHE, HF_HUB_OFFLINE, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, deprecate, get_class_from_dynamic_module, is_accelerate_available, is_accelerate_version, is_compiled_module, is_safetensors_available, is_torch_version, is_transformers_available, logging, numpy_to_pil, ) if is_transformers_available(): import transformers from transformers import PreTrainedModel from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME from ..utils import FLAX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME if is_accelerate_available(): import accelerate INDEX_FILE = "diffusion_pytorch_model.bin" CUSTOM_PIPELINE_FILE_NAME = "pipeline.py" DUMMY_MODULES_FOLDER = "diffusers.utils" TRANSFORMERS_DUMMY_MODULES_FOLDER = "transformers.utils" logger = logging.get_logger(__name__) LOADABLE_CLASSES = { "diffusers": { "ModelMixin": ["save_pretrained", "from_pretrained"], "SchedulerMixin": ["save_pretrained", "from_pretrained"], "DiffusionPipeline": ["save_pretrained", "from_pretrained"], "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"], }, "transformers": { "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"], "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"], "PreTrainedModel": ["save_pretrained", "from_pretrained"], "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"], "ProcessorMixin": ["save_pretrained", "from_pretrained"], "ImageProcessingMixin": ["save_pretrained", "from_pretrained"], }, "onnxruntime.training": { "ORTModule": ["save_pretrained", "from_pretrained"], }, } ALL_IMPORTABLE_CLASSES = {} for library in LOADABLE_CLASSES: ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library]) @dataclass class ImagePipelineOutput(BaseOutput): """ Output class for image pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. """ images: Union[List[PIL.Image.Image], np.ndarray] @dataclass class AudioPipelineOutput(BaseOutput): """ Output class for audio pipelines. Args: audios (`np.ndarray`) List of denoised samples of shape `(batch_size, num_channels, sample_rate)`. Numpy array present the denoised audio samples of the diffusion pipeline. """ audios: np.ndarray def is_safetensors_compatible(filenames, variant=None, passed_components=None) -> bool: """ Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of default pytorch files to know which safetensors files are needed. - The model is safetensors compatible only if there is a matching safetensors file for every default pytorch file. Converting default pytorch serialized filenames to safetensors serialized filenames: - For models from the diffusers library, just replace the ".bin" extension with ".safetensors" - For models from the transformers library, the filename changes from "pytorch_model" to "model", and the ".bin" extension is replaced with ".safetensors" """ pt_filenames = [] sf_filenames = set() passed_components = passed_components or [] for filename in filenames: _, extension = os.path.splitext(filename) if len(filename.split("/")) == 2 and filename.split("/")[0] in passed_components: continue if extension == ".bin": pt_filenames.append(filename) elif extension == ".safetensors": sf_filenames.add(filename) for filename in pt_filenames: # filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extention = '.bam' path, filename = os.path.split(filename) filename, extension = os.path.splitext(filename) if filename.startswith("pytorch_model"): filename = filename.replace("pytorch_model", "model") else: filename = filename expected_sf_filename = os.path.join(path, filename) expected_sf_filename = f"{expected_sf_filename}.safetensors" if expected_sf_filename not in sf_filenames: logger.warning(f"{expected_sf_filename} not found") return False return True def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLike], str]: weight_names = [ WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME, FLAX_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ] if is_transformers_available(): weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME] # model_pytorch, diffusion_model_pytorch, ... weight_prefixes = [w.split(".")[0] for w in weight_names] # .bin, .safetensors, ... weight_suffixs = [w.split(".")[-1] for w in weight_names] # -00001-of-00002 transformers_index_format = r"\d{5}-of-\d{5}" if variant is not None: # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetenstors` variant_file_re = re.compile( rf"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$" ) # `text_encoder/pytorch_model.bin.index.fp16.json` variant_index_re = re.compile( rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$" ) # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetenstors` non_variant_file_re = re.compile( rf"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$" ) # `text_encoder/pytorch_model.bin.index.json` non_variant_index_re = re.compile(rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json") if variant is not None: variant_weights = {f for f in filenames if variant_file_re.match(f.split("/")[-1]) is not None} variant_indexes = {f for f in filenames if variant_index_re.match(f.split("/")[-1]) is not None} variant_filenames = variant_weights | variant_indexes else: variant_filenames = set() non_variant_weights = {f for f in filenames if non_variant_file_re.match(f.split("/")[-1]) is not None} non_variant_indexes = {f for f in filenames if non_variant_index_re.match(f.split("/")[-1]) is not None} non_variant_filenames = non_variant_weights | non_variant_indexes # all variant filenames will be used by default usable_filenames = set(variant_filenames) def convert_to_variant(filename): if "index" in filename: variant_filename = filename.replace("index", f"index.{variant}") elif re.compile(f"^(.*?){transformers_index_format}").match(filename) is not None: variant_filename = f"{filename.split('-')[0]}.{variant}-{'-'.join(filename.split('-')[1:])}" else: variant_filename = f"{filename.split('.')[0]}.{variant}.{filename.split('.')[1]}" return variant_filename for f in non_variant_filenames: variant_filename = convert_to_variant(f) if variant_filename not in usable_filenames: usable_filenames.add(f) return usable_filenames, variant_filenames def warn_deprecated_model_variant(pretrained_model_name_or_path, use_auth_token, variant, revision, model_filenames): info = model_info( pretrained_model_name_or_path, use_auth_token=use_auth_token, revision=None, ) filenames = {sibling.rfilename for sibling in info.siblings} comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision) comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames] if set(comp_model_filenames) == set(model_filenames): warnings.warn( f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` even though you can load it via `variant=`{revision}`. Loading model variants via `revision='{revision}'` is deprecated and will be removed in diffusers v1. Please use `variant='{revision}'` instead.", FutureWarning, ) else: warnings.warn( f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have the required variant filenames in the 'main' branch. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {revision} files' so that the correct variant file can be added.", FutureWarning, ) def maybe_raise_or_warn( library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module ): """Simple helper method to raise or warn in case incorrect module has been passed""" if not is_pipeline_module: library = importlib.import_module(library_name) class_obj = getattr(library, class_name) class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()} expected_class_obj = None for class_name, class_candidate in class_candidates.items(): if class_candidate is not None and issubclass(class_obj, class_candidate): expected_class_obj = class_candidate # Dynamo wraps the original model in a private class. # I didn't find a public API to get the original class. sub_model = passed_class_obj[name] model_cls = sub_model.__class__ if is_compiled_module(sub_model): model_cls = sub_model._orig_mod.__class__ if not issubclass(model_cls, expected_class_obj): raise ValueError( f"{passed_class_obj[name]} is of type: {model_cls}, but should be" f" {expected_class_obj}" ) else: logger.warning( f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it" " has the correct type" ) def get_class_obj_and_candidates(library_name, class_name, importable_classes, pipelines, is_pipeline_module): """Simple helper method to retrieve class object of module as well as potential parent class objects""" if is_pipeline_module: pipeline_module = getattr(pipelines, library_name) class_obj = getattr(pipeline_module, class_name) class_candidates = {c: class_obj for c in importable_classes.keys()} else: # else we just import it from the library. library = importlib.import_module(library_name) class_obj = getattr(library, class_name) class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()} return class_obj, class_candidates def _get_pipeline_class(class_obj, config, custom_pipeline=None, cache_dir=None, revision=None): if custom_pipeline is not None: if custom_pipeline.endswith(".py"): path = Path(custom_pipeline) # decompose into folder & file file_name = path.name custom_pipeline = path.parent.absolute() else: file_name = CUSTOM_PIPELINE_FILE_NAME return get_class_from_dynamic_module( custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=revision ) if class_obj != DiffusionPipeline: return class_obj diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0]) return getattr(diffusers_module, config["_class_name"]) def load_sub_model( library_name: str, class_name: str, importable_classes: List[Any], pipelines: Any, is_pipeline_module: bool, pipeline_class: Any, torch_dtype: torch.dtype, provider: Any, sess_options: Any, device_map: Optional[Union[Dict[str, torch.device], str]], model_variants: Dict[str, str], name: str, from_flax: bool, variant: str, low_cpu_mem_usage: bool, cached_folder: Union[str, os.PathLike], ): """Helper method to load the module `name` from `library_name` and `class_name`""" # retrieve class candidates class_obj, class_candidates = get_class_obj_and_candidates( library_name, class_name, importable_classes, pipelines, is_pipeline_module ) load_method_name = None # retrive load method name for class_name, class_candidate in class_candidates.items(): if class_candidate is not None and issubclass(class_obj, class_candidate): load_method_name = importable_classes[class_name][1] # if load method name is None, then we have a dummy module -> raise Error if load_method_name is None: none_module = class_obj.__module__ is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith( TRANSFORMERS_DUMMY_MODULES_FOLDER ) if is_dummy_path and "dummy" in none_module: # call class_obj for nice error message of missing requirements class_obj() raise ValueError( f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have" f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}." ) load_method = getattr(class_obj, load_method_name) # add kwargs to loading method loading_kwargs = {} if issubclass(class_obj, torch.nn.Module): loading_kwargs["torch_dtype"] = torch_dtype if issubclass(class_obj, diffusers.OnnxRuntimeModel): loading_kwargs["provider"] = provider loading_kwargs["sess_options"] = sess_options is_diffusers_model = issubclass(class_obj, diffusers.ModelMixin) if is_transformers_available(): transformers_version = version.parse(version.parse(transformers.__version__).base_version) else: transformers_version = "N/A" is_transformers_model = ( is_transformers_available() and issubclass(class_obj, PreTrainedModel) and transformers_version >= version.parse("4.20.0") ) # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers. # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default. # This makes sure that the weights won't be initialized which significantly speeds up loading. if is_diffusers_model or is_transformers_model: loading_kwargs["device_map"] = device_map loading_kwargs["variant"] = model_variants.pop(name, None) if from_flax: loading_kwargs["from_flax"] = True # the following can be deleted once the minimum required `transformers` version # is higher than 4.27 if ( is_transformers_model and loading_kwargs["variant"] is not None and transformers_version < version.parse("4.27.0") ): raise ImportError( f"When passing `variant='{variant}'`, please make sure to upgrade your `transformers` version to at least 4.27.0.dev0" ) elif is_transformers_model and loading_kwargs["variant"] is None: loading_kwargs.pop("variant") # if `from_flax` and model is transformer model, can currently not load with `low_cpu_mem_usage` if not (from_flax and is_transformers_model): loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage else: loading_kwargs["low_cpu_mem_usage"] = False # check if the module is in a subdirectory if os.path.isdir(os.path.join(cached_folder, name)): loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs) else: # else load from the root directory loaded_sub_model = load_method(cached_folder, **loading_kwargs) return loaded_sub_model class DiffusionPipeline(ConfigMixin): r""" Base class for all models. [`DiffusionPipeline`] takes care of storing all components (models, schedulers, processors) for diffusion pipelines and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines to: - move all PyTorch modules to the device of your choice - enabling/disabling the progress bar for the denoising iteration Class attributes: - **config_name** (`str`) -- name of the config file that will store the class and module names of all components of the diffusion pipeline. - **_optional_components** (List[`str`]) -- list of all components that are optional so they don't have to be passed for the pipeline to function (should be overridden by subclasses). """ config_name = "model_index.json" _optional_components = [] def register_modules(self, **kwargs): # import it here to avoid circular import from diffusers import pipelines for name, module in kwargs.items(): # retrieve library if module is None: register_dict = {name: (None, None)} else: # register the original module, not the dynamo compiled one if is_compiled_module(module): module = module._orig_mod library = module.__module__.split(".")[0] # check if the module is a pipeline module pipeline_dir = module.__module__.split(".")[-2] if len(module.__module__.split(".")) > 2 else None path = module.__module__.split(".") is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir) # if library is not in LOADABLE_CLASSES, then it is a custom module. # Or if it's a pipeline module, then the module is inside the pipeline # folder so we set the library to module name. if library not in LOADABLE_CLASSES or is_pipeline_module: library = pipeline_dir # retrieve class_name class_name = module.__class__.__name__ register_dict = {name: (library, class_name)} # save model index config self.register_to_config(**register_dict) # set models setattr(self, name, module) def __setattr__(self, name: str, value: Any): if name in self.__dict__ and hasattr(self.config, name): # We need to overwrite the config if name exists in config if isinstance(getattr(self.config, name), (tuple, list)): if value is not None and self.config[name][0] is not None: class_library_tuple = (value.__module__.split(".")[0], value.__class__.__name__) else: class_library_tuple = (None, None) self.register_to_config(**{name: class_library_tuple}) else: self.register_to_config(**{name: value}) super().__setattr__(name, value) def save_pretrained( self, save_directory: Union[str, os.PathLike], safe_serialization: bool = False, variant: Optional[str] = None, ): """ Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading method. The pipeline can easily be re-loaded using the [`~DiffusionPipeline.from_pretrained`] class method. Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). variant (`str`, *optional*): If specified, weights are saved in the format pytorch_model..bin. """ model_index_dict = dict(self.config) model_index_dict.pop("_class_name", None) model_index_dict.pop("_diffusers_version", None) model_index_dict.pop("_module", None) expected_modules, optional_kwargs = self._get_signature_keys(self) def is_saveable_module(name, value): if name not in expected_modules: return False if name in self._optional_components and value[0] is None: return False return True model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)} for pipeline_component_name in model_index_dict.keys(): sub_model = getattr(self, pipeline_component_name) model_cls = sub_model.__class__ # Dynamo wraps the original model in a private class. # I didn't find a public API to get the original class. if is_compiled_module(sub_model): sub_model = sub_model._orig_mod model_cls = sub_model.__class__ save_method_name = None # search for the model's base class in LOADABLE_CLASSES for library_name, library_classes in LOADABLE_CLASSES.items(): if library_name in sys.modules: library = importlib.import_module(library_name) else: logger.info( f"{library_name} is not installed. Cannot save {pipeline_component_name} as {library_classes} from {library_name}" ) for base_class, save_load_methods in library_classes.items(): class_candidate = getattr(library, base_class, None) if class_candidate is not None and issubclass(model_cls, class_candidate): # if we found a suitable base class in LOADABLE_CLASSES then grab its save method save_method_name = save_load_methods[0] break if save_method_name is not None: break if save_method_name is None: logger.warn(f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved.") # make sure that unsaveable components are not tried to be loaded afterward self.register_to_config(**{pipeline_component_name: (None, None)}) continue save_method = getattr(sub_model, save_method_name) # Call the save method with the argument safe_serialization only if it's supported save_method_signature = inspect.signature(save_method) save_method_accept_safe = "safe_serialization" in save_method_signature.parameters save_method_accept_variant = "variant" in save_method_signature.parameters save_kwargs = {} if save_method_accept_safe: save_kwargs["safe_serialization"] = safe_serialization if save_method_accept_variant: save_kwargs["variant"] = variant save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs) # finally save the config self.save_config(save_directory) def to( self, torch_device: Optional[Union[str, torch.device]] = None, torch_dtype: Optional[torch.dtype] = None, silence_dtype_warnings: bool = False, ): if torch_device is None and torch_dtype is None: return self # throw warning if pipeline is in "offloaded"-mode but user tries to manually set to GPU. def module_is_sequentially_offloaded(module): if not is_accelerate_available() or is_accelerate_version("<", "0.14.0"): return False return hasattr(module, "_hf_hook") and not isinstance( module._hf_hook, (accelerate.hooks.CpuOffload, accelerate.hooks.AlignDevicesHook) ) def module_is_offloaded(module): if not is_accelerate_available() or is_accelerate_version("<", "0.17.0.dev0"): return False return hasattr(module, "_hf_hook") and isinstance(module._hf_hook, accelerate.hooks.CpuOffload) # .to("cuda") would raise an error if the pipeline is sequentially offloaded, so we raise our own to make it clearer pipeline_is_sequentially_offloaded = any( module_is_sequentially_offloaded(module) for _, module in self.components.items() ) if pipeline_is_sequentially_offloaded and torch.device(torch_device).type == "cuda": raise ValueError( "It seems like you have activated sequential model offloading by calling `enable_sequential_cpu_offload`, but are now attempting to move the pipeline to GPU. This is not compatible with offloading. Please, move your pipeline `.to('cpu')` or consider removing the move altogether if you use sequential offloading." ) # Display a warning in this case (the operation succeeds but the benefits are lost) pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items()) if pipeline_is_offloaded and torch.device(torch_device).type == "cuda": logger.warning( f"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading." ) module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module)] is_offloaded = pipeline_is_offloaded or pipeline_is_sequentially_offloaded for module in modules: is_loaded_in_8bit = hasattr(module, "is_loaded_in_8bit") and module.is_loaded_in_8bit if is_loaded_in_8bit and torch_dtype is not None: logger.warning( f"The module '{module.__class__.__name__}' has been loaded in 8bit and conversion to {torch_dtype} is not yet supported. Module is still in 8bit precision." ) if is_loaded_in_8bit and torch_device is not None: logger.warning( f"The module '{module.__class__.__name__}' has been loaded in 8bit and moving it to {torch_dtype} via `.to()` is not yet supported. Module is still on {module.device}." ) else: module.to(torch_device, torch_dtype) if ( module.dtype == torch.float16 and str(torch_device) in ["cpu"] and not silence_dtype_warnings and not is_offloaded ): logger.warning( "Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` device. It" " is not recommended to move them to `cpu` as running them will fail. Please make" " sure to use an accelerator to run the pipeline in inference, due to the lack of" " support for`float16` operations on this device in PyTorch. Please, remove the" " `torch_dtype=torch.float16` argument, or use another device for inference." ) return self @property def device(self) -> torch.device: r""" Returns: `torch.device`: The torch device on which the pipeline is located. """ module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module)] for module in modules: return module.device return torch.device("cpu") @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): r""" Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights. The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task. The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those weights are discarded. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* of a pretrained pipeline hosted inside a model repo on https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like `CompVis/ldm-text2im-large-256`. - A path to a *directory* containing pipeline weights saved using [`~DiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`. torch_dtype (`str` or `torch.dtype`, *optional*): Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype will be automatically derived from the model's weights. custom_pipeline (`str`, *optional*): This is an experimental feature and is likely to change in the future. Can be either: - A string, the *repo id* of a custom pipeline hosted inside a model repo on https://huggingface.co/. Valid repo ids have to be located under a user or organization name, like `hf-internal-testing/diffusers-dummy-pipeline`. It is required that the model repo has a file, called `pipeline.py` that defines the custom pipeline. - A string, the *file name* of a community pipeline hosted on GitHub under https://github.com/huggingface/diffusers/tree/main/examples/community. Valid file names have to match exactly the file name without `.py` located under the above link, *e.g.* `clip_guided_stable_diffusion`. Community pipelines are always loaded from the current `main` branch of GitHub. - A path to a *directory* containing a custom pipeline, e.g., `./my_pipeline_directory/`. It is required that the directory has a file, called `pipeline.py` that defines the custom pipeline. For more information on how to load and create custom pipelines, please have a look at [Loading and Adding Custom Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview) force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. custom_revision (`str`, *optional*, defaults to `"main"` when loading from the Hub and to local version of `diffusers` when loading from GitHub): The specific model version to use. It can be a branch name, a tag name, or a commit id similar to `revision` when loading a custom pipeline from the Hub. It can be a diffusers version when loading a custom pipeline from GitHub. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. specify the folder name here. device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading by not initializing the weights and only loading the pre-trained weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch, setting this argument to `True` will raise an error. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the pipeline will load the `safetensors` weights if they're available **and** if the `safetensors` library is installed. If set to `True`, the pipeline will forcibly load the models from `safetensors` weights. If set to `False` the pipeline will *not* use `safetensors`. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the specific pipeline class. The overwritten components are then directly passed to the pipelines `__init__` method. See example below for more information. variant (`str`, *optional*): If specified load weights from `variant` filename, *e.g.* pytorch_model..bin. `variant` is ignored when using `from_flax`. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"` Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a firewalled environment. Examples: ```py >>> from diffusers import DiffusionPipeline >>> # Download pipeline from huggingface.co and cache. >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256") >>> # Download pipeline that requires an authorization token >>> # For more information on access tokens, please refer to this section >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens) >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") >>> # Use a different scheduler >>> from diffusers import LMSDiscreteScheduler >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config) >>> pipeline.scheduler = scheduler ``` """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) resume_download = kwargs.pop("resume_download", False) force_download = kwargs.pop("force_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) from_flax = kwargs.pop("from_flax", False) torch_dtype = kwargs.pop("torch_dtype", None) custom_pipeline = kwargs.pop("custom_pipeline", None) custom_revision = kwargs.pop("custom_revision", None) provider = kwargs.pop("provider", None) sess_options = kwargs.pop("sess_options", None) device_map = kwargs.pop("device_map", None) low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) variant = kwargs.pop("variant", None) use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False) # 1. Download the checkpoints and configs # use snapshot download here to get it working from from_pretrained if not os.path.isdir(pretrained_model_name_or_path): cached_folder = cls.download( pretrained_model_name_or_path, cache_dir=cache_dir, resume_download=resume_download, force_download=force_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, from_flax=from_flax, use_safetensors=use_safetensors, custom_pipeline=custom_pipeline, custom_revision=custom_revision, variant=variant, **kwargs, ) else: cached_folder = pretrained_model_name_or_path config_dict = cls.load_config(cached_folder) # pop out "_ignore_files" as it is only needed for download config_dict.pop("_ignore_files", None) # 2. Define which model components should load variants # We retrieve the information by matching whether variant # model checkpoints exist in the subfolders model_variants = {} if variant is not None: for folder in os.listdir(cached_folder): folder_path = os.path.join(cached_folder, folder) is_folder = os.path.isdir(folder_path) and folder in config_dict variant_exists = is_folder and any( p.split(".")[1].startswith(variant) for p in os.listdir(folder_path) ) if variant_exists: model_variants[folder] = variant # 3. Load the pipeline class, if using custom module then load it from the hub # if we load from explicit class, let's use it pipeline_class = _get_pipeline_class( cls, config_dict, custom_pipeline=custom_pipeline, cache_dir=cache_dir, revision=custom_revision ) # DEPRECATED: To be removed in 1.0.0 if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse( version.parse(config_dict["_diffusers_version"]).base_version ) <= version.parse("0.5.1"): from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy pipeline_class = StableDiffusionInpaintPipelineLegacy deprecation_message = ( "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the" f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For" " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting" " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your" f" checkpoint {pretrained_model_name_or_path} to the format of" " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain" " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0." ) deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False) # 4. Define expected modules given pipeline signature # and define non-None initialized modules (=`init_kwargs`) # some modules can be passed directly to the init # in this case they are already instantiated in `kwargs` # extract them here expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class) passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs} passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs} init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs) # define init kwargs init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict} init_kwargs = {**init_kwargs, **passed_pipe_kwargs} # remove `null` components def load_module(name, value): if value[0] is None: return False if name in passed_class_obj and passed_class_obj[name] is None: return False return True init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)} # Special case: safety_checker must be loaded separately when using `from_flax` if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj: raise NotImplementedError( "The safety checker cannot be automatically loaded when loading weights `from_flax`." " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker" " separately if you need it." ) # 5. Throw nice warnings / errors for fast accelerate loading if len(unused_kwargs) > 0: logger.warning( f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored." ) if low_cpu_mem_usage and not is_accelerate_available(): low_cpu_mem_usage = False logger.warning( "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" " install accelerate\n```\n." ) if device_map is not None and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" " `device_map=None`." ) if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" " `low_cpu_mem_usage=False`." ) if low_cpu_mem_usage is False and device_map is not None: raise ValueError( f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and" " dispatching. Please make sure to set `low_cpu_mem_usage=True`." ) # import it here to avoid circular import from diffusers import pipelines # 6. Load each module in the pipeline for name, (library_name, class_name) in init_dict.items(): # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names if class_name.startswith("Flax"): class_name = class_name[4:] # 6.2 Define all importable classes is_pipeline_module = hasattr(pipelines, library_name) importable_classes = ALL_IMPORTABLE_CLASSES if is_pipeline_module else LOADABLE_CLASSES[library_name] loaded_sub_model = None # 6.3 Use passed sub model or load class_name from library_name if name in passed_class_obj: # if the model is in a pipeline module, then we load it from the pipeline # check that passed_class_obj has correct parent class maybe_raise_or_warn( library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module ) loaded_sub_model = passed_class_obj[name] else: # load sub model loaded_sub_model = load_sub_model( library_name=library_name, class_name=class_name, importable_classes=importable_classes, pipelines=pipelines, is_pipeline_module=is_pipeline_module, pipeline_class=pipeline_class, torch_dtype=torch_dtype, provider=provider, sess_options=sess_options, device_map=device_map, model_variants=model_variants, name=name, from_flax=from_flax, variant=variant, low_cpu_mem_usage=low_cpu_mem_usage, cached_folder=cached_folder, ) init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...) # 7. Potentially add passed objects if expected missing_modules = set(expected_modules) - set(init_kwargs.keys()) passed_modules = list(passed_class_obj.keys()) optional_modules = pipeline_class._optional_components if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules): for module in missing_modules: init_kwargs[module] = passed_class_obj.get(module, None) elif len(missing_modules) > 0: passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs raise ValueError( f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed." ) # 8. Instantiate the pipeline model = pipeline_class(**init_kwargs) return_cached_folder = kwargs.pop("return_cached_folder", False) if return_cached_folder: message = f"Passing `return_cached_folder=True` is deprecated and will be removed in `diffusers=0.18.0`. Please do the following instead: \n 1. Load the cached_folder via `cached_folder={cls}.download({pretrained_model_name_or_path})`. \n 2. Load the pipeline by loading from the cached folder: `pipeline={cls}.from_pretrained(cached_folder)`." deprecate("return_cached_folder", "0.18.0", message) return model, cached_folder return model @classmethod def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]: r""" Download and cache a PyTorch diffusion pipeline from pre-trained pipeline weights. Parameters: pretrained_model_name (`str` or `os.PathLike`, *optional*): Should be a string, the *repo id* of a pretrained pipeline hosted inside a model repo on https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like `CompVis/ldm-text2im-large-256`. custom_pipeline (`str`, *optional*): This is an experimental feature and is likely to change in the future. Can be either: - A string, the *repo id* of a custom pipeline hosted inside a model repo on https://huggingface.co/. Valid repo ids have to be located under a user or organization name, like `hf-internal-testing/diffusers-dummy-pipeline`. It is required that the model repo has a file, called `pipeline.py` that defines the custom pipeline. - A string, the *file name* of a community pipeline hosted on GitHub under https://github.com/huggingface/diffusers/tree/main/examples/community. Valid file names have to match exactly the file name without `.py` located under the above link, *e.g.* `clip_guided_stable_diffusion`. Community pipelines are always loaded from the current `main` branch of GitHub. - A path to a *directory* containing a custom pipeline, e.g., `./my_pipeline_directory/`. It is required that the directory has a file, called `pipeline.py` that defines the custom pipeline. For more information on how to load and create custom pipelines, please have a look at [Loading and Adding Custom Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview) force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. custom_revision (`str`, *optional*, defaults to `"main"` when loading from the Hub and to local version of `diffusers` when loading from GitHub): The specific model version to use. It can be a branch name, a tag name, or a commit id similar to `revision` when loading a custom pipeline from the Hub. It can be a diffusers version when loading a custom pipeline from GitHub. mirror (`str`, *optional*): Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. specify the folder name here. variant (`str`, *optional*): If specified load weights from `variant` filename, *e.g.* pytorch_model..bin. `variant` is ignored when using `from_flax`. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models) """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) resume_download = kwargs.pop("resume_download", False) force_download = kwargs.pop("force_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) from_flax = kwargs.pop("from_flax", False) custom_pipeline = kwargs.pop("custom_pipeline", None) custom_revision = kwargs.pop("custom_revision", None) variant = kwargs.pop("variant", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True pipeline_is_cached = False allow_patterns = None ignore_patterns = None if not local_files_only: config_file = hf_hub_download( pretrained_model_name, cls.config_name, cache_dir=cache_dir, revision=revision, proxies=proxies, force_download=force_download, resume_download=resume_download, use_auth_token=use_auth_token, ) info = model_info( pretrained_model_name, use_auth_token=use_auth_token, revision=revision, ) config_dict = cls._dict_from_json_file(config_file) ignore_filenames = config_dict.pop("_ignore_files", []) # retrieve all folder_names that contain relevant files folder_names = [k for k, v in config_dict.items() if isinstance(v, list)] filenames = {sibling.rfilename for sibling in info.siblings} model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant) # remove ignored filenames model_filenames = set(model_filenames) - set(ignore_filenames) variant_filenames = set(variant_filenames) - set(ignore_filenames) # if the whole pipeline is cached we don't have to ping the Hub if revision in DEPRECATED_REVISION_ARGS and version.parse( version.parse(__version__).base_version ) >= version.parse("0.18.0"): warn_deprecated_model_variant( pretrained_model_name, use_auth_token, variant, revision, model_filenames ) model_folder_names = {os.path.split(f)[0] for f in model_filenames} # all filenames compatible with variant will be added allow_patterns = list(model_filenames) # allow all patterns from non-model folders # this enables downloading schedulers, tokenizers, ... allow_patterns += [f"{k}/*" for k in folder_names if k not in model_folder_names] # also allow downloading config.json files with the model allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names] allow_patterns += [ SCHEDULER_CONFIG_NAME, CONFIG_NAME, cls.config_name, CUSTOM_PIPELINE_FILE_NAME, ] # retrieve passed components that should not be downloaded pipeline_class = _get_pipeline_class( cls, config_dict, custom_pipeline=custom_pipeline, cache_dir=cache_dir, revision=custom_revision ) expected_components, _ = cls._get_signature_keys(pipeline_class) passed_components = [k for k in expected_components if k in kwargs] if ( use_safetensors and not allow_pickle and not is_safetensors_compatible( model_filenames, variant=variant, passed_components=passed_components ) ): raise EnvironmentError( f"Could not found the necessary `safetensors` weights in {model_filenames} (variant={variant})" ) if from_flax: ignore_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pb"] elif use_safetensors and is_safetensors_compatible( model_filenames, variant=variant, passed_components=passed_components ): ignore_patterns = ["*.bin", "*.msgpack"] safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")} safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")} if ( len(safetensors_variant_filenames) > 0 and safetensors_model_filenames != safetensors_variant_filenames ): logger.warn( f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(safetensors_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(safetensors_model_filenames - safetensors_variant_filenames)}\nIf this behavior is not expected, please check your folder structure." ) else: ignore_patterns = ["*.safetensors", "*.msgpack"] bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")} bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")} if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames: logger.warn( f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure." ) # Don't download any objects that are passed allow_patterns = [ p for p in allow_patterns if not (len(p.split("/")) == 2 and p.split("/")[0] in passed_components) ] # Don't download index files of forbidden patterns either ignore_patterns = ignore_patterns + [f"{i}.index.*json" for i in ignore_patterns] re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns] re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns] expected_files = [f for f in filenames if not any(p.match(f) for p in re_ignore_pattern)] expected_files = [f for f in expected_files if any(p.match(f) for p in re_allow_pattern)] snapshot_folder = Path(config_file).parent pipeline_is_cached = all((snapshot_folder / f).is_file() for f in expected_files) if pipeline_is_cached: # if the pipeline is cached, we can directly return it # else call snapshot_download return snapshot_folder user_agent = {"pipeline_class": cls.__name__} if custom_pipeline is not None and not custom_pipeline.endswith(".py"): user_agent["custom_pipeline"] = custom_pipeline # download all allow_patterns - ignore_patterns cached_folder = snapshot_download( pretrained_model_name, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, allow_patterns=allow_patterns, ignore_patterns=ignore_patterns, user_agent=user_agent, ) return cached_folder @staticmethod def _get_signature_keys(obj): parameters = inspect.signature(obj.__init__).parameters required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) expected_modules = set(required_parameters.keys()) - {"self"} return expected_modules, optional_parameters @property def components(self) -> Dict[str, Any]: r""" The `self.components` property can be useful to run different pipelines with the same weights and configurations to not have to re-allocate memory. Examples: ```py >>> from diffusers import ( ... StableDiffusionPipeline, ... StableDiffusionImg2ImgPipeline, ... StableDiffusionInpaintPipeline, ... ) >>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") >>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components) >>> inpaint = StableDiffusionInpaintPipeline(**text2img.components) ``` Returns: A dictionary containing all the modules needed to initialize the pipeline. """ expected_modules, optional_parameters = self._get_signature_keys(self) components = { k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters } if set(components.keys()) != expected_modules: raise ValueError( f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected" f" {expected_modules} to be defined, but {components.keys()} are defined." ) return components @staticmethod def numpy_to_pil(images): """ Convert a numpy image or a batch of images to a PIL image. """ return numpy_to_pil(images) def progress_bar(self, iterable=None, total=None): if not hasattr(self, "_progress_bar_config"): self._progress_bar_config = {} elif not isinstance(self._progress_bar_config, dict): raise ValueError( f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}." ) if iterable is not None: return tqdm(iterable, **self._progress_bar_config) elif total is not None: return tqdm(total=total, **self._progress_bar_config) else: raise ValueError("Either `total` or `iterable` has to be defined.") def set_progress_bar_config(self, **kwargs): self._progress_bar_config = kwargs def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None): r""" Enable memory efficient attention as implemented in xformers. When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed. Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used. Parameters: attention_op (`Callable`, *optional*): Override the default `None` operator for use as `op` argument to the [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention) function of xFormers. Examples: ```py >>> import torch >>> from diffusers import DiffusionPipeline >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp >>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) >>> # Workaround for not accepting attention shape using VAE for Flash Attention >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None) ``` """ self.set_use_memory_efficient_attention_xformers(True, attention_op) def disable_xformers_memory_efficient_attention(self): r""" Disable memory efficient attention as implemented in xformers. """ self.set_use_memory_efficient_attention_xformers(False) def set_use_memory_efficient_attention_xformers( self, valid: bool, attention_op: Optional[Callable] = None ) -> None: # Recursively walk through all the children. # Any children which exposes the set_use_memory_efficient_attention_xformers method # gets the message def fn_recursive_set_mem_eff(module: torch.nn.Module): if hasattr(module, "set_use_memory_efficient_attention_xformers"): module.set_use_memory_efficient_attention_xformers(valid, attention_op) for child in module.children(): fn_recursive_set_mem_eff(child) module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module)] for module in modules: fn_recursive_set_mem_eff(module) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ self.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) def set_attention_slice(self, slice_size: Optional[int]): module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module) and hasattr(m, "set_attention_slice")] for module in modules: module.set_attention_slice(slice_size) ================================================ FILE: diffusers/pipelines/pndm/__init__.py ================================================ from .pipeline_pndm import PNDMPipeline ================================================ FILE: diffusers/pipelines/pndm/pipeline_pndm.py ================================================ # Copyright 2023 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 List, Optional, Tuple, Union import torch from ...models import UNet2DModel from ...schedulers import PNDMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class PNDMPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: unet (`UNet2DModel`): U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): The `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image. """ unet: UNet2DModel scheduler: PNDMScheduler def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler): super().__init__() scheduler = PNDMScheduler.from_config(scheduler.config) self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, num_inference_steps: int = 50, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[ImagePipelineOutput, Tuple]: r""" Args: batch_size (`int`, `optional`, defaults to 1): The number of images to generate. num_inference_steps (`int`, `optional`, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. generator (`torch.Generator`, `optional`): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. output_type (`str`, `optional`, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, `optional`, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ # For more information on the sampling method you can take a look at Algorithm 2 of # the official paper: https://arxiv.org/pdf/2202.09778.pdf # Sample gaussian noise to begin loop image = randn_tensor( (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), generator=generator, device=self.device, ) self.scheduler.set_timesteps(num_inference_steps) for t in self.progress_bar(self.scheduler.timesteps): model_output = self.unet(image, t).sample image = self.scheduler.step(model_output, t, image).prev_sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/repaint/__init__.py ================================================ from .pipeline_repaint import RePaintPipeline ================================================ FILE: diffusers/pipelines/repaint/pipeline_repaint.py ================================================ # Copyright 2023 ETH Zurich Computer Vision Lab and 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 List, Optional, Tuple, Union import numpy as np import PIL import torch from ...models import UNet2DModel from ...schedulers import RePaintScheduler from ...utils import PIL_INTERPOLATION, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]): if isinstance(mask, torch.Tensor): return mask elif isinstance(mask, PIL.Image.Image): mask = [mask] if isinstance(mask[0], PIL.Image.Image): w, h = mask[0].size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask] mask = np.concatenate(mask, axis=0) mask = mask.astype(np.float32) / 255.0 mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask = torch.from_numpy(mask) elif isinstance(mask[0], torch.Tensor): mask = torch.cat(mask, dim=0) return mask class RePaintPipeline(DiffusionPipeline): unet: UNet2DModel scheduler: RePaintScheduler def __init__(self, unet, scheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, image: Union[torch.Tensor, PIL.Image.Image], mask_image: Union[torch.Tensor, PIL.Image.Image], num_inference_steps: int = 250, eta: float = 0.0, jump_length: int = 10, jump_n_sample: int = 10, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, ) -> Union[ImagePipelineOutput, Tuple]: r""" Args: image (`torch.FloatTensor` or `PIL.Image.Image`): The original image to inpaint on. mask_image (`torch.FloatTensor` or `PIL.Image.Image`): The mask_image where 0.0 values define which part of the original image to inpaint (change). num_inference_steps (`int`, *optional*, defaults to 1000): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. eta (`float`): The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM and 1.0 is DDPM scheduler respectively. jump_length (`int`, *optional*, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump ("j" in RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf. jump_n_sample (`int`, *optional*, defaults to 10): The number of times we will make forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ original_image = image original_image = _preprocess_image(original_image) original_image = original_image.to(device=self.device, dtype=self.unet.dtype) mask_image = _preprocess_mask(mask_image) mask_image = mask_image.to(device=self.device, dtype=self.unet.dtype) batch_size = original_image.shape[0] # sample gaussian noise to begin the loop if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) image_shape = original_image.shape image = randn_tensor(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype) # set step values self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device) self.scheduler.eta = eta t_last = self.scheduler.timesteps[0] + 1 generator = generator[0] if isinstance(generator, list) else generator for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): if t < t_last: # predict the noise residual model_output = self.unet(image, t).sample # compute previous image: x_t -> x_t-1 image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample else: # compute the reverse: x_t-1 -> x_t image = self.scheduler.undo_step(image, t_last, generator) t_last = t image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/score_sde_ve/__init__.py ================================================ from .pipeline_score_sde_ve import ScoreSdeVePipeline ================================================ FILE: diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py ================================================ # Copyright 2023 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 List, Optional, Tuple, Union import torch from ...models import UNet2DModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class ScoreSdeVePipeline(DiffusionPipeline): r""" Parameters: This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]): The [`ScoreSdeVeScheduler`] scheduler to be used in combination with `unet` to denoise the encoded image. """ unet: UNet2DModel scheduler: ScoreSdeVeScheduler def __init__(self, unet: UNet2DModel, scheduler: ScoreSdeVeScheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, num_inference_steps: int = 2000, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[ImagePipelineOutput, Tuple]: r""" Args: batch_size (`int`, *optional*, defaults to 1): The number of images to generate. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ img_size = self.unet.config.sample_size shape = (batch_size, 3, img_size, img_size) model = self.unet sample = randn_tensor(shape, generator=generator) * self.scheduler.init_noise_sigma sample = sample.to(self.device) self.scheduler.set_timesteps(num_inference_steps) self.scheduler.set_sigmas(num_inference_steps) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device) # correction step for _ in range(self.scheduler.config.correct_steps): model_output = self.unet(sample, sigma_t).sample sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample # prediction step model_output = model(sample, sigma_t).sample output = self.scheduler.step_pred(model_output, t, sample, generator=generator) sample, sample_mean = output.prev_sample, output.prev_sample_mean sample = sample_mean.clamp(0, 1) sample = sample.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": sample = self.numpy_to_pil(sample) if not return_dict: return (sample,) return ImagePipelineOutput(images=sample) ================================================ FILE: diffusers/pipelines/semantic_stable_diffusion/__init__.py ================================================ from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import BaseOutput, is_torch_available, is_transformers_available @dataclass class SemanticStableDiffusionPipelineOutput(BaseOutput): """ Output class for Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. nsfw_content_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, or `None` if safety checking could not be performed. """ images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] if is_transformers_available() and is_torch_available(): from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline ================================================ FILE: diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py ================================================ import inspect import warnings from itertools import repeat from typing import Callable, List, Optional, Union import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, UNet2DConditionModel from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from ...schedulers import KarrasDiffusionSchedulers from ...utils import logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import SemanticStableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import SemanticStableDiffusionPipeline >>> pipe = SemanticStableDiffusionPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> out = pipe( ... prompt="a photo of the face of a woman", ... num_images_per_prompt=1, ... guidance_scale=7, ... editing_prompt=[ ... "smiling, smile", # Concepts to apply ... "glasses, wearing glasses", ... "curls, wavy hair, curly hair", ... "beard, full beard, mustache", ... ], ... reverse_editing_direction=[ ... False, ... False, ... False, ... False, ... ], # Direction of guidance i.e. increase all concepts ... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept ... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept ... edit_threshold=[ ... 0.99, ... 0.975, ... 0.925, ... 0.96, ... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions ... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance ... edit_mom_beta=0.6, # Momentum beta ... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other ... ) >>> image = out.images[0] ``` """ class SemanticStableDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation with latent editing. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) This model builds on the implementation of ['StableDiffusionPipeline'] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`Q16SafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: int = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, editing_prompt: Optional[Union[str, List[str]]] = None, editing_prompt_embeddings: Optional[torch.Tensor] = None, reverse_editing_direction: Optional[Union[bool, List[bool]]] = False, edit_guidance_scale: Optional[Union[float, List[float]]] = 5, edit_warmup_steps: Optional[Union[int, List[int]]] = 10, edit_cooldown_steps: Optional[Union[int, List[int]]] = None, edit_threshold: Optional[Union[float, List[float]]] = 0.9, edit_momentum_scale: Optional[float] = 0.1, edit_mom_beta: Optional[float] = 0.4, edit_weights: Optional[List[float]] = None, sem_guidance: Optional[List[torch.Tensor]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. editing_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting `editing_prompt = None`. Guidance direction of prompt should be specified via `reverse_editing_direction`. editing_prompt_embeddings (`torch.Tensor>`, *optional*): Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be specified via `reverse_editing_direction`. reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`): Whether the corresponding prompt in `editing_prompt` should be increased or decreased. edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5): Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf). edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10): Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum will still be calculated for those steps and applied once all warmup periods are over. `edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf). edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`): Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied. edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9): Threshold of semantic guidance. edit_momentum_scale (`float`, *optional*, defaults to 0.1): Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0 momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf). edit_mom_beta (`float`, *optional*, defaults to 0.4): Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf). edit_weights (`List[float]`, *optional*, defaults to `None`): Indicates how much each individual concept should influence the overall guidance. If no weights are provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf). sem_guidance (`List[torch.Tensor]`, *optional*): List of pre-generated guidance vectors to be applied at generation. Length of the list has to correspond to `num_inference_steps`. Returns: [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps) # 2. Define call parameters batch_size = 1 if isinstance(prompt, str) else len(prompt) if editing_prompt: enable_edit_guidance = True if isinstance(editing_prompt, str): editing_prompt = [editing_prompt] enabled_editing_prompts = len(editing_prompt) elif editing_prompt_embeddings is not None: enable_edit_guidance = True enabled_editing_prompts = editing_prompt_embeddings.shape[0] else: enabled_editing_prompts = 0 enable_edit_guidance = False # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ) text_input_ids = text_inputs.input_ids if text_input_ids.shape[-1] > self.tokenizer.model_max_length: removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = text_embeddings.shape text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) if enable_edit_guidance: # get safety text embeddings if editing_prompt_embeddings is None: edit_concepts_input = self.tokenizer( [x for item in editing_prompt for x in repeat(item, batch_size)], padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ) edit_concepts_input_ids = edit_concepts_input.input_ids if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length: removed_text = self.tokenizer.batch_decode( edit_concepts_input_ids[:, self.tokenizer.model_max_length :] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length] edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0] else: edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1) # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed_edit, seq_len_edit, _ = edit_concepts.shape edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1) edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = uncond_embeddings.shape[1] uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1) uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes if enable_edit_guidance: text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts]) else: text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) # get the initial random noise unless the user supplied it # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=self.device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, text_embeddings.dtype, self.device, generator, latents, ) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # Initialize edit_momentum to None edit_momentum = None self.uncond_estimates = None self.text_estimates = None self.edit_estimates = None self.sem_guidance = None for i, t in enumerate(self.progress_bar(timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample # perform guidance if do_classifier_free_guidance: noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts) # [b,4, 64, 64] noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1] noise_pred_edit_concepts = noise_pred_out[2:] # default text guidance noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond) # noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0]) if self.uncond_estimates is None: self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape)) self.uncond_estimates[i] = noise_pred_uncond.detach().cpu() if self.text_estimates is None: self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape)) self.text_estimates[i] = noise_pred_text.detach().cpu() if self.edit_estimates is None and enable_edit_guidance: self.edit_estimates = torch.zeros( (num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape) ) if self.sem_guidance is None: self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape)) if edit_momentum is None: edit_momentum = torch.zeros_like(noise_guidance) if enable_edit_guidance: concept_weights = torch.zeros( (len(noise_pred_edit_concepts), noise_guidance.shape[0]), device=self.device, dtype=noise_guidance.dtype, ) noise_guidance_edit = torch.zeros( (len(noise_pred_edit_concepts), *noise_guidance.shape), device=self.device, dtype=noise_guidance.dtype, ) # noise_guidance_edit = torch.zeros_like(noise_guidance) warmup_inds = [] for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts): self.edit_estimates[i, c] = noise_pred_edit_concept if isinstance(edit_guidance_scale, list): edit_guidance_scale_c = edit_guidance_scale[c] else: edit_guidance_scale_c = edit_guidance_scale if isinstance(edit_threshold, list): edit_threshold_c = edit_threshold[c] else: edit_threshold_c = edit_threshold if isinstance(reverse_editing_direction, list): reverse_editing_direction_c = reverse_editing_direction[c] else: reverse_editing_direction_c = reverse_editing_direction if edit_weights: edit_weight_c = edit_weights[c] else: edit_weight_c = 1.0 if isinstance(edit_warmup_steps, list): edit_warmup_steps_c = edit_warmup_steps[c] else: edit_warmup_steps_c = edit_warmup_steps if isinstance(edit_cooldown_steps, list): edit_cooldown_steps_c = edit_cooldown_steps[c] elif edit_cooldown_steps is None: edit_cooldown_steps_c = i + 1 else: edit_cooldown_steps_c = edit_cooldown_steps if i >= edit_warmup_steps_c: warmup_inds.append(c) if i >= edit_cooldown_steps_c: noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept) continue noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond # tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3)) tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3)) tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts) if reverse_editing_direction_c: noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1 concept_weights[c, :] = tmp_weights noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c # torch.quantile function expects float32 if noise_guidance_edit_tmp.dtype == torch.float32: tmp = torch.quantile( torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2), edit_threshold_c, dim=2, keepdim=False, ) else: tmp = torch.quantile( torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32), edit_threshold_c, dim=2, keepdim=False, ).to(noise_guidance_edit_tmp.dtype) noise_guidance_edit_tmp = torch.where( torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None], noise_guidance_edit_tmp, torch.zeros_like(noise_guidance_edit_tmp), ) noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp # noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp warmup_inds = torch.tensor(warmup_inds).to(self.device) if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0: concept_weights = concept_weights.to("cpu") # Offload to cpu noise_guidance_edit = noise_guidance_edit.to("cpu") concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds) concept_weights_tmp = torch.where( concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp ) concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0) # concept_weights_tmp = torch.nan_to_num(concept_weights_tmp) noise_guidance_edit_tmp = torch.index_select( noise_guidance_edit.to(self.device), 0, warmup_inds ) noise_guidance_edit_tmp = torch.einsum( "cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp ) noise_guidance_edit_tmp = noise_guidance_edit_tmp noise_guidance = noise_guidance + noise_guidance_edit_tmp self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu() del noise_guidance_edit_tmp del concept_weights_tmp concept_weights = concept_weights.to(self.device) noise_guidance_edit = noise_guidance_edit.to(self.device) concept_weights = torch.where( concept_weights < 0, torch.zeros_like(concept_weights), concept_weights ) concept_weights = torch.nan_to_num(concept_weights) noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit) noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit if warmup_inds.shape[0] == len(noise_pred_edit_concepts): noise_guidance = noise_guidance + noise_guidance_edit self.sem_guidance[i] = noise_guidance_edit.detach().cpu() if sem_guidance is not None: edit_guidance = sem_guidance[i].to(self.device) noise_guidance = noise_guidance + edit_guidance noise_pred = noise_pred_uncond + noise_guidance # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 8. Post-processing if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/spectrogram_diffusion/__init__.py ================================================ # flake8: noqa from ...utils import is_note_seq_available, is_transformers_available, is_torch_available from ...utils import OptionalDependencyNotAvailable try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .notes_encoder import SpectrogramNotesEncoder from .continous_encoder import SpectrogramContEncoder from .pipeline_spectrogram_diffusion import ( SpectrogramContEncoder, SpectrogramDiffusionPipeline, T5FilmDecoder, ) try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .midi_utils import MidiProcessor ================================================ FILE: diffusers/pipelines/spectrogram_diffusion/continous_encoder.py ================================================ # Copyright 2022 The Music Spectrogram Diffusion Authors. # Copyright 2023 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. import torch import torch.nn as nn from transformers.modeling_utils import ModuleUtilsMixin from transformers.models.t5.modeling_t5 import ( T5Block, T5Config, T5LayerNorm, ) from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class SpectrogramContEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin): @register_to_config def __init__( self, input_dims: int, targets_context_length: int, d_model: int, dropout_rate: float, num_layers: int, num_heads: int, d_kv: int, d_ff: int, feed_forward_proj: str, is_decoder: bool = False, ): super().__init__() self.input_proj = nn.Linear(input_dims, d_model, bias=False) self.position_encoding = nn.Embedding(targets_context_length, d_model) self.position_encoding.weight.requires_grad = False self.dropout_pre = nn.Dropout(p=dropout_rate) t5config = T5Config( d_model=d_model, num_heads=num_heads, d_kv=d_kv, d_ff=d_ff, feed_forward_proj=feed_forward_proj, dropout_rate=dropout_rate, is_decoder=is_decoder, is_encoder_decoder=False, ) self.encoders = nn.ModuleList() for lyr_num in range(num_layers): lyr = T5Block(t5config) self.encoders.append(lyr) self.layer_norm = T5LayerNorm(d_model) self.dropout_post = nn.Dropout(p=dropout_rate) def forward(self, encoder_inputs, encoder_inputs_mask): x = self.input_proj(encoder_inputs) # terminal relative positional encodings max_positions = encoder_inputs.shape[1] input_positions = torch.arange(max_positions, device=encoder_inputs.device) seq_lens = encoder_inputs_mask.sum(-1) input_positions = torch.roll(input_positions.unsqueeze(0), tuple(seq_lens.tolist()), dims=0) x += self.position_encoding(input_positions) x = self.dropout_pre(x) # inverted the attention mask input_shape = encoder_inputs.size() extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape) for lyr in self.encoders: x = lyr(x, extended_attention_mask)[0] x = self.layer_norm(x) return self.dropout_post(x), encoder_inputs_mask ================================================ FILE: diffusers/pipelines/spectrogram_diffusion/midi_utils.py ================================================ # Copyright 2022 The Music Spectrogram Diffusion Authors. # Copyright 2023 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. import dataclasses import math import os from typing import Any, Callable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union import numpy as np import torch import torch.nn.functional as F from ...utils import is_note_seq_available from .pipeline_spectrogram_diffusion import TARGET_FEATURE_LENGTH if is_note_seq_available(): import note_seq else: raise ImportError("Please install note-seq via `pip install note-seq`") INPUT_FEATURE_LENGTH = 2048 SAMPLE_RATE = 16000 HOP_SIZE = 320 FRAME_RATE = int(SAMPLE_RATE // HOP_SIZE) DEFAULT_STEPS_PER_SECOND = 100 DEFAULT_MAX_SHIFT_SECONDS = 10 DEFAULT_NUM_VELOCITY_BINS = 1 SLAKH_CLASS_PROGRAMS = { "Acoustic Piano": 0, "Electric Piano": 4, "Chromatic Percussion": 8, "Organ": 16, "Acoustic Guitar": 24, "Clean Electric Guitar": 26, "Distorted Electric Guitar": 29, "Acoustic Bass": 32, "Electric Bass": 33, "Violin": 40, "Viola": 41, "Cello": 42, "Contrabass": 43, "Orchestral Harp": 46, "Timpani": 47, "String Ensemble": 48, "Synth Strings": 50, "Choir and Voice": 52, "Orchestral Hit": 55, "Trumpet": 56, "Trombone": 57, "Tuba": 58, "French Horn": 60, "Brass Section": 61, "Soprano/Alto Sax": 64, "Tenor Sax": 66, "Baritone Sax": 67, "Oboe": 68, "English Horn": 69, "Bassoon": 70, "Clarinet": 71, "Pipe": 73, "Synth Lead": 80, "Synth Pad": 88, } @dataclasses.dataclass class NoteRepresentationConfig: """Configuration note representations.""" onsets_only: bool include_ties: bool @dataclasses.dataclass class NoteEventData: pitch: int velocity: Optional[int] = None program: Optional[int] = None is_drum: Optional[bool] = None instrument: Optional[int] = None @dataclasses.dataclass class NoteEncodingState: """Encoding state for note transcription, keeping track of active pitches.""" # velocity bin for active pitches and programs active_pitches: MutableMapping[Tuple[int, int], int] = dataclasses.field(default_factory=dict) @dataclasses.dataclass class EventRange: type: str min_value: int max_value: int @dataclasses.dataclass class Event: type: str value: int class Tokenizer: def __init__(self, regular_ids: int): # The special tokens: 0=PAD, 1=EOS, and 2=UNK self._num_special_tokens = 3 self._num_regular_tokens = regular_ids def encode(self, token_ids): encoded = [] for token_id in token_ids: if not 0 <= token_id < self._num_regular_tokens: raise ValueError( f"token_id {token_id} does not fall within valid range of [0, {self._num_regular_tokens})" ) encoded.append(token_id + self._num_special_tokens) # Add EOS token encoded.append(1) # Pad to till INPUT_FEATURE_LENGTH encoded = encoded + [0] * (INPUT_FEATURE_LENGTH - len(encoded)) return encoded class Codec: """Encode and decode events. Useful for declaring what certain ranges of a vocabulary should be used for. This is intended to be used from Python before encoding or after decoding with GenericTokenVocabulary. This class is more lightweight and does not include things like EOS or UNK token handling. To ensure that 'shift' events are always the first block of the vocab and start at 0, that event type is required and specified separately. """ def __init__(self, max_shift_steps: int, steps_per_second: float, event_ranges: List[EventRange]): """Define Codec. Args: max_shift_steps: Maximum number of shift steps that can be encoded. steps_per_second: Shift steps will be interpreted as having a duration of 1 / steps_per_second. event_ranges: Other supported event types and their ranges. """ self.steps_per_second = steps_per_second self._shift_range = EventRange(type="shift", min_value=0, max_value=max_shift_steps) self._event_ranges = [self._shift_range] + event_ranges # Ensure all event types have unique names. assert len(self._event_ranges) == len({er.type for er in self._event_ranges}) @property def num_classes(self) -> int: return sum(er.max_value - er.min_value + 1 for er in self._event_ranges) # The next couple methods are simplified special case methods just for shift # events that are intended to be used from within autograph functions. def is_shift_event_index(self, index: int) -> bool: return (self._shift_range.min_value <= index) and (index <= self._shift_range.max_value) @property def max_shift_steps(self) -> int: return self._shift_range.max_value def encode_event(self, event: Event) -> int: """Encode an event to an index.""" offset = 0 for er in self._event_ranges: if event.type == er.type: if not er.min_value <= event.value <= er.max_value: raise ValueError( f"Event value {event.value} is not within valid range " f"[{er.min_value}, {er.max_value}] for type {event.type}" ) return offset + event.value - er.min_value offset += er.max_value - er.min_value + 1 raise ValueError(f"Unknown event type: {event.type}") def event_type_range(self, event_type: str) -> Tuple[int, int]: """Return [min_id, max_id] for an event type.""" offset = 0 for er in self._event_ranges: if event_type == er.type: return offset, offset + (er.max_value - er.min_value) offset += er.max_value - er.min_value + 1 raise ValueError(f"Unknown event type: {event_type}") def decode_event_index(self, index: int) -> Event: """Decode an event index to an Event.""" offset = 0 for er in self._event_ranges: if offset <= index <= offset + er.max_value - er.min_value: return Event(type=er.type, value=er.min_value + index - offset) offset += er.max_value - er.min_value + 1 raise ValueError(f"Unknown event index: {index}") @dataclasses.dataclass class ProgramGranularity: # both tokens_map_fn and program_map_fn should be idempotent tokens_map_fn: Callable[[Sequence[int], Codec], Sequence[int]] program_map_fn: Callable[[int], int] def drop_programs(tokens, codec: Codec): """Drops program change events from a token sequence.""" min_program_id, max_program_id = codec.event_type_range("program") return tokens[(tokens < min_program_id) | (tokens > max_program_id)] def programs_to_midi_classes(tokens, codec): """Modifies program events to be the first program in the MIDI class.""" min_program_id, max_program_id = codec.event_type_range("program") is_program = (tokens >= min_program_id) & (tokens <= max_program_id) return np.where(is_program, min_program_id + 8 * ((tokens - min_program_id) // 8), tokens) PROGRAM_GRANULARITIES = { # "flat" granularity; drop program change tokens and set NoteSequence # programs to zero "flat": ProgramGranularity(tokens_map_fn=drop_programs, program_map_fn=lambda program: 0), # map each program to the first program in its MIDI class "midi_class": ProgramGranularity( tokens_map_fn=programs_to_midi_classes, program_map_fn=lambda program: 8 * (program // 8) ), # leave programs as is "full": ProgramGranularity(tokens_map_fn=lambda tokens, codec: tokens, program_map_fn=lambda program: program), } def frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1): """ equivalent of tf.signal.frame """ signal_length = signal.shape[axis] if pad_end: frames_overlap = frame_length - frame_step rest_samples = np.abs(signal_length - frames_overlap) % np.abs(frame_length - frames_overlap) pad_size = int(frame_length - rest_samples) if pad_size != 0: pad_axis = [0] * signal.ndim pad_axis[axis] = pad_size signal = F.pad(signal, pad_axis, "constant", pad_value) frames = signal.unfold(axis, frame_length, frame_step) return frames def program_to_slakh_program(program): # this is done very hackily, probably should use a custom mapping for slakh_program in sorted(SLAKH_CLASS_PROGRAMS.values(), reverse=True): if program >= slakh_program: return slakh_program def audio_to_frames( samples, hop_size: int, frame_rate: int, ) -> Tuple[Sequence[Sequence[int]], torch.Tensor]: """Convert audio samples to non-overlapping frames and frame times.""" frame_size = hop_size samples = np.pad(samples, [0, frame_size - len(samples) % frame_size], mode="constant") # Split audio into frames. frames = frame( torch.Tensor(samples).unsqueeze(0), frame_length=frame_size, frame_step=frame_size, pad_end=False, # TODO check why its off by 1 here when True ) num_frames = len(samples) // frame_size times = np.arange(num_frames) / frame_rate return frames, times def note_sequence_to_onsets_and_offsets_and_programs( ns: note_seq.NoteSequence, ) -> Tuple[Sequence[float], Sequence[NoteEventData]]: """Extract onset & offset times and pitches & programs from a NoteSequence. The onset & offset times will not necessarily be in sorted order. Args: ns: NoteSequence from which to extract onsets and offsets. Returns: times: A list of note onset and offset times. values: A list of NoteEventData objects where velocity is zero for note offsets. """ # Sort by program and pitch and put offsets before onsets as a tiebreaker for # subsequent stable sort. notes = sorted(ns.notes, key=lambda note: (note.is_drum, note.program, note.pitch)) times = [note.end_time for note in notes if not note.is_drum] + [note.start_time for note in notes] values = [ NoteEventData(pitch=note.pitch, velocity=0, program=note.program, is_drum=False) for note in notes if not note.is_drum ] + [ NoteEventData(pitch=note.pitch, velocity=note.velocity, program=note.program, is_drum=note.is_drum) for note in notes ] return times, values def num_velocity_bins_from_codec(codec: Codec): """Get number of velocity bins from event codec.""" lo, hi = codec.event_type_range("velocity") return hi - lo # segment an array into segments of length n def segment(a, n): return [a[i : i + n] for i in range(0, len(a), n)] def velocity_to_bin(velocity, num_velocity_bins): if velocity == 0: return 0 else: return math.ceil(num_velocity_bins * velocity / note_seq.MAX_MIDI_VELOCITY) def note_event_data_to_events( state: Optional[NoteEncodingState], value: NoteEventData, codec: Codec, ) -> Sequence[Event]: """Convert note event data to a sequence of events.""" if value.velocity is None: # onsets only, no program or velocity return [Event("pitch", value.pitch)] else: num_velocity_bins = num_velocity_bins_from_codec(codec) velocity_bin = velocity_to_bin(value.velocity, num_velocity_bins) if value.program is None: # onsets + offsets + velocities only, no programs if state is not None: state.active_pitches[(value.pitch, 0)] = velocity_bin return [Event("velocity", velocity_bin), Event("pitch", value.pitch)] else: if value.is_drum: # drum events use a separate vocabulary return [Event("velocity", velocity_bin), Event("drum", value.pitch)] else: # program + velocity + pitch if state is not None: state.active_pitches[(value.pitch, value.program)] = velocity_bin return [ Event("program", value.program), Event("velocity", velocity_bin), Event("pitch", value.pitch), ] def note_encoding_state_to_events(state: NoteEncodingState) -> Sequence[Event]: """Output program and pitch events for active notes plus a final tie event.""" events = [] for pitch, program in sorted(state.active_pitches.keys(), key=lambda k: k[::-1]): if state.active_pitches[(pitch, program)]: events += [Event("program", program), Event("pitch", pitch)] events.append(Event("tie", 0)) return events def encode_and_index_events( state, event_times, event_values, codec, frame_times, encode_event_fn, encoding_state_to_events_fn=None ): """Encode a sequence of timed events and index to audio frame times. Encodes time shifts as repeated single step shifts for later run length encoding. Optionally, also encodes a sequence of "state events", keeping track of the current encoding state at each audio frame. This can be used e.g. to prepend events representing the current state to a targets segment. Args: state: Initial event encoding state. event_times: Sequence of event times. event_values: Sequence of event values. encode_event_fn: Function that transforms event value into a sequence of one or more Event objects. codec: An Codec object that maps Event objects to indices. frame_times: Time for every audio frame. encoding_state_to_events_fn: Function that transforms encoding state into a sequence of one or more Event objects. Returns: events: Encoded events and shifts. event_start_indices: Corresponding start event index for every audio frame. Note: one event can correspond to multiple audio indices due to sampling rate differences. This makes splitting sequences tricky because the same event can appear at the end of one sequence and the beginning of another. event_end_indices: Corresponding end event index for every audio frame. Used to ensure when slicing that one chunk ends where the next begins. Should always be true that event_end_indices[i] = event_start_indices[i + 1]. state_events: Encoded "state" events representing the encoding state before each event. state_event_indices: Corresponding state event index for every audio frame. """ indices = np.argsort(event_times, kind="stable") event_steps = [round(event_times[i] * codec.steps_per_second) for i in indices] event_values = [event_values[i] for i in indices] events = [] state_events = [] event_start_indices = [] state_event_indices = [] cur_step = 0 cur_event_idx = 0 cur_state_event_idx = 0 def fill_event_start_indices_to_cur_step(): while ( len(event_start_indices) < len(frame_times) and frame_times[len(event_start_indices)] < cur_step / codec.steps_per_second ): event_start_indices.append(cur_event_idx) state_event_indices.append(cur_state_event_idx) for event_step, event_value in zip(event_steps, event_values): while event_step > cur_step: events.append(codec.encode_event(Event(type="shift", value=1))) cur_step += 1 fill_event_start_indices_to_cur_step() cur_event_idx = len(events) cur_state_event_idx = len(state_events) if encoding_state_to_events_fn: # Dump state to state events *before* processing the next event, because # we want to capture the state prior to the occurrence of the event. for e in encoding_state_to_events_fn(state): state_events.append(codec.encode_event(e)) for e in encode_event_fn(state, event_value, codec): events.append(codec.encode_event(e)) # After the last event, continue filling out the event_start_indices array. # The inequality is not strict because if our current step lines up exactly # with (the start of) an audio frame, we need to add an additional shift event # to "cover" that frame. while cur_step / codec.steps_per_second <= frame_times[-1]: events.append(codec.encode_event(Event(type="shift", value=1))) cur_step += 1 fill_event_start_indices_to_cur_step() cur_event_idx = len(events) # Now fill in event_end_indices. We need this extra array to make sure that # when we slice events, each slice ends exactly where the subsequent slice # begins. event_end_indices = event_start_indices[1:] + [len(events)] events = np.array(events).astype(np.int32) state_events = np.array(state_events).astype(np.int32) event_start_indices = segment(np.array(event_start_indices).astype(np.int32), TARGET_FEATURE_LENGTH) event_end_indices = segment(np.array(event_end_indices).astype(np.int32), TARGET_FEATURE_LENGTH) state_event_indices = segment(np.array(state_event_indices).astype(np.int32), TARGET_FEATURE_LENGTH) outputs = [] for start_indices, end_indices, event_indices in zip(event_start_indices, event_end_indices, state_event_indices): outputs.append( { "inputs": events, "event_start_indices": start_indices, "event_end_indices": end_indices, "state_events": state_events, "state_event_indices": event_indices, } ) return outputs def extract_sequence_with_indices(features, state_events_end_token=None, feature_key="inputs"): """Extract target sequence corresponding to audio token segment.""" features = features.copy() start_idx = features["event_start_indices"][0] end_idx = features["event_end_indices"][-1] features[feature_key] = features[feature_key][start_idx:end_idx] if state_events_end_token is not None: # Extract the state events corresponding to the audio start token, and # prepend them to the targets array. state_event_start_idx = features["state_event_indices"][0] state_event_end_idx = state_event_start_idx + 1 while features["state_events"][state_event_end_idx - 1] != state_events_end_token: state_event_end_idx += 1 features[feature_key] = np.concatenate( [ features["state_events"][state_event_start_idx:state_event_end_idx], features[feature_key], ], axis=0, ) return features def map_midi_programs( feature, codec: Codec, granularity_type: str = "full", feature_key: str = "inputs" ) -> Mapping[str, Any]: """Apply MIDI program map to token sequences.""" granularity = PROGRAM_GRANULARITIES[granularity_type] feature[feature_key] = granularity.tokens_map_fn(feature[feature_key], codec) return feature def run_length_encode_shifts_fn( features, codec: Codec, feature_key: str = "inputs", state_change_event_types: Sequence[str] = (), ) -> Callable[[Mapping[str, Any]], Mapping[str, Any]]: """Return a function that run-length encodes shifts for a given codec. Args: codec: The Codec to use for shift events. feature_key: The feature key for which to run-length encode shifts. state_change_event_types: A list of event types that represent state changes; tokens corresponding to these event types will be interpreted as state changes and redundant ones will be removed. Returns: A preprocessing function that run-length encodes single-step shifts. """ state_change_event_ranges = [codec.event_type_range(event_type) for event_type in state_change_event_types] def run_length_encode_shifts(features: MutableMapping[str, Any]) -> Mapping[str, Any]: """Combine leading/interior shifts, trim trailing shifts. Args: features: Dict of features to process. Returns: A dict of features. """ events = features[feature_key] shift_steps = 0 total_shift_steps = 0 output = np.array([], dtype=np.int32) current_state = np.zeros(len(state_change_event_ranges), dtype=np.int32) for event in events: if codec.is_shift_event_index(event): shift_steps += 1 total_shift_steps += 1 else: # If this event is a state change and has the same value as the current # state, we can skip it entirely. is_redundant = False for i, (min_index, max_index) in enumerate(state_change_event_ranges): if (min_index <= event) and (event <= max_index): if current_state[i] == event: is_redundant = True current_state[i] = event if is_redundant: continue # Once we've reached a non-shift event, RLE all previous shift events # before outputting the non-shift event. if shift_steps > 0: shift_steps = total_shift_steps while shift_steps > 0: output_steps = np.minimum(codec.max_shift_steps, shift_steps) output = np.concatenate([output, [output_steps]], axis=0) shift_steps -= output_steps output = np.concatenate([output, [event]], axis=0) features[feature_key] = output return features return run_length_encode_shifts(features) def note_representation_processor_chain(features, codec: Codec, note_representation_config: NoteRepresentationConfig): tie_token = codec.encode_event(Event("tie", 0)) state_events_end_token = tie_token if note_representation_config.include_ties else None features = extract_sequence_with_indices( features, state_events_end_token=state_events_end_token, feature_key="inputs" ) features = map_midi_programs(features, codec) features = run_length_encode_shifts_fn(features, codec, state_change_event_types=["velocity", "program"]) return features class MidiProcessor: def __init__(self): self.codec = Codec( max_shift_steps=DEFAULT_MAX_SHIFT_SECONDS * DEFAULT_STEPS_PER_SECOND, steps_per_second=DEFAULT_STEPS_PER_SECOND, event_ranges=[ EventRange("pitch", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITCH), EventRange("velocity", 0, DEFAULT_NUM_VELOCITY_BINS), EventRange("tie", 0, 0), EventRange("program", note_seq.MIN_MIDI_PROGRAM, note_seq.MAX_MIDI_PROGRAM), EventRange("drum", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITCH), ], ) self.tokenizer = Tokenizer(self.codec.num_classes) self.note_representation_config = NoteRepresentationConfig(onsets_only=False, include_ties=True) def __call__(self, midi: Union[bytes, os.PathLike, str]): if not isinstance(midi, bytes): with open(midi, "rb") as f: midi = f.read() ns = note_seq.midi_to_note_sequence(midi) ns_sus = note_seq.apply_sustain_control_changes(ns) for note in ns_sus.notes: if not note.is_drum: note.program = program_to_slakh_program(note.program) samples = np.zeros(int(ns_sus.total_time * SAMPLE_RATE)) _, frame_times = audio_to_frames(samples, HOP_SIZE, FRAME_RATE) times, values = note_sequence_to_onsets_and_offsets_and_programs(ns_sus) events = encode_and_index_events( state=NoteEncodingState(), event_times=times, event_values=values, frame_times=frame_times, codec=self.codec, encode_event_fn=note_event_data_to_events, encoding_state_to_events_fn=note_encoding_state_to_events, ) events = [ note_representation_processor_chain(event, self.codec, self.note_representation_config) for event in events ] input_tokens = [self.tokenizer.encode(event["inputs"]) for event in events] return input_tokens ================================================ FILE: diffusers/pipelines/spectrogram_diffusion/notes_encoder.py ================================================ # Copyright 2022 The Music Spectrogram Diffusion Authors. # Copyright 2023 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. import torch import torch.nn as nn from transformers.modeling_utils import ModuleUtilsMixin from transformers.models.t5.modeling_t5 import T5Block, T5Config, T5LayerNorm from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin): @register_to_config def __init__( self, max_length: int, vocab_size: int, d_model: int, dropout_rate: float, num_layers: int, num_heads: int, d_kv: int, d_ff: int, feed_forward_proj: str, is_decoder: bool = False, ): super().__init__() self.token_embedder = nn.Embedding(vocab_size, d_model) self.position_encoding = nn.Embedding(max_length, d_model) self.position_encoding.weight.requires_grad = False self.dropout_pre = nn.Dropout(p=dropout_rate) t5config = T5Config( vocab_size=vocab_size, d_model=d_model, num_heads=num_heads, d_kv=d_kv, d_ff=d_ff, dropout_rate=dropout_rate, feed_forward_proj=feed_forward_proj, is_decoder=is_decoder, is_encoder_decoder=False, ) self.encoders = nn.ModuleList() for lyr_num in range(num_layers): lyr = T5Block(t5config) self.encoders.append(lyr) self.layer_norm = T5LayerNorm(d_model) self.dropout_post = nn.Dropout(p=dropout_rate) def forward(self, encoder_input_tokens, encoder_inputs_mask): x = self.token_embedder(encoder_input_tokens) seq_length = encoder_input_tokens.shape[1] inputs_positions = torch.arange(seq_length, device=encoder_input_tokens.device) x += self.position_encoding(inputs_positions) x = self.dropout_pre(x) # inverted the attention mask input_shape = encoder_input_tokens.size() extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape) for lyr in self.encoders: x = lyr(x, extended_attention_mask)[0] x = self.layer_norm(x) return self.dropout_post(x), encoder_inputs_mask ================================================ FILE: diffusers/pipelines/spectrogram_diffusion/pipeline_spectrogram_diffusion.py ================================================ # Copyright 2022 The Music Spectrogram Diffusion Authors. # Copyright 2023 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. import math from typing import Any, Callable, List, Optional, Tuple, Union import numpy as np import torch from ...models import T5FilmDecoder from ...schedulers import DDPMScheduler from ...utils import is_onnx_available, logging, randn_tensor if is_onnx_available(): from ..onnx_utils import OnnxRuntimeModel from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline from .continous_encoder import SpectrogramContEncoder from .notes_encoder import SpectrogramNotesEncoder logger = logging.get_logger(__name__) # pylint: disable=invalid-name TARGET_FEATURE_LENGTH = 256 class SpectrogramDiffusionPipeline(DiffusionPipeline): _optional_components = ["melgan"] def __init__( self, notes_encoder: SpectrogramNotesEncoder, continuous_encoder: SpectrogramContEncoder, decoder: T5FilmDecoder, scheduler: DDPMScheduler, melgan: OnnxRuntimeModel if is_onnx_available() else Any, ) -> None: super().__init__() # From MELGAN self.min_value = math.log(1e-5) # Matches MelGAN training. self.max_value = 4.0 # Largest value for most examples self.n_dims = 128 self.register_modules( notes_encoder=notes_encoder, continuous_encoder=continuous_encoder, decoder=decoder, scheduler=scheduler, melgan=melgan, ) def scale_features(self, features, output_range=(-1.0, 1.0), clip=False): """Linearly scale features to network outputs range.""" min_out, max_out = output_range if clip: features = torch.clip(features, self.min_value, self.max_value) # Scale to [0, 1]. zero_one = (features - self.min_value) / (self.max_value - self.min_value) # Scale to [min_out, max_out]. return zero_one * (max_out - min_out) + min_out def scale_to_features(self, outputs, input_range=(-1.0, 1.0), clip=False): """Invert by linearly scaling network outputs to features range.""" min_out, max_out = input_range outputs = torch.clip(outputs, min_out, max_out) if clip else outputs # Scale to [0, 1]. zero_one = (outputs - min_out) / (max_out - min_out) # Scale to [self.min_value, self.max_value]. return zero_one * (self.max_value - self.min_value) + self.min_value def encode(self, input_tokens, continuous_inputs, continuous_mask): tokens_mask = input_tokens > 0 tokens_encoded, tokens_mask = self.notes_encoder( encoder_input_tokens=input_tokens, encoder_inputs_mask=tokens_mask ) continuous_encoded, continuous_mask = self.continuous_encoder( encoder_inputs=continuous_inputs, encoder_inputs_mask=continuous_mask ) return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)] def decode(self, encodings_and_masks, input_tokens, noise_time): timesteps = noise_time if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(input_tokens.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device) logits = self.decoder( encodings_and_masks=encodings_and_masks, decoder_input_tokens=input_tokens, decoder_noise_time=timesteps ) return logits @torch.no_grad() def __call__( self, input_tokens: List[List[int]], generator: Optional[torch.Generator] = None, num_inference_steps: int = 100, return_dict: bool = True, output_type: str = "numpy", callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ) -> Union[AudioPipelineOutput, Tuple]: if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) pred_mel = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.float32) full_pred_mel = np.zeros([1, 0, self.n_dims], np.float32) ones = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device) for i, encoder_input_tokens in enumerate(input_tokens): if i == 0: encoder_continuous_inputs = torch.from_numpy(pred_mel[:1].copy()).to( device=self.device, dtype=self.decoder.dtype ) # The first chunk has no previous context. encoder_continuous_mask = torch.zeros((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device) else: # The full song pipeline does not feed in a context feature, so the mask # will be all 0s after the feature converter. Because we know we're # feeding in a full context chunk from the previous prediction, set it # to all 1s. encoder_continuous_mask = ones encoder_continuous_inputs = self.scale_features( encoder_continuous_inputs, output_range=[-1.0, 1.0], clip=True ) encodings_and_masks = self.encode( input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device), continuous_inputs=encoder_continuous_inputs, continuous_mask=encoder_continuous_mask, ) # Sample encoder_continuous_inputs shaped gaussian noise to begin loop x = randn_tensor( shape=encoder_continuous_inputs.shape, generator=generator, device=self.device, dtype=self.decoder.dtype, ) # set step values self.scheduler.set_timesteps(num_inference_steps) # Denoising diffusion loop for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)): output = self.decode( encodings_and_masks=encodings_and_masks, input_tokens=x, noise_time=t / self.scheduler.config.num_train_timesteps, # rescale to [0, 1) ) # Compute previous output: x_t -> x_t-1 x = self.scheduler.step(output, t, x, generator=generator).prev_sample mel = self.scale_to_features(x, input_range=[-1.0, 1.0]) encoder_continuous_inputs = mel[:1] pred_mel = mel.cpu().float().numpy() full_pred_mel = np.concatenate([full_pred_mel, pred_mel[:1]], axis=1) # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, full_pred_mel) logger.info("Generated segment", i) if output_type == "numpy" and not is_onnx_available(): raise ValueError( "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'." ) elif output_type == "numpy" and self.melgan is None: raise ValueError( "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'." ) if output_type == "numpy": output = self.melgan(input_features=full_pred_mel.astype(np.float32)) else: output = full_pred_mel if not return_dict: return (output,) return AudioPipelineOutput(audios=output) ================================================ FILE: diffusers/pipelines/stable_diffusion/README.md ================================================ # Stable Diffusion ## Overview Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team. The summary of the model is the following: *Stable Diffusion is a text-to-image model that will empower billions of people to create stunning art within seconds. It is a breakthrough in speed and quality meaning that it can run on consumer GPUs. You can see some of the amazing output that has been created by this model without pre or post-processing on this page. The model itself builds upon the work of the team at CompVis and Runway in their widely used latent diffusion model combined with insights from the conditional diffusion models by our lead generative AI developer Katherine Crowson, Dall-E 2 by Open AI, Imagen by Google Brain and many others. We are delighted that AI media generation is a cooperative field and hope it can continue this way to bring the gift of creativity to all.* ## Tips: - Stable Diffusion has the same architecture as [Latent Diffusion](https://arxiv.org/abs/2112.10752) but uses a frozen CLIP Text Encoder instead of training the text encoder jointly with the diffusion model. - An in-detail explanation of the Stable Diffusion model can be found under [Stable Diffusion with 🧨 Diffusers](https://huggingface.co/blog/stable_diffusion). - If you don't want to rely on the Hugging Face Hub and having to pass a authentication token, you can download the weights with `git lfs install; git clone https://huggingface.co/runwayml/stable-diffusion-v1-5` and instead pass the local path to the cloned folder to `from_pretrained` as shown below. - Stable Diffusion can work with a variety of different samplers as is shown below. ## Available Pipelines: | Pipeline | Tasks | Colab |---|---|:---:| | [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | [pipeline_stable_diffusion_img2img](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [pipeline_stable_diffusion_inpaint](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) ## Examples: ### Using Stable Diffusion without being logged into the Hub. If you want to download the model weights using a single Python line, you need to be logged in via `huggingface-cli login`. ```python from diffusers import DiffusionPipeline pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") ``` This however can make it difficult to build applications on top of `diffusers` as you will always have to pass the token around. A potential way to solve this issue is by downloading the weights to a local path `"./stable-diffusion-v1-5"`: ``` git lfs install git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 ``` and simply passing the local path to `from_pretrained`: ```python from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5") ``` ### Text-to-Image with default PLMS scheduler ```python # make sure you're logged in with `huggingface-cli login` from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### Text-to-Image with DDIM scheduler ```python # make sure you're logged in with `huggingface-cli login` from diffusers import StableDiffusionPipeline, DDIMScheduler scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", scheduler=scheduler, ).to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### Text-to-Image with K-LMS scheduler ```python # make sure you're logged in with `huggingface-cli login` from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler lms = LMSDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", scheduler=lms, ).to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### CycleDiffusion using Stable Diffusion and DDIM scheduler ```python import requests import torch from PIL import Image from io import BytesIO from diffusers import CycleDiffusionPipeline, DDIMScheduler # load the scheduler. CycleDiffusion only supports stochastic schedulers. # load the pipeline # make sure you're logged in with `huggingface-cli login` model_id_or_path = "CompVis/stable-diffusion-v1-4" scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler") pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda") # let's download an initial image url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png" response = requests.get(url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((512, 512)) init_image.save("horse.png") # let's specify a prompt source_prompt = "An astronaut riding a horse" prompt = "An astronaut riding an elephant" # call the pipeline image = pipe( prompt=prompt, source_prompt=source_prompt, image=init_image, num_inference_steps=100, eta=0.1, strength=0.8, guidance_scale=2, source_guidance_scale=1, ).images[0] image.save("horse_to_elephant.png") # let's try another example # See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png" response = requests.get(url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((512, 512)) init_image.save("black.png") source_prompt = "A black colored car" prompt = "A blue colored car" # call the pipeline torch.manual_seed(0) image = pipe( prompt=prompt, source_prompt=source_prompt, image=init_image, num_inference_steps=100, eta=0.1, strength=0.85, guidance_scale=3, source_guidance_scale=1, ).images[0] image.save("black_to_blue.png") ``` ================================================ FILE: diffusers/pipelines/stable_diffusion/__init__.py ================================================ from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import ( BaseOutput, OptionalDependencyNotAvailable, is_flax_available, is_k_diffusion_available, is_k_diffusion_version, is_onnx_available, is_torch_available, is_transformers_available, is_transformers_version, ) @dataclass class StableDiffusionPipelineOutput(BaseOutput): """ Output class for Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. nsfw_content_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, or `None` if safety checking could not be performed. """ images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_cycle_diffusion import CycleDiffusionPipeline from .pipeline_stable_diffusion import StableDiffusionPipeline from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy from .pipeline_stable_diffusion_instruct_pix2pix import StableDiffusionInstructPix2PixPipeline from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline from .pipeline_stable_unclip import StableUnCLIPPipeline from .pipeline_stable_unclip_img2img import StableUnCLIPImg2ImgPipeline from .safety_checker import StableDiffusionSafetyChecker from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline else: from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import ( StableDiffusionDepth2ImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionPix2PixZeroPipeline, ) else: from .pipeline_stable_diffusion_depth2img import StableDiffusionDepth2ImgPipeline from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline from .pipeline_stable_diffusion_pix2pix_zero import StableDiffusionPix2PixZeroPipeline try: if not ( is_torch_available() and is_transformers_available() and is_k_diffusion_available() and is_k_diffusion_version(">=", "0.0.12") ): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline try: if not (is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_onnx_objects import * # noqa F403 else: from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline from .pipeline_onnx_stable_diffusion_img2img import OnnxStableDiffusionImg2ImgPipeline from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline if is_transformers_available() and is_flax_available(): import flax @flax.struct.dataclass class FlaxStableDiffusionPipelineOutput(BaseOutput): """ Output class for Stable Diffusion pipelines. Args: images (`np.ndarray`) Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline. nsfw_content_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content. """ images: np.ndarray nsfw_content_detected: List[bool] from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline from .pipeline_flax_stable_diffusion_img2img import FlaxStableDiffusionImg2ImgPipeline from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline from .safety_checker_flax import FlaxStableDiffusionSafetyChecker ================================================ FILE: diffusers/pipelines/stable_diffusion/convert_from_ckpt.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ Conversion script for the Stable Diffusion checkpoints.""" import re from io import BytesIO from typing import Optional import requests import torch from transformers import ( AutoFeatureExtractor, BertTokenizerFast, CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection, ) from ...models import ( AutoencoderKL, ControlNetModel, PriorTransformer, UNet2DConditionModel, ) from ...schedulers import ( DDIMScheduler, DDPMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UnCLIPScheduler, ) from ...utils import is_omegaconf_available, is_safetensors_available, logging from ...utils.import_utils import BACKENDS_MAPPING from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel from ..paint_by_example import PaintByExampleImageEncoder from ..pipeline_utils import DiffusionPipeline from .safety_checker import StableDiffusionSafetyChecker from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer logger = logging.get_logger(__name__) # pylint: disable=invalid-name def shave_segments(path, n_shave_prefix_segments=1): """ Removes segments. Positive values shave the first segments, negative shave the last segments. """ if n_shave_prefix_segments >= 0: return ".".join(path.split(".")[n_shave_prefix_segments:]) else: return ".".join(path.split(".")[:n_shave_prefix_segments]) def renew_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item.replace("in_layers.0", "norm1") new_item = new_item.replace("in_layers.2", "conv1") new_item = new_item.replace("out_layers.0", "norm2") new_item = new_item.replace("out_layers.3", "conv2") new_item = new_item.replace("emb_layers.1", "time_emb_proj") new_item = new_item.replace("skip_connection", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("nin_shortcut", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item # new_item = new_item.replace('norm.weight', 'group_norm.weight') # new_item = new_item.replace('norm.bias', 'group_norm.bias') # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("norm.weight", "group_norm.weight") new_item = new_item.replace("norm.bias", "group_norm.bias") new_item = new_item.replace("q.weight", "to_q.weight") new_item = new_item.replace("q.bias", "to_q.bias") new_item = new_item.replace("k.weight", "to_k.weight") new_item = new_item.replace("k.bias", "to_k.bias") new_item = new_item.replace("v.weight", "to_v.weight") new_item = new_item.replace("v.bias", "to_v.bias") new_item = new_item.replace("proj_out.weight", "to_out.0.weight") new_item = new_item.replace("proj_out.bias", "to_out.0.bias") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def assign_to_checkpoint( paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None ): """ This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits attention layers, and takes into account additional replacements that may arise. Assigns the weights to the new checkpoint. """ assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." # Splits the attention layers into three variables. if attention_paths_to_split is not None: for path, path_map in attention_paths_to_split.items(): old_tensor = old_checkpoint[path] channels = old_tensor.shape[0] // 3 target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) query, key, value = old_tensor.split(channels // num_heads, dim=1) checkpoint[path_map["query"]] = query.reshape(target_shape) checkpoint[path_map["key"]] = key.reshape(target_shape) checkpoint[path_map["value"]] = value.reshape(target_shape) for path in paths: new_path = path["new"] # These have already been assigned if attention_paths_to_split is not None and new_path in attention_paths_to_split: continue # Global renaming happens here new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") if additional_replacements is not None: for replacement in additional_replacements: new_path = new_path.replace(replacement["old"], replacement["new"]) # proj_attn.weight has to be converted from conv 1D to linear is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) shape = old_checkpoint[path["old"]].shape if is_attn_weight and len(shape) == 3: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] elif is_attn_weight and len(shape) == 4: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] else: checkpoint[new_path] = old_checkpoint[path["old"]] def conv_attn_to_linear(checkpoint): keys = list(checkpoint.keys()) attn_keys = ["query.weight", "key.weight", "value.weight"] for key in keys: if ".".join(key.split(".")[-2:]) in attn_keys: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0, 0] elif "proj_attn.weight" in key: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0] def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): """ Creates a config for the diffusers based on the config of the LDM model. """ if controlnet: unet_params = original_config.model.params.control_stage_config.params else: unet_params = original_config.model.params.unet_config.params vae_params = original_config.model.params.first_stage_config.params.ddconfig block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] down_block_types = [] resolution = 1 for i in range(len(block_out_channels)): block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D" down_block_types.append(block_type) if i != len(block_out_channels) - 1: resolution *= 2 up_block_types = [] for i in range(len(block_out_channels)): block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D" up_block_types.append(block_type) resolution //= 2 vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) head_dim = unet_params.num_heads if "num_heads" in unet_params else None use_linear_projection = ( unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False ) if use_linear_projection: # stable diffusion 2-base-512 and 2-768 if head_dim is None: head_dim = [5, 10, 20, 20] class_embed_type = None projection_class_embeddings_input_dim = None if "num_classes" in unet_params: if unet_params.num_classes == "sequential": class_embed_type = "projection" assert "adm_in_channels" in unet_params projection_class_embeddings_input_dim = unet_params.adm_in_channels else: raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}") config = { "sample_size": image_size // vae_scale_factor, "in_channels": unet_params.in_channels, "down_block_types": tuple(down_block_types), "block_out_channels": tuple(block_out_channels), "layers_per_block": unet_params.num_res_blocks, "cross_attention_dim": unet_params.context_dim, "attention_head_dim": head_dim, "use_linear_projection": use_linear_projection, "class_embed_type": class_embed_type, "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, } if not controlnet: config["out_channels"] = unet_params.out_channels config["up_block_types"] = tuple(up_block_types) return config def create_vae_diffusers_config(original_config, image_size: int): """ Creates a config for the diffusers based on the config of the LDM model. """ vae_params = original_config.model.params.first_stage_config.params.ddconfig _ = original_config.model.params.first_stage_config.params.embed_dim block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) config = { "sample_size": image_size, "in_channels": vae_params.in_channels, "out_channels": vae_params.out_ch, "down_block_types": tuple(down_block_types), "up_block_types": tuple(up_block_types), "block_out_channels": tuple(block_out_channels), "latent_channels": vae_params.z_channels, "layers_per_block": vae_params.num_res_blocks, } return config def create_diffusers_schedular(original_config): schedular = DDIMScheduler( num_train_timesteps=original_config.model.params.timesteps, beta_start=original_config.model.params.linear_start, beta_end=original_config.model.params.linear_end, beta_schedule="scaled_linear", ) return schedular def create_ldm_bert_config(original_config): bert_params = original_config.model.parms.cond_stage_config.params config = LDMBertConfig( d_model=bert_params.n_embed, encoder_layers=bert_params.n_layer, encoder_ffn_dim=bert_params.n_embed * 4, ) return config def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False, controlnet=False): """ Takes a state dict and a config, and returns a converted checkpoint. """ # extract state_dict for UNet unet_state_dict = {} keys = list(checkpoint.keys()) if controlnet: unet_key = "control_model." else: unet_key = "model.diffusion_model." # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: print(f"Checkpoint {path} has both EMA and non-EMA weights.") print( "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." ) for key in keys: if key.startswith("model.diffusion_model"): flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) else: if sum(k.startswith("model_ema") for k in keys) > 100: print( "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" " weights (usually better for inference), please make sure to add the `--extract_ema` flag." ) for key in keys: if key.startswith(unet_key): unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) new_checkpoint = {} new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] if config["class_embed_type"] is None: # No parameters to port ... elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] else: raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] if not controlnet: new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] # Retrieves the keys for the input blocks only num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) input_blocks = { layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] for layer_id in range(num_input_blocks) } # Retrieves the keys for the middle blocks only num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) middle_blocks = { layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] for layer_id in range(num_middle_blocks) } # Retrieves the keys for the output blocks only num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) output_blocks = { layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] for layer_id in range(num_output_blocks) } for i in range(1, num_input_blocks): block_id = (i - 1) // (config["layers_per_block"] + 1) layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) resnets = [ key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key ] attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] if f"input_blocks.{i}.0.op.weight" in unet_state_dict: new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( f"input_blocks.{i}.0.op.weight" ) new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( f"input_blocks.{i}.0.op.bias" ) paths = renew_resnet_paths(resnets) meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) if len(attentions): paths = renew_attention_paths(attentions) meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) resnet_0 = middle_blocks[0] attentions = middle_blocks[1] resnet_1 = middle_blocks[2] resnet_0_paths = renew_resnet_paths(resnet_0) assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) resnet_1_paths = renew_resnet_paths(resnet_1) assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) attentions_paths = renew_attention_paths(attentions) meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} assign_to_checkpoint( attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) for i in range(num_output_blocks): block_id = i // (config["layers_per_block"] + 1) layer_in_block_id = i % (config["layers_per_block"] + 1) output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] output_block_list = {} for layer in output_block_layers: layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) if layer_id in output_block_list: output_block_list[layer_id].append(layer_name) else: output_block_list[layer_id] = [layer_name] if len(output_block_list) > 1: resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] resnet_0_paths = renew_resnet_paths(resnets) paths = renew_resnet_paths(resnets) meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) output_block_list = {k: sorted(v) for k, v in output_block_list.items()} if ["conv.bias", "conv.weight"] in output_block_list.values(): index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.weight" ] new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ f"output_blocks.{i}.{index}.conv.bias" ] # Clear attentions as they have been attributed above. if len(attentions) == 2: attentions = [] if len(attentions): paths = renew_attention_paths(attentions) meta_path = { "old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", } assign_to_checkpoint( paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) else: resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) for path in resnet_0_paths: old_path = ".".join(["output_blocks", str(i), path["old"]]) new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) new_checkpoint[new_path] = unet_state_dict[old_path] if controlnet: # conditioning embedding orig_index = 0 new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop( f"input_hint_block.{orig_index}.weight" ) new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( f"input_hint_block.{orig_index}.bias" ) orig_index += 2 diffusers_index = 0 while diffusers_index < 6: new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop( f"input_hint_block.{orig_index}.weight" ) new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop( f"input_hint_block.{orig_index}.bias" ) diffusers_index += 1 orig_index += 2 new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop( f"input_hint_block.{orig_index}.weight" ) new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( f"input_hint_block.{orig_index}.bias" ) # down blocks for i in range(num_input_blocks): new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight") new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias") # mid block new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight") new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias") return new_checkpoint def convert_ldm_vae_checkpoint(checkpoint, config): # extract state dict for VAE vae_state_dict = {} vae_key = "first_stage_model." keys = list(checkpoint.keys()) for key in keys: if key.startswith(vae_key): vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) new_checkpoint = {} new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] # Retrieves the keys for the encoder down blocks only num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) down_blocks = { layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) } # Retrieves the keys for the decoder up blocks only num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) up_blocks = { layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) } for i in range(num_down_blocks): resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.weight" ) new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.bias" ) paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] num_mid_res_blocks = 2 for i in range(1, num_mid_res_blocks + 1): resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] paths = renew_vae_attention_paths(mid_attentions) meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) conv_attn_to_linear(new_checkpoint) for i in range(num_up_blocks): block_id = num_up_blocks - 1 - i resnets = [ key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key ] if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ f"decoder.up.{block_id}.upsample.conv.weight" ] new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ f"decoder.up.{block_id}.upsample.conv.bias" ] paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] num_mid_res_blocks = 2 for i in range(1, num_mid_res_blocks + 1): resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] paths = renew_vae_attention_paths(mid_attentions) meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) conv_attn_to_linear(new_checkpoint) return new_checkpoint def convert_ldm_bert_checkpoint(checkpoint, config): def _copy_attn_layer(hf_attn_layer, pt_attn_layer): hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias def _copy_linear(hf_linear, pt_linear): hf_linear.weight = pt_linear.weight hf_linear.bias = pt_linear.bias def _copy_layer(hf_layer, pt_layer): # copy layer norms _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0]) _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0]) # copy attn _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1]) # copy MLP pt_mlp = pt_layer[1][1] _copy_linear(hf_layer.fc1, pt_mlp.net[0][0]) _copy_linear(hf_layer.fc2, pt_mlp.net[2]) def _copy_layers(hf_layers, pt_layers): for i, hf_layer in enumerate(hf_layers): if i != 0: i += i pt_layer = pt_layers[i : i + 2] _copy_layer(hf_layer, pt_layer) hf_model = LDMBertModel(config).eval() # copy embeds hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight # copy layer norm _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm) # copy hidden layers _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers) _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits) return hf_model def convert_ldm_clip_checkpoint(checkpoint): text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") keys = list(checkpoint.keys()) text_model_dict = {} for key in keys: if key.startswith("cond_stage_model.transformer"): text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key] text_model.load_state_dict(text_model_dict) return text_model textenc_conversion_lst = [ ("cond_stage_model.model.positional_embedding", "text_model.embeddings.position_embedding.weight"), ("cond_stage_model.model.token_embedding.weight", "text_model.embeddings.token_embedding.weight"), ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"), ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"), ] textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst} textenc_transformer_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[0]): x[1] for x in textenc_transformer_conversion_lst} textenc_pattern = re.compile("|".join(protected.keys())) def convert_paint_by_example_checkpoint(checkpoint): config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14") model = PaintByExampleImageEncoder(config) keys = list(checkpoint.keys()) text_model_dict = {} for key in keys: if key.startswith("cond_stage_model.transformer"): text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key] # load clip vision model.model.load_state_dict(text_model_dict) # load mapper keys_mapper = { k[len("cond_stage_model.mapper.res") :]: v for k, v in checkpoint.items() if k.startswith("cond_stage_model.mapper") } MAPPING = { "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"], "attn.c_proj": ["attn1.to_out.0"], "ln_1": ["norm1"], "ln_2": ["norm3"], "mlp.c_fc": ["ff.net.0.proj"], "mlp.c_proj": ["ff.net.2"], } mapped_weights = {} for key, value in keys_mapper.items(): prefix = key[: len("blocks.i")] suffix = key.split(prefix)[-1].split(".")[-1] name = key.split(prefix)[-1].split(suffix)[0][1:-1] mapped_names = MAPPING[name] num_splits = len(mapped_names) for i, mapped_name in enumerate(mapped_names): new_name = ".".join([prefix, mapped_name, suffix]) shape = value.shape[0] // num_splits mapped_weights[new_name] = value[i * shape : (i + 1) * shape] model.mapper.load_state_dict(mapped_weights) # load final layer norm model.final_layer_norm.load_state_dict( { "bias": checkpoint["cond_stage_model.final_ln.bias"], "weight": checkpoint["cond_stage_model.final_ln.weight"], } ) # load final proj model.proj_out.load_state_dict( { "bias": checkpoint["proj_out.bias"], "weight": checkpoint["proj_out.weight"], } ) # load uncond vector model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"]) return model def convert_open_clip_checkpoint(checkpoint): text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder") keys = list(checkpoint.keys()) text_model_dict = {} if "cond_stage_model.model.text_projection" in checkpoint: d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0]) else: d_model = 1024 text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids") for key in keys: if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer continue if key in textenc_conversion_map: text_model_dict[textenc_conversion_map[key]] = checkpoint[key] if key.startswith("cond_stage_model.model.transformer."): new_key = key[len("cond_stage_model.model.transformer.") :] if new_key.endswith(".in_proj_weight"): new_key = new_key[: -len(".in_proj_weight")] new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] elif new_key.endswith(".in_proj_bias"): new_key = new_key[: -len(".in_proj_bias")] new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] else: new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key] = checkpoint[key] text_model.load_state_dict(text_model_dict) return text_model def stable_unclip_image_encoder(original_config): """ Returns the image processor and clip image encoder for the img2img unclip pipeline. We currently know of two types of stable unclip models which separately use the clip and the openclip image encoders. """ image_embedder_config = original_config.model.params.embedder_config sd_clip_image_embedder_class = image_embedder_config.target sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1] if sd_clip_image_embedder_class == "ClipImageEmbedder": clip_model_name = image_embedder_config.params.model if clip_model_name == "ViT-L/14": feature_extractor = CLIPImageProcessor() image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") else: raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}") elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder": feature_extractor = CLIPImageProcessor() image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") else: raise NotImplementedError( f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}" ) return feature_extractor, image_encoder def stable_unclip_image_noising_components( original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None ): """ Returns the noising components for the img2img and txt2img unclip pipelines. Converts the stability noise augmentor into 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats 2. a `DDPMScheduler` for holding the noise schedule If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided. """ noise_aug_config = original_config.model.params.noise_aug_config noise_aug_class = noise_aug_config.target noise_aug_class = noise_aug_class.split(".")[-1] if noise_aug_class == "CLIPEmbeddingNoiseAugmentation": noise_aug_config = noise_aug_config.params embedding_dim = noise_aug_config.timestep_dim max_noise_level = noise_aug_config.noise_schedule_config.timesteps beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim) image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule) if "clip_stats_path" in noise_aug_config: if clip_stats_path is None: raise ValueError("This stable unclip config requires a `clip_stats_path`") clip_mean, clip_std = torch.load(clip_stats_path, map_location=device) clip_mean = clip_mean[None, :] clip_std = clip_std[None, :] clip_stats_state_dict = { "mean": clip_mean, "std": clip_std, } image_normalizer.load_state_dict(clip_stats_state_dict) else: raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}") return image_normalizer, image_noising_scheduler def convert_controlnet_checkpoint( checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema ): ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True) ctrlnet_config["upcast_attention"] = upcast_attention ctrlnet_config.pop("sample_size") controlnet_model = ControlNetModel(**ctrlnet_config) converted_ctrl_checkpoint = convert_ldm_unet_checkpoint( checkpoint, ctrlnet_config, path=checkpoint_path, extract_ema=extract_ema, controlnet=True ) controlnet_model.load_state_dict(converted_ctrl_checkpoint) return controlnet_model def download_from_original_stable_diffusion_ckpt( checkpoint_path: str, original_config_file: str = None, image_size: int = 512, prediction_type: str = None, model_type: str = None, extract_ema: bool = False, scheduler_type: str = "pndm", num_in_channels: Optional[int] = None, upcast_attention: Optional[bool] = None, device: str = None, from_safetensors: bool = False, stable_unclip: Optional[str] = None, stable_unclip_prior: Optional[str] = None, clip_stats_path: Optional[str] = None, controlnet: Optional[bool] = None, load_safety_checker: bool = True, pipeline_class: DiffusionPipeline = None, ) -> DiffusionPipeline: """ Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is recommended that you override the default values and/or supply an `original_config_file` wherever possible. Args: checkpoint_path (`str`): Path to `.ckpt` file. original_config_file (`str`): Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically inferred by looking for a key that only exists in SD2.0 models. image_size (`int`, *optional*, defaults to 512): The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use 768 for Stable Diffusion v2. prediction_type (`str`, *optional*): The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2. num_in_channels (`int`, *optional*, defaults to None): The number of input channels. If `None`, it will be automatically inferred. scheduler_type (`str`, *optional*, defaults to 'pndm'): Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", "ddim"]`. model_type (`str`, *optional*, defaults to `None`): The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder", "FrozenCLIPEmbedder", "PaintByExample"]`. is_img2img (`bool`, *optional*, defaults to `False`): Whether the model should be loaded as an img2img pipeline. extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning. upcast_attention (`bool`, *optional*, defaults to `None`): Whether the attention computation should always be upcasted. This is necessary when running stable diffusion 2.1. device (`str`, *optional*, defaults to `None`): The device to use. Pass `None` to determine automatically. from_safetensors (`str`, *optional*, defaults to `False`): If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. load_safety_checker (`bool`, *optional*, defaults to `True`): Whether to load the safety checker or not. Defaults to `True`. pipeline_class (`str`, *optional*, defaults to `None`): The pipeline class to use. Pass `None` to determine automatically. return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file. """ # import pipelines here to avoid circular import error when using from_ckpt method from diffusers import ( LDMTextToImagePipeline, PaintByExamplePipeline, StableDiffusionControlNetPipeline, StableDiffusionPipeline, StableUnCLIPImg2ImgPipeline, StableUnCLIPPipeline, ) if pipeline_class is None: pipeline_class = StableDiffusionPipeline if prediction_type == "v-prediction": prediction_type = "v_prediction" if not is_omegaconf_available(): raise ValueError(BACKENDS_MAPPING["omegaconf"][1]) from omegaconf import OmegaConf if from_safetensors: if not is_safetensors_available(): raise ValueError(BACKENDS_MAPPING["safetensors"][1]) from safetensors import safe_open checkpoint = {} with safe_open(checkpoint_path, framework="pt", device="cpu") as f: for key in f.keys(): checkpoint[key] = f.get_tensor(key) else: if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" checkpoint = torch.load(checkpoint_path, map_location=device) else: checkpoint = torch.load(checkpoint_path, map_location=device) # Sometimes models don't have the global_step item if "global_step" in checkpoint: global_step = checkpoint["global_step"] else: print("global_step key not found in model") global_step = None # NOTE: this while loop isn't great but this controlnet checkpoint has one additional # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21 while "state_dict" in checkpoint: checkpoint = checkpoint["state_dict"] if original_config_file is None: key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight" # model_type = "v1" config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024: # model_type = "v2" config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml" if global_step == 110000: # v2.1 needs to upcast attention upcast_attention = True original_config_file = BytesIO(requests.get(config_url).content) original_config = OmegaConf.load(original_config_file) if num_in_channels is not None: original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels if ( "parameterization" in original_config["model"]["params"] and original_config["model"]["params"]["parameterization"] == "v" ): if prediction_type is None: # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"` # as it relies on a brittle global step parameter here prediction_type = "epsilon" if global_step == 875000 else "v_prediction" if image_size is None: # NOTE: For stable diffusion 2 base one has to pass `image_size==512` # as it relies on a brittle global step parameter here image_size = 512 if global_step == 875000 else 768 else: if prediction_type is None: prediction_type = "epsilon" if image_size is None: image_size = 512 if controlnet is None: controlnet = "control_stage_config" in original_config.model.params if controlnet: controlnet_model = convert_controlnet_checkpoint( checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema ) num_train_timesteps = original_config.model.params.timesteps beta_start = original_config.model.params.linear_start beta_end = original_config.model.params.linear_end scheduler = DDIMScheduler( beta_end=beta_end, beta_schedule="scaled_linear", beta_start=beta_start, num_train_timesteps=num_train_timesteps, steps_offset=1, clip_sample=False, set_alpha_to_one=False, prediction_type=prediction_type, ) # make sure scheduler works correctly with DDIM scheduler.register_to_config(clip_sample=False) if scheduler_type == "pndm": config = dict(scheduler.config) config["skip_prk_steps"] = True scheduler = PNDMScheduler.from_config(config) elif scheduler_type == "lms": scheduler = LMSDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "heun": scheduler = HeunDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "euler": scheduler = EulerDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "euler-ancestral": scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) elif scheduler_type == "dpm": scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) elif scheduler_type == "ddim": scheduler = scheduler else: raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") # Convert the UNet2DConditionModel model. unet_config = create_unet_diffusers_config(original_config, image_size=image_size) unet_config["upcast_attention"] = upcast_attention unet = UNet2DConditionModel(**unet_config) converted_unet_checkpoint = convert_ldm_unet_checkpoint( checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema ) unet.load_state_dict(converted_unet_checkpoint) # Convert the VAE model. vae_config = create_vae_diffusers_config(original_config, image_size=image_size) converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) vae = AutoencoderKL(**vae_config) vae.load_state_dict(converted_vae_checkpoint) # Convert the text model. if model_type is None: model_type = original_config.model.params.cond_stage_config.target.split(".")[-1] logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}") if model_type == "FrozenOpenCLIPEmbedder": text_model = convert_open_clip_checkpoint(checkpoint) tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer") if stable_unclip is None: if controlnet: pipe = StableDiffusionControlNetPipeline( vae=vae, text_encoder=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler, controlnet=controlnet_model, safety_checker=None, feature_extractor=None, requires_safety_checker=False, ) else: pipe = pipeline_class( vae=vae, text_encoder=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=None, requires_safety_checker=False, ) else: image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components( original_config, clip_stats_path=clip_stats_path, device=device ) if stable_unclip == "img2img": feature_extractor, image_encoder = stable_unclip_image_encoder(original_config) pipe = StableUnCLIPImg2ImgPipeline( # image encoding components feature_extractor=feature_extractor, image_encoder=image_encoder, # image noising components image_normalizer=image_normalizer, image_noising_scheduler=image_noising_scheduler, # regular denoising components tokenizer=tokenizer, text_encoder=text_model, unet=unet, scheduler=scheduler, # vae vae=vae, ) elif stable_unclip == "txt2img": if stable_unclip_prior is None or stable_unclip_prior == "karlo": karlo_model = "kakaobrain/karlo-v1-alpha" prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior") prior_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") prior_text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler") prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config) else: raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}") pipe = StableUnCLIPPipeline( # prior components prior_tokenizer=prior_tokenizer, prior_text_encoder=prior_text_model, prior=prior, prior_scheduler=prior_scheduler, # image noising components image_normalizer=image_normalizer, image_noising_scheduler=image_noising_scheduler, # regular denoising components tokenizer=tokenizer, text_encoder=text_model, unet=unet, scheduler=scheduler, # vae vae=vae, ) else: raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}") elif model_type == "PaintByExample": vision_model = convert_paint_by_example_checkpoint(checkpoint) tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") pipe = PaintByExamplePipeline( vae=vae, image_encoder=vision_model, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor, ) elif model_type == "FrozenCLIPEmbedder": text_model = convert_ldm_clip_checkpoint(checkpoint) tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") if load_safety_checker: safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") else: safety_checker = None feature_extractor = None if controlnet: pipe = StableDiffusionControlNetPipeline( vae=vae, text_encoder=text_model, tokenizer=tokenizer, unet=unet, controlnet=controlnet_model, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) else: pipe = pipeline_class( vae=vae, text_encoder=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) else: text_config = create_ldm_bert_config(original_config) text_model = convert_ldm_bert_checkpoint(checkpoint, text_config) tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler) return pipe def download_controlnet_from_original_ckpt( checkpoint_path: str, original_config_file: str, image_size: int = 512, extract_ema: bool = False, num_in_channels: Optional[int] = None, upcast_attention: Optional[bool] = None, device: str = None, from_safetensors: bool = False, ) -> DiffusionPipeline: if not is_omegaconf_available(): raise ValueError(BACKENDS_MAPPING["omegaconf"][1]) from omegaconf import OmegaConf if from_safetensors: if not is_safetensors_available(): raise ValueError(BACKENDS_MAPPING["safetensors"][1]) from safetensors import safe_open checkpoint = {} with safe_open(checkpoint_path, framework="pt", device="cpu") as f: for key in f.keys(): checkpoint[key] = f.get_tensor(key) else: if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" checkpoint = torch.load(checkpoint_path, map_location=device) else: checkpoint = torch.load(checkpoint_path, map_location=device) # NOTE: this while loop isn't great but this controlnet checkpoint has one additional # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21 while "state_dict" in checkpoint: checkpoint = checkpoint["state_dict"] original_config = OmegaConf.load(original_config_file) if num_in_channels is not None: original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels if "control_stage_config" not in original_config.model.params: raise ValueError("`control_stage_config` not present in original config") controlnet_model = convert_controlnet_checkpoint( checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema ) return controlnet_model ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers.utils import is_accelerate_available, is_accelerate_version from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMScheduler from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta): # 1. get previous step value (=t-1) prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps if prev_timestep <= 0: return clean_latents # 2. compute alphas, betas alpha_prod_t = scheduler.alphas_cumprod[timestep] alpha_prod_t_prev = ( scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod ) variance = scheduler._get_variance(timestep, prev_timestep) std_dev_t = eta * variance ** (0.5) # direction pointing to x_t e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5) dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t noise = std_dev_t * randn_tensor( clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator ) prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise return prev_latents def compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta): # 1. get previous step value (=t-1) prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps # 2. compute alphas, betas alpha_prod_t = scheduler.alphas_cumprod[timestep] alpha_prod_t_prev = ( scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod ) beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) # 4. Clip "predicted x_0" if scheduler.config.clip_sample: pred_original_sample = torch.clamp(pred_original_sample, -1, 1) # 5. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) variance = scheduler._get_variance(timestep, prev_timestep) std_dev_t = eta * variance ** (0.5) # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred noise = (prev_latents - (alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction)) / ( variance ** (0.5) * eta ) return noise class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: DDIMScheduler, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): image = image.to(device=device, dtype=dtype) batch_size = image.shape[0] if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0) # add noise to latents using the timestep shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents clean_latents = init_latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents, clean_latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], source_prompt: Union[str, List[str]], image: Union[torch.FloatTensor, PIL.Image.Image] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, source_guidance_scale: Optional[float] = 1, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. source_guidance_scale (`float`, *optional*, defaults to 1): Guidance scale for the source prompt. This is useful to control the amount of influence the source prompt for encoding. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.1): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs(prompt, strength, callback_steps) # 2. Define call parameters batch_size = 1 if isinstance(prompt, str) else len(prompt) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, prompt_embeds=prompt_embeds, ) source_prompt_embeds = self._encode_prompt( source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, None ) # 4. Preprocess image image = preprocess(image) # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables latents, clean_latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator ) source_latents = latents # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) generator = extra_step_kwargs.pop("generator", None) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) source_latent_model_input = torch.cat([source_latents] * 2) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) source_latent_model_input = self.scheduler.scale_model_input(source_latent_model_input, t) # predict the noise residual concat_latent_model_input = torch.stack( [ source_latent_model_input[0], latent_model_input[0], source_latent_model_input[1], latent_model_input[1], ], dim=0, ) concat_prompt_embeds = torch.stack( [ source_prompt_embeds[0], prompt_embeds[0], source_prompt_embeds[1], prompt_embeds[1], ], dim=0, ) concat_noise_pred = self.unet( concat_latent_model_input, t, encoder_hidden_states=concat_prompt_embeds ).sample # perform guidance ( source_noise_pred_uncond, noise_pred_uncond, source_noise_pred_text, noise_pred_text, ) = concat_noise_pred.chunk(4, dim=0) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) source_noise_pred = source_noise_pred_uncond + source_guidance_scale * ( source_noise_pred_text - source_noise_pred_uncond ) # Sample source_latents from the posterior distribution. prev_source_latents = posterior_sample( self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs ) # Compute noise. noise = compute_noise( self.scheduler, prev_source_latents, source_latents, t, source_noise_pred, **extra_step_kwargs ) source_latents = prev_source_latents # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( noise_pred, t, latents, variance_noise=noise, **extra_step_kwargs ).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 9. Post-processing if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py ================================================ # Copyright 2023 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. import warnings from functools import partial from typing import Dict, List, Optional, Union import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict from flax.jax_utils import unreplicate from flax.training.common_utils import shard from packaging import version from PIL import Image from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel from ...schedulers import ( FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, ) from ...utils import deprecate, logging, replace_example_docstring from ..pipeline_flax_utils import FlaxDiffusionPipeline from . import FlaxStableDiffusionPipelineOutput from .safety_checker_flax import FlaxStableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Set to True to use python for loop instead of jax.fori_loop for easier debugging DEBUG = False EXAMPLE_DOC_STRING = """ Examples: ```py >>> import jax >>> import numpy as np >>> from flax.jax_utils import replicate >>> from flax.training.common_utils import shard >>> from diffusers import FlaxStableDiffusionPipeline >>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16 ... ) >>> prompt = "a photo of an astronaut riding a horse on mars" >>> prng_seed = jax.random.PRNGKey(0) >>> num_inference_steps = 50 >>> num_samples = jax.device_count() >>> prompt = num_samples * [prompt] >>> prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng >>> params = replicate(params) >>> prng_seed = jax.random.split(prng_seed, jax.device_count()) >>> prompt_ids = shard(prompt_ids) >>> images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` """ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`FlaxAutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`FlaxCLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or [`FlaxDPMSolverMultistepScheduler`]. safety_checker ([`FlaxStableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: FlaxAutoencoderKL, text_encoder: FlaxCLIPTextModel, tokenizer: CLIPTokenizer, unet: FlaxUNet2DConditionModel, scheduler: Union[ FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler ], safety_checker: FlaxStableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, dtype: jnp.dtype = jnp.float32, ): super().__init__() self.dtype = dtype if safety_checker is None: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) def prepare_inputs(self, prompt: Union[str, List[str]]): if not isinstance(prompt, (str, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) return text_input.input_ids def _get_has_nsfw_concepts(self, features, params): has_nsfw_concepts = self.safety_checker(features, params) return has_nsfw_concepts def _run_safety_checker(self, images, safety_model_params, jit=False): # safety_model_params should already be replicated when jit is True pil_images = [Image.fromarray(image) for image in images] features = self.feature_extractor(pil_images, return_tensors="np").pixel_values if jit: features = shard(features) has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params) has_nsfw_concepts = unshard(has_nsfw_concepts) safety_model_params = unreplicate(safety_model_params) else: has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params) images_was_copied = False for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: if not images_was_copied: images_was_copied = True images = images.copy() images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image if any(has_nsfw_concepts): warnings.warn( "Potential NSFW content was detected in one or more images. A black image will be returned" " instead. Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts def _generate( self, prompt_ids: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, num_inference_steps: int, height: int, width: int, guidance_scale: float, latents: Optional[jnp.array] = None, neg_prompt_ids: Optional[jnp.array] = None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") # get prompt text embeddings prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0] # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0` # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0` batch_size = prompt_ids.shape[0] max_length = prompt_ids.shape[-1] if neg_prompt_ids is None: uncond_input = self.tokenizer( [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np" ).input_ids else: uncond_input = neg_prompt_ids negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0] context = jnp.concatenate([negative_prompt_embeds, prompt_embeds]) # Ensure model output will be `float32` before going into the scheduler guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32) latents_shape = ( batch_size, self.unet.config.in_channels, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if latents is None: latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") def loop_body(step, args): latents, scheduler_state = args # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes latents_input = jnp.concatenate([latents] * 2) t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step] timestep = jnp.broadcast_to(t, latents_input.shape[0]) latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t) # predict the noise residual noise_pred = self.unet.apply( {"params": params["unet"]}, jnp.array(latents_input), jnp.array(timestep, dtype=jnp.int32), encoder_hidden_states=context, ).sample # perform guidance noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple() return latents, scheduler_state scheduler_state = self.scheduler.set_timesteps( params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape ) # scale the initial noise by the standard deviation required by the scheduler latents = latents * params["scheduler"].init_noise_sigma if DEBUG: # run with python for loop for i in range(num_inference_steps): latents, scheduler_state = loop_body(i, (latents, scheduler_state)) else: latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state)) # scale and decode the image latents with vae latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1) return image @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt_ids: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, num_inference_steps: int = 50, height: Optional[int] = None, width: Optional[int] = None, guidance_scale: Union[float, jnp.array] = 7.5, latents: jnp.array = None, neg_prompt_ids: jnp.array = None, return_dict: bool = True, jit: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. latents (`jnp.array`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. tensor will ge generated by sampling using the supplied random `generator`. jit (`bool`, defaults to `False`): Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of a plain tuple. Examples: Returns: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor if isinstance(guidance_scale, float): # Convert to a tensor so each device gets a copy. Follow the prompt_ids for # shape information, as they may be sharded (when `jit` is `True`), or not. guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0]) if len(prompt_ids.shape) > 2: # Assume sharded guidance_scale = guidance_scale[:, None] if jit: images = _p_generate( self, prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ) else: images = self._generate( prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ) if self.safety_checker is not None: safety_params = params["safety_checker"] images_uint8_casted = (images * 255).round().astype("uint8") num_devices, batch_size = images.shape[:2] images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3) images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit) images = np.asarray(images) # block images if any(has_nsfw_concept): for i, is_nsfw in enumerate(has_nsfw_concept): if is_nsfw: images[i] = np.asarray(images_uint8_casted[i]) images = images.reshape(num_devices, batch_size, height, width, 3) else: images = np.asarray(images) has_nsfw_concept = False if not return_dict: return (images, has_nsfw_concept) return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept) # Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation. # Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`). @partial( jax.pmap, in_axes=(None, 0, 0, 0, None, None, None, 0, 0, 0), static_broadcasted_argnums=(0, 4, 5, 6), ) def _p_generate( pipe, prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ): return pipe._generate( prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ) @partial(jax.pmap, static_broadcasted_argnums=(0,)) def _p_get_has_nsfw_concepts(pipe, features, params): return pipe._get_has_nsfw_concepts(features, params) def unshard(x: jnp.ndarray): # einops.rearrange(x, 'd b ... -> (d b) ...') num_devices, batch_size = x.shape[:2] rest = x.shape[2:] return x.reshape(num_devices * batch_size, *rest) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py ================================================ # Copyright 2023 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. # NOTE: This file is deprecated and will be removed in a future version. # It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works from ...utils import deprecate from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401 deprecate( "stable diffusion controlnet", "0.22.0", "Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.", standard_warn=False, stacklevel=3, ) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py ================================================ # Copyright 2023 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. import warnings from functools import partial from typing import Dict, List, Optional, Union import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict from flax.jax_utils import unreplicate from flax.training.common_utils import shard from PIL import Image from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel from ...schedulers import ( FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, ) from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring from ..pipeline_flax_utils import FlaxDiffusionPipeline from . import FlaxStableDiffusionPipelineOutput from .safety_checker_flax import FlaxStableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Set to True to use python for loop instead of jax.fori_loop for easier debugging DEBUG = False EXAMPLE_DOC_STRING = """ Examples: ```py >>> import jax >>> import numpy as np >>> import jax.numpy as jnp >>> from flax.jax_utils import replicate >>> from flax.training.common_utils import shard >>> import requests >>> from io import BytesIO >>> from PIL import Image >>> from diffusers import FlaxStableDiffusionImg2ImgPipeline >>> def create_key(seed=0): ... return jax.random.PRNGKey(seed) >>> rng = create_key(0) >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> init_img = Image.open(BytesIO(response.content)).convert("RGB") >>> init_img = init_img.resize((768, 512)) >>> prompts = "A fantasy landscape, trending on artstation" >>> pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained( ... "CompVis/stable-diffusion-v1-4", ... revision="flax", ... dtype=jnp.bfloat16, ... ) >>> num_samples = jax.device_count() >>> rng = jax.random.split(rng, jax.device_count()) >>> prompt_ids, processed_image = pipeline.prepare_inputs( ... prompt=[prompts] * num_samples, image=[init_img] * num_samples ... ) >>> p_params = replicate(params) >>> prompt_ids = shard(prompt_ids) >>> processed_image = shard(processed_image) >>> output = pipeline( ... prompt_ids=prompt_ids, ... image=processed_image, ... params=p_params, ... prng_seed=rng, ... strength=0.75, ... num_inference_steps=50, ... jit=True, ... height=512, ... width=768, ... ).images >>> output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:]))) ``` """ class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline): r""" Pipeline for image-to-image generation using Stable Diffusion. This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`FlaxAutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`FlaxCLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or [`FlaxDPMSolverMultistepScheduler`]. safety_checker ([`FlaxStableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: FlaxAutoencoderKL, text_encoder: FlaxCLIPTextModel, tokenizer: CLIPTokenizer, unet: FlaxUNet2DConditionModel, scheduler: Union[ FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler ], safety_checker: FlaxStableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, dtype: jnp.dtype = jnp.float32, ): super().__init__() self.dtype = dtype if safety_checker is None: logger.warn( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) def prepare_inputs(self, prompt: Union[str, List[str]], image: Union[Image.Image, List[Image.Image]]): if not isinstance(prompt, (str, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if not isinstance(image, (Image.Image, list)): raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}") if isinstance(image, Image.Image): image = [image] processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image]) text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) return text_input.input_ids, processed_images def _get_has_nsfw_concepts(self, features, params): has_nsfw_concepts = self.safety_checker(features, params) return has_nsfw_concepts def _run_safety_checker(self, images, safety_model_params, jit=False): # safety_model_params should already be replicated when jit is True pil_images = [Image.fromarray(image) for image in images] features = self.feature_extractor(pil_images, return_tensors="np").pixel_values if jit: features = shard(features) has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params) has_nsfw_concepts = unshard(has_nsfw_concepts) safety_model_params = unreplicate(safety_model_params) else: has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params) images_was_copied = False for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: if not images_was_copied: images_was_copied = True images = images.copy() images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image if any(has_nsfw_concepts): warnings.warn( "Potential NSFW content was detected in one or more images. A black image will be returned" " instead. Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts def get_timestep_start(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) return t_start def _generate( self, prompt_ids: jnp.array, image: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, start_timestep: int, num_inference_steps: int, height: int, width: int, guidance_scale: float, noise: Optional[jnp.array] = None, neg_prompt_ids: Optional[jnp.array] = None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") # get prompt text embeddings prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0] # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0` # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0` batch_size = prompt_ids.shape[0] max_length = prompt_ids.shape[-1] if neg_prompt_ids is None: uncond_input = self.tokenizer( [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np" ).input_ids else: uncond_input = neg_prompt_ids negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0] context = jnp.concatenate([negative_prompt_embeds, prompt_embeds]) latents_shape = ( batch_size, self.unet.config.in_channels, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if noise is None: noise = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32) else: if noise.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {noise.shape}, expected {latents_shape}") # Create init_latents init_latent_dist = self.vae.apply({"params": params["vae"]}, image, method=self.vae.encode).latent_dist init_latents = init_latent_dist.sample(key=prng_seed).transpose((0, 3, 1, 2)) init_latents = self.vae.config.scaling_factor * init_latents def loop_body(step, args): latents, scheduler_state = args # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes latents_input = jnp.concatenate([latents] * 2) t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step] timestep = jnp.broadcast_to(t, latents_input.shape[0]) latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t) # predict the noise residual noise_pred = self.unet.apply( {"params": params["unet"]}, jnp.array(latents_input), jnp.array(timestep, dtype=jnp.int32), encoder_hidden_states=context, ).sample # perform guidance noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple() return latents, scheduler_state scheduler_state = self.scheduler.set_timesteps( params["scheduler"], num_inference_steps=num_inference_steps, shape=latents_shape ) latent_timestep = scheduler_state.timesteps[start_timestep : start_timestep + 1].repeat(batch_size) latents = self.scheduler.add_noise(params["scheduler"], init_latents, noise, latent_timestep) # scale the initial noise by the standard deviation required by the scheduler latents = latents * params["scheduler"].init_noise_sigma if DEBUG: # run with python for loop for i in range(start_timestep, num_inference_steps): latents, scheduler_state = loop_body(i, (latents, scheduler_state)) else: latents, _ = jax.lax.fori_loop(start_timestep, num_inference_steps, loop_body, (latents, scheduler_state)) # scale and decode the image latents with vae latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1) return image @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt_ids: jnp.array, image: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, strength: float = 0.8, num_inference_steps: int = 50, height: Optional[int] = None, width: Optional[int] = None, guidance_scale: Union[float, jnp.array] = 7.5, noise: jnp.array = None, neg_prompt_ids: jnp.array = None, return_dict: bool = True, jit: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt_ids (`jnp.array`): The prompt or prompts to guide the image generation. image (`jnp.array`): Array representing an image batch, that will be used as the starting point for the process. params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. noise (`jnp.array`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. tensor will ge generated by sampling using the supplied random `generator`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of a plain tuple. jit (`bool`, defaults to `False`): Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release. Examples: Returns: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor if isinstance(guidance_scale, float): # Convert to a tensor so each device gets a copy. Follow the prompt_ids for # shape information, as they may be sharded (when `jit` is `True`), or not. guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0]) if len(prompt_ids.shape) > 2: # Assume sharded guidance_scale = guidance_scale[:, None] start_timestep = self.get_timestep_start(num_inference_steps, strength) if jit: images = _p_generate( self, prompt_ids, image, params, prng_seed, start_timestep, num_inference_steps, height, width, guidance_scale, noise, neg_prompt_ids, ) else: images = self._generate( prompt_ids, image, params, prng_seed, start_timestep, num_inference_steps, height, width, guidance_scale, noise, neg_prompt_ids, ) if self.safety_checker is not None: safety_params = params["safety_checker"] images_uint8_casted = (images * 255).round().astype("uint8") num_devices, batch_size = images.shape[:2] images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3) images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit) images = np.asarray(images) # block images if any(has_nsfw_concept): for i, is_nsfw in enumerate(has_nsfw_concept): if is_nsfw: images[i] = np.asarray(images_uint8_casted[i]) images = images.reshape(num_devices, batch_size, height, width, 3) else: images = np.asarray(images) has_nsfw_concept = False if not return_dict: return (images, has_nsfw_concept) return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept) # Static argnums are pipe, start_timestep, num_inference_steps, height, width. A change would trigger recompilation. # Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`). @partial( jax.pmap, in_axes=(None, 0, 0, 0, 0, None, None, None, None, 0, 0, 0), static_broadcasted_argnums=(0, 5, 6, 7, 8), ) def _p_generate( pipe, prompt_ids, image, params, prng_seed, start_timestep, num_inference_steps, height, width, guidance_scale, noise, neg_prompt_ids, ): return pipe._generate( prompt_ids, image, params, prng_seed, start_timestep, num_inference_steps, height, width, guidance_scale, noise, neg_prompt_ids, ) @partial(jax.pmap, static_broadcasted_argnums=(0,)) def _p_get_has_nsfw_concepts(pipe, features, params): return pipe._get_has_nsfw_concepts(features, params) def unshard(x: jnp.ndarray): # einops.rearrange(x, 'd b ... -> (d b) ...') num_devices, batch_size = x.shape[:2] rest = x.shape[2:] return x.reshape(num_devices * batch_size, *rest) def preprocess(image, dtype): w, h = image.size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = jnp.array(image).astype(dtype) / 255.0 image = image[None].transpose(0, 3, 1, 2) return 2.0 * image - 1.0 ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py ================================================ # Copyright 2023 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. import warnings from functools import partial from typing import Dict, List, Optional, Union import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict from flax.jax_utils import unreplicate from flax.training.common_utils import shard from packaging import version from PIL import Image from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel from ...schedulers import ( FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, ) from ...utils import PIL_INTERPOLATION, deprecate, logging, replace_example_docstring from ..pipeline_flax_utils import FlaxDiffusionPipeline from . import FlaxStableDiffusionPipelineOutput from .safety_checker_flax import FlaxStableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Set to True to use python for loop instead of jax.fori_loop for easier debugging DEBUG = False EXAMPLE_DOC_STRING = """ Examples: ```py >>> import jax >>> import numpy as np >>> from flax.jax_utils import replicate >>> from flax.training.common_utils import shard >>> import PIL >>> import requests >>> from io import BytesIO >>> from diffusers import FlaxStableDiffusionInpaintPipeline >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" >>> init_image = download_image(img_url).resize((512, 512)) >>> mask_image = download_image(mask_url).resize((512, 512)) >>> pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained( ... "xvjiarui/stable-diffusion-2-inpainting" ... ) >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench" >>> prng_seed = jax.random.PRNGKey(0) >>> num_inference_steps = 50 >>> num_samples = jax.device_count() >>> prompt = num_samples * [prompt] >>> init_image = num_samples * [init_image] >>> mask_image = num_samples * [mask_image] >>> prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs( ... prompt, init_image, mask_image ... ) # shard inputs and rng >>> params = replicate(params) >>> prng_seed = jax.random.split(prng_seed, jax.device_count()) >>> prompt_ids = shard(prompt_ids) >>> processed_masked_images = shard(processed_masked_images) >>> processed_masks = shard(processed_masks) >>> images = pipeline( ... prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True ... ).images >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` """ class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`FlaxAutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`FlaxCLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or [`FlaxDPMSolverMultistepScheduler`]. safety_checker ([`FlaxStableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: FlaxAutoencoderKL, text_encoder: FlaxCLIPTextModel, tokenizer: CLIPTokenizer, unet: FlaxUNet2DConditionModel, scheduler: Union[ FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler ], safety_checker: FlaxStableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, dtype: jnp.dtype = jnp.float32, ): super().__init__() self.dtype = dtype if safety_checker is None: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) def prepare_inputs( self, prompt: Union[str, List[str]], image: Union[Image.Image, List[Image.Image]], mask: Union[Image.Image, List[Image.Image]], ): if not isinstance(prompt, (str, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if not isinstance(image, (Image.Image, list)): raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}") if isinstance(image, Image.Image): image = [image] if not isinstance(mask, (Image.Image, list)): raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}") if isinstance(mask, Image.Image): mask = [mask] processed_images = jnp.concatenate([preprocess_image(img, jnp.float32) for img in image]) processed_masks = jnp.concatenate([preprocess_mask(m, jnp.float32) for m in mask]) # processed_masks[processed_masks < 0.5] = 0 processed_masks = processed_masks.at[processed_masks < 0.5].set(0) # processed_masks[processed_masks >= 0.5] = 1 processed_masks = processed_masks.at[processed_masks >= 0.5].set(1) processed_masked_images = processed_images * (processed_masks < 0.5) text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) return text_input.input_ids, processed_masked_images, processed_masks def _get_has_nsfw_concepts(self, features, params): has_nsfw_concepts = self.safety_checker(features, params) return has_nsfw_concepts def _run_safety_checker(self, images, safety_model_params, jit=False): # safety_model_params should already be replicated when jit is True pil_images = [Image.fromarray(image) for image in images] features = self.feature_extractor(pil_images, return_tensors="np").pixel_values if jit: features = shard(features) has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params) has_nsfw_concepts = unshard(has_nsfw_concepts) safety_model_params = unreplicate(safety_model_params) else: has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params) images_was_copied = False for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: if not images_was_copied: images_was_copied = True images = images.copy() images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image if any(has_nsfw_concepts): warnings.warn( "Potential NSFW content was detected in one or more images. A black image will be returned" " instead. Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts def _generate( self, prompt_ids: jnp.array, mask: jnp.array, masked_image: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, num_inference_steps: int, height: int, width: int, guidance_scale: float, latents: Optional[jnp.array] = None, neg_prompt_ids: Optional[jnp.array] = None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") # get prompt text embeddings prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0] # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0` # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0` batch_size = prompt_ids.shape[0] max_length = prompt_ids.shape[-1] if neg_prompt_ids is None: uncond_input = self.tokenizer( [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np" ).input_ids else: uncond_input = neg_prompt_ids negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0] context = jnp.concatenate([negative_prompt_embeds, prompt_embeds]) latents_shape = ( batch_size, self.vae.config.latent_channels, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if latents is None: latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=self.dtype) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") prng_seed, mask_prng_seed = jax.random.split(prng_seed) masked_image_latent_dist = self.vae.apply( {"params": params["vae"]}, masked_image, method=self.vae.encode ).latent_dist masked_image_latents = masked_image_latent_dist.sample(key=mask_prng_seed).transpose((0, 3, 1, 2)) masked_image_latents = self.vae.config.scaling_factor * masked_image_latents del mask_prng_seed mask = jax.image.resize(mask, (*mask.shape[:-2], *masked_image_latents.shape[-2:]), method="nearest") # 8. Check that sizes of mask, masked image and latents match num_channels_latents = self.vae.config.latent_channels num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.unet` or your `mask_image` or `image` input." ) def loop_body(step, args): latents, mask, masked_image_latents, scheduler_state = args # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes latents_input = jnp.concatenate([latents] * 2) mask_input = jnp.concatenate([mask] * 2) masked_image_latents_input = jnp.concatenate([masked_image_latents] * 2) t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step] timestep = jnp.broadcast_to(t, latents_input.shape[0]) latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t) # concat latents, mask, masked_image_latents in the channel dimension latents_input = jnp.concatenate([latents_input, mask_input, masked_image_latents_input], axis=1) # predict the noise residual noise_pred = self.unet.apply( {"params": params["unet"]}, jnp.array(latents_input), jnp.array(timestep, dtype=jnp.int32), encoder_hidden_states=context, ).sample # perform guidance noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple() return latents, mask, masked_image_latents, scheduler_state scheduler_state = self.scheduler.set_timesteps( params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape ) # scale the initial noise by the standard deviation required by the scheduler latents = latents * params["scheduler"].init_noise_sigma if DEBUG: # run with python for loop for i in range(num_inference_steps): latents, mask, masked_image_latents, scheduler_state = loop_body( i, (latents, mask, masked_image_latents, scheduler_state) ) else: latents, _, _, _ = jax.lax.fori_loop( 0, num_inference_steps, loop_body, (latents, mask, masked_image_latents, scheduler_state) ) # scale and decode the image latents with vae latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1) return image @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt_ids: jnp.array, mask: jnp.array, masked_image: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.random.KeyArray, num_inference_steps: int = 50, height: Optional[int] = None, width: Optional[int] = None, guidance_scale: Union[float, jnp.array] = 7.5, latents: jnp.array = None, neg_prompt_ids: jnp.array = None, return_dict: bool = True, jit: bool = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. latents (`jnp.array`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. tensor will ge generated by sampling using the supplied random `generator`. jit (`bool`, defaults to `False`): Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of a plain tuple. Examples: Returns: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor masked_image = jax.image.resize(masked_image, (*masked_image.shape[:-2], height, width), method="bicubic") mask = jax.image.resize(mask, (*mask.shape[:-2], height, width), method="nearest") if isinstance(guidance_scale, float): # Convert to a tensor so each device gets a copy. Follow the prompt_ids for # shape information, as they may be sharded (when `jit` is `True`), or not. guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0]) if len(prompt_ids.shape) > 2: # Assume sharded guidance_scale = guidance_scale[:, None] if jit: images = _p_generate( self, prompt_ids, mask, masked_image, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ) else: images = self._generate( prompt_ids, mask, masked_image, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ) if self.safety_checker is not None: safety_params = params["safety_checker"] images_uint8_casted = (images * 255).round().astype("uint8") num_devices, batch_size = images.shape[:2] images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3) images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit) images = np.asarray(images) # block images if any(has_nsfw_concept): for i, is_nsfw in enumerate(has_nsfw_concept): if is_nsfw: images[i] = np.asarray(images_uint8_casted[i]) images = images.reshape(num_devices, batch_size, height, width, 3) else: images = np.asarray(images) has_nsfw_concept = False if not return_dict: return (images, has_nsfw_concept) return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept) # Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation. # Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`). @partial( jax.pmap, in_axes=(None, 0, 0, 0, 0, 0, None, None, None, 0, 0, 0), static_broadcasted_argnums=(0, 6, 7, 8), ) def _p_generate( pipe, prompt_ids, mask, masked_image, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ): return pipe._generate( prompt_ids, mask, masked_image, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, ) @partial(jax.pmap, static_broadcasted_argnums=(0,)) def _p_get_has_nsfw_concepts(pipe, features, params): return pipe._get_has_nsfw_concepts(features, params) def unshard(x: jnp.ndarray): # einops.rearrange(x, 'd b ... -> (d b) ...') num_devices, batch_size = x.shape[:2] rest = x.shape[2:] return x.reshape(num_devices * batch_size, *rest) def preprocess_image(image, dtype): w, h = image.size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = jnp.array(image).astype(dtype) / 255.0 image = image[None].transpose(0, 3, 1, 2) return 2.0 * image - 1.0 def preprocess_mask(mask, dtype): w, h = mask.size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = mask.resize((w, h)) mask = jnp.array(mask.convert("L")).astype(dtype) / 255.0 mask = jnp.expand_dims(mask, axis=(0, 1)) return mask ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py ================================================ # Copyright 2023 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. import inspect from typing import Callable, List, Optional, Union import numpy as np import torch from transformers import CLIPImageProcessor, CLIPTokenizer from ...configuration_utils import FrozenDict from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from ...utils import deprecate, logging from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) class OnnxStableDiffusionPipeline(DiffusionPipeline): vae_encoder: OnnxRuntimeModel vae_decoder: OnnxRuntimeModel text_encoder: OnnxRuntimeModel tokenizer: CLIPTokenizer unet: OnnxRuntimeModel scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] safety_checker: OnnxRuntimeModel feature_extractor: CLIPImageProcessor _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae_encoder: OnnxRuntimeModel, vae_decoder: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: CLIPTokenizer, unet: OnnxRuntimeModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: OnnxRuntimeModel, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.register_to_config(requires_safety_checker=requires_safety_checker) def _encode_prompt( self, prompt: Union[str, List[str]], num_images_per_prompt: Optional[int], do_classifier_free_guidance: bool, negative_prompt: Optional[str], prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids if not np.array_equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0] prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] * batch_size elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="np", ) negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0] if do_classifier_free_guidance: negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def check_inputs( self, prompt: Union[str, List[str]], height: Optional[int], width: Optional[int], callback_steps: int, negative_prompt: Optional[str] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[np.random.RandomState] = None, latents: Optional[np.ndarray] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, np.ndarray], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`): `Image`, or tensor representing an image batch which will be upscaled. * num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`np.random.RandomState`, *optional*): One or a list of [numpy generator(s)](TODO) to make generation deterministic. latents (`np.ndarray`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if generator is None: generator = np.random # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 prompt_embeds = self._encode_prompt( prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # get the initial random noise unless the user supplied it latents_dtype = prompt_embeds.dtype latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8) if latents is None: latents = generator.randn(*latents_shape).astype(latents_dtype) elif latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") # set timesteps self.scheduler.set_timesteps(num_inference_steps) latents = latents * np.float64(self.scheduler.init_noise_sigma) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta timestep_dtype = next( (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)" ) timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype] for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t) latent_model_input = latent_model_input.cpu().numpy() # predict the noise residual timestep = np.array([t], dtype=timestep_dtype) noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds) noise_pred = noise_pred[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 scheduler_output = self.scheduler.step( torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs ) latents = scheduler_output.prev_sample.numpy() # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) latents = 1 / 0.18215 * latents # image = self.vae_decoder(latent_sample=latents)[0] # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1 image = np.concatenate( [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])] ) image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) if self.safety_checker is not None: safety_checker_input = self.feature_extractor( self.numpy_to_pil(image), return_tensors="np" ).pixel_values.astype(image.dtype) images, has_nsfw_concept = [], [] for i in range(image.shape[0]): image_i, has_nsfw_concept_i = self.safety_checker( clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1] ) images.append(image_i) has_nsfw_concept.append(has_nsfw_concept_i[0]) image = np.concatenate(images) else: has_nsfw_concept = None if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) class StableDiffusionOnnxPipeline(OnnxStableDiffusionPipeline): def __init__( self, vae_encoder: OnnxRuntimeModel, vae_decoder: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: CLIPTokenizer, unet: OnnxRuntimeModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: OnnxRuntimeModel, feature_extractor: CLIPImageProcessor, ): deprecation_message = "Please use `OnnxStableDiffusionPipeline` instead of `StableDiffusionOnnxPipeline`." deprecate("StableDiffusionOnnxPipeline", "1.0.0", deprecation_message) super().__init__( vae_encoder=vae_encoder, vae_decoder=vae_decoder, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py ================================================ # Copyright 2023 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. import inspect from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, CLIPTokenizer from ...configuration_utils import FrozenDict from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from ...utils import PIL_INTERPOLATION, deprecate, logging from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess with 8->64 def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline): r""" Pipeline for text-guided image to image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ vae_encoder: OnnxRuntimeModel vae_decoder: OnnxRuntimeModel text_encoder: OnnxRuntimeModel tokenizer: CLIPTokenizer unet: OnnxRuntimeModel scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] safety_checker: OnnxRuntimeModel feature_extractor: CLIPImageProcessor _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae_encoder: OnnxRuntimeModel, vae_decoder: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: CLIPTokenizer, unet: OnnxRuntimeModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: OnnxRuntimeModel, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt: Union[str, List[str]], num_images_per_prompt: Optional[int], do_classifier_free_guidance: bool, negative_prompt: Optional[str], prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids if not np.array_equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0] prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] * batch_size elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="np", ) negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0] if do_classifier_free_guidance: negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def check_inputs( self, prompt: Union[str, List[str]], callback_steps: int, negative_prompt: Optional[Union[str, List[str]]] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def __call__( self, prompt: Union[str, List[str]], image: Union[np.ndarray, PIL.Image.Image] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[np.random.RandomState] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, np.ndarray], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`np.ndarray` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`np.random.RandomState`, *optional*): A np.random.RandomState to make generation deterministic. prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # check inputs. Raise error if not correct self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if generator is None: generator = np.random # set timesteps self.scheduler.set_timesteps(num_inference_steps) image = preprocess(image).cpu().numpy() # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 prompt_embeds = self._encode_prompt( prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) latents_dtype = prompt_embeds.dtype image = image.astype(latents_dtype) # encode the init image into latents and scale the latents init_latents = self.vae_encoder(sample=image)[0] init_latents = 0.18215 * init_latents if isinstance(prompt, str): prompt = [prompt] if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = len(prompt) // init_latents.shape[0] init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0) elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts." ) else: init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0) # get the original timestep using init_timestep offset = self.scheduler.config.get("steps_offset", 0) init_timestep = int(num_inference_steps * strength) + offset init_timestep = min(init_timestep, num_inference_steps) timesteps = self.scheduler.timesteps.numpy()[-init_timestep] timesteps = np.array([timesteps] * batch_size * num_images_per_prompt) # add noise to latents using the timesteps noise = generator.randn(*init_latents.shape).astype(latents_dtype) init_latents = self.scheduler.add_noise( torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps) ) init_latents = init_latents.numpy() # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta latents = init_latents t_start = max(num_inference_steps - init_timestep + offset, 0) timesteps = self.scheduler.timesteps[t_start:].numpy() timestep_dtype = next( (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)" ) timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype] for i, t in enumerate(self.progress_bar(timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t) latent_model_input = latent_model_input.cpu().numpy() # predict the noise residual timestep = np.array([t], dtype=timestep_dtype) noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[ 0 ] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 scheduler_output = self.scheduler.step( torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs ) latents = scheduler_output.prev_sample.numpy() # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) latents = 1 / 0.18215 * latents # image = self.vae_decoder(latent_sample=latents)[0] # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1 image = np.concatenate( [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])] ) image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) if self.safety_checker is not None: safety_checker_input = self.feature_extractor( self.numpy_to_pil(image), return_tensors="np" ).pixel_values.astype(image.dtype) # safety_checker does not support batched inputs yet images, has_nsfw_concept = [], [] for i in range(image.shape[0]): image_i, has_nsfw_concept_i = self.safety_checker( clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1] ) images.append(image_i) has_nsfw_concept.append(has_nsfw_concept_i[0]) image = np.concatenate(images) else: has_nsfw_concept = None if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py ================================================ # Copyright 2023 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. import inspect from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, CLIPTokenizer from ...configuration_utils import FrozenDict from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from ...utils import PIL_INTERPOLATION, deprecate, logging from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name NUM_UNET_INPUT_CHANNELS = 9 NUM_LATENT_CHANNELS = 4 def prepare_mask_and_masked_image(image, mask, latents_shape): image = np.array(image.convert("RGB").resize((latents_shape[1] * 8, latents_shape[0] * 8))) image = image[None].transpose(0, 3, 1, 2) image = image.astype(np.float32) / 127.5 - 1.0 image_mask = np.array(mask.convert("L").resize((latents_shape[1] * 8, latents_shape[0] * 8))) masked_image = image * (image_mask < 127.5) mask = mask.resize((latents_shape[1], latents_shape[0]), PIL_INTERPOLATION["nearest"]) mask = np.array(mask.convert("L")) mask = mask.astype(np.float32) / 255.0 mask = mask[None, None] mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 return mask, masked_image class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ vae_encoder: OnnxRuntimeModel vae_decoder: OnnxRuntimeModel text_encoder: OnnxRuntimeModel tokenizer: CLIPTokenizer unet: OnnxRuntimeModel scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] safety_checker: OnnxRuntimeModel feature_extractor: CLIPImageProcessor _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae_encoder: OnnxRuntimeModel, vae_decoder: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: CLIPTokenizer, unet: OnnxRuntimeModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: OnnxRuntimeModel, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() logger.info("`OnnxStableDiffusionInpaintPipeline` is experimental and will very likely change in the future.") if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt: Union[str, List[str]], num_images_per_prompt: Optional[int], do_classifier_free_guidance: bool, negative_prompt: Optional[str], prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids if not np.array_equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0] prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] * batch_size elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="np", ) negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0] if do_classifier_free_guidance: negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline.check_inputs def check_inputs( self, prompt: Union[str, List[str]], height: Optional[int], width: Optional[int], callback_steps: int, negative_prompt: Optional[str] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], image: PIL.Image.Image, mask_image: PIL.Image.Image, height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[np.random.RandomState] = None, latents: Optional[np.ndarray] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, np.ndarray], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`PIL.Image.Image`): `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will be masked out with `mask_image` and repainted according to `prompt`. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`np.random.RandomState`, *optional*): A np.random.RandomState to make generation deterministic. latents (`np.ndarray`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if generator is None: generator = np.random # set timesteps self.scheduler.set_timesteps(num_inference_steps) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 prompt_embeds = self._encode_prompt( prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) num_channels_latents = NUM_LATENT_CHANNELS latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8) latents_dtype = prompt_embeds.dtype if latents is None: latents = generator.randn(*latents_shape).astype(latents_dtype) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") # prepare mask and masked_image mask, masked_image = prepare_mask_and_masked_image(image, mask_image, latents_shape[-2:]) mask = mask.astype(latents.dtype) masked_image = masked_image.astype(latents.dtype) masked_image_latents = self.vae_encoder(sample=masked_image)[0] masked_image_latents = 0.18215 * masked_image_latents # duplicate mask and masked_image_latents for each generation per prompt mask = mask.repeat(batch_size * num_images_per_prompt, 0) masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 0) mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask masked_image_latents = ( np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] unet_input_channels = NUM_UNET_INPUT_CHANNELS if num_channels_latents + num_channels_mask + num_channels_masked_image != unet_input_channels: raise ValueError( "Incorrect configuration settings! The config of `pipeline.unet` expects" f" {unet_input_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.unet` or your `mask_image` or `image` input." ) # set timesteps self.scheduler.set_timesteps(num_inference_steps) # scale the initial noise by the standard deviation required by the scheduler latents = latents * np.float64(self.scheduler.init_noise_sigma) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta timestep_dtype = next( (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)" ) timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype] for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latnets in the channel dimension latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t) latent_model_input = latent_model_input.cpu().numpy() latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1) # predict the noise residual timestep = np.array([t], dtype=timestep_dtype) noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[ 0 ] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 scheduler_output = self.scheduler.step( torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs ) latents = scheduler_output.prev_sample.numpy() # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) latents = 1 / 0.18215 * latents # image = self.vae_decoder(latent_sample=latents)[0] # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1 image = np.concatenate( [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])] ) image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) if self.safety_checker is not None: safety_checker_input = self.feature_extractor( self.numpy_to_pil(image), return_tensors="np" ).pixel_values.astype(image.dtype) # safety_checker does not support batched inputs yet images, has_nsfw_concept = [], [] for i in range(image.shape[0]): image_i, has_nsfw_concept_i = self.safety_checker( clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1] ) images.append(image_i) has_nsfw_concept.append(has_nsfw_concept_i[0]) image = np.concatenate(images) else: has_nsfw_concept = None if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py ================================================ import inspect from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, CLIPTokenizer from ...configuration_utils import FrozenDict from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from ...utils import deprecate, logging from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name def preprocess(image): w, h = image.size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL.Image.LANCZOS) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) return 2.0 * image - 1.0 def preprocess_mask(mask, scale_factor=8): mask = mask.convert("L") w, h = mask.size w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) mask = mask[None].transpose(0, 1, 2, 3) # what does this step do? mask = 1 - mask # repaint white, keep black return mask class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline): r""" Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] vae_encoder: OnnxRuntimeModel vae_decoder: OnnxRuntimeModel text_encoder: OnnxRuntimeModel tokenizer: CLIPTokenizer unet: OnnxRuntimeModel scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] safety_checker: OnnxRuntimeModel feature_extractor: CLIPImageProcessor def __init__( self, vae_encoder: OnnxRuntimeModel, vae_decoder: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: CLIPTokenizer, unet: OnnxRuntimeModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: OnnxRuntimeModel, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt: Union[str, List[str]], num_images_per_prompt: Optional[int], do_classifier_free_guidance: bool, negative_prompt: Optional[str], prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids if not np.array_equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0] prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] * batch_size elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="np", ) negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0] if do_classifier_free_guidance: negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def check_inputs( self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def __call__( self, prompt: Union[str, List[str]], image: Union[np.ndarray, PIL.Image.Image] = None, mask_image: Union[np.ndarray, PIL.Image.Image] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[np.random.RandomState] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, np.ndarray], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`nd.ndarray` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. This is the image whose masked region will be inpainted. mask_image (`nd.ndarray` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.uu strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (?) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`np.random.RandomState`, *optional*): A np.random.RandomState to make generation deterministic. prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # check inputs. Raise error if not correct self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if generator is None: generator = np.random # set timesteps self.scheduler.set_timesteps(num_inference_steps) if isinstance(image, PIL.Image.Image): image = preprocess(image) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 prompt_embeds = self._encode_prompt( prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) latents_dtype = prompt_embeds.dtype image = image.astype(latents_dtype) # encode the init image into latents and scale the latents init_latents = self.vae_encoder(sample=image)[0] init_latents = 0.18215 * init_latents # Expand init_latents for batch_size and num_images_per_prompt init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0) init_latents_orig = init_latents # preprocess mask if not isinstance(mask_image, np.ndarray): mask_image = preprocess_mask(mask_image, 8) mask_image = mask_image.astype(latents_dtype) mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0) # check sizes if not mask.shape == init_latents.shape: raise ValueError("The mask and image should be the same size!") # get the original timestep using init_timestep offset = self.scheduler.config.get("steps_offset", 0) init_timestep = int(num_inference_steps * strength) + offset init_timestep = min(init_timestep, num_inference_steps) timesteps = self.scheduler.timesteps.numpy()[-init_timestep] timesteps = np.array([timesteps] * batch_size * num_images_per_prompt) # add noise to latents using the timesteps noise = generator.randn(*init_latents.shape).astype(latents_dtype) init_latents = self.scheduler.add_noise( torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps) ) init_latents = init_latents.numpy() # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (?) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to ? in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta latents = init_latents t_start = max(num_inference_steps - init_timestep + offset, 0) timesteps = self.scheduler.timesteps[t_start:].numpy() timestep_dtype = next( (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)" ) timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype] for i, t in enumerate(self.progress_bar(timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual timestep = np.array([t], dtype=timestep_dtype) noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[ 0 ] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs ).prev_sample latents = latents.numpy() init_latents_proper = self.scheduler.add_noise( torch.from_numpy(init_latents_orig), torch.from_numpy(noise), torch.from_numpy(np.array([t])) ) init_latents_proper = init_latents_proper.numpy() latents = (init_latents_proper * mask) + (latents * (1 - mask)) # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) latents = 1 / 0.18215 * latents # image = self.vae_decoder(latent_sample=latents)[0] # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1 image = np.concatenate( [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])] ) image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) if self.safety_checker is not None: safety_checker_input = self.feature_extractor( self.numpy_to_pil(image), return_tensors="np" ).pixel_values.astype(image.dtype) # There will throw an error if use safety_checker batchsize>1 images, has_nsfw_concept = [], [] for i in range(image.shape[0]): image_i, has_nsfw_concept_i = self.safety_checker( clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1] ) images.append(image_i) has_nsfw_concept.append(has_nsfw_concept_i[0]) image = np.concatenate(images) else: has_nsfw_concept = None if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py ================================================ from logging import getLogger from typing import Any, Callable, List, Optional, Union import numpy as np import PIL import torch from ...schedulers import DDPMScheduler from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel from ..pipeline_utils import ImagePipelineOutput from . import StableDiffusionUpscalePipeline logger = getLogger(__name__) NUM_LATENT_CHANNELS = 4 NUM_UNET_INPUT_CHANNELS = 7 ORT_TO_PT_TYPE = { "float16": torch.float16, "float32": torch.float32, } def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 32 image = [np.array(i.resize((w, h)))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline): def __init__( self, vae: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: Any, unet: OnnxRuntimeModel, low_res_scheduler: DDPMScheduler, scheduler: Any, max_noise_level: int = 350, ): super().__init__( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, low_res_scheduler=low_res_scheduler, scheduler=scheduler, safety_checker=None, feature_extractor=None, watermarker=None, max_noise_level=max_noise_level, ) def __call__( self, prompt: Union[str, List[str]], image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]], num_inference_steps: int = 75, guidance_scale: float = 9.0, noise_level: int = 20, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`np.ndarray` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. noise_level TODO negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`np.random.RandomState`, *optional*): A np.random.RandomState to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`np.ndarray`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`np.ndarray`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs(prompt, image, noise_level, callback_steps) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt text_embeddings = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) latents_dtype = ORT_TO_PT_TYPE[str(text_embeddings.dtype)] # 4. Preprocess image image = preprocess(image) image = image.cpu() # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Add noise to image noise_level = torch.tensor([noise_level], dtype=torch.long, device=device) noise = torch.randn(image.shape, generator=generator, device=device, dtype=latents_dtype) image = self.low_res_scheduler.add_noise(image, noise, noise_level) batch_multiplier = 2 if do_classifier_free_guidance else 1 image = np.concatenate([image] * batch_multiplier * num_images_per_prompt) noise_level = np.concatenate([noise_level] * image.shape[0]) # 6. Prepare latent variables height, width = image.shape[2:] latents = self.prepare_latents( batch_size * num_images_per_prompt, NUM_LATENT_CHANNELS, height, width, latents_dtype, device, generator, latents, ) # 7. Check that sizes of image and latents match num_channels_image = image.shape[1] if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS: raise ValueError( "Incorrect configuration settings! The config of `pipeline.unet` expects" f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +" f" `num_channels_image`: {num_channels_image} " f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of" " `pipeline.unet` or your `image` input." ) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) timestep_dtype = next( (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)" ) timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype] # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = np.concatenate([latent_model_input, image], axis=1) # timestep to tensor timestep = np.array([t], dtype=timestep_dtype) # predict the noise residual noise_pred = self.unet( sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings, class_labels=noise_level.astype(np.int64), )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs ).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 10. Post-processing image = self.decode_latents(latents.float()) # 11. Convert to PIL if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) def decode_latents(self, latents): latents = 1 / 0.08333 * latents image = self.vae(latent_sample=latents)[0] image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) return image def _encode_prompt( self, prompt: Union[str, List[str]], device, num_images_per_prompt: Optional[int], do_classifier_free_guidance: bool, negative_prompt: Optional[str], prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) # no positional arguments to text_encoder prompt_embeds = self.text_encoder( input_ids=text_input_ids.int().to(device), # attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt) prompt_embeds = prompt_embeds.reshape(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) # if hasattr(uncond_input, "attention_mask"): # attention_mask = uncond_input.attention_mask.to(device) # else: # attention_mask = None uncond_embeddings = self.text_encoder( input_ids=uncond_input.input_ids.int().to(device), # attention_mask=attention_mask, ) uncond_embeddings = uncond_embeddings[0] if do_classifier_free_guidance: seq_len = uncond_embeddings.shape[1] # duplicate unconditional embeddings for each generation per prompt, using mps friendly method uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt) uncond_embeddings = uncond_embeddings.reshape(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = np.concatenate([uncond_embeddings, prompt_embeds]) return prompt_embeds ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import FromCkptMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionPipeline >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt).images[0] ``` """ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromCkptMixin): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py ================================================ # Copyright 2023 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. import inspect import math import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import torch from torch.nn import functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import Attention from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionAttendAndExcitePipeline >>> pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained( ... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16 ... ).to("cuda") >>> prompt = "a cat and a frog" >>> # use get_indices function to find out indices of the tokens you want to alter >>> pipe.get_indices(prompt) {0: '<|startoftext|>', 1: 'a', 2: 'cat', 3: 'and', 4: 'a', 5: 'frog', 6: '<|endoftext|>'} >>> token_indices = [2, 5] >>> seed = 6141 >>> generator = torch.Generator("cuda").manual_seed(seed) >>> images = pipe( ... prompt=prompt, ... token_indices=token_indices, ... guidance_scale=7.5, ... generator=generator, ... num_inference_steps=50, ... max_iter_to_alter=25, ... ).images >>> image = images[0] >>> image.save(f"../images/{prompt}_{seed}.png") ``` """ class AttentionStore: @staticmethod def get_empty_store(): return {"down": [], "mid": [], "up": []} def __call__(self, attn, is_cross: bool, place_in_unet: str): if self.cur_att_layer >= 0 and is_cross: if attn.shape[1] == np.prod(self.attn_res): self.step_store[place_in_unet].append(attn) self.cur_att_layer += 1 if self.cur_att_layer == self.num_att_layers: self.cur_att_layer = 0 self.between_steps() def between_steps(self): self.attention_store = self.step_store self.step_store = self.get_empty_store() def get_average_attention(self): average_attention = self.attention_store return average_attention def aggregate_attention(self, from_where: List[str]) -> torch.Tensor: """Aggregates the attention across the different layers and heads at the specified resolution.""" out = [] attention_maps = self.get_average_attention() for location in from_where: for item in attention_maps[location]: cross_maps = item.reshape(-1, self.attn_res[0], self.attn_res[1], item.shape[-1]) out.append(cross_maps) out = torch.cat(out, dim=0) out = out.sum(0) / out.shape[0] return out def reset(self): self.cur_att_layer = 0 self.step_store = self.get_empty_store() self.attention_store = {} def __init__(self, attn_res): """ Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion process """ self.num_att_layers = -1 self.cur_att_layer = 0 self.step_store = self.get_empty_store() self.attention_store = {} self.curr_step_index = 0 self.attn_res = attn_res class AttendExciteAttnProcessor: def __init__(self, attnstore, place_in_unet): super().__init__() self.attnstore = attnstore self.place_in_unet = place_in_unet def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) query = attn.to_q(hidden_states) is_cross = encoder_hidden_states is not None encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) # only need to store attention maps during the Attend and Excite process if attention_probs.requires_grad: self.attnstore(attention_probs, is_cross, self.place_in_unet) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) return hidden_states class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion and Attend and Excite. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, indices, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) indices_is_list_ints = isinstance(indices, list) and isinstance(indices[0], int) indices_is_list_list_ints = ( isinstance(indices, list) and isinstance(indices[0], list) and isinstance(indices[0][0], int) ) if not indices_is_list_ints and not indices_is_list_list_ints: raise TypeError("`indices` must be a list of ints or a list of a list of ints") if indices_is_list_ints: indices_batch_size = 1 elif indices_is_list_list_ints: indices_batch_size = len(indices) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if indices_batch_size != prompt_batch_size: raise ValueError( f"indices batch size must be same as prompt batch size. indices batch size: {indices_batch_size}, prompt batch size: {prompt_batch_size}" ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @staticmethod def _compute_max_attention_per_index( attention_maps: torch.Tensor, indices: List[int], ) -> List[torch.Tensor]: """Computes the maximum attention value for each of the tokens we wish to alter.""" attention_for_text = attention_maps[:, :, 1:-1] attention_for_text *= 100 attention_for_text = torch.nn.functional.softmax(attention_for_text, dim=-1) # Shift indices since we removed the first token indices = [index - 1 for index in indices] # Extract the maximum values max_indices_list = [] for i in indices: image = attention_for_text[:, :, i] smoothing = GaussianSmoothing().to(attention_maps.device) input = F.pad(image.unsqueeze(0).unsqueeze(0), (1, 1, 1, 1), mode="reflect") image = smoothing(input).squeeze(0).squeeze(0) max_indices_list.append(image.max()) return max_indices_list def _aggregate_and_get_max_attention_per_token( self, indices: List[int], ): """Aggregates the attention for each token and computes the max activation value for each token to alter.""" attention_maps = self.attention_store.aggregate_attention( from_where=("up", "down", "mid"), ) max_attention_per_index = self._compute_max_attention_per_index( attention_maps=attention_maps, indices=indices, ) return max_attention_per_index @staticmethod def _compute_loss(max_attention_per_index: List[torch.Tensor]) -> torch.Tensor: """Computes the attend-and-excite loss using the maximum attention value for each token.""" losses = [max(0, 1.0 - curr_max) for curr_max in max_attention_per_index] loss = max(losses) return loss @staticmethod def _update_latent(latents: torch.Tensor, loss: torch.Tensor, step_size: float) -> torch.Tensor: """Update the latent according to the computed loss.""" grad_cond = torch.autograd.grad(loss.requires_grad_(True), [latents], retain_graph=True)[0] latents = latents - step_size * grad_cond return latents def _perform_iterative_refinement_step( self, latents: torch.Tensor, indices: List[int], loss: torch.Tensor, threshold: float, text_embeddings: torch.Tensor, step_size: float, t: int, max_refinement_steps: int = 20, ): """ Performs the iterative latent refinement introduced in the paper. Here, we continuously update the latent code according to our loss objective until the given threshold is reached for all tokens. """ iteration = 0 target_loss = max(0, 1.0 - threshold) while loss > target_loss: iteration += 1 latents = latents.clone().detach().requires_grad_(True) self.unet(latents, t, encoder_hidden_states=text_embeddings).sample self.unet.zero_grad() # Get max activation value for each subject token max_attention_per_index = self._aggregate_and_get_max_attention_per_token( indices=indices, ) loss = self._compute_loss(max_attention_per_index) if loss != 0: latents = self._update_latent(latents, loss, step_size) logger.info(f"\t Try {iteration}. loss: {loss}") if iteration >= max_refinement_steps: logger.info(f"\t Exceeded max number of iterations ({max_refinement_steps})! ") break # Run one more time but don't compute gradients and update the latents. # We just need to compute the new loss - the grad update will occur below latents = latents.clone().detach().requires_grad_(True) _ = self.unet(latents, t, encoder_hidden_states=text_embeddings).sample self.unet.zero_grad() # Get max activation value for each subject token max_attention_per_index = self._aggregate_and_get_max_attention_per_token( indices=indices, ) loss = self._compute_loss(max_attention_per_index) logger.info(f"\t Finished with loss of: {loss}") return loss, latents, max_attention_per_index def register_attention_control(self): attn_procs = {} cross_att_count = 0 for name in self.unet.attn_processors.keys(): if name.startswith("mid_block"): place_in_unet = "mid" elif name.startswith("up_blocks"): place_in_unet = "up" elif name.startswith("down_blocks"): place_in_unet = "down" else: continue cross_att_count += 1 attn_procs[name] = AttendExciteAttnProcessor(attnstore=self.attention_store, place_in_unet=place_in_unet) self.unet.set_attn_processor(attn_procs) self.attention_store.num_att_layers = cross_att_count def get_indices(self, prompt: str) -> Dict[str, int]: """Utility function to list the indices of the tokens you wish to alte""" ids = self.tokenizer(prompt).input_ids indices = {i: tok for tok, i in zip(self.tokenizer.convert_ids_to_tokens(ids), range(len(ids)))} return indices @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]], token_indices: Union[List[int], List[List[int]]], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: int = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, max_iter_to_alter: int = 25, thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8}, scale_factor: int = 20, attn_res: Optional[Tuple[int]] = (16, 16), ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. token_indices (`List[int]`): The token indices to alter with attend-and-excite. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). max_iter_to_alter (`int`, *optional*, defaults to `25`): Number of denoising steps to apply attend-and-excite. The first denoising steps are where the attend-and-excite is applied. I.e. if `max_iter_to_alter` is 25 and there are a total of `30` denoising steps, the first 25 denoising steps will apply attend-and-excite and the last 5 will not apply attend-and-excite. thresholds (`dict`, *optional*, defaults to `{0: 0.05, 10: 0.5, 20: 0.8}`): Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in. scale_factor (`int`, *optional*, default to 20): Scale factor that controls the step size of each Attend and Excite update. attn_res (`tuple`, *optional*, default computed from width and height): The 2D resolution of the semantic attention map. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. :type attention_store: object """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, token_indices, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) if attn_res is None: attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32)) self.attention_store = AttentionStore(attn_res) self.register_attention_control() # default config for step size from original repo scale_range = np.linspace(1.0, 0.5, len(self.scheduler.timesteps)) step_size = scale_factor * np.sqrt(scale_range) text_embeddings = ( prompt_embeds[batch_size * num_images_per_prompt :] if do_classifier_free_guidance else prompt_embeds ) if isinstance(token_indices[0], int): token_indices = [token_indices] indices = [] for ind in token_indices: indices = indices + [ind] * num_images_per_prompt # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Attend and excite process with torch.enable_grad(): latents = latents.clone().detach().requires_grad_(True) updated_latents = [] for latent, index, text_embedding in zip(latents, indices, text_embeddings): # Forward pass of denoising with text conditioning latent = latent.unsqueeze(0) text_embedding = text_embedding.unsqueeze(0) self.unet( latent, t, encoder_hidden_states=text_embedding, cross_attention_kwargs=cross_attention_kwargs, ).sample self.unet.zero_grad() # Get max activation value for each subject token max_attention_per_index = self._aggregate_and_get_max_attention_per_token( indices=index, ) loss = self._compute_loss(max_attention_per_index=max_attention_per_index) # If this is an iterative refinement step, verify we have reached the desired threshold for all if i in thresholds.keys() and loss > 1.0 - thresholds[i]: loss, latent, max_attention_per_index = self._perform_iterative_refinement_step( latents=latent, indices=index, loss=loss, threshold=thresholds[i], text_embeddings=text_embedding, step_size=step_size[i], t=t, ) # Perform gradient update if i < max_iter_to_alter: if loss != 0: latent = self._update_latent( latents=latent, loss=loss, step_size=step_size[i], ) logger.info(f"Iteration {i} | Loss: {loss:0.4f}") updated_latents.append(latent) latents = torch.cat(updated_latents, dim=0) # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 8. Post-processing if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) class GaussianSmoothing(torch.nn.Module): """ Arguments: Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input using a depthwise convolution. channels (int, sequence): Number of channels of the input tensors. Output will have this number of channels as well. kernel_size (int, sequence): Size of the gaussian kernel. sigma (float, sequence): Standard deviation of the gaussian kernel. dim (int, optional): The number of dimensions of the data. Default value is 2 (spatial). """ # channels=1, kernel_size=kernel_size, sigma=sigma, dim=2 def __init__( self, channels: int = 1, kernel_size: int = 3, sigma: float = 0.5, dim: int = 2, ): super().__init__() if isinstance(kernel_size, int): kernel_size = [kernel_size] * dim if isinstance(sigma, float): sigma = [sigma] * dim # The gaussian kernel is the product of the # gaussian function of each dimension. kernel = 1 meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size]) for size, std, mgrid in zip(kernel_size, sigma, meshgrids): mean = (size - 1) / 2 kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2)) # Make sure sum of values in gaussian kernel equals 1. kernel = kernel / torch.sum(kernel) # Reshape to depthwise convolutional weight kernel = kernel.view(1, 1, *kernel.size()) kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) self.register_buffer("weight", kernel) self.groups = channels if dim == 1: self.conv = F.conv1d elif dim == 2: self.conv = F.conv2d elif dim == 3: self.conv = F.conv3d else: raise RuntimeError("Only 1, 2 and 3 dimensions are supported. Received {}.".format(dim)) def forward(self, input): """ Arguments: Apply gaussian filter to input. input (torch.Tensor): Input to apply gaussian filter on. Returns: filtered (torch.Tensor): Filtered output. """ return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py ================================================ # Copyright 2023 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. # NOTE: This file is deprecated and will be removed in a future version. # It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works from ...utils import deprecate from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401 from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401 deprecate( "stable diffusion controlnet", "0.22.0", "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.", standard_warn=False, stacklevel=3, ) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py ================================================ # Copyright 2023 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. import contextlib import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import PIL_INTERPOLATION, deprecate, is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, depth_estimator: DPTForDepthEstimation, feature_extractor: DPTFeatureExtractor, ): super().__init__() is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, depth_estimator=depth_estimator, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.depth_estimator]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device): if isinstance(image, PIL.Image.Image): image = [image] else: image = list(image) if isinstance(image[0], PIL.Image.Image): width, height = image[0].size else: height, width = image[0].shape[-2:] if depth_map is None: pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values pixel_values = pixel_values.to(device=device) # The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16. # So we use `torch.autocast` here for half precision inference. context_manger = torch.autocast("cuda", dtype=dtype) if device.type == "cuda" else contextlib.nullcontext() with context_manger: depth_map = self.depth_estimator(pixel_values).predicted_depth else: depth_map = depth_map.to(device=device, dtype=dtype) depth_map = torch.nn.functional.interpolate( depth_map.unsqueeze(1), size=(height // self.vae_scale_factor, width // self.vae_scale_factor), mode="bicubic", align_corners=False, ) depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0 depth_map = depth_map.to(dtype) # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if depth_map.shape[0] < batch_size: repeat_by = batch_size // depth_map.shape[0] depth_map = depth_map.repeat(repeat_by, 1, 1, 1) depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map return depth_map @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, depth_map: Optional[torch.FloatTensor] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> import torch >>> import requests >>> from PIL import Image >>> from diffusers import StableDiffusionDepth2ImgPipeline >>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( ... "stabilityai/stable-diffusion-2-depth", ... torch_dtype=torch.float16, ... ) >>> pipe.to("cuda") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> init_image = Image.open(requests.get(url, stream=True).raw) >>> prompt = "two tigers" >>> n_propmt = "bad, deformed, ugly, bad anotomy" >>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0] ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs( prompt, strength, callback_steps, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) if image is None: raise ValueError("`image` input cannot be undefined.") # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare depth mask depth_mask = self.prepare_depth_map( image, depth_map, batch_size * num_images_per_prompt, do_classifier_free_guidance, prompt_embeds.dtype, device, ) # 5. Preprocess image image = preprocess(image) # 6. Set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 7. Prepare latent variables latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator ) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds, return_dict=False)[ 0 ] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_diffedit.py ================================================ # Copyright 2023 DiffEdit Authors and Pix2Pix Zero Authors and 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. import inspect import warnings from dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMInverseScheduler, KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, BaseOutput, deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class DiffEditInversionPipelineOutput(BaseOutput): """ Output class for Stable Diffusion pipelines. Args: latents (`torch.FloatTensor`) inverted latents tensor images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `num_timesteps * batch_size` or numpy array of shape `(num_timesteps, batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. """ latents: torch.FloatTensor images: Union[List[PIL.Image.Image], np.ndarray] EXAMPLE_DOC_STRING = """ ```py >>> import PIL >>> import requests >>> import torch >>> from io import BytesIO >>> from diffusers import StableDiffusionDiffEditPipeline >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png" >>> init_image = download_image(img_url).resize((768, 768)) >>> pipe = StableDiffusionDiffEditPipeline.from_pretrained( ... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config) >>> pipeline.enable_model_cpu_offload() >>> mask_prompt = "A bowl of fruits" >>> prompt = "A bowl of pears" >>> mask_image = pipe.generate_mask(image=init_image, source_prompt=prompt, target_prompt=mask_prompt) >>> image_latents = pipe.invert(image=init_image, prompt=mask_prompt).latents >>> image = pipe(prompt=prompt, mask_image=mask_image, image_latents=image_latents).images[0] ``` """ EXAMPLE_INVERT_DOC_STRING = """ ```py >>> import PIL >>> import requests >>> import torch >>> from io import BytesIO >>> from diffusers import StableDiffusionDiffEditPipeline >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png" >>> init_image = download_image(img_url).resize((768, 768)) >>> pipe = StableDiffusionDiffEditPipeline.from_pretrained( ... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config) >>> pipeline.enable_model_cpu_offload() >>> prompt = "A bowl of fruits" >>> inverted_latents = pipe.invert(image=init_image, prompt=prompt).latents ``` """ def auto_corr_loss(hidden_states, generator=None): reg_loss = 0.0 for i in range(hidden_states.shape[0]): for j in range(hidden_states.shape[1]): noise = hidden_states[i : i + 1, j : j + 1, :, :] while True: roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item() reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2 reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2 if noise.shape[2] <= 8: break noise = torch.nn.functional.avg_pool2d(noise, kernel_size=2) return reg_loss def kl_divergence(hidden_states): return hidden_states.var() + hidden_states.mean() ** 2 - 1 - torch.log(hidden_states.var() + 1e-7) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image def preprocess_mask(mask, batch_size: int = 1): if not isinstance(mask, torch.Tensor): # preprocess mask if isinstance(mask, PIL.Image.Image) or isinstance(mask, np.ndarray): mask = [mask] if isinstance(mask, list): if isinstance(mask[0], PIL.Image.Image): mask = [np.array(m.convert("L")).astype(np.float32) / 255.0 for m in mask] if isinstance(mask[0], np.ndarray): mask = np.stack(mask, axis=0) if mask[0].ndim < 3 else np.concatenate(mask, axis=0) mask = torch.from_numpy(mask) elif isinstance(mask[0], torch.Tensor): mask = torch.stack(mask, dim=0) if mask[0].ndim < 3 else torch.cat(mask, dim=0) # Batch and add channel dim for single mask if mask.ndim == 2: mask = mask.unsqueeze(0).unsqueeze(0) # Batch single mask or add channel dim if mask.ndim == 3: # Single batched mask, no channel dim or single mask not batched but channel dim if mask.shape[0] == 1: mask = mask.unsqueeze(0) # Batched masks no channel dim else: mask = mask.unsqueeze(1) # Check mask shape if batch_size > 1: if mask.shape[0] == 1: mask = torch.cat([mask] * batch_size) elif mask.shape[0] > 1 and mask.shape[0] != batch_size: raise ValueError( f"`mask_image` with batch size {mask.shape[0]} cannot be broadcasted to batch size {batch_size} " f"inferred by prompt inputs" ) if mask.shape[1] != 1: raise ValueError(f"`mask_image` must have 1 channel, but has {mask.shape[1]} channels") # Check mask is in [0, 1] if mask.min() < 0 or mask.max() > 1: raise ValueError("`mask_image` should be in [0, 1] range") # Binarize mask mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 return mask class StableDiffusionDiffEditPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): r""" Pipeline for text-guided image inpainting using Stable Diffusion using DiffEdit. *This is an experimental feature*. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. inverse_scheduler (`[DDIMInverseScheduler]`): A scheduler to be used in combination with `unet` to fill in the unmasked part of the input latents safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor", "inverse_scheduler"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, inverse_scheduler: DDIMInverseScheduler, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration" " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make" " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to" " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face" " Hub, it would be very nice if you could open a Pull request for the" " `scheduler/scheduler_config.json` file" ) deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["skip_prk_steps"] = True scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, inverse_scheduler=inverse_scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (strength is None) or (strength is not None and (strength < 0 or strength > 1)): raise ValueError( f"The value of `strength` should in [0.0, 1.0] but is, but is {strength} of type {type(strength)}." ) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def check_source_inputs( self, source_prompt=None, source_negative_prompt=None, source_prompt_embeds=None, source_negative_prompt_embeds=None, ): if source_prompt is not None and source_prompt_embeds is not None: raise ValueError( f"Cannot forward both `source_prompt`: {source_prompt} and `source_prompt_embeds`: {source_prompt_embeds}." " Please make sure to only forward one of the two." ) elif source_prompt is None and source_prompt_embeds is None: raise ValueError( "Provide either `source_image` or `source_prompt_embeds`. Cannot leave all both of the arguments undefined." ) elif source_prompt is not None and ( not isinstance(source_prompt, str) and not isinstance(source_prompt, list) ): raise ValueError(f"`source_prompt` has to be of type `str` or `list` but is {type(source_prompt)}") if source_negative_prompt is not None and source_negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `source_negative_prompt`: {source_negative_prompt} and `source_negative_prompt_embeds`:" f" {source_negative_prompt_embeds}. Please make sure to only forward one of the two." ) if source_prompt_embeds is not None and source_negative_prompt_embeds is not None: if source_prompt_embeds.shape != source_negative_prompt_embeds.shape: raise ValueError( "`source_prompt_embeds` and `source_negative_prompt_embeds` must have the same shape when passed" f" directly, but got: `source_prompt_embeds` {source_prompt_embeds.shape} !=" f" `source_negative_prompt_embeds` {source_negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start def get_inverse_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) # safety for t_start overflow to prevent empty timsteps slice if t_start == 0: return self.inverse_scheduler.timesteps, num_inference_steps timesteps = self.inverse_scheduler.timesteps[:-t_start] return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.StableDiffusionPix2PixZeroPipeline.prepare_image_latents def prepare_image_latents(self, image, batch_size, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): latents = [self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)] latents = torch.cat(latents, dim=0) else: latents = self.vae.encode(image).latent_dist.sample(generator) latents = self.vae.config.scaling_factor * latents if batch_size != latents.shape[0]: if batch_size % latents.shape[0] == 0: # expand image_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_latents_per_image = batch_size // latents.shape[0] latents = torch.cat([latents] * additional_latents_per_image, dim=0) else: raise ValueError( f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts." ) else: latents = torch.cat([latents], dim=0) return latents def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int): pred_type = self.inverse_scheduler.config.prediction_type alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep] beta_prod_t = 1 - alpha_prod_t if pred_type == "epsilon": return model_output elif pred_type == "sample": return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5) elif pred_type == "v_prediction": return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`" ) @torch.no_grad() def generate_mask( self, image: Union[torch.FloatTensor, PIL.Image.Image] = None, target_prompt: Optional[Union[str, List[str]]] = None, target_negative_prompt: Optional[Union[str, List[str]]] = None, target_prompt_embeds: Optional[torch.FloatTensor] = None, target_negative_prompt_embeds: Optional[torch.FloatTensor] = None, source_prompt: Optional[Union[str, List[str]]] = None, source_negative_prompt: Optional[Union[str, List[str]]] = None, source_prompt_embeds: Optional[torch.FloatTensor] = None, source_negative_prompt_embeds: Optional[torch.FloatTensor] = None, num_maps_per_mask: Optional[int] = 10, mask_encode_strength: Optional[float] = 0.5, mask_thresholding_ratio: Optional[float] = 3.0, num_inference_steps: int = 50, guidance_scale: float = 7.5, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "np", cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function used to generate a latent mask given a mask prompt, a target prompt, and an image. Args: image (`PIL.Image.Image`): `Image`, or tensor representing an image batch which will be used for computing the mask. target_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the semantic mask generation. If not defined, one has to pass `prompt_embeds`. instead. target_negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). target_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. target_negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. source_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the semantic mask generation using the method in [DiffEdit: Diffusion-Based Semantic Image Editing with Mask Guidance](https://arxiv.org/pdf/2210.11427.pdf). If not defined, one has to pass `source_prompt_embeds` or `source_image` instead. source_negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the semantic mask generation away from using the method in [DiffEdit: Diffusion-Based Semantic Image Editing with Mask Guidance](https://arxiv.org/pdf/2210.11427.pdf). If not defined, one has to pass `source_negative_prompt_embeds` or `source_image` instead. source_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings to guide the semantic mask generation. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `source_prompt` input argument. source_negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings to negatively guide the semantic mask generation. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `source_negative_prompt` input argument. num_maps_per_mask (`int`, *optional*, defaults to 10): The number of noise maps sampled to generate the semantic mask using the method in [DiffEdit: Diffusion-Based Semantic Image Editing with Mask Guidance](https://arxiv.org/pdf/2210.11427.pdf). mask_encode_strength (`float`, *optional*, defaults to 0.5): Conceptually, the strength of the noise maps sampled to generate the semantic mask using the method in [DiffEdit: Diffusion-Based Semantic Image Editing with Mask Guidance]( https://arxiv.org/pdf/2210.11427.pdf). Must be between 0 and 1. mask_thresholding_ratio (`float`, *optional*, defaults to 3.0): The maximum multiple of the mean absolute difference used to clamp the semantic guidance map before mask binarization. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: `List[PIL.Image.Image]` or `np.array`: `List[PIL.Image.Image]` if `output_type` is `"pil"`, otherwise a `np.array`. When returning a `List[PIL.Image.Image]`, the list will consist of a batch of single-channel binary image with dimensions `(height // self.vae_scale_factor, width // self.vae_scale_factor)`, otherwise the `np.array` will have shape `(batch_size, height // self.vae_scale_factor, width // self.vae_scale_factor)`. """ # 1. Check inputs (Provide dummy argument for callback_steps) self.check_inputs( target_prompt, mask_encode_strength, 1, target_negative_prompt, target_prompt_embeds, target_negative_prompt_embeds, ) self.check_source_inputs( source_prompt, source_negative_prompt, source_prompt_embeds, source_negative_prompt_embeds, ) if (num_maps_per_mask is None) or ( num_maps_per_mask is not None and (not isinstance(num_maps_per_mask, int) or num_maps_per_mask <= 0) ): raise ValueError( f"`num_maps_per_mask` has to be a positive integer but is {num_maps_per_mask} of type" f" {type(num_maps_per_mask)}." ) if mask_thresholding_ratio is None or mask_thresholding_ratio <= 0: raise ValueError( f"`mask_thresholding_ratio` has to be positive but is {mask_thresholding_ratio} of type" f" {type(mask_thresholding_ratio)}." ) # 2. Define call parameters if target_prompt is not None and isinstance(target_prompt, str): batch_size = 1 elif target_prompt is not None and isinstance(target_prompt, list): batch_size = len(target_prompt) else: batch_size = target_prompt_embeds.shape[0] if cross_attention_kwargs is None: cross_attention_kwargs = {} device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompts target_prompt_embeds = self._encode_prompt( target_prompt, device, num_maps_per_mask, do_classifier_free_guidance, target_negative_prompt, prompt_embeds=target_prompt_embeds, negative_prompt_embeds=target_negative_prompt_embeds, ) source_prompt_embeds = self._encode_prompt( source_prompt, device, num_maps_per_mask, do_classifier_free_guidance, source_negative_prompt, prompt_embeds=source_prompt_embeds, negative_prompt_embeds=source_negative_prompt_embeds, ) # 4. Preprocess image image = preprocess(image).repeat_interleave(num_maps_per_mask, dim=0) # 5. Set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, _ = self.get_timesteps(num_inference_steps, mask_encode_strength, device) encode_timestep = timesteps[0] # 6. Prepare image latents and add noise with specified strength image_latents = self.prepare_image_latents( image, batch_size * num_maps_per_mask, self.vae.dtype, device, generator ) noise = randn_tensor(image_latents.shape, generator=generator, device=device, dtype=self.vae.dtype) image_latents = self.scheduler.add_noise(image_latents, noise, encode_timestep) latent_model_input = torch.cat([image_latents] * (4 if do_classifier_free_guidance else 2)) latent_model_input = self.scheduler.scale_model_input(latent_model_input, encode_timestep) # 7. Predict the noise residual prompt_embeds = torch.cat([source_prompt_embeds, target_prompt_embeds]) noise_pred = self.unet( latent_model_input, encode_timestep, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample if do_classifier_free_guidance: noise_pred_neg_src, noise_pred_source, noise_pred_uncond, noise_pred_target = noise_pred.chunk(4) noise_pred_source = noise_pred_neg_src + guidance_scale * (noise_pred_source - noise_pred_neg_src) noise_pred_target = noise_pred_uncond + guidance_scale * (noise_pred_target - noise_pred_uncond) else: noise_pred_source, noise_pred_target = noise_pred.chunk(2) # 8. Compute the mask from the absolute difference of predicted noise residuals # TODO: Consider smoothing mask guidance map mask_guidance_map = ( torch.abs(noise_pred_target - noise_pred_source) .reshape(batch_size, num_maps_per_mask, *noise_pred_target.shape[-3:]) .mean([1, 2]) ) clamp_magnitude = mask_guidance_map.mean() * mask_thresholding_ratio semantic_mask_image = mask_guidance_map.clamp(0, clamp_magnitude) / clamp_magnitude semantic_mask_image = torch.where(semantic_mask_image <= 0.5, 0, 1) mask_image = semantic_mask_image.cpu().numpy() # 9. Convert to Numpy array or PIL. if output_type == "pil": mask_image = self.image_processor.numpy_to_pil(mask_image) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() return mask_image @torch.no_grad() @replace_example_docstring(EXAMPLE_INVERT_DOC_STRING) def invert( self, prompt: Optional[Union[str, List[str]]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, num_inference_steps: int = 50, inpaint_strength: float = 0.8, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, decode_latents: bool = False, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, lambda_auto_corr: float = 20.0, lambda_kl: float = 20.0, num_reg_steps: int = 0, num_auto_corr_rolls: int = 5, ): r""" Function used to generate inverted latents given a prompt and image. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`PIL.Image.Image`): `Image`, or tensor representing an image batch to produce the inverted latents, guided by `prompt`. inpaint_strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how far into the noising process to run latent inversion. Must be between 0 and 1. When `strength` is 1, the inversion process will be run for the full number of iterations specified in `num_inference_steps`. `image` will be used as a reference for the inversion process, adding more noise the larger the `strength`. If `strength` is 0, no inpainting will occur. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. decode_latents (`bool`, *optional*, defaults to `False`): Whether or not to decode the inverted latents into a generated image. Setting this argument to `True` will decode all inverted latents for each timestep into a list of generated images. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.DiffEditInversionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). lambda_auto_corr (`float`, *optional*, defaults to 20.0): Lambda parameter to control auto correction lambda_kl (`float`, *optional*, defaults to 20.0): Lambda parameter to control Kullback–Leibler divergence output num_reg_steps (`int`, *optional*, defaults to 0): Number of regularization loss steps num_auto_corr_rolls (`int`, *optional*, defaults to 5): Number of auto correction roll steps Examples: Returns: [`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] if `return_dict` is `True`, otherwise a `tuple`. When returning a tuple, the first element is the inverted latents tensors ordered by increasing noise, and then second is the corresponding decoded images if `decode_latents` is `True`, otherwise `None`. """ # 1. Check inputs self.check_inputs( prompt, inpaint_strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) if image is None: raise ValueError("`image` input cannot be undefined.") # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if cross_attention_kwargs is None: cross_attention_kwargs = {} device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Preprocess image image = preprocess(image) # 4. Prepare latent variables num_images_per_prompt = 1 latents = self.prepare_image_latents( image, batch_size * num_images_per_prompt, self.vae.dtype, device, generator ) # 5. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 6. Prepare timesteps self.inverse_scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_inverse_timesteps(num_inference_steps, inpaint_strength, device) # 7. Noising loop where we obtain the intermediate noised latent image for each timestep. num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order inverted_latents = [latents.detach().clone()] with self.progress_bar(total=num_inference_steps - 1) as progress_bar: for i, t in enumerate(timesteps[:-1]): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # regularization of the noise prediction (not in original code or paper but borrowed from Pix2PixZero) if num_reg_steps > 0: with torch.enable_grad(): for _ in range(num_reg_steps): if lambda_auto_corr > 0: for _ in range(num_auto_corr_rolls): var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True) # Derive epsilon from model output before regularizing to IID standard normal var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t) l_ac = auto_corr_loss(var_epsilon, generator=generator) l_ac.backward() grad = var.grad.detach() / num_auto_corr_rolls noise_pred = noise_pred - lambda_auto_corr * grad if lambda_kl > 0: var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True) # Derive epsilon from model output before regularizing to IID standard normal var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t) l_kld = kl_divergence(var_epsilon) l_kld.backward() grad = var.grad.detach() noise_pred = noise_pred - lambda_kl * grad noise_pred = noise_pred.detach() # compute the previous noisy sample x_t -> x_t-1 latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample inverted_latents.append(latents.detach().clone()) # call the callback, if provided if i == len(timesteps) - 1 or ( (i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0 ): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) assert len(inverted_latents) == len(timesteps) latents = torch.stack(list(reversed(inverted_latents)), 1) # 8. Post-processing image = None if decode_latents: image = self.decode_latents(latents.flatten(0, 1).detach()) # 9. Convert to PIL. if decode_latents and output_type == "pil": image = self.image_processor.numpy_to_pil(image) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (latents, image) return DiffEditInversionPipelineOutput(latents=latents, images=image) @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Optional[Union[str, List[str]]] = None, mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None, image_latents: torch.FloatTensor = None, inpaint_strength: Optional[float] = 0.8, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask the generated image. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, 1, H, W)`. image_latents (`PIL.Image.Image` or `torch.FloatTensor`): Partially noised image latents from the inversion process to be used as inputs for image generation. inpaint_strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength` is 1, the denoising process will be run on the masked area for the full number of iterations specified in `num_inference_steps`. `image_latents` will be used as a reference for the masked area, adding more noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs( prompt, inpaint_strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) if mask_image is None: raise ValueError( "`mask_image` input cannot be undefined. Use `generate_mask()` to compute `mask_image` from text prompts." ) if image_latents is None: raise ValueError( "`image_latents` input cannot be undefined. Use `invert()` to compute `image_latents` from input images." ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if cross_attention_kwargs is None: cross_attention_kwargs = {} device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Preprocess mask mask_image = preprocess_mask(mask_image, batch_size) latent_height, latent_width = mask_image.shape[-2:] mask_image = torch.cat([mask_image] * num_images_per_prompt) mask_image = mask_image.to(device=device, dtype=prompt_embeds.dtype) # 5. Set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, inpaint_strength, device) # 6. Preprocess image latents image_latents = preprocess(image_latents) latent_shape = (self.vae.config.latent_channels, latent_height, latent_width) if image_latents.shape[-3:] != latent_shape: raise ValueError( f"Each latent image in `image_latents` must have shape {latent_shape}, " f"but has shape {image_latents.shape[-3:]}" ) if image_latents.ndim == 4: image_latents = image_latents.reshape(batch_size, len(timesteps), *latent_shape) if image_latents.shape[:2] != (batch_size, len(timesteps)): raise ValueError( f"`image_latents` must have batch size {batch_size} with latent images from {len(timesteps)} timesteps, " f"but has batch size {image_latents.shape[0]} with latent images from {image_latents.shape[1]} timesteps." ) image_latents = image_latents.transpose(0, 1).repeat_interleave(num_images_per_prompt, dim=1) image_latents = image_latents.to(device=device, dtype=prompt_embeds.dtype) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop latents = image_latents[0].detach().clone() num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # mask with inverted latents from appropriate timestep - use original image latent for last step latents = latents * mask_image + image_latents[i] * (1 - mask_image) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Union import PIL import torch from packaging import version from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import deprecate, is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name class StableDiffusionImageVariationPipeline(DiffusionPipeline): r""" Pipeline to generate variations from an input image using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. image_encoder ([`CLIPVisionModelWithProjection`]): Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ # TODO: feature_extractor is required to encode images (if they are in PIL format), # we should give a descriptive message if the pipeline doesn't have one. _optional_components = ["safety_checker"] def __init__( self, vae: AutoencoderKL, image_encoder: CLIPVisionModelWithProjection, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warn( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, image_encoder=image_encoder, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.image_encoder, self.vae, self.safety_checker]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeddings = self.image_encoder(image).image_embeds image_embeddings = image_embeddings.unsqueeze(1) # duplicate image embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = image_embeddings.shape image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1) image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) if do_classifier_free_guidance: negative_prompt_embeds = torch.zeros_like(image_embeddings) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings]) return image_embeddings # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs(self, image, height, width, callback_steps): if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The image or images to guide the image generation. If you provide a tensor, it needs to comply with the configuration of [this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json) `CLIPImageProcessor` height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(image, height, width, callback_steps) # 2. Define call parameters if isinstance(image, PIL.Image.Image): batch_size = 1 elif isinstance(image, list): batch_size = len(image) else: batch_size = image.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input image image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, image_embeddings.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import FromCkptMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import requests >>> import torch >>> from PIL import Image >>> from io import BytesIO >>> from diffusers import StableDiffusionImg2ImgPipeline >>> device = "cuda" >>> model_id_or_path = "runwayml/stable-diffusion-v1-5" >>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) >>> pipe = pipe.to(device) >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> init_image = Image.open(BytesIO(response.content)).convert("RGB") >>> init_image = init_image.resize((768, 512)) >>> prompt = "A fantasy landscape, trending on artstation" >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images >>> images[0].save("fantasy_landscape.png") ``` """ def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class StableDiffusionImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromCkptMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Preprocess image image = self.image_processor.preprocess(image) # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False): """ Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the ``image`` and ``1`` for the ``mask``. The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be binarized (``mask > 0.5``) and cast to ``torch.float32`` too. Args: image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint. It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``. mask (_type_): The mask to apply to the image, i.e. regions to inpaint. It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``. Raises: ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not (ot the other way around). Returns: tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4 dimensions: ``batch x channels x height x width``. """ if image is None: raise ValueError("`image` input cannot be undefined.") if mask is None: raise ValueError("`mask_image` input cannot be undefined.") if isinstance(image, torch.Tensor): if not isinstance(mask, torch.Tensor): raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not") # Batch single image if image.ndim == 3: assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)" image = image.unsqueeze(0) # Batch and add channel dim for single mask if mask.ndim == 2: mask = mask.unsqueeze(0).unsqueeze(0) # Batch single mask or add channel dim if mask.ndim == 3: # Single batched mask, no channel dim or single mask not batched but channel dim if mask.shape[0] == 1: mask = mask.unsqueeze(0) # Batched masks no channel dim else: mask = mask.unsqueeze(1) assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" # Check image is in [-1, 1] if image.min() < -1 or image.max() > 1: raise ValueError("Image should be in [-1, 1] range") # Check mask is in [0, 1] if mask.min() < 0 or mask.max() > 1: raise ValueError("Mask should be in [0, 1] range") # Binarize mask mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 # Image as float32 image = image.to(dtype=torch.float32) elif isinstance(mask, torch.Tensor): raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not") else: # preprocess image if isinstance(image, (PIL.Image.Image, np.ndarray)): image = [image] if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): # resize all images w.r.t passed height an width image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image] image = [np.array(i.convert("RGB"))[None, :] for i in image] image = np.concatenate(image, axis=0) elif isinstance(image, list) and isinstance(image[0], np.ndarray): image = np.concatenate([i[None, :] for i in image], axis=0) image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 # preprocess mask if isinstance(mask, (PIL.Image.Image, np.ndarray)): mask = [mask] if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image): mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask] mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) mask = mask.astype(np.float32) / 255.0 elif isinstance(mask, list) and isinstance(mask[0], np.ndarray): mask = np.concatenate([m[None, None, :] for m in mask], axis=0) mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask = torch.from_numpy(mask) masked_image = image * (mask < 0.5) # n.b. ensure backwards compatibility as old function does not return image if return_image: return mask, masked_image, image return mask, masked_image class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration" " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make" " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to" " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face" " Hub, it would be very nice if you could open a Pull request for the" " `scheduler/scheduler_config.json` file" ) deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["skip_prk_steps"] = True scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4 if unet.config.in_channels != 9: logger.warning( f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default," f" {self.__class__} assumes that `pipeline.unet` has 9 input channels: 4 for `num_channels_latents`," " 1 for `num_channels_mask`, and 4 for `num_channels_masked_image`. If you did not intend to modify" " this behavior, please check whether you have loaded the right checkpoint." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def check_inputs( self, prompt, height, width, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, image=None, timestep=None, is_strength_max=True, ): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if (image is None or timestep is None) and not is_strength_max: raise ValueError( "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise." "However, either the image or the noise timestep has not been provided." ) if latents is None: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) if is_strength_max: # if strength is 100% then simply initialise the latents to noise latents = noise else: # otherwise initialise latents as init image + noise image = image.to(device=device, dtype=dtype) if isinstance(generator, list): image_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(batch_size) ] else: image_latents = self.vae.encode(image).latent_dist.sample(generator=generator) image_latents = self.vae.config.scaling_factor * image_latents latents = self.scheduler.add_noise(image_latents, noise, timestep) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask = torch.nn.functional.interpolate( mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) ) mask = mask.to(device=device, dtype=dtype) masked_image = masked_image.to(device=device, dtype=dtype) # encode the mask image into latents space so we can concatenate it to the latents if isinstance(generator, list): masked_image_latents = [ self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) for i in range(batch_size) ] masked_image_latents = torch.cat(masked_image_latents, dim=0) else: masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) masked_image_latents = self.vae.config.scaling_factor * masked_image_latents # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if mask.shape[0] < batch_size: if not batch_size % mask.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1) mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask masked_image_latents = ( torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return mask, masked_image_latents # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 1.0, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`PIL.Image.Image`): `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will be masked out with `mask_image` and repainted according to `prompt`. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. strength (`float`, *optional*, defaults to 1.): Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked portion of the reference `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: ```py >>> import PIL >>> import requests >>> import torch >>> from io import BytesIO >>> from diffusers import StableDiffusionInpaintPipeline >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" >>> init_image = download_image(img_url).resize((512, 512)) >>> mask_image = download_image(mask_url).resize((512, 512)) >>> pipe = StableDiffusionInpaintPipeline.from_pretrained( ... "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench" >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs self.check_inputs( prompt, height, width, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps( num_inference_steps=num_inference_steps, strength=strength, device=device ) # at which timestep to set the initial noise (n.b. 50% if strength is 0.5) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise is_strength_max = strength == 1.0 # 5. Preprocess mask and image mask, masked_image, init_image = prepare_mask_and_masked_image( image, mask_image, height, width, return_image=True ) # 6. Prepare latent variables num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, image=init_image, timestep=latent_timestep, is_strength_max=is_strength_max, ) # 7. Prepare mask latent variables mask, masked_image_latents = self.prepare_mask_latents( mask, masked_image, batch_size * num_images_per_prompt, height, width, prompt_embeds.dtype, device, generator, do_classifier_free_guidance, ) # 8. Check that sizes of mask, masked image and latents match num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.unet` or your `mask_image` or `image` input." ) # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 10. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor from ...loaders import FromCkptMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, ) from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) def preprocess_image(image, batch_size): w, h = image.size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size) image = torch.from_numpy(image) return 2.0 * image - 1.0 def preprocess_mask(mask, batch_size, scale_factor=8): if not isinstance(mask, torch.FloatTensor): mask = mask.convert("L") w, h = mask.size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) mask = np.vstack([mask[None]] * batch_size) mask = 1 - mask # repaint white, keep black mask = torch.from_numpy(mask) return mask else: valid_mask_channel_sizes = [1, 3] # if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W) if mask.shape[3] in valid_mask_channel_sizes: mask = mask.permute(0, 3, 1, 2) elif mask.shape[1] not in valid_mask_channel_sizes: raise ValueError( f"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension," f" but received mask of shape {tuple(mask.shape)}" ) # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape mask = mask.mean(dim=1, keepdim=True) h, w = mask.shape[-2:] h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8 mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor)) return mask class StableDiffusionInpaintPipelineLegacy( DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromCkptMixin ): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["feature_extractor"] # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.__init__ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs def check_inputs( self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start def prepare_latents(self, image, timestep, num_images_per_prompt, dtype, device, generator): image = image.to(device=self.device, dtype=dtype) init_latent_dist = self.vae.encode(image).latent_dist init_latents = init_latent_dist.sample(generator=generator) init_latents = self.vae.config.scaling_factor * init_latents # Expand init_latents for batch_size and num_images_per_prompt init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0) init_latents_orig = init_latents # add noise to latents using the timesteps noise = randn_tensor(init_latents.shape, generator=generator, device=self.device, dtype=dtype) init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents, init_latents_orig, noise @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None, strength: float = 0.8, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, add_predicted_noise: Optional[bool] = False, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. This is the image whose masked region will be inpainted. mask_image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If mask is a tensor, the expected shape should be either `(B, H, W, C)` or `(B, C, H, W)`, where C is 1 or 3. strength (`float`, *optional*, defaults to 0.8): Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength` is 1, the denoising process will be run on the masked area for the full number of iterations specified in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur. num_inference_steps (`int`, *optional*, defaults to 50): The reference number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter will be modulated by `strength`, as explained above. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. add_predicted_noise (`bool`, *optional*, defaults to True): Use predicted noise instead of random noise when constructing noisy versions of the original image in the reverse diffusion process eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Preprocess image and mask if not isinstance(image, torch.FloatTensor): image = preprocess_image(image, batch_size) mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor) # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 6. Prepare latent variables # encode the init image into latents and scale the latents latents, init_latents_orig, noise = self.prepare_latents( image, latent_timestep, num_images_per_prompt, prompt_embeds.dtype, device, generator ) # 7. Prepare mask latent mask = mask_image.to(device=self.device, dtype=latents.dtype) mask = torch.cat([mask] * num_images_per_prompt) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds, return_dict=False)[ 0 ] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # masking if add_predicted_noise: init_latents_proper = self.scheduler.add_noise( init_latents_orig, noise_pred_uncond, torch.tensor([t]) ) else: init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t])) latents = (init_latents_proper * mask) + (latents * (1 - mask)) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # use original latents corresponding to unmasked portions of the image latents = (init_latents_orig * mask) + (latents * (1 - mask)) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py ================================================ # Copyright 2023 The InstructPix2Pix Authors and 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. import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, ) from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): r""" Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, num_inference_steps: int = 100, guidance_scale: float = 7.5, image_guidance_scale: float = 1.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`PIL.Image.Image`): `Image`, or tensor representing an image batch which will be repainted according to `prompt`. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. This pipeline requires a value of at least `1`. image_guidance_scale (`float`, *optional*, defaults to 1.5): Image guidance scale is to push the generated image towards the inital image `image`. Image guidance scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to generate images that are closely linked to the source image `image`, usually at the expense of lower image quality. This pipeline requires a value of at least `1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> import PIL >>> import requests >>> import torch >>> from io import BytesIO >>> from diffusers import StableDiffusionInstructPix2PixPipeline >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" >>> image = download_image(img_url).resize((512, 512)) >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( ... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "make the mountains snowy" >>> image = pipe(prompt=prompt, image=image).images[0] ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Check inputs self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) if image is None: raise ValueError("`image` input cannot be undefined.") # 1. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0 # check if scheduler is in sigmas space scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas") # 2. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 3. Preprocess image image = preprocess(image) height, width = image.shape[-2:] # 4. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare Image latents image_latents = self.prepare_image_latents( image, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, do_classifier_free_guidance, generator, ) # 6. Prepare latent variables num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 7. Check that shapes of latents and image match the UNet channels num_channels_image = image_latents.shape[1] if num_channels_latents + num_channels_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_image`: {num_channels_image} " f" = {num_channels_latents+num_channels_image}. Please verify the config of" " `pipeline.unet` or your `image` input." ) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Expand the latents if we are doing classifier free guidance. # The latents are expanded 3 times because for pix2pix the guidance\ # is applied for both the text and the input image. latent_model_input = torch.cat([latents] * 3) if do_classifier_free_guidance else latents # concat latents, image_latents in the channel dimension scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1) # predict the noise residual noise_pred = self.unet( scaled_latent_model_input, t, encoder_hidden_states=prompt_embeds, return_dict=False )[0] # Hack: # For karras style schedulers the model does classifer free guidance using the # predicted_original_sample instead of the noise_pred. So we need to compute the # predicted_original_sample here if we are using a karras style scheduler. if scheduler_is_in_sigma_space: step_index = (self.scheduler.timesteps == t).nonzero().item() sigma = self.scheduler.sigmas[step_index] noise_pred = latent_model_input - sigma * noise_pred # perform guidance if do_classifier_free_guidance: noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3) noise_pred = ( noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_image) + image_guidance_scale * (noise_pred_image - noise_pred_uncond) ) # Hack: # For karras style schedulers the model does classifer free guidance using the # predicted_original_sample instead of the noise_pred. But the scheduler.step function # expects the noise_pred and computes the predicted_original_sample internally. So we # need to overwrite the noise_pred here such that the value of the computed # predicted_original_sample is correct. if scheduler_is_in_sigma_space: noise_pred = (noise_pred - latents) / (-sigma) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_ prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes # pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds] prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def check_inputs( self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def prepare_image_latents( self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None ): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): image_latents = [self.vae.encode(image[i : i + 1]).latent_dist.mode() for i in range(batch_size)] image_latents = torch.cat(image_latents, dim=0) else: image_latents = self.vae.encode(image).latent_dist.mode() if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: # expand image_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_image_per_prompt = batch_size // image_latents.shape[0] image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." ) else: image_latents = torch.cat([image_latents], dim=0) if do_classifier_free_guidance: uncond_image_latents = torch.zeros_like(image_latents) image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0) return image_latents ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py ================================================ # Copyright 2023 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. import importlib import warnings from typing import Callable, List, Optional, Union import torch from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser from k_diffusion.sampling import get_sigmas_karras from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...pipelines import DiffusionPipeline from ...schedulers import LMSDiscreteScheduler from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name class ModelWrapper: def __init__(self, model, alphas_cumprod): self.model = model self.alphas_cumprod = alphas_cumprod def apply_model(self, *args, **kwargs): if len(args) == 3: encoder_hidden_states = args[-1] args = args[:2] if kwargs.get("cond", None) is not None: encoder_hidden_states = kwargs.pop("cond") return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample class StableDiffusionKDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) This is an experimental pipeline and is likely to change in the future. Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker: bool = True, ): super().__init__() logger.info( f"{self.__class__} is an experimntal pipeline and is likely to change in the future. We recommend to use" " this pipeline for fast experimentation / iteration if needed, but advice to rely on existing pipelines" " as defined in https://huggingface.co/docs/diffusers/api/schedulers#implemented-schedulers for" " production settings." ) # get correct sigmas from LMS scheduler = LMSDiscreteScheduler.from_config(scheduler.config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.register_to_config(requires_safety_checker=requires_safety_checker) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) model = ModelWrapper(unet, scheduler.alphas_cumprod) if scheduler.config.prediction_type == "v_prediction": self.k_diffusion_model = CompVisVDenoiser(model) else: self.k_diffusion_model = CompVisDenoiser(model) def set_scheduler(self, scheduler_type: str): library = importlib.import_module("k_diffusion") sampling = getattr(library, "sampling") self.sampler = getattr(sampling, scheduler_type) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, use_karras_sigmas: Optional[bool] = False, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. use_karras_sigmas (`bool`, *optional*, defaults to `False`): Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to `DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M Karras`. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = True if guidance_scale <= 1.0: raise ValueError("has to use guidance_scale") # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=prompt_embeds.device) # 5. Prepare sigmas if use_karras_sigmas: sigma_min: float = self.k_diffusion_model.sigmas[0].item() sigma_max: float = self.k_diffusion_model.sigmas[-1].item() sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max) sigmas = sigmas.to(device) else: sigmas = self.scheduler.sigmas sigmas = sigmas.to(prompt_embeds.dtype) # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) latents = latents * sigmas[0] self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device) self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device) # 7. Define model function def model_fn(x, t): latent_model_input = torch.cat([x] * 2) t = torch.cat([t] * 2) noise_pred = self.k_diffusion_model(latent_model_input, t, cond=prompt_embeds) noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) return noise_pred # 8. Run k-diffusion solver latents = self.sampler(model_fn, latents, sigmas) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py ================================================ # Copyright 2023 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. import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch import torch.nn.functional as F from transformers import CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import EulerDiscreteScheduler from ...utils import is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = [np.array(i.resize((w, h)))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class StableDiffusionLatentUpscalePipeline(DiffusionPipeline): r""" Pipeline to upscale the resolution of Stable Diffusion output images by a factor of 2. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`EulerDiscreteScheduler`]. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: EulerDiscreteScheduler, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt(self, prompt, device, do_classifier_free_guidance, negative_prompt): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `list(int)`): prompt to be encoded device: (`torch.device`): torch device do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). """ batch_size = len(prompt) if isinstance(prompt, list) else 1 text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_length=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) text_encoder_out = self.text_encoder( text_input_ids.to(device), output_hidden_states=True, ) text_embeddings = text_encoder_out.hidden_states[-1] text_pooler_out = text_encoder_out.pooler_output # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_length=True, return_tensors="pt", ) uncond_encoder_out = self.text_encoder( uncond_input.input_ids.to(device), output_hidden_states=True, ) uncond_embeddings = uncond_encoder_out.hidden_states[-1] uncond_pooler_out = uncond_encoder_out.pooler_output # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) text_pooler_out = torch.cat([uncond_pooler_out, text_pooler_out]) return text_embeddings, text_pooler_out # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def check_inputs(self, prompt, image, callback_steps): if not isinstance(prompt, str) and not isinstance(prompt, list): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}" ) # verify batch size of prompt and image are same if image is a list or tensor if isinstance(image, list) or isinstance(image, torch.Tensor): if isinstance(prompt, str): batch_size = 1 else: batch_size = len(prompt) if isinstance(image, list): image_batch_size = len(image) else: image_batch_size = image.shape[0] if image.ndim == 4 else 1 if batch_size != image_batch_size: raise ValueError( f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}." " Please make sure that passed `prompt` matches the batch size of `image`." ) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height, width) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]], num_inference_steps: int = 75, guidance_scale: float = 9.0, negative_prompt: Optional[Union[str, List[str]]] = None, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image upscaling. image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`): `Image`, or tensor representing an image batch which will be upscaled. If it's a tensor, it can be either a latent output from a stable diffusion model, or an image tensor in the range `[-1, 1]`. It will be considered a `latent` if `image.shape[1]` is `4`; otherwise, it will be considered to be an image representation and encoded using this pipeline's `vae` encoder. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline >>> import torch >>> pipeline = StableDiffusionPipeline.from_pretrained( ... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16 ... ) >>> pipeline.to("cuda") >>> model_id = "stabilityai/sd-x2-latent-upscaler" >>> upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) >>> upscaler.to("cuda") >>> prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic" >>> generator = torch.manual_seed(33) >>> low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images >>> with torch.no_grad(): ... image = pipeline.decode_latents(low_res_latents) >>> image = pipeline.numpy_to_pil(image)[0] >>> image.save("../images/a1.png") >>> upscaled_image = upscaler( ... prompt=prompt, ... image=low_res_latents, ... num_inference_steps=20, ... guidance_scale=0, ... generator=generator, ... ).images[0] >>> upscaled_image.save("../images/a2.png") ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs(prompt, image, callback_steps) # 2. Define call parameters batch_size = 1 if isinstance(prompt, str) else len(prompt) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 if guidance_scale == 0: prompt = [""] * batch_size # 3. Encode input prompt text_embeddings, text_pooler_out = self._encode_prompt( prompt, device, do_classifier_free_guidance, negative_prompt ) # 4. Preprocess image image = preprocess(image) image = image.to(dtype=text_embeddings.dtype, device=device) if image.shape[1] == 3: # encode image if not in latent-space yet image = self.vae.encode(image).latent_dist.sample() * self.vae.config.scaling_factor # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps batch_multiplier = 2 if do_classifier_free_guidance else 1 image = image[None, :] if image.ndim == 3 else image image = torch.cat([image] * batch_multiplier) # 5. Add noise to image (set to be 0): # (see below notes from the author): # "the This step theoretically can make the model work better on out-of-distribution inputs, but mostly just seems to make it match the input less, so it's turned off by default." noise_level = torch.tensor([0.0], dtype=torch.float32, device=device) noise_level = torch.cat([noise_level] * image.shape[0]) inv_noise_level = (noise_level**2 + 1) ** (-0.5) image_cond = F.interpolate(image, scale_factor=2, mode="nearest") * inv_noise_level[:, None, None, None] image_cond = image_cond.to(text_embeddings.dtype) noise_level_embed = torch.cat( [ torch.ones(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device), torch.zeros(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device), ], dim=1, ) timestep_condition = torch.cat([noise_level_embed, text_pooler_out], dim=1) # 6. Prepare latent variables height, width = image.shape[2:] num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size, num_channels_latents, height * 2, # 2x upscale width * 2, text_embeddings.dtype, device, generator, latents, ) # 7. Check that sizes of image and latents match num_channels_image = image.shape[1] if num_channels_latents + num_channels_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_image`: {num_channels_image} " f" = {num_channels_latents+num_channels_image}. Please verify the config of" " `pipeline.unet` or your `image` input." ) # 9. Denoising loop num_warmup_steps = 0 with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): sigma = self.scheduler.sigmas[i] # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t) scaled_model_input = torch.cat([scaled_model_input, image_cond], dim=1) # preconditioning parameter based on Karras et al. (2022) (table 1) timestep = torch.log(sigma) * 0.25 noise_pred = self.unet( scaled_model_input, timestep, encoder_hidden_states=text_embeddings, timestep_cond=timestep_condition, ).sample # in original repo, the output contains a variance channel that's not used noise_pred = noise_pred[:, :-1] # apply preconditioning, based on table 1 in Karras et al. (2022) inv_sigma = 1 / (sigma**2 + 1) noise_pred = inv_sigma * latent_model_input + self.scheduler.scale_model_input(sigma, t) * noise_pred # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py ================================================ # Copyright 2023 TIME Authors and 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. import copy import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import PNDMScheduler from ...schedulers.scheduling_utils import SchedulerMixin from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name AUGS_CONST = ["A photo of ", "An image of ", "A picture of "] EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionModelEditingPipeline >>> model_ckpt = "CompVis/stable-diffusion-v1-4" >>> pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt) >>> pipe = pipe.to("cuda") >>> source_prompt = "A pack of roses" >>> destination_prompt = "A pack of blue roses" >>> pipe.edit_model(source_prompt, destination_prompt) >>> prompt = "A field of roses" >>> image = pipe(prompt).images[0] ``` """ class StableDiffusionModelEditingPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image model editing using "Editing Implicit Assumptions in Text-to-Image Diffusion Models". This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.). Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. with_to_k ([`bool`]): Whether to edit the key projection matrices along wiht the value projection matrices. with_augs ([`list`]): Textual augmentations to apply while editing the text-to-image model. Set to [] for no augmentations. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: SchedulerMixin, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, requires_safety_checker: bool = True, with_to_k: bool = True, with_augs: list = AUGS_CONST, ): super().__init__() if isinstance(scheduler, PNDMScheduler): logger.error("PNDMScheduler for this pipeline is currently not supported.") if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) self.with_to_k = with_to_k self.with_augs = with_augs # get cross-attention layers ca_layers = [] def append_ca(net_): if net_.__class__.__name__ == "CrossAttention": ca_layers.append(net_) elif hasattr(net_, "children"): for net__ in net_.children(): append_ca(net__) # recursively find all cross-attention layers in unet for net in self.unet.named_children(): if "down" in net[0]: append_ca(net[1]) elif "up" in net[0]: append_ca(net[1]) elif "mid" in net[0]: append_ca(net[1]) # get projection matrices self.ca_clip_layers = [l for l in ca_layers if l.to_v.in_features == 768] self.projection_matrices = [l.to_v for l in self.ca_clip_layers] self.og_matrices = [copy.deepcopy(l.to_v) for l in self.ca_clip_layers] if self.with_to_k: self.projection_matrices = self.projection_matrices + [l.to_k for l in self.ca_clip_layers] self.og_matrices = self.og_matrices + [copy.deepcopy(l.to_k) for l in self.ca_clip_layers] # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def edit_model( self, source_prompt: str, destination_prompt: str, lamb: float = 0.1, restart_params: bool = True, ): r""" Apply model editing via closed-form solution (see Eq. 5 in the TIME paper https://arxiv.org/abs/2303.08084) Args: source_prompt (`str`): The source prompt containing the concept to be edited. destination_prompt (`str`): The destination prompt. Must contain all words from source_prompt with additional ones to specify the target edit. lamb (`float`, *optional*, defaults to 0.1): The lambda parameter specifying the regularization intesity. Smaller values increase the editing power. restart_params (`bool`, *optional*, defaults to True): Restart the model parameters to their pre-trained version before editing. This is done to avoid edit compounding. When it is False, edits accumulate. """ # restart LDM parameters if restart_params: num_ca_clip_layers = len(self.ca_clip_layers) for idx_, l in enumerate(self.ca_clip_layers): l.to_v = copy.deepcopy(self.og_matrices[idx_]) self.projection_matrices[idx_] = l.to_v if self.with_to_k: l.to_k = copy.deepcopy(self.og_matrices[num_ca_clip_layers + idx_]) self.projection_matrices[num_ca_clip_layers + idx_] = l.to_k # set up sentences old_texts = [source_prompt] new_texts = [destination_prompt] # add augmentations base = old_texts[0] if old_texts[0][0:1] != "A" else "a" + old_texts[0][1:] for aug in self.with_augs: old_texts.append(aug + base) base = new_texts[0] if new_texts[0][0:1] != "A" else "a" + new_texts[0][1:] for aug in self.with_augs: new_texts.append(aug + base) # prepare input k* and v* old_embs, new_embs = [], [] for old_text, new_text in zip(old_texts, new_texts): text_input = self.tokenizer( [old_text, new_text], padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] old_emb, new_emb = text_embeddings old_embs.append(old_emb) new_embs.append(new_emb) # identify corresponding destinations for each token in old_emb idxs_replaces = [] for old_text, new_text in zip(old_texts, new_texts): tokens_a = self.tokenizer(old_text).input_ids tokens_b = self.tokenizer(new_text).input_ids tokens_a = [self.tokenizer.encode("a ")[1] if self.tokenizer.decode(t) == "an" else t for t in tokens_a] tokens_b = [self.tokenizer.encode("a ")[1] if self.tokenizer.decode(t) == "an" else t for t in tokens_b] num_orig_tokens = len(tokens_a) idxs_replace = [] j = 0 for i in range(num_orig_tokens): curr_token = tokens_a[i] while tokens_b[j] != curr_token: j += 1 idxs_replace.append(j) j += 1 while j < 77: idxs_replace.append(j) j += 1 while len(idxs_replace) < 77: idxs_replace.append(76) idxs_replaces.append(idxs_replace) # prepare batch: for each pair of setences, old context and new values contexts, valuess = [], [] for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces): context = old_emb.detach() values = [] with torch.no_grad(): for layer in self.projection_matrices: values.append(layer(new_emb[idxs_replace]).detach()) contexts.append(context) valuess.append(values) # edit the model for layer_num in range(len(self.projection_matrices)): # mat1 = \lambda W + \sum{v k^T} mat1 = lamb * self.projection_matrices[layer_num].weight # mat2 = \lambda I + \sum{k k^T} mat2 = lamb * torch.eye( self.projection_matrices[layer_num].weight.shape[1], device=self.projection_matrices[layer_num].weight.device, ) # aggregate sums for mat1, mat2 for context, values in zip(contexts, valuess): context_vector = context.reshape(context.shape[0], context.shape[1], 1) context_vector_T = context.reshape(context.shape[0], 1, context.shape[1]) value_vector = values[layer_num].reshape(values[layer_num].shape[0], values[layer_num].shape[1], 1) for_mat1 = (value_vector @ context_vector_T).sum(dim=0) for_mat2 = (context_vector @ context_vector_T).sum(dim=0) mat1 += for_mat1 mat2 += for_mat2 # update projection matrix self.projection_matrices[layer_num].weight = torch.nn.Parameter(mat1 @ torch.inverse(mat2)) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py ================================================ # Copyright 2023 MultiDiffusion Authors and 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMScheduler, PNDMScheduler from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler >>> model_ckpt = "stabilityai/stable-diffusion-2-base" >>> scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") >>> pipe = StableDiffusionPanoramaPipeline.from_pretrained( ... model_ckpt, scheduler=scheduler, torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of the dolomites" >>> image = pipe(prompt).images[0] ``` """ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation". This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.). To generate panorama-like images, be sure to pass the `width` parameter accordingly when using the pipeline. Our recommendation for the `width` value is 2048. This is the default value of the `width` parameter for this pipeline. Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. The original work on Multi Diffsion used the [`DDIMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: DDIMScheduler, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() if isinstance(scheduler, PNDMScheduler): logger.error("PNDMScheduler for this pipeline is currently not supported.") if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def get_views(self, panorama_height, panorama_width, window_size=64, stride=8): # Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113) panorama_height /= 8 panorama_width /= 8 num_blocks_height = (panorama_height - window_size) // stride + 1 num_blocks_width = (panorama_width - window_size) // stride + 1 total_num_blocks = int(num_blocks_height * num_blocks_width) views = [] for i in range(total_num_blocks): h_start = int((i // num_blocks_width) * stride) h_end = h_start + window_size w_start = int((i % num_blocks_width) * stride) w_end = w_start + window_size views.append((h_start, h_end, w_start, w_end)) return views @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = 512, width: Optional[int] = 2048, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to 512: The height in pixels of the generated image. width (`int`, *optional*, defaults to 2048): The width in pixels of the generated image. The width is kept to a high number because the pipeline is supposed to be used for generating panorama-like images. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Define panorama grid and initialize views for synthesis. views = self.get_views(height, width) count = torch.zeros_like(latents) value = torch.zeros_like(latents) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop # Each denoising step also includes refinement of the latents with respect to the # views. num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): count.zero_() value.zero_() # generate views # Here, we iterate through different spatial crops of the latents and denoise them. These # denoised (latent) crops are then averaged to produce the final latent # for the current timestep via MultiDiffusion. Please see Sec. 4.1 in the # MultiDiffusion paper for more details: https://arxiv.org/abs/2302.08113 for h_start, h_end, w_start, w_end in views: # get the latents corresponding to the current view coordinates latents_for_view = latents[:, :, h_start:h_end, w_start:w_end] # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents_for_view] * 2) if do_classifier_free_guidance else latents_for_view ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents_view_denoised = self.scheduler.step( noise_pred, t, latents_for_view, **extra_step_kwargs ).prev_sample value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised count[:, :, h_start:h_end, w_start:w_end] += 1 # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113 latents = torch.where(count > 0, value / count, value) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py ================================================ # Copyright 2023 Pix2Pix Zero Authors and 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. import inspect import warnings from dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch import torch.nn.functional as F from transformers import ( BlipForConditionalGeneration, BlipProcessor, CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, ) from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import Attention from ...schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler from ...schedulers.scheduling_ddim_inverse import DDIMInverseScheduler from ...utils import ( PIL_INTERPOLATION, BaseOutput, deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin): """ Output class for Stable Diffusion pipelines. Args: latents (`torch.FloatTensor`) inverted latents tensor images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. """ latents: torch.FloatTensor images: Union[List[PIL.Image.Image], np.ndarray] EXAMPLE_DOC_STRING = """ Examples: ```py >>> import requests >>> import torch >>> from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline >>> def download(embedding_url, local_filepath): ... r = requests.get(embedding_url) ... with open(local_filepath, "wb") as f: ... f.write(r.content) >>> model_ckpt = "CompVis/stable-diffusion-v1-4" >>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16) >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) >>> pipeline.to("cuda") >>> prompt = "a high resolution painting of a cat in the style of van gough" >>> source_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/cat.pt" >>> target_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/dog.pt" >>> for url in [source_emb_url, target_emb_url]: ... download(url, url.split("/")[-1]) >>> src_embeds = torch.load(source_emb_url.split("/")[-1]) >>> target_embeds = torch.load(target_emb_url.split("/")[-1]) >>> images = pipeline( ... prompt, ... source_embeds=src_embeds, ... target_embeds=target_embeds, ... num_inference_steps=50, ... cross_attention_guidance_amount=0.15, ... ).images >>> images[0].save("edited_image_dog.png") ``` """ EXAMPLE_INVERT_DOC_STRING = """ Examples: ```py >>> import torch >>> from transformers import BlipForConditionalGeneration, BlipProcessor >>> from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline >>> import requests >>> from PIL import Image >>> captioner_id = "Salesforce/blip-image-captioning-base" >>> processor = BlipProcessor.from_pretrained(captioner_id) >>> model = BlipForConditionalGeneration.from_pretrained( ... captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True ... ) >>> sd_model_ckpt = "CompVis/stable-diffusion-v1-4" >>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained( ... sd_model_ckpt, ... caption_generator=model, ... caption_processor=processor, ... torch_dtype=torch.float16, ... safety_checker=None, ... ) >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config) >>> pipeline.enable_model_cpu_offload() >>> img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png" >>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512)) >>> # generate caption >>> caption = pipeline.generate_caption(raw_image) >>> # "a photography of a cat with flowers and dai dai daie - daie - daie kasaii" >>> inv_latents = pipeline.invert(caption, image=raw_image).latents >>> # we need to generate source and target embeds >>> source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"] >>> target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"] >>> source_embeds = pipeline.get_embeds(source_prompts) >>> target_embeds = pipeline.get_embeds(target_prompts) >>> # the latents can then be used to edit a real image >>> # when using Stable Diffusion 2 or other models that use v-prediction >>> # set `cross_attention_guidance_amount` to 0.01 or less to avoid input latent gradient explosion >>> image = pipeline( ... caption, ... source_embeds=source_embeds, ... target_embeds=target_embeds, ... num_inference_steps=50, ... cross_attention_guidance_amount=0.15, ... generator=generator, ... latents=inv_latents, ... negative_prompt=caption, ... ).images[0] >>> image.save("edited_image.png") ``` """ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image def prepare_unet(unet: UNet2DConditionModel): """Modifies the UNet (`unet`) to perform Pix2Pix Zero optimizations.""" pix2pix_zero_attn_procs = {} for name in unet.attn_processors.keys(): module_name = name.replace(".processor", "") module = unet.get_submodule(module_name) if "attn2" in name: pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=True) module.requires_grad_(True) else: pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=False) module.requires_grad_(False) unet.set_attn_processor(pix2pix_zero_attn_procs) return unet class Pix2PixZeroL2Loss: def __init__(self): self.loss = 0.0 def compute_loss(self, predictions, targets): self.loss += ((predictions - targets) ** 2).sum((1, 2)).mean(0) class Pix2PixZeroAttnProcessor: """An attention processor class to store the attention weights. In Pix2Pix Zero, it happens during computations in the cross-attention blocks.""" def __init__(self, is_pix2pix_zero=False): self.is_pix2pix_zero = is_pix2pix_zero if self.is_pix2pix_zero: self.reference_cross_attn_map = {} def __call__( self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, timestep=None, loss=None, ): batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) if self.is_pix2pix_zero and timestep is not None: # new bookkeeping to save the attention weights. if loss is None: self.reference_cross_attn_map[timestep.item()] = attention_probs.detach().cpu() # compute loss elif loss is not None: prev_attn_probs = self.reference_cross_attn_map.pop(timestep.item()) loss.compute_loss(attention_probs, prev_attn_probs.to(attention_probs.device)) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) return hidden_states class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline): r""" Pipeline for pixel-levl image editing using Pix2Pix Zero. Based on Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. requires_safety_checker (bool): Whether the pipeline requires a safety checker. We recommend setting it to True if you're using the pipeline publicly. """ _optional_components = [ "safety_checker", "feature_extractor", "caption_generator", "caption_processor", "inverse_scheduler", ] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DDPMScheduler, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler], feature_extractor: CLIPImageProcessor, safety_checker: StableDiffusionSafetyChecker, inverse_scheduler: DDIMInverseScheduler, caption_generator: BlipForConditionalGeneration, caption_processor: BlipProcessor, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, caption_processor=caption_processor, caption_generator=caption_generator, inverse_scheduler=inverse_scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") hook = None for cpu_offloaded_model in [self.vae, self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) if self.safety_checker is not None: _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, source_embeds, target_embeds, callback_steps, prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if source_embeds is None and target_embeds is None: raise ValueError("`source_embeds` and `target_embeds` cannot be undefined.") if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def generate_caption(self, images): """Generates caption for a given image.""" text = "a photography of" prev_device = self.caption_generator.device device = self._execution_device inputs = self.caption_processor(images, text, return_tensors="pt").to( device=device, dtype=self.caption_generator.dtype ) self.caption_generator.to(device) outputs = self.caption_generator.generate(**inputs, max_new_tokens=128) # offload caption generator self.caption_generator.to(prev_device) caption = self.caption_processor.batch_decode(outputs, skip_special_tokens=True)[0] return caption def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor): """Constructs the edit direction to steer the image generation process semantically.""" return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0) @torch.no_grad() def get_embeds(self, prompt: List[str], batch_size: int = 16) -> torch.FloatTensor: num_prompts = len(prompt) embeds = [] for i in range(0, num_prompts, batch_size): prompt_slice = prompt[i : i + batch_size] input_ids = self.tokenizer( prompt_slice, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ).input_ids input_ids = input_ids.to(self.text_encoder.device) embeds.append(self.text_encoder(input_ids)[0]) return torch.cat(embeds, dim=0).mean(0)[None] def prepare_image_latents(self, image, batch_size, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if isinstance(generator, list): latents = [self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)] latents = torch.cat(latents, dim=0) else: latents = self.vae.encode(image).latent_dist.sample(generator) latents = self.vae.config.scaling_factor * latents if batch_size != latents.shape[0]: if batch_size % latents.shape[0] == 0: # expand image_latents for batch_size deprecation_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial" " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" " your script to pass as many initial images as text prompts to suppress this warning." ) deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) additional_latents_per_image = batch_size // latents.shape[0] latents = torch.cat([latents] * additional_latents_per_image, dim=0) else: raise ValueError( f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts." ) else: latents = torch.cat([latents], dim=0) return latents def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int): pred_type = self.inverse_scheduler.config.prediction_type alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep] beta_prod_t = 1 - alpha_prod_t if pred_type == "epsilon": return model_output elif pred_type == "sample": return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5) elif pred_type == "v_prediction": return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`" ) def auto_corr_loss(self, hidden_states, generator=None): reg_loss = 0.0 for i in range(hidden_states.shape[0]): for j in range(hidden_states.shape[1]): noise = hidden_states[i : i + 1, j : j + 1, :, :] while True: roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item() reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2 reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2 if noise.shape[2] <= 8: break noise = F.avg_pool2d(noise, kernel_size=2) return reg_loss def kl_divergence(self, hidden_states): mean = hidden_states.mean() var = hidden_states.var() return var + mean**2 - 1 - torch.log(var + 1e-7) @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Optional[Union[str, List[str]]] = None, image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None, source_embeds: torch.Tensor = None, target_embeds: torch.Tensor = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, cross_attention_guidance_amount: float = 0.1, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. source_embeds (`torch.Tensor`): Source concept embeddings. Generation of the embeddings as per the [original paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction. target_embeds (`torch.Tensor`): Target concept embeddings. Generation of the embeddings as per the [original paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. cross_attention_guidance_amount (`float`, defaults to 0.1): Amount of guidance needed from the reference cross-attention maps. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Define the spatial resolutions. height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, image, source_embeds, target_embeds, callback_steps, prompt_embeds, ) # 3. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if cross_attention_kwargs is None: cross_attention_kwargs = {} device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Generate the inverted noise from the input image or any other image # generated from the input prompt. num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) latents_init = latents.clone() # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Rejig the UNet so that we can obtain the cross-attenion maps and # use them for guiding the subsequent image generation. self.unet = prepare_unet(self.unet) # 7. Denoising loop where we obtain the cross-attention maps. num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs={"timestep": t}, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 8. Compute the edit directions. edit_direction = self.construct_direction(source_embeds, target_embeds).to(prompt_embeds.device) # 9. Edit the prompt embeddings as per the edit directions discovered. prompt_embeds_edit = prompt_embeds.clone() prompt_embeds_edit[1:2] += edit_direction # 10. Second denoising loop to generate the edited image. latents = latents_init num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # we want to learn the latent such that it steers the generation # process towards the edited direction, so make the make initial # noise learnable x_in = latent_model_input.detach().clone() x_in.requires_grad = True # optimizer opt = torch.optim.SGD([x_in], lr=cross_attention_guidance_amount) with torch.enable_grad(): # initialize loss loss = Pix2PixZeroL2Loss() # predict the noise residual noise_pred = self.unet( x_in, t, encoder_hidden_states=prompt_embeds_edit.detach(), cross_attention_kwargs={"timestep": t, "loss": loss}, ).sample loss.loss.backward(retain_graph=False) opt.step() # recompute the noise noise_pred = self.unet( x_in.detach(), t, encoder_hidden_states=prompt_embeds_edit, cross_attention_kwargs={"timestep": None}, ).sample latents = x_in.detach().chunk(2)[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) @torch.no_grad() @replace_example_docstring(EXAMPLE_INVERT_DOC_STRING) def invert( self, prompt: Optional[str] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, num_inference_steps: int = 50, guidance_scale: float = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, cross_attention_guidance_amount: float = 0.1, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, lambda_auto_corr: float = 20.0, lambda_kl: float = 20.0, num_reg_steps: int = 5, num_auto_corr_rolls: int = 5, ): r""" Function used to generate inverted latents given a prompt and image. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`PIL.Image.Image`, *optional*): `Image`, or tensor representing an image batch which will be used for conditioning. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 1): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. cross_attention_guidance_amount (`float`, defaults to 0.1): Amount of guidance needed from the reference cross-attention maps. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. lambda_auto_corr (`float`, *optional*, defaults to 20.0): Lambda parameter to control auto correction lambda_kl (`float`, *optional*, defaults to 20.0): Lambda parameter to control Kullback–Leibler divergence output num_reg_steps (`int`, *optional*, defaults to 5): Number of regularization loss steps num_auto_corr_rolls (`int`, *optional*, defaults to 5): Number of auto correction roll steps Examples: Returns: [`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is the inverted latents tensor and then second is the corresponding decoded image. """ # 1. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if cross_attention_kwargs is None: cross_attention_kwargs = {} device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Preprocess image image = preprocess(image) # 4. Prepare latent variables latents = self.prepare_image_latents(image, batch_size, self.vae.dtype, device, generator) # 5. Encode input prompt num_images_per_prompt = 1 prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, prompt_embeds=prompt_embeds, ) # 4. Prepare timesteps self.inverse_scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.inverse_scheduler.timesteps # 6. Rejig the UNet so that we can obtain the cross-attenion maps and # use them for guiding the subsequent image generation. self.unet = prepare_unet(self.unet) # 7. Denoising loop where we obtain the cross-attention maps. num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order with self.progress_bar(total=num_inference_steps - 1) as progress_bar: for i, t in enumerate(timesteps[:-1]): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs={"timestep": t}, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # regularization of the noise prediction with torch.enable_grad(): for _ in range(num_reg_steps): if lambda_auto_corr > 0: for _ in range(num_auto_corr_rolls): var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True) # Derive epsilon from model output before regularizing to IID standard normal var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t) l_ac = self.auto_corr_loss(var_epsilon, generator=generator) l_ac.backward() grad = var.grad.detach() / num_auto_corr_rolls noise_pred = noise_pred - lambda_auto_corr * grad if lambda_kl > 0: var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True) # Derive epsilon from model output before regularizing to IID standard normal var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t) l_kld = self.kl_divergence(var_epsilon) l_kld.backward() grad = var.grad.detach() noise_pred = noise_pred - lambda_kl * grad noise_pred = noise_pred.detach() # compute the previous noisy sample x_t -> x_t-1 latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ( (i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0 ): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) inverted_latents = latents.detach().clone() # 8. Post-processing image = self.decode_latents(latents.detach()) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() # 9. Convert to PIL. if output_type == "pil": image = self.image_processor.numpy_to_pil(image) if not return_dict: return (inverted_latents, image) return Pix2PixInversionPipelineOutput(latents=inverted_latents, images=image) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py ================================================ # Copyright 2023 Susung Hong and 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput from .safety_checker import StableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionSAGPipeline >>> pipe = StableDiffusionSAGPipeline.from_pretrained( ... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt, sag_scale=0.75).images[0] ``` """ # processes and stores attention probabilities class CrossAttnStoreProcessor: def __init__(self): self.attention_probs = None def __call__( self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, ): batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) 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) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) self.attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(self.attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) return hidden_states # Modified to get self-attention guidance scale in this paper (https://arxiv.org/pdf/2210.00939.pdf) as an input class StableDiffusionSAGPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) if self.safety_checker is not None: cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, sag_scale: float = 0.75, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. sag_scale (`float`, *optional*, defaults to 0.75): SAG scale as defined in [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance] (https://arxiv.org/abs/2210.00939). `sag_scale` is defined as `s_s` of equation (24) of SAG paper: https://arxiv.org/pdf/2210.00939.pdf. Typically chosen between [0, 1.0] for better quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # and `sag_scale` is` `s` of equation (16) # of the self-attentnion guidance paper: https://arxiv.org/pdf/2210.00939.pdf # `sag_scale = 0` means no self-attention guidance do_self_attention_guidance = sag_scale > 0.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop store_processor = CrossAttnStoreProcessor() self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order map_size = None def get_map_size(module, input, output): nonlocal map_size map_size = output[0].shape[-2:] with self.unet.mid_block.attentions[0].register_forward_hook(get_map_size): with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # perform self-attention guidance with the stored self-attentnion map if do_self_attention_guidance: # classifier-free guidance produces two chunks of attention map # and we only use unconditional one according to equation (25) # in https://arxiv.org/pdf/2210.00939.pdf if do_classifier_free_guidance: # DDIM-like prediction of x0 pred_x0 = self.pred_x0(latents, noise_pred_uncond, t) # get the stored attention maps uncond_attn, cond_attn = store_processor.attention_probs.chunk(2) # self-attention-based degrading of latents degraded_latents = self.sag_masking( pred_x0, uncond_attn, map_size, t, self.pred_epsilon(latents, noise_pred_uncond, t) ) uncond_emb, _ = prompt_embeds.chunk(2) # forward and give guidance degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=uncond_emb).sample noise_pred += sag_scale * (noise_pred_uncond - degraded_pred) else: # DDIM-like prediction of x0 pred_x0 = self.pred_x0(latents, noise_pred, t) # get the stored attention maps cond_attn = store_processor.attention_probs # self-attention-based degrading of latents degraded_latents = self.sag_masking( pred_x0, cond_attn, map_size, t, self.pred_epsilon(latents, noise_pred, t) ) # forward and give guidance degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=prompt_embeds).sample noise_pred += sag_scale * (noise_pred - degraded_pred) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) def sag_masking(self, original_latents, attn_map, map_size, t, eps): # Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf bh, hw1, hw2 = attn_map.shape b, latent_channel, latent_h, latent_w = original_latents.shape h = self.unet.config.attention_head_dim if isinstance(h, list): h = h[-1] # Produce attention mask attn_map = attn_map.reshape(b, h, hw1, hw2) attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0 attn_mask = ( attn_mask.reshape(b, map_size[0], map_size[1]) .unsqueeze(1) .repeat(1, latent_channel, 1, 1) .type(attn_map.dtype) ) attn_mask = F.interpolate(attn_mask, (latent_h, latent_w)) # Blur according to the self-attention mask degraded_latents = gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0) degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask) # Noise it again to match the noise level degraded_latents = self.scheduler.add_noise(degraded_latents, noise=eps, timesteps=t) return degraded_latents # Modified from diffusers.schedulers.scheduling_ddim.DDIMScheduler.step # Note: there are some schedulers that clip or do not return x_0 (PNDMScheduler, DDIMScheduler, etc.) def pred_x0(self, sample, model_output, timestep): alpha_prod_t = self.scheduler.alphas_cumprod[timestep] beta_prod_t = 1 - alpha_prod_t if self.scheduler.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) elif self.scheduler.config.prediction_type == "sample": pred_original_sample = model_output elif self.scheduler.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output # predict V model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`," " or `v_prediction`" ) return pred_original_sample def pred_epsilon(self, sample, model_output, timestep): alpha_prod_t = self.scheduler.alphas_cumprod[timestep] beta_prod_t = 1 - alpha_prod_t if self.scheduler.config.prediction_type == "epsilon": pred_eps = model_output elif self.scheduler.config.prediction_type == "sample": pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (beta_prod_t**0.5) elif self.scheduler.config.prediction_type == "v_prediction": pred_eps = (beta_prod_t**0.5) * sample + (alpha_prod_t**0.5) * model_output else: raise ValueError( f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`," " or `v_prediction`" ) return pred_eps # Gaussian blur def gaussian_blur_2d(img, kernel_size, sigma): ksize_half = (kernel_size - 1) * 0.5 x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) pdf = torch.exp(-0.5 * (x / sigma).pow(2)) x_kernel = pdf / pdf.sum() x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] img = F.pad(img, padding, mode="reflect") img = F.conv2d(img, kernel2d, groups=img.shape[-3]) return img ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import AttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor from ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name def preprocess(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): image = [image] if isinstance(image[0], PIL.Image.Image): w, h = image[0].size w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = [np.array(i.resize((w, h)))[None, :] for i in image] image = np.concatenate(image, axis=0) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image class StableDiffusionUpscalePipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image super-resolution using Stable Diffusion 2. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. low_res_scheduler ([`SchedulerMixin`]): A scheduler used to add initial noise to the low res conditioning image. It must be an instance of [`DDPMScheduler`]. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ _optional_components = ["watermarker", "safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, low_res_scheduler: DDPMScheduler, scheduler: KarrasDiffusionSchedulers, safety_checker: Optional[Any] = None, feature_extractor: Optional[CLIPImageProcessor] = None, watermarker: Optional[Any] = None, max_noise_level: int = 350, ): super().__init__() if hasattr( vae, "config" ): # check if vae has a config attribute `scaling_factor` and if it is set to 0.08333, else set it to 0.08333 and deprecate is_vae_scaling_factor_set_to_0_08333 = ( hasattr(vae.config, "scaling_factor") and vae.config.scaling_factor == 0.08333 ) if not is_vae_scaling_factor_set_to_0_08333: deprecation_message = ( "The configuration file of the vae does not contain `scaling_factor` or it is set to" f" {vae.config.scaling_factor}, which seems highly unlikely. If your checkpoint is a fine-tuned" " version of `stabilityai/stable-diffusion-x4-upscaler` you should change 'scaling_factor' to" " 0.08333 Please make sure to update the config accordingly, as not doing so might lead to" " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging" " Face Hub, it would be very nice if you could open a Pull Request for the `vae/config.json` file" ) deprecate("wrong scaling_factor", "1.0.0", deprecation_message, standard_warn=False) vae.register_to_config(scaling_factor=0.08333) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, low_res_scheduler=low_res_scheduler, scheduler=scheduler, safety_checker=safety_checker, watermarker=watermarker, feature_extractor=feature_extractor, ) self.register_to_config(max_noise_level=max_noise_level) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, nsfw_detected, watermark_detected = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype=dtype), ) else: nsfw_detected = None watermark_detected = None if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None: self.unet_offload_hook.offload() return image, nsfw_detected, watermark_detected # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image def check_inputs( self, prompt, image, noise_level, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}" ) # verify batch size of prompt and image are same if image is a list or tensor if isinstance(image, list) or isinstance(image, torch.Tensor): if isinstance(prompt, str): batch_size = 1 else: batch_size = len(prompt) if isinstance(image, list): image_batch_size = len(image) else: image_batch_size = image.shape[0] if batch_size != image_batch_size: raise ValueError( f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}." " Please make sure that passed `prompt` matches the batch size of `image`." ) # check noise level if noise_level > self.config.max_noise_level: raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height, width) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None, num_inference_steps: int = 75, guidance_scale: float = 9.0, noise_level: int = 20, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`): `Image`, or tensor representing an image batch which will be upscaled. * num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> import requests >>> from PIL import Image >>> from io import BytesIO >>> from diffusers import StableDiffusionUpscalePipeline >>> import torch >>> # load model and scheduler >>> model_id = "stabilityai/stable-diffusion-x4-upscaler" >>> pipeline = StableDiffusionUpscalePipeline.from_pretrained( ... model_id, revision="fp16", torch_dtype=torch.float16 ... ) >>> pipeline = pipeline.to("cuda") >>> # let's download an image >>> url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png" >>> response = requests.get(url) >>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB") >>> low_res_img = low_res_img.resize((128, 128)) >>> prompt = "a white cat" >>> upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] >>> upscaled_image.save("upsampled_cat.png") ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs self.check_inputs( prompt, image, noise_level, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) if image is None: raise ValueError("`image` input cannot be undefined.") # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Preprocess image image = preprocess(image) image = image.to(dtype=prompt_embeds.dtype, device=device) # 5. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Add noise to image noise_level = torch.tensor([noise_level], dtype=torch.long, device=device) noise = randn_tensor(image.shape, generator=generator, device=device, dtype=prompt_embeds.dtype) image = self.low_res_scheduler.add_noise(image, noise, noise_level) batch_multiplier = 2 if do_classifier_free_guidance else 1 image = torch.cat([image] * batch_multiplier * num_images_per_prompt) noise_level = torch.cat([noise_level] * image.shape[0]) # 6. Prepare latent variables height, width = image.shape[2:] num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 7. Check that sizes of image and latents match num_channels_image = image.shape[1] if num_channels_latents + num_channels_image != self.unet.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_image`: {num_channels_image} " f" = {num_channels_latents+num_channels_image}. Please verify the config of" " `pipeline.unet` or your `image` input." ) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 9. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = torch.cat([latent_model_input, image], dim=1) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, class_labels=noise_level, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 10. Post-processing # make sure the VAE is in float32 mode, as it overflows in float16 self.vae.to(dtype=torch.float32) use_torch_2_0_or_xformers = self.vae.decoder.mid_block.attentions[0].processor in [ AttnProcessor2_0, XFormersAttnProcessor, LoRAXFormersAttnProcessor, ] # if xformers or torch_2_0 is used attention block does not need # to be in float32 which can save lots of memory if not use_torch_2_0_or_xformers: self.vae.post_quant_conv.to(latents.dtype) self.vae.decoder.conv_in.to(latents.dtype) self.vae.decoder.mid_block.to(latents.dtype) else: latents = latents.float() # 11. Convert to PIL if output_type == "pil": image = self.decode_latents(latents) image, has_nsfw_concept, _ = self.run_safety_checker(image, device, prompt_embeds.dtype) image = self.numpy_to_pil(image) # 11. Apply watermark if self.watermarker is not None: image = self.watermarker.apply_watermark(image) elif output_type == "pt": latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents).sample has_nsfw_concept = None else: image = self.decode_latents(latents) has_nsfw_concept = None # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers.models.clip.modeling_clip import CLIPTextModelOutput from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel from ...models.embeddings import get_timestep_embedding from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableUnCLIPPipeline >>> pipe = StableUnCLIPPipeline.from_pretrained( ... "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16 ... ) # TODO update model path >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> images = pipe(prompt).images >>> images[0].save("astronaut_horse.png") ``` """ class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin): """ Pipeline for text-to-image generation using stable unCLIP. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: prior_tokenizer ([`CLIPTokenizer`]): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). prior_text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. prior ([`PriorTransformer`]): The canonincal unCLIP prior to approximate the image embedding from the text embedding. prior_scheduler ([`KarrasDiffusionSchedulers`]): Scheduler used in the prior denoising process. image_normalizer ([`StableUnCLIPImageNormalizer`]): Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image embeddings after the noise has been applied. image_noising_scheduler ([`KarrasDiffusionSchedulers`]): Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined by `noise_level` in `StableUnCLIPPipeline.__call__`. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). text_encoder ([`CLIPTextModel`]): Frozen text-encoder. unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`KarrasDiffusionSchedulers`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. """ # prior components prior_tokenizer: CLIPTokenizer prior_text_encoder: CLIPTextModelWithProjection prior: PriorTransformer prior_scheduler: KarrasDiffusionSchedulers # image noising components image_normalizer: StableUnCLIPImageNormalizer image_noising_scheduler: KarrasDiffusionSchedulers # regular denoising components tokenizer: CLIPTokenizer text_encoder: CLIPTextModel unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vae: AutoencoderKL def __init__( self, # prior components prior_tokenizer: CLIPTokenizer, prior_text_encoder: CLIPTextModelWithProjection, prior: PriorTransformer, prior_scheduler: KarrasDiffusionSchedulers, # image noising components image_normalizer: StableUnCLIPImageNormalizer, image_noising_scheduler: KarrasDiffusionSchedulers, # regular denoising components tokenizer: CLIPTokenizer, text_encoder: CLIPTextModelWithProjection, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, # vae vae: AutoencoderKL, ): super().__init__() self.register_modules( prior_tokenizer=prior_tokenizer, prior_text_encoder=prior_text_encoder, prior=prior, prior_scheduler=prior_scheduler, image_normalizer=image_normalizer, image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, vae=vae, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") # TODO: self.prior.post_process_latents and self.image_noiser.{scale,unscale} are not covered by the offload hooks, so they fails if added to the list models = [ self.prior_text_encoder, self.text_encoder, self.unet, self.vae, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.prior_text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt with _encode_prompt->_encode_prior_prompt, tokenizer->prior_tokenizer, text_encoder->prior_text_encoder def _encode_prior_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None, text_attention_mask: Optional[torch.Tensor] = None, ): if text_model_output is None: batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings text_inputs = self.prior_tokenizer( prompt, padding="max_length", max_length=self.prior_tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids text_mask = text_inputs.attention_mask.bool().to(device) untruncated_ids = self.prior_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.prior_tokenizer.batch_decode( untruncated_ids[:, self.prior_tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.prior_tokenizer.model_max_length} tokens: {removed_text}" ) text_input_ids = text_input_ids[:, : self.prior_tokenizer.model_max_length] prior_text_encoder_output = self.prior_text_encoder(text_input_ids.to(device)) prompt_embeds = prior_text_encoder_output.text_embeds prior_text_encoder_hidden_states = prior_text_encoder_output.last_hidden_state else: batch_size = text_model_output[0].shape[0] prompt_embeds, prior_text_encoder_hidden_states = text_model_output[0], text_model_output[1] text_mask = text_attention_mask prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) prior_text_encoder_hidden_states = prior_text_encoder_hidden_states.repeat_interleave( num_images_per_prompt, dim=0 ) text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) if do_classifier_free_guidance: uncond_tokens = [""] * batch_size uncond_input = self.prior_tokenizer( uncond_tokens, padding="max_length", max_length=self.prior_tokenizer.model_max_length, truncation=True, return_tensors="pt", ) uncond_text_mask = uncond_input.attention_mask.bool().to(device) negative_prompt_embeds_prior_text_encoder_output = self.prior_text_encoder( uncond_input.input_ids.to(device) ) negative_prompt_embeds = negative_prompt_embeds_prior_text_encoder_output.text_embeds uncond_prior_text_encoder_hidden_states = ( negative_prompt_embeds_prior_text_encoder_output.last_hidden_state ) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len) seq_len = uncond_prior_text_encoder_hidden_states.shape[1] uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.repeat( 1, num_images_per_prompt, 1 ) uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.view( batch_size * num_images_per_prompt, seq_len, -1 ) uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0) # done duplicates # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prior_text_encoder_hidden_states = torch.cat( [uncond_prior_text_encoder_hidden_states, prior_text_encoder_hidden_states] ) text_mask = torch.cat([uncond_text_mask, text_mask]) return prompt_embeds, prior_text_encoder_hidden_states, text_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs with prepare_extra_step_kwargs->prepare_prior_extra_step_kwargs, scheduler->prior_scheduler def prepare_prior_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the prior_scheduler step, since not all prior_schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other prior_schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.prior_scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the prior_scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.prior_scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, height, width, callback_steps, noise_level, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two." ) if prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( "Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined." ) if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps: raise ValueError( f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive." ) # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) latents = latents * scheduler.init_noise_sigma return latents def noise_image_embeddings( self, image_embeds: torch.Tensor, noise_level: int, noise: Optional[torch.FloatTensor] = None, generator: Optional[torch.Generator] = None, ): """ Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher `noise_level` increases the variance in the final un-noised images. The noise is applied in two ways 1. A noise schedule is applied directly to the embeddings 2. A vector of sinusoidal time embeddings are appended to the output. In both cases, the amount of noise is controlled by the same `noise_level`. The embeddings are normalized before the noise is applied and un-normalized after the noise is applied. """ if noise is None: noise = randn_tensor( image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype ) noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device) self.image_normalizer.to(image_embeds.device) image_embeds = self.image_normalizer.scale(image_embeds) image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise) image_embeds = self.image_normalizer.unscale(image_embeds) noise_level = get_timestep_embedding( timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0 ) # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors, # but we might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. noise_level = noise_level.to(image_embeds.dtype) image_embeds = torch.cat((image_embeds, noise_level), 1) return image_embeds @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, # regular denoising process args prompt: Optional[Union[str, List[str]]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 20, guidance_scale: float = 10.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, noise_level: int = 0, # prior args prior_num_inference_steps: int = 25, prior_guidance_scale: float = 4.0, prior_latents: Optional[torch.FloatTensor] = None, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 20): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 10.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). noise_level (`int`, *optional*, defaults to `0`): The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in the final un-noised images. See `StableUnCLIPPipeline.noise_image_embeddings` for details. prior_num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps in the prior denoising process. More denoising steps usually lead to a higher quality image at the expense of slower inference. prior_guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale for the prior denoising process as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `prior_guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. prior_latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image embedding generation in the prior denoising process. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt=prompt, height=height, width=width, callback_steps=callback_steps, noise_level=noise_level, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] batch_size = batch_size * num_images_per_prompt device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. prior_do_classifier_free_guidance = prior_guidance_scale > 1.0 # 3. Encode input prompt prior_prompt_embeds, prior_text_encoder_hidden_states, prior_text_mask = self._encode_prior_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=prior_do_classifier_free_guidance, ) # 4. Prepare prior timesteps self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device) prior_timesteps_tensor = self.prior_scheduler.timesteps # 5. Prepare prior latent variables embedding_dim = self.prior.config.embedding_dim prior_latents = self.prepare_latents( (batch_size, embedding_dim), prior_prompt_embeds.dtype, device, generator, prior_latents, self.prior_scheduler, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline prior_extra_step_kwargs = self.prepare_prior_extra_step_kwargs(generator, eta) # 7. Prior denoising loop for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([prior_latents] * 2) if prior_do_classifier_free_guidance else prior_latents latent_model_input = self.prior_scheduler.scale_model_input(latent_model_input, t) predicted_image_embedding = self.prior( latent_model_input, timestep=t, proj_embedding=prior_prompt_embeds, encoder_hidden_states=prior_text_encoder_hidden_states, attention_mask=prior_text_mask, ).predicted_image_embedding if prior_do_classifier_free_guidance: predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2) predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * ( predicted_image_embedding_text - predicted_image_embedding_uncond ) prior_latents = self.prior_scheduler.step( predicted_image_embedding, timestep=t, sample=prior_latents, **prior_extra_step_kwargs, return_dict=False, )[0] if callback is not None and i % callback_steps == 0: callback(i, t, prior_latents) prior_latents = self.prior.post_process_latents(prior_latents) image_embeds = prior_latents # done prior # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 8. Encode input prompt prompt_embeds = self._encode_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 9. Prepare image embeddings image_embeds = self.noise_image_embeddings( image_embeds=image_embeds, noise_level=noise_level, generator=generator, ) if do_classifier_free_guidance: negative_prompt_embeds = torch.zeros_like(image_embeds) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes image_embeds = torch.cat([negative_prompt_embeds, image_embeds]) # 10. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 11. Prepare latent variables num_channels_latents = self.unet.config.in_channels shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) latents = self.prepare_latents( shape=shape, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=latents, scheduler=self.scheduler, ) # 12. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 13. Denoising loop for i, t in enumerate(self.progress_bar(timesteps)): latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, class_labels=image_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import PIL import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from diffusers.utils.import_utils import is_accelerate_available from ...image_processor import VaeImageProcessor from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.embeddings import get_timestep_embedding from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_version, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import requests >>> import torch >>> from PIL import Image >>> from io import BytesIO >>> from diffusers import StableUnCLIPImg2ImgPipeline >>> pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( ... "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16 ... ) # TODO update model path >>> pipe = pipe.to("cuda") >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> response = requests.get(url) >>> init_image = Image.open(BytesIO(response.content)).convert("RGB") >>> init_image = init_image.resize((768, 512)) >>> prompt = "A fantasy landscape, trending on artstation" >>> images = pipe(prompt, init_image).images >>> images[0].save("fantasy_landscape.png") ``` """ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): """ Pipeline for text-guided image to image generation using stable unCLIP. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: feature_extractor ([`CLIPImageProcessor`]): Feature extractor for image pre-processing before being encoded. image_encoder ([`CLIPVisionModelWithProjection`]): CLIP vision model for encoding images. image_normalizer ([`StableUnCLIPImageNormalizer`]): Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image embeddings after the noise has been applied. image_noising_scheduler ([`KarrasDiffusionSchedulers`]): Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined by `noise_level` in `StableUnCLIPPipeline.__call__`. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). text_encoder ([`CLIPTextModel`]): Frozen text-encoder. unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`KarrasDiffusionSchedulers`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. """ # image encoding components feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection # image noising components image_normalizer: StableUnCLIPImageNormalizer image_noising_scheduler: KarrasDiffusionSchedulers # regular denoising components tokenizer: CLIPTokenizer text_encoder: CLIPTextModel unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vae: AutoencoderKL def __init__( self, # image encoding components feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection, # image noising components image_normalizer: StableUnCLIPImageNormalizer, image_noising_scheduler: KarrasDiffusionSchedulers, # regular denoising components tokenizer: CLIPTokenizer, text_encoder: CLIPTextModel, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, # vae vae: AutoencoderKL, ): super().__init__() self.register_modules( feature_extractor=feature_extractor, image_encoder=image_encoder, image_normalizer=image_normalizer, image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, vae=vae, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") # TODO: self.image_normalizer.{scale,unscale} are not covered by the offload hooks, so they fails if added to the list models = [ self.image_encoder, self.text_encoder, self.unet, self.vae, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.image_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def _encode_image( self, image, device, batch_size, num_images_per_prompt, do_classifier_free_guidance, noise_level, generator, image_embeds, ): dtype = next(self.image_encoder.parameters()).dtype if isinstance(image, PIL.Image.Image): # the image embedding should repeated so it matches the total batch size of the prompt repeat_by = batch_size else: # assume the image input is already properly batched and just needs to be repeated so # it matches the num_images_per_prompt. # # NOTE(will) this is probably missing a few number of side cases. I.e. batched/non-batched # `image_embeds`. If those happen to be common use cases, let's think harder about # what the expected dimensions of inputs should be and how we handle the encoding. repeat_by = num_images_per_prompt if image_embeds is None: if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeds = self.image_encoder(image).image_embeds image_embeds = self.noise_image_embeddings( image_embeds=image_embeds, noise_level=noise_level, generator=generator, ) # duplicate image embeddings for each generation per prompt, using mps friendly method image_embeds = image_embeds.unsqueeze(1) bs_embed, seq_len, _ = image_embeds.shape image_embeds = image_embeds.repeat(1, repeat_by, 1) image_embeds = image_embeds.view(bs_embed * repeat_by, seq_len, -1) image_embeds = image_embeds.squeeze(1) if do_classifier_free_guidance: negative_prompt_embeds = torch.zeros_like(image_embeds) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes image_embeds = torch.cat([negative_prompt_embeds, image_embeds]) return image_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, image, height, width, callback_steps, noise_level, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, image_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two." ) if prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( "Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined." ) if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps: raise ValueError( f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive." ) if image is not None and image_embeds is not None: raise ValueError( "Provide either `image` or `image_embeds`. Please make sure to define only one of the two." ) if image is None and image_embeds is None: raise ValueError( "Provide either `image` or `image_embeds`. Cannot leave both `image` and `image_embeds` undefined." ) if image is not None: if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_unclip.StableUnCLIPPipeline.noise_image_embeddings def noise_image_embeddings( self, image_embeds: torch.Tensor, noise_level: int, noise: Optional[torch.FloatTensor] = None, generator: Optional[torch.Generator] = None, ): """ Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher `noise_level` increases the variance in the final un-noised images. The noise is applied in two ways 1. A noise schedule is applied directly to the embeddings 2. A vector of sinusoidal time embeddings are appended to the output. In both cases, the amount of noise is controlled by the same `noise_level`. The embeddings are normalized before the noise is applied and un-normalized after the noise is applied. """ if noise is None: noise = randn_tensor( image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype ) noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device) self.image_normalizer.to(image_embeds.device) image_embeds = self.image_normalizer.scale(image_embeds) image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise) image_embeds = self.image_normalizer.unscale(image_embeds) noise_level = get_timestep_embedding( timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0 ) # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors, # but we might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. noise_level = noise_level.to(image_embeds.dtype) image_embeds = torch.cat((image_embeds, noise_level), 1) return image_embeds @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, image: Union[torch.FloatTensor, PIL.Image.Image] = None, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 20, guidance_scale: float = 10, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, noise_level: int = 0, image_embeds: Optional[torch.FloatTensor] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, either `prompt_embeds` will be used or prompt is initialized to `""`. image (`torch.FloatTensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch. The image will be encoded to its CLIP embedding which the unet will be conditioned on. Note that the image is _not_ encoded by the vae and then used as the latents in the denoising process such as in the standard stable diffusion text guided image variation process. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 20): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 10.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). noise_level (`int`, *optional*, defaults to `0`): The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in the final un-noised images. See `StableUnCLIPPipeline.noise_image_embeddings` for details. image_embeds (`torch.FloatTensor`, *optional*): Pre-generated CLIP embeddings to condition the unet on. Note that these are not latents to be used in the denoising process. If you want to provide pre-generated latents, pass them to `__call__` as `latents`. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor if prompt is None and prompt_embeds is None: prompt = len(image) * [""] if isinstance(image, list) else "" # 1. Check inputs. Raise error if not correct self.check_inputs( prompt=prompt, image=image, height=height, width=width, callback_steps=callback_steps, noise_level=noise_level, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, image_embeds=image_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] batch_size = batch_size * num_images_per_prompt device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Encoder input image noise_level = torch.tensor([noise_level], device=device) image_embeds = self._encode_image( image=image, device=device, batch_size=batch_size, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, noise_level=noise_level, generator=generator, image_embeds=image_embeds, ) # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size=batch_size, num_channels_latents=num_channels_latents, height=height, width=width, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=latents, ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 8. Denoising loop for i, t in enumerate(self.progress_bar(timesteps)): latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, class_labels=image_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 9. Post-processing if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/stable_diffusion/safety_checker.py ================================================ # Copyright 2023 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. import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from ...utils import logging logger = logging.get_logger(__name__) def cosine_distance(image_embeds, text_embeds): normalized_image_embeds = nn.functional.normalize(image_embeds) normalized_text_embeds = nn.functional.normalize(text_embeds) return torch.mm(normalized_image_embeds, normalized_text_embeds.t()) class StableDiffusionSafetyChecker(PreTrainedModel): config_class = CLIPConfig _no_split_modules = ["CLIPEncoderLayer"] def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModel(config.vision_config) self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False) self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False) self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False) self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False) self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False) @torch.no_grad() def forward(self, clip_input, images): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy() cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy() result = [] batch_size = image_embeds.shape[0] for i in range(batch_size): result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []} # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 for concept_idx in range(len(special_cos_dist[0])): concept_cos = special_cos_dist[i][concept_idx] concept_threshold = self.special_care_embeds_weights[concept_idx].item() result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["special_scores"][concept_idx] > 0: result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]}) adjustment = 0.01 for concept_idx in range(len(cos_dist[0])): concept_cos = cos_dist[i][concept_idx] concept_threshold = self.concept_embeds_weights[concept_idx].item() result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["concept_scores"][concept_idx] > 0: result_img["bad_concepts"].append(concept_idx) result.append(result_img) has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result] for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: if torch.is_tensor(images) or torch.is_tensor(images[0]): images[idx] = torch.zeros_like(images[idx]) # black image else: images[idx] = np.zeros(images[idx].shape) # black image if any(has_nsfw_concepts): logger.warning( "Potential NSFW content was detected in one or more images. A black image will be returned instead." " Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts @torch.no_grad() def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds) cos_dist = cosine_distance(image_embeds, self.concept_embeds) # increase this value to create a stronger `nsfw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment # special_scores = special_scores.round(decimals=3) special_care = torch.any(special_scores > 0, dim=1) special_adjustment = special_care * 0.01 special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1]) concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment # concept_scores = concept_scores.round(decimals=3) has_nsfw_concepts = torch.any(concept_scores > 0, dim=1) images[has_nsfw_concepts] = 0.0 # black image return images, has_nsfw_concepts ================================================ FILE: diffusers/pipelines/stable_diffusion/safety_checker_flax.py ================================================ # Copyright 2023 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, Tuple import jax import jax.numpy as jnp from flax import linen as nn from flax.core.frozen_dict import FrozenDict from transformers import CLIPConfig, FlaxPreTrainedModel from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule def jax_cosine_distance(emb_1, emb_2, eps=1e-12): norm_emb_1 = jnp.divide(emb_1.T, jnp.clip(jnp.linalg.norm(emb_1, axis=1), a_min=eps)).T norm_emb_2 = jnp.divide(emb_2.T, jnp.clip(jnp.linalg.norm(emb_2, axis=1), a_min=eps)).T return jnp.matmul(norm_emb_1, norm_emb_2.T) class FlaxStableDiffusionSafetyCheckerModule(nn.Module): config: CLIPConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.vision_model = FlaxCLIPVisionModule(self.config.vision_config) self.visual_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype) self.concept_embeds = self.param("concept_embeds", jax.nn.initializers.ones, (17, self.config.projection_dim)) self.special_care_embeds = self.param( "special_care_embeds", jax.nn.initializers.ones, (3, self.config.projection_dim) ) self.concept_embeds_weights = self.param("concept_embeds_weights", jax.nn.initializers.ones, (17,)) self.special_care_embeds_weights = self.param("special_care_embeds_weights", jax.nn.initializers.ones, (3,)) def __call__(self, clip_input): pooled_output = self.vision_model(clip_input)[1] image_embeds = self.visual_projection(pooled_output) special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds) cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds) # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign image inputs adjustment = 0.0 special_scores = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment special_scores = jnp.round(special_scores, 3) is_special_care = jnp.any(special_scores > 0, axis=1, keepdims=True) # Use a lower threshold if an image has any special care concept special_adjustment = is_special_care * 0.01 concept_scores = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment concept_scores = jnp.round(concept_scores, 3) has_nsfw_concepts = jnp.any(concept_scores > 0, axis=1) return has_nsfw_concepts class FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel): config_class = CLIPConfig main_input_name = "clip_input" module_class = FlaxStableDiffusionSafetyCheckerModule def __init__( self, config: CLIPConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): if input_shape is None: input_shape = (1, 224, 224, 3) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.KeyArray, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor clip_input = jax.random.normal(rng, input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, clip_input)["params"] return random_params def __call__( self, clip_input, params: dict = None, ): clip_input = jnp.transpose(clip_input, (0, 2, 3, 1)) return self.module.apply( {"params": params or self.params}, jnp.array(clip_input, dtype=jnp.float32), rngs={}, ) ================================================ FILE: diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py ================================================ # Copyright 2023 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, Union import torch from torch import nn from ...configuration_utils import ConfigMixin, register_to_config from ...models.modeling_utils import ModelMixin class StableUnCLIPImageNormalizer(ModelMixin, ConfigMixin): """ This class is used to hold the mean and standard deviation of the CLIP embedder used in stable unCLIP. It is used to normalize the image embeddings before the noise is applied and un-normalize the noised image embeddings. """ @register_to_config def __init__( self, embedding_dim: int = 768, ): super().__init__() self.mean = nn.Parameter(torch.zeros(1, embedding_dim)) self.std = nn.Parameter(torch.ones(1, embedding_dim)) def to( self, torch_device: Optional[Union[str, torch.device]] = None, torch_dtype: Optional[torch.dtype] = None, ): self.mean = nn.Parameter(self.mean.to(torch_device).to(torch_dtype)) self.std = nn.Parameter(self.std.to(torch_device).to(torch_dtype)) return self def scale(self, embeds): embeds = (embeds - self.mean) * 1.0 / self.std return embeds def unscale(self, embeds): embeds = (embeds * self.std) + self.mean return embeds ================================================ FILE: diffusers/pipelines/stable_diffusion_safe/__init__.py ================================================ from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import BaseOutput, is_torch_available, is_transformers_available @dataclass class SafetyConfig(object): WEAK = { "sld_warmup_steps": 15, "sld_guidance_scale": 20, "sld_threshold": 0.0, "sld_momentum_scale": 0.0, "sld_mom_beta": 0.0, } MEDIUM = { "sld_warmup_steps": 10, "sld_guidance_scale": 1000, "sld_threshold": 0.01, "sld_momentum_scale": 0.3, "sld_mom_beta": 0.4, } STRONG = { "sld_warmup_steps": 7, "sld_guidance_scale": 2000, "sld_threshold": 0.025, "sld_momentum_scale": 0.5, "sld_mom_beta": 0.7, } MAX = { "sld_warmup_steps": 0, "sld_guidance_scale": 5000, "sld_threshold": 1.0, "sld_momentum_scale": 0.5, "sld_mom_beta": 0.7, } @dataclass class StableDiffusionSafePipelineOutput(BaseOutput): """ Output class for Safe Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. nsfw_content_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, or `None` if safety checking could not be performed. images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images that were flagged by the safety checker any may contain "not-safe-for-work" (nsfw) content, or `None` if no safety check was performed or no images were flagged. applied_safety_concept (`str`) The safety concept that was applied for safety guidance, or `None` if safety guidance was disabled """ images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] unsafe_images: Optional[Union[List[PIL.Image.Image], np.ndarray]] applied_safety_concept: Optional[str] if is_transformers_available() and is_torch_available(): from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe from .safety_checker import SafeStableDiffusionSafetyChecker ================================================ FILE: diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py ================================================ import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import torch from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import deprecate, is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionSafePipelineOutput from .safety_checker import SafeStableDiffusionSafetyChecker logger = logging.get_logger(__name__) # pylint: disable=invalid-name class StableDiffusionPipelineSafe(DiffusionPipeline): r""" Pipeline for text-to-image generation using Safe Latent Diffusion. The implementation is based on the [`StableDiffusionPipeline`] This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ["safety_checker", "feature_extractor"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: SafeStableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__() safety_concept: Optional[str] = ( "an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity," " bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child" " abuse, brutality, cruelty" ) if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self._safety_text_concept = safety_concept self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.register_to_config(requires_safety_checker=requires_safety_checker) @property def safety_concept(self): r""" Getter method for the safety concept used with SLD Returns: `str`: The text describing the safety concept """ return self._safety_text_concept @safety_concept.setter def safety_concept(self, concept): r""" Setter method for the safety concept used with SLD Args: concept (`str`): The text of the new safety concept """ self._safety_text_concept = concept def enable_sequential_cpu_offload(self): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device("cuda") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). """ batch_size = len(prompt) if isinstance(prompt, list) else 1 text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids if not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # Encode the safety concept text if enable_safety_guidance: safety_concept_input = self.tokenizer( [self._safety_text_concept], padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) safety_embeddings = self.text_encoder(safety_concept_input.input_ids.to(self.device))[0] # duplicate safety embeddings for each generation per prompt, using mps friendly method seq_len = safety_embeddings.shape[1] safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1) safety_embeddings = safety_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance + sld, we need to do three forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing three forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, safety_embeddings]) else: # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def run_safety_checker(self, image, device, dtype, enable_safety_guidance): if self.safety_checker is not None: images = image.copy() safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) flagged_images = np.zeros((2, *image.shape[1:])) if any(has_nsfw_concept): logger.warning( "Potential NSFW content was detected in one or more images. A black image will be returned" " instead." f"{'You may look at this images in the `unsafe_images` variable of the output at your own discretion.' if enable_safety_guidance else 'Try again with a different prompt and/or seed.'}" ) for idx, has_nsfw_concept in enumerate(has_nsfw_concept): if has_nsfw_concept: flagged_images[idx] = images[idx] image[idx] = np.zeros(image[idx].shape) # black image else: has_nsfw_concept = None flagged_images = None return image, has_nsfw_concept, flagged_images # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def perform_safety_guidance( self, enable_safety_guidance, safety_momentum, noise_guidance, noise_pred_out, i, sld_guidance_scale, sld_warmup_steps, sld_threshold, sld_momentum_scale, sld_mom_beta, ): # Perform SLD guidance if enable_safety_guidance: if safety_momentum is None: safety_momentum = torch.zeros_like(noise_guidance) noise_pred_text, noise_pred_uncond = noise_pred_out[0], noise_pred_out[1] noise_pred_safety_concept = noise_pred_out[2] # Equation 6 scale = torch.clamp(torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0) # Equation 6 safety_concept_scale = torch.where( (noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale ) # Equation 4 noise_guidance_safety = torch.mul((noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale) # Equation 7 noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum # Equation 8 safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety if i >= sld_warmup_steps: # Warmup # Equation 3 noise_guidance = noise_guidance - noise_guidance_safety return noise_guidance, safety_momentum @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, sld_guidance_scale: Optional[float] = 1000, sld_warmup_steps: Optional[int] = 10, sld_threshold: Optional[float] = 0.01, sld_momentum_scale: Optional[float] = 0.3, sld_mom_beta: Optional[float] = 0.4, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. sld_guidance_scale (`float`, *optional*, defaults to 1000): Safe latent guidance as defined in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105). `sld_guidance_scale` is defined as sS of Eq. 6. If set to be less than 1, safety guidance will be disabled. sld_warmup_steps (`int`, *optional*, defaults to 10): Number of warmup steps for safety guidance. SLD will only be applied for diffusion steps greater than `sld_warmup_steps`. `sld_warmup_steps` is defined as `delta` of [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105). sld_threshold (`float`, *optional*, defaults to 0.01): Threshold that separates the hyperplane between appropriate and inappropriate images. `sld_threshold` is defined as `lamda` of Eq. 5 in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105). sld_momentum_scale (`float`, *optional*, defaults to 0.3): Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0 momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller than `sld_warmup_steps`. `sld_momentum_scale` is defined as `sm` of Eq. 7 in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105). sld_mom_beta (`float`, *optional*, defaults to 0.4): Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller than `sld_warmup_steps`. `sld_mom_beta` is defined as `beta m` of Eq. 8 in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105). Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps) # 2. Define call parameters batch_size = 1 if isinstance(prompt, str) else len(prompt) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 enable_safety_guidance = sld_guidance_scale > 1.0 and do_classifier_free_guidance if not enable_safety_guidance: warnings.warn("Safety checker disabled!") # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) safety_momentum = None num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents] * (3 if enable_safety_guidance else 2)) if do_classifier_free_guidance else latents ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample # perform guidance if do_classifier_free_guidance: noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2)) noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1] # default classifier free guidance noise_guidance = noise_pred_text - noise_pred_uncond # Perform SLD guidance if enable_safety_guidance: if safety_momentum is None: safety_momentum = torch.zeros_like(noise_guidance) noise_pred_safety_concept = noise_pred_out[2] # Equation 6 scale = torch.clamp( torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0 ) # Equation 6 safety_concept_scale = torch.where( (noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale, ) # Equation 4 noise_guidance_safety = torch.mul( (noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale ) # Equation 7 noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum # Equation 8 safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety if i >= sld_warmup_steps: # Warmup # Equation 3 noise_guidance = noise_guidance - noise_guidance_safety noise_pred = noise_pred_uncond + guidance_scale * noise_guidance # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 8. Post-processing image = self.decode_latents(latents) # 9. Run safety checker image, has_nsfw_concept, flagged_images = self.run_safety_checker( image, device, prompt_embeds.dtype, enable_safety_guidance ) # 10. Convert to PIL if output_type == "pil": image = self.numpy_to_pil(image) if flagged_images is not None: flagged_images = self.numpy_to_pil(flagged_images) if not return_dict: return ( image, has_nsfw_concept, self._safety_text_concept if enable_safety_guidance else None, flagged_images, ) return StableDiffusionSafePipelineOutput( images=image, nsfw_content_detected=has_nsfw_concept, applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None, unsafe_images=flagged_images, ) ================================================ FILE: diffusers/pipelines/stable_diffusion_safe/safety_checker.py ================================================ # Copyright 2023 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. import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from ...utils import logging logger = logging.get_logger(__name__) def cosine_distance(image_embeds, text_embeds): normalized_image_embeds = nn.functional.normalize(image_embeds) normalized_text_embeds = nn.functional.normalize(text_embeds) return torch.mm(normalized_image_embeds, normalized_text_embeds.t()) class SafeStableDiffusionSafetyChecker(PreTrainedModel): config_class = CLIPConfig _no_split_modules = ["CLIPEncoderLayer"] def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModel(config.vision_config) self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False) self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False) self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False) self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False) self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False) @torch.no_grad() def forward(self, clip_input, images): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy() cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy() result = [] batch_size = image_embeds.shape[0] for i in range(batch_size): result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []} # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 for concept_idx in range(len(special_cos_dist[0])): concept_cos = special_cos_dist[i][concept_idx] concept_threshold = self.special_care_embeds_weights[concept_idx].item() result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["special_scores"][concept_idx] > 0: result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]}) adjustment = 0.01 for concept_idx in range(len(cos_dist[0])): concept_cos = cos_dist[i][concept_idx] concept_threshold = self.concept_embeds_weights[concept_idx].item() result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["concept_scores"][concept_idx] > 0: result_img["bad_concepts"].append(concept_idx) result.append(result_img) has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result] return images, has_nsfw_concepts @torch.no_grad() def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds) cos_dist = cosine_distance(image_embeds, self.concept_embeds) # increase this value to create a stronger `nsfw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment # special_scores = special_scores.round(decimals=3) special_care = torch.any(special_scores > 0, dim=1) special_adjustment = special_care * 0.01 special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1]) concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment # concept_scores = concept_scores.round(decimals=3) has_nsfw_concepts = torch.any(concept_scores > 0, dim=1) return images, has_nsfw_concepts ================================================ FILE: diffusers/pipelines/stochastic_karras_ve/__init__.py ================================================ from .pipeline_stochastic_karras_ve import KarrasVePipeline ================================================ FILE: diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py ================================================ # Copyright 2023 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 List, Optional, Tuple, Union import torch from ...models import UNet2DModel from ...schedulers import KarrasVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class KarrasVePipeline(DiffusionPipeline): r""" Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and the VE column of Table 1 from [1] for reference. [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://arxiv.org/abs/2011.13456 Parameters: unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`KarrasVeScheduler`]): Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image. """ # add type hints for linting unet: UNet2DModel scheduler: KarrasVeScheduler def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, batch_size: int = 1, num_inference_steps: int = 50, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[Tuple, ImagePipelineOutput]: r""" Args: batch_size (`int`, *optional*, defaults to 1): The number of images to generate. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ img_size = self.unet.config.sample_size shape = (batch_size, 3, img_size, img_size) model = self.unet # sample x_0 ~ N(0, sigma_0^2 * I) sample = randn_tensor(shape, generator=generator, device=self.device) * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(num_inference_steps) for t in self.progress_bar(self.scheduler.timesteps): # here sigma_t == t_i from the paper sigma = self.scheduler.schedule[t] sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0 # 1. Select temporarily increased noise level sigma_hat # 2. Add new noise to move from sample_i to sample_hat sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator) # 3. Predict the noise residual given the noise magnitude `sigma_hat` # The model inputs and output are adjusted by following eq. (213) in [1]. model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample # 4. Evaluate dx/dt at sigma_hat # 5. Take Euler step from sigma to sigma_prev step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat) if sigma_prev != 0: # 6. Apply 2nd order correction # The model inputs and output are adjusted by following eq. (213) in [1]. model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample step_output = self.scheduler.step_correct( model_output, sigma_hat, sigma_prev, sample_hat, step_output.prev_sample, step_output["derivative"], ) sample = step_output.prev_sample sample = (sample / 2 + 0.5).clamp(0, 1) image = sample.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/text_to_video_synthesis/__init__.py ================================================ from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class TextToVideoSDPipelineOutput(BaseOutput): """ Output class for text to video pipelines. Args: frames (`List[np.ndarray]` or `torch.FloatTensor`) List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as a `torch` tensor. NumPy array present the denoised images of the diffusion pipeline. The length of the list denotes the video length i.e., the number of frames. """ frames: Union[List[np.ndarray], torch.FloatTensor] try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline ================================================ FILE: diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py ================================================ # Copyright 2023 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. import inspect from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import torch from transformers import CLIPTextModel, CLIPTokenizer from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet3DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) from ..pipeline_utils import DiffusionPipeline from . import TextToVideoSDPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import TextToVideoSDPipeline >>> from diffusers.utils import export_to_video >>> pipe = TextToVideoSDPipeline.from_pretrained( ... "damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16" ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "Spiderman is surfing" >>> video_frames = pipe(prompt).frames >>> video_path = export_to_video(video_frames) >>> video_path ``` """ def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]: # This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78 # reshape to ncfhw mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1) std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1) # unnormalize back to [0,1] video = video.mul_(std).add_(mean) video.clamp_(0, 1) # prepare the final outputs i, c, f, h, w = video.shape images = video.permute(2, 3, 0, 4, 1).reshape( f, h, i * w, c ) # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c) images = images.unbind(dim=0) # prepare a list of indvidual (consecutive frames) images = [(image.cpu().numpy() * 255).astype("uint8") for image in images] # f h w c return images class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-video generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Same as Stable Diffusion 2. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet3DConditionModel`]): Conditional U-Net architecture to denoise the encoded video latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet3DConditionModel, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. """ if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): from accelerate import cpu_offload else: raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: cpu_offload(cpu_offloaded_model, device) def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. """ if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def decode_latents(self, latents): latents = 1 / self.vae.config.scaling_factor * latents batch_size, channels, num_frames, height, width = latents.shape latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width) image = self.vae.decode(latents).sample video = ( image[None, :] .reshape( ( batch_size, num_frames, -1, ) + image.shape[2:] ) .permute(0, 2, 1, 3, 4) ) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 video = video.float() return video # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) def prepare_latents( self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None ): shape = ( batch_size, num_channels_latents, num_frames, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_frames: int = 16, num_inference_steps: int = 50, guidance_scale: float = 9.0, negative_prompt: Optional[Union[str, List[str]]] = None, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "np", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated video. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated video. num_frames (`int`, *optional*, defaults to 16): The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds amounts to 2 seconds of video. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality videos at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate videos that are closely linked to the text `prompt`, usually at the expense of lower video quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the video generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. Latents should be of shape `(batch_size, num_channel, num_frames, height, width)`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"np"`): The output format of the generate video. Choose between `torch.FloatTensor` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated frames. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor num_images_per_prompt = 1 # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # reshape latents bsz, channel, frames, width, height = latents.shape latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height) noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # reshape latents back latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) video_tensor = self.decode_latents(latents) if output_type == "pt": video = video_tensor else: video = tensor2vid(video_tensor) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (video,) return TextToVideoSDPipelineOutput(frames=video) ================================================ FILE: diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py ================================================ import copy from dataclasses import dataclass from typing import Callable, List, Optional, Union import numpy as np import PIL import torch import torch.nn.functional as F from torch.nn.functional import grid_sample from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import BaseOutput def rearrange_0(tensor, f): F, C, H, W = tensor.size() tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4)) return tensor def rearrange_1(tensor): B, C, F, H, W = tensor.size() return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W)) def rearrange_3(tensor, f): F, D, C = tensor.size() return torch.reshape(tensor, (F // f, f, D, C)) def rearrange_4(tensor): B, F, D, C = tensor.size() return torch.reshape(tensor, (B * F, D, C)) class CrossFrameAttnProcessor: """ Cross frame attention processor. For each frame the self-attention is replaced with attention with first frame Args: batch_size: The number that represents actual batch size, other than the frames. For example, using calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to 2, due to classifier-free guidance. """ def __init__(self, batch_size=2): self.batch_size = batch_size def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None): batch_size, sequence_length, _ = hidden_states.shape attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) query = attn.to_q(hidden_states) is_cross_attention = encoder_hidden_states is not None 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) # Sparse Attention if not is_cross_attention: video_length = key.size()[0] // self.batch_size first_frame_index = [0] * video_length # rearrange keys to have batch and frames in the 1st and 2nd dims respectively key = rearrange_3(key, video_length) key = key[:, first_frame_index] # rearrange values to have batch and frames in the 1st and 2nd dims respectively value = rearrange_3(value, video_length) value = value[:, first_frame_index] # rearrange back to original shape key = rearrange_4(key) value = rearrange_4(value) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) return hidden_states @dataclass class TextToVideoPipelineOutput(BaseOutput): images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] def coords_grid(batch, ht, wd, device): # Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device)) coords = torch.stack(coords[::-1], dim=0).float() return coords[None].repeat(batch, 1, 1, 1) def warp_single_latent(latent, reference_flow): """ Warp latent of a single frame with given flow Args: latent: latent code of a single frame reference_flow: flow which to warp the latent with Returns: warped: warped latent """ _, _, H, W = reference_flow.size() _, _, h, w = latent.size() coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype) coords_t0 = coords0 + reference_flow coords_t0[:, 0] /= W coords_t0[:, 1] /= H coords_t0 = coords_t0 * 2.0 - 1.0 coords_t0 = F.interpolate(coords_t0, size=(h, w), mode="bilinear") coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1)) warped = grid_sample(latent, coords_t0, mode="nearest", padding_mode="reflection") return warped def create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype): """ Create translation motion field Args: motion_field_strength_x: motion strength along x-axis motion_field_strength_y: motion strength along y-axis frame_ids: indexes of the frames the latents of which are being processed. This is needed when we perform chunk-by-chunk inference device: device dtype: dtype Returns: """ seq_length = len(frame_ids) reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype) for fr_idx in range(seq_length): reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx]) reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx]) return reference_flow def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents): """ Creates translation motion and warps the latents accordingly Args: motion_field_strength_x: motion strength along x-axis motion_field_strength_y: motion strength along y-axis frame_ids: indexes of the frames the latents of which are being processed. This is needed when we perform chunk-by-chunk inference latents: latent codes of frames Returns: warped_latents: warped latents """ motion_field = create_motion_field( motion_field_strength_x=motion_field_strength_x, motion_field_strength_y=motion_field_strength_y, frame_ids=frame_ids, device=latents.device, dtype=latents.dtype, ) warped_latents = latents.clone().detach() for i in range(len(warped_latents)): warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None]) return warped_latents class TextToVideoZeroPipeline(StableDiffusionPipeline): r""" Pipeline for zero-shot text-to-video generation using Stable Diffusion. This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, ): super().__init__( vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker ) self.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) def forward_loop(self, x_t0, t0, t1, generator): """ Perform ddpm forward process from time t0 to t1. This is the same as adding noise with corresponding variance. Args: x_t0: latent code at time t0 t0: t0 t1: t1 generator: torch.Generator object Returns: x_t1: forward process applied to x_t0 from time t0 to t1. """ eps = torch.randn(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device) alpha_vec = torch.prod(self.scheduler.alphas[t0:t1]) x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps return x_t1 def backward_loop( self, latents, timesteps, prompt_embeds, guidance_scale, callback, callback_steps, num_warmup_steps, extra_step_kwargs, cross_attention_kwargs=None, ): """ Perform backward process given list of time steps Args: latents: Latents at time timesteps[0]. timesteps: time steps, along which to perform backward process. prompt_embeds: Pre-generated text embeddings guidance_scale: Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. extra_step_kwargs: extra_step_kwargs. cross_attention_kwargs: cross_attention_kwargs. num_warmup_steps: number of warmup steps. Returns: latents: latents of backward process output at time timesteps[-1] """ do_classifier_free_guidance = guidance_scale > 1.0 num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order with self.progress_bar(total=num_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) return latents.clone().detach() @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], video_length: Optional[int] = 8, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_videos_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, motion_field_strength_x: float = 12, motion_field_strength_y: float = 12, output_type: Optional[str] = "tensor", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, t0: int = 44, t1: int = 47, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. video_length (`int`, *optional*, defaults to 8): The number of generated video frames height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of videos to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"numpy"`): The output format of the generated image. Choose between `"latent"` and `"numpy"`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. motion_field_strength_x (`float`, *optional*, defaults to 12): Strength of motion in generated video along x-axis. See the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1. motion_field_strength_y (`float`, *optional*, defaults to 12): Strength of motion in generated video along y-axis. See the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1. t0 (`int`, *optional*, defaults to 44): Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1. t1 (`int`, *optional*, defaults to 47): Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1. Returns: [`~pipelines.text_to_video_synthesis.TextToVideoPipelineOutput`]: The output contains a ndarray of the generated images, when output_type != 'latent', otherwise a latent codes of generated image, and a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ assert video_length > 0 frame_ids = list(range(video_length)) assert num_videos_per_prompt == 1 if isinstance(prompt, str): prompt = [prompt] if isinstance(negative_prompt, str): negative_prompt = [negative_prompt] # Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps) # Define call parameters batch_size = 1 if isinstance(prompt, str) else len(prompt) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt ) # Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_videos_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order # Perform the first backward process up to time T_1 x_1_t1 = self.backward_loop( timesteps=timesteps[: -t1 - 1], prompt_embeds=prompt_embeds, latents=latents, guidance_scale=guidance_scale, callback=callback, callback_steps=callback_steps, extra_step_kwargs=extra_step_kwargs, num_warmup_steps=num_warmup_steps, ) scheduler_copy = copy.deepcopy(self.scheduler) # Perform the second backward process up to time T_0 x_1_t0 = self.backward_loop( timesteps=timesteps[-t1 - 1 : -t0 - 1], prompt_embeds=prompt_embeds, latents=x_1_t1, guidance_scale=guidance_scale, callback=callback, callback_steps=callback_steps, extra_step_kwargs=extra_step_kwargs, num_warmup_steps=0, ) # Propagate first frame latents at time T_0 to remaining frames x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1) # Add motion in latents at time T_0 x_2k_t0 = create_motion_field_and_warp_latents( motion_field_strength_x=motion_field_strength_x, motion_field_strength_y=motion_field_strength_y, latents=x_2k_t0, frame_ids=frame_ids[1:], ) # Perform forward process up to time T_1 x_2k_t1 = self.forward_loop( x_t0=x_2k_t0, t0=timesteps[-t0 - 1].item(), t1=timesteps[-t1 - 1].item(), generator=generator, ) # Perform backward process from time T_1 to 0 x_1k_t1 = torch.cat([x_1_t1, x_2k_t1]) b, l, d = prompt_embeds.size() prompt_embeds = prompt_embeds[:, None].repeat(1, video_length, 1, 1).reshape(b * video_length, l, d) self.scheduler = scheduler_copy x_1k_0 = self.backward_loop( timesteps=timesteps[-t1 - 1 :], prompt_embeds=prompt_embeds, latents=x_1k_t1, guidance_scale=guidance_scale, callback=callback, callback_steps=callback_steps, extra_step_kwargs=extra_step_kwargs, num_warmup_steps=0, ) latents = x_1k_0 # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") torch.cuda.empty_cache() if output_type == "latent": image = latents has_nsfw_concept = None else: image = self.decode_latents(latents) # Run safety checker image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image, has_nsfw_concept) return TextToVideoPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: diffusers/pipelines/unclip/__init__.py ================================================ from ...utils import ( OptionalDependencyNotAvailable, is_torch_available, is_transformers_available, is_transformers_version, ) try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline else: from .pipeline_unclip import UnCLIPPipeline from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline from .text_proj import UnCLIPTextProjModel ================================================ FILE: diffusers/pipelines/unclip/pipeline_unclip.py ================================================ # Copyright 2023 Kakao Brain and 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. import inspect from typing import List, Optional, Tuple, Union import torch from torch.nn import functional as F from transformers import CLIPTextModelWithProjection, CLIPTokenizer from transformers.models.clip.modeling_clip import CLIPTextModelOutput from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel from ...pipelines import DiffusionPipeline from ...pipelines.pipeline_utils import ImagePipelineOutput from ...schedulers import UnCLIPScheduler from ...utils import is_accelerate_available, logging, randn_tensor from .text_proj import UnCLIPTextProjModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name class UnCLIPPipeline(DiffusionPipeline): """ Pipeline for text-to-image generation using unCLIP This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). prior ([`PriorTransformer`]): The canonincal unCLIP prior to approximate the image embedding from the text embedding. text_proj ([`UnCLIPTextProjModel`]): Utility class to prepare and combine the embeddings before they are passed to the decoder. decoder ([`UNet2DConditionModel`]): The decoder to invert the image embedding into an image. super_res_first ([`UNet2DModel`]): Super resolution unet. Used in all but the last step of the super resolution diffusion process. super_res_last ([`UNet2DModel`]): Super resolution unet. Used in the last step of the super resolution diffusion process. prior_scheduler ([`UnCLIPScheduler`]): Scheduler used in the prior denoising process. Just a modified DDPMScheduler. decoder_scheduler ([`UnCLIPScheduler`]): Scheduler used in the decoder denoising process. Just a modified DDPMScheduler. super_res_scheduler ([`UnCLIPScheduler`]): Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler. """ prior: PriorTransformer decoder: UNet2DConditionModel text_proj: UnCLIPTextProjModel text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer super_res_first: UNet2DModel super_res_last: UNet2DModel prior_scheduler: UnCLIPScheduler decoder_scheduler: UnCLIPScheduler super_res_scheduler: UnCLIPScheduler def __init__( self, prior: PriorTransformer, decoder: UNet2DConditionModel, text_encoder: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, text_proj: UnCLIPTextProjModel, super_res_first: UNet2DModel, super_res_last: UNet2DModel, prior_scheduler: UnCLIPScheduler, decoder_scheduler: UnCLIPScheduler, super_res_scheduler: UnCLIPScheduler, ): super().__init__() self.register_modules( prior=prior, decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, text_proj=text_proj, super_res_first=super_res_first, super_res_last=super_res_last, prior_scheduler=prior_scheduler, decoder_scheduler=decoder_scheduler, super_res_scheduler=super_res_scheduler, ) def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) latents = latents * scheduler.init_noise_sigma return latents def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None, text_attention_mask: Optional[torch.Tensor] = None, ): if text_model_output is None: batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids text_mask = text_inputs.attention_mask.bool().to(device) untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] text_encoder_output = self.text_encoder(text_input_ids.to(device)) prompt_embeds = text_encoder_output.text_embeds text_encoder_hidden_states = text_encoder_output.last_hidden_state else: batch_size = text_model_output[0].shape[0] prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1] text_mask = text_attention_mask prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) if do_classifier_free_guidance: uncond_tokens = [""] * batch_size uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) uncond_text_mask = uncond_input.attention_mask.bool().to(device) negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device)) negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len) seq_len = uncond_text_encoder_hidden_states.shape[1] uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1) uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view( batch_size * num_images_per_prompt, seq_len, -1 ) uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0) # done duplicates # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]) text_mask = torch.cat([uncond_text_mask, text_mask]) return prompt_embeds, text_encoder_hidden_states, text_mask def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") # TODO: self.prior.post_process_latents is not covered by the offload hooks, so it fails if added to the list models = [ self.decoder, self.text_proj, self.text_encoder, self.super_res_first, self.super_res_last, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"): return self.device for module in self.decoder.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() def __call__( self, prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: int = 1, prior_num_inference_steps: int = 25, decoder_num_inference_steps: int = 25, super_res_num_inference_steps: int = 7, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prior_latents: Optional[torch.FloatTensor] = None, decoder_latents: Optional[torch.FloatTensor] = None, super_res_latents: Optional[torch.FloatTensor] = None, text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None, text_attention_mask: Optional[torch.Tensor] = None, prior_guidance_scale: float = 4.0, decoder_guidance_scale: float = 8.0, output_type: Optional[str] = "pil", return_dict: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. This can only be left undefined if `text_model_output` and `text_attention_mask` is passed. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prior_num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps for the prior. More denoising steps usually lead to a higher quality image at the expense of slower inference. decoder_num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality image at the expense of slower inference. super_res_num_inference_steps (`int`, *optional*, defaults to 7): The number of denoising steps for super resolution. More denoising steps usually lead to a higher quality image at the expense of slower inference. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. prior_latents (`torch.FloatTensor` of shape (batch size, embeddings dimension), *optional*): Pre-generated noisy latents to be used as inputs for the prior. decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*): Pre-generated noisy latents to be used as inputs for the decoder. super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*): Pre-generated noisy latents to be used as inputs for the decoder. prior_guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. decoder_guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. text_model_output (`CLIPTextModelOutput`, *optional*): Pre-defined CLIPTextModel outputs that can be derived from the text encoder. Pre-defined text outputs can be passed for tasks like text embedding interpolations. Make sure to also pass `text_attention_mask` in this case. `prompt` can the be left to `None`. text_attention_mask (`torch.Tensor`, *optional*): Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention masks are necessary when passing `text_model_output`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. """ if prompt is not None: if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") else: batch_size = text_model_output[0].shape[0] device = self._execution_device batch_size = batch_size * num_images_per_prompt do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0 prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask ) # prior self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device) prior_timesteps_tensor = self.prior_scheduler.timesteps embedding_dim = self.prior.config.embedding_dim prior_latents = self.prepare_latents( (batch_size, embedding_dim), prompt_embeds.dtype, device, generator, prior_latents, self.prior_scheduler, ) for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents predicted_image_embedding = self.prior( latent_model_input, timestep=t, proj_embedding=prompt_embeds, encoder_hidden_states=text_encoder_hidden_states, attention_mask=text_mask, ).predicted_image_embedding if do_classifier_free_guidance: predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2) predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * ( predicted_image_embedding_text - predicted_image_embedding_uncond ) if i + 1 == prior_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = prior_timesteps_tensor[i + 1] prior_latents = self.prior_scheduler.step( predicted_image_embedding, timestep=t, sample=prior_latents, generator=generator, prev_timestep=prev_timestep, ).prev_sample prior_latents = self.prior.post_process_latents(prior_latents) image_embeddings = prior_latents # done prior # decoder text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj( image_embeddings=image_embeddings, prompt_embeds=prompt_embeds, text_encoder_hidden_states=text_encoder_hidden_states, do_classifier_free_guidance=do_classifier_free_guidance, ) if device.type == "mps": # HACK: MPS: There is a panic when padding bool tensors, # so cast to int tensor for the pad and back to bool afterwards text_mask = text_mask.type(torch.int) decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1) decoder_text_mask = decoder_text_mask.type(torch.bool) else: decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True) self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device) decoder_timesteps_tensor = self.decoder_scheduler.timesteps num_channels_latents = self.decoder.config.in_channels height = self.decoder.config.sample_size width = self.decoder.config.sample_size decoder_latents = self.prepare_latents( (batch_size, num_channels_latents, height, width), text_encoder_hidden_states.dtype, device, generator, decoder_latents, self.decoder_scheduler, ) for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents noise_pred = self.decoder( sample=latent_model_input, timestep=t, encoder_hidden_states=text_encoder_hidden_states, class_labels=additive_clip_time_embeddings, attention_mask=decoder_text_mask, ).sample if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if i + 1 == decoder_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = decoder_timesteps_tensor[i + 1] # compute the previous noisy sample x_t -> x_t-1 decoder_latents = self.decoder_scheduler.step( noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator ).prev_sample decoder_latents = decoder_latents.clamp(-1, 1) image_small = decoder_latents # done decoder # super res self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device) super_res_timesteps_tensor = self.super_res_scheduler.timesteps channels = self.super_res_first.config.in_channels // 2 height = self.super_res_first.config.sample_size width = self.super_res_first.config.sample_size super_res_latents = self.prepare_latents( (batch_size, channels, height, width), image_small.dtype, device, generator, super_res_latents, self.super_res_scheduler, ) if device.type == "mps": # MPS does not support many interpolations image_upscaled = F.interpolate(image_small, size=[height, width]) else: interpolate_antialias = {} if "antialias" in inspect.signature(F.interpolate).parameters: interpolate_antialias["antialias"] = True image_upscaled = F.interpolate( image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias ) for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)): # no classifier free guidance if i == super_res_timesteps_tensor.shape[0] - 1: unet = self.super_res_last else: unet = self.super_res_first latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1) noise_pred = unet( sample=latent_model_input, timestep=t, ).sample if i + 1 == super_res_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = super_res_timesteps_tensor[i + 1] # compute the previous noisy sample x_t -> x_t-1 super_res_latents = self.super_res_scheduler.step( noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator ).prev_sample image = super_res_latents # done super res # post processing image = image * 0.5 + 0.5 image = image.clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/unclip/pipeline_unclip_image_variation.py ================================================ # Copyright 2023 Kakao Brain and 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. import inspect from typing import List, Optional, Union import PIL import torch from torch.nn import functional as F from transformers import ( CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection, ) from ...models import UNet2DConditionModel, UNet2DModel from ...pipelines import DiffusionPipeline, ImagePipelineOutput from ...schedulers import UnCLIPScheduler from ...utils import is_accelerate_available, logging, randn_tensor from .text_proj import UnCLIPTextProjModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name class UnCLIPImageVariationPipeline(DiffusionPipeline): """ Pipeline to generate variations from an input image using unCLIP This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `image_encoder`. image_encoder ([`CLIPVisionModelWithProjection`]): Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. text_proj ([`UnCLIPTextProjModel`]): Utility class to prepare and combine the embeddings before they are passed to the decoder. decoder ([`UNet2DConditionModel`]): The decoder to invert the image embedding into an image. super_res_first ([`UNet2DModel`]): Super resolution unet. Used in all but the last step of the super resolution diffusion process. super_res_last ([`UNet2DModel`]): Super resolution unet. Used in the last step of the super resolution diffusion process. decoder_scheduler ([`UnCLIPScheduler`]): Scheduler used in the decoder denoising process. Just a modified DDPMScheduler. super_res_scheduler ([`UnCLIPScheduler`]): Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler. """ decoder: UNet2DConditionModel text_proj: UnCLIPTextProjModel text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection super_res_first: UNet2DModel super_res_last: UNet2DModel decoder_scheduler: UnCLIPScheduler super_res_scheduler: UnCLIPScheduler def __init__( self, decoder: UNet2DConditionModel, text_encoder: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, text_proj: UnCLIPTextProjModel, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection, super_res_first: UNet2DModel, super_res_last: UNet2DModel, decoder_scheduler: UnCLIPScheduler, super_res_scheduler: UnCLIPScheduler, ): super().__init__() self.register_modules( decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, text_proj=text_proj, feature_extractor=feature_extractor, image_encoder=image_encoder, super_res_first=super_res_first, super_res_last=super_res_last, decoder_scheduler=decoder_scheduler, super_res_scheduler=super_res_scheduler, ) # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) latents = latents * scheduler.init_noise_sigma return latents def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance): batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ) text_input_ids = text_inputs.input_ids text_mask = text_inputs.attention_mask.bool().to(device) text_encoder_output = self.text_encoder(text_input_ids.to(device)) prompt_embeds = text_encoder_output.text_embeds text_encoder_hidden_states = text_encoder_output.last_hidden_state prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) if do_classifier_free_guidance: uncond_tokens = [""] * batch_size max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) uncond_text_mask = uncond_input.attention_mask.bool().to(device) negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device)) negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len) seq_len = uncond_text_encoder_hidden_states.shape[1] uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1) uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view( batch_size * num_images_per_prompt, seq_len, -1 ) uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0) # done duplicates # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]) text_mask = torch.cat([uncond_text_mask, text_mask]) return prompt_embeds, text_encoder_hidden_states, text_mask def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None): dtype = next(self.image_encoder.parameters()).dtype if image_embeddings is None: if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeddings = self.image_encoder(image).image_embeds image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0) return image_embeddings def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") models = [ self.decoder, self.text_proj, self.text_encoder, self.super_res_first, self.super_res_last, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"): return self.device for module in self.decoder.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device @torch.no_grad() def __call__( self, image: Optional[Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]] = None, num_images_per_prompt: int = 1, decoder_num_inference_steps: int = 25, super_res_num_inference_steps: int = 7, generator: Optional[torch.Generator] = None, decoder_latents: Optional[torch.FloatTensor] = None, super_res_latents: Optional[torch.FloatTensor] = None, image_embeddings: Optional[torch.Tensor] = None, decoder_guidance_scale: float = 8.0, output_type: Optional[str] = "pil", return_dict: bool = True, ): """ Function invoked when calling the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The image or images to guide the image generation. If you provide a tensor, it needs to comply with the configuration of [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json) `CLIPImageProcessor`. Can be left to `None` only when `image_embeddings` are passed. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. decoder_num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality image at the expense of slower inference. super_res_num_inference_steps (`int`, *optional*, defaults to 7): The number of denoising steps for super resolution. More denoising steps usually lead to a higher quality image at the expense of slower inference. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*): Pre-generated noisy latents to be used as inputs for the decoder. super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*): Pre-generated noisy latents to be used as inputs for the decoder. decoder_guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. image_embeddings (`torch.Tensor`, *optional*): Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings can be passed for tasks like image interpolations. `image` can the be left to `None`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. """ if image is not None: if isinstance(image, PIL.Image.Image): batch_size = 1 elif isinstance(image, list): batch_size = len(image) else: batch_size = image.shape[0] else: batch_size = image_embeddings.shape[0] prompt = [""] * batch_size device = self._execution_device batch_size = batch_size * num_images_per_prompt do_classifier_free_guidance = decoder_guidance_scale > 1.0 prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance ) image_embeddings = self._encode_image(image, device, num_images_per_prompt, image_embeddings) # decoder text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj( image_embeddings=image_embeddings, prompt_embeds=prompt_embeds, text_encoder_hidden_states=text_encoder_hidden_states, do_classifier_free_guidance=do_classifier_free_guidance, ) if device.type == "mps": # HACK: MPS: There is a panic when padding bool tensors, # so cast to int tensor for the pad and back to bool afterwards text_mask = text_mask.type(torch.int) decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1) decoder_text_mask = decoder_text_mask.type(torch.bool) else: decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True) self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device) decoder_timesteps_tensor = self.decoder_scheduler.timesteps num_channels_latents = self.decoder.config.in_channels height = self.decoder.config.sample_size width = self.decoder.config.sample_size if decoder_latents is None: decoder_latents = self.prepare_latents( (batch_size, num_channels_latents, height, width), text_encoder_hidden_states.dtype, device, generator, decoder_latents, self.decoder_scheduler, ) for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents noise_pred = self.decoder( sample=latent_model_input, timestep=t, encoder_hidden_states=text_encoder_hidden_states, class_labels=additive_clip_time_embeddings, attention_mask=decoder_text_mask, ).sample if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) if i + 1 == decoder_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = decoder_timesteps_tensor[i + 1] # compute the previous noisy sample x_t -> x_t-1 decoder_latents = self.decoder_scheduler.step( noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator ).prev_sample decoder_latents = decoder_latents.clamp(-1, 1) image_small = decoder_latents # done decoder # super res self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device) super_res_timesteps_tensor = self.super_res_scheduler.timesteps channels = self.super_res_first.config.in_channels // 2 height = self.super_res_first.config.sample_size width = self.super_res_first.config.sample_size if super_res_latents is None: super_res_latents = self.prepare_latents( (batch_size, channels, height, width), image_small.dtype, device, generator, super_res_latents, self.super_res_scheduler, ) if device.type == "mps": # MPS does not support many interpolations image_upscaled = F.interpolate(image_small, size=[height, width]) else: interpolate_antialias = {} if "antialias" in inspect.signature(F.interpolate).parameters: interpolate_antialias["antialias"] = True image_upscaled = F.interpolate( image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias ) for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)): # no classifier free guidance if i == super_res_timesteps_tensor.shape[0] - 1: unet = self.super_res_last else: unet = self.super_res_first latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1) noise_pred = unet( sample=latent_model_input, timestep=t, ).sample if i + 1 == super_res_timesteps_tensor.shape[0]: prev_timestep = None else: prev_timestep = super_res_timesteps_tensor[i + 1] # compute the previous noisy sample x_t -> x_t-1 super_res_latents = self.super_res_scheduler.step( noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator ).prev_sample image = super_res_latents # done super res # post processing image = image * 0.5 + 0.5 image = image.clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).float().numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/unclip/text_proj.py ================================================ # Copyright 2023 Kakao Brain and 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. import torch from torch import nn from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin class UnCLIPTextProjModel(ModelMixin, ConfigMixin): """ Utility class for CLIP embeddings. Used to combine the image and text embeddings into a format usable by the decoder. For more details, see the original paper: https://arxiv.org/abs/2204.06125 section 2.1 """ @register_to_config def __init__( self, *, clip_extra_context_tokens: int = 4, clip_embeddings_dim: int = 768, time_embed_dim: int, cross_attention_dim, ): super().__init__() self.learned_classifier_free_guidance_embeddings = nn.Parameter(torch.zeros(clip_embeddings_dim)) # parameters for additional clip time embeddings self.embedding_proj = nn.Linear(clip_embeddings_dim, time_embed_dim) self.clip_image_embeddings_project_to_time_embeddings = nn.Linear(clip_embeddings_dim, time_embed_dim) # parameters for encoder hidden states self.clip_extra_context_tokens = clip_extra_context_tokens self.clip_extra_context_tokens_proj = nn.Linear( clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim ) self.encoder_hidden_states_proj = nn.Linear(clip_embeddings_dim, cross_attention_dim) self.text_encoder_hidden_states_norm = nn.LayerNorm(cross_attention_dim) def forward(self, *, image_embeddings, prompt_embeds, text_encoder_hidden_states, do_classifier_free_guidance): if do_classifier_free_guidance: # Add the classifier free guidance embeddings to the image embeddings image_embeddings_batch_size = image_embeddings.shape[0] classifier_free_guidance_embeddings = self.learned_classifier_free_guidance_embeddings.unsqueeze(0) classifier_free_guidance_embeddings = classifier_free_guidance_embeddings.expand( image_embeddings_batch_size, -1 ) image_embeddings = torch.cat([classifier_free_guidance_embeddings, image_embeddings], dim=0) # The image embeddings batch size and the text embeddings batch size are equal assert image_embeddings.shape[0] == prompt_embeds.shape[0] batch_size = prompt_embeds.shape[0] # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and # adding CLIP embeddings to the existing timestep embedding, ... time_projected_prompt_embeds = self.embedding_proj(prompt_embeds) time_projected_image_embeddings = self.clip_image_embeddings_project_to_time_embeddings(image_embeddings) additive_clip_time_embeddings = time_projected_image_embeddings + time_projected_prompt_embeds # ... and by projecting CLIP embeddings into four # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder" clip_extra_context_tokens = self.clip_extra_context_tokens_proj(image_embeddings) clip_extra_context_tokens = clip_extra_context_tokens.reshape(batch_size, -1, self.clip_extra_context_tokens) clip_extra_context_tokens = clip_extra_context_tokens.permute(0, 2, 1) text_encoder_hidden_states = self.encoder_hidden_states_proj(text_encoder_hidden_states) text_encoder_hidden_states = self.text_encoder_hidden_states_norm(text_encoder_hidden_states) text_encoder_hidden_states = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states], dim=1) return text_encoder_hidden_states, additive_clip_time_embeddings ================================================ FILE: diffusers/pipelines/versatile_diffusion/__init__.py ================================================ from ...utils import ( OptionalDependencyNotAvailable, is_torch_available, is_transformers_available, is_transformers_version, ) try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import ( VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, ) else: from .modeling_text_unet import UNetFlatConditionModel from .pipeline_versatile_diffusion import VersatileDiffusionPipeline from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline ================================================ FILE: diffusers/pipelines/versatile_diffusion/modeling_text_unet.py ================================================ from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin from ...models.attention import Attention from ...models.attention_processor import ( AttentionProcessor, AttnAddedKVProcessor, AttnAddedKVProcessor2_0, AttnProcessor, ) from ...models.dual_transformer_2d import DualTransformer2DModel from ...models.embeddings import GaussianFourierProjection, TextTimeEmbedding, TimestepEmbedding, Timesteps from ...models.transformer_2d import Transformer2DModel from ...models.unet_2d_condition import UNet2DConditionOutput from ...utils import is_torch_version, logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_down_block( down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, downsample_padding=None, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", resnet_skip_time_act=False, resnet_out_scale_factor=1.0, cross_attention_norm=None, ): down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type if down_block_type == "DownBlockFlat": return DownBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "CrossAttnDownBlockFlat": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat") return CrossAttnDownBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) raise ValueError(f"{down_block_type} is not supported.") def get_up_block( up_block_type, num_layers, in_channels, out_channels, prev_output_channel, temb_channels, add_upsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", resnet_skip_time_act=False, resnet_out_scale_factor=1.0, cross_attention_norm=None, ): up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type if up_block_type == "UpBlockFlat": return UpBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "CrossAttnUpBlockFlat": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat") return CrossAttnUpBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) raise ValueError(f"{up_block_type} is not supported.") # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat class UNetFlatConditionModel(ModelMixin, ConfigMixin): r""" UNetFlatConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): The number of channels in the output. center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. flip_sin_to_cos (`bool`, *optional*, defaults to `False`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`): The tuple of downsample blocks to use. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlockFlatCrossAttn"`): The mid block type. Choose from `UNetMidBlockFlatCrossAttn` or `UNetMidBlockFlatSimpleCrossAttn`, will skip the mid block layer if `None`. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat",)`): The tuple of upsample blocks to use. only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): Whether to include self-attention in the basic transformer blocks, see [`~models.attention.BasicTransformerBlock`]. block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. If `None`, it will skip the normalization and activation layers in post-processing norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): The dimension of the cross attention features. encoder_hid_dim (`int`, *optional*, defaults to None): If given, `encoder_hidden_states` will be projected from this dimension to `cross_attention_dim`. attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for resnet blocks, see [`~models.resnet.ResnetBlockFlat`]. Choose from `default` or `scale_shift`. class_embed_type (`str`, *optional*, defaults to None): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. addition_embed_type (`str`, *optional*, defaults to None): Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or "text". "text" will use the `TextTimeEmbedding` layer. num_class_embeds (`int`, *optional*, defaults to None): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`. time_embedding_type (`str`, *optional*, default to `positional`): The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. time_embedding_dim (`int`, *optional*, default to `None`): An optional override for the dimension of the projected time embedding. time_embedding_act_fn (`str`, *optional*, default to `None`): Optional activation function to use on the time embeddings only one time before they as passed to the rest of the unet. Choose from `silu`, `mish`, `gelu`, and `swish`. timestep_post_act (`str, *optional*, default to `None`): The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. time_cond_proj_dim (`int`, *optional*, default to `None`): The dimension of `cond_proj` layer in timestep embedding. conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when using the "projection" `class_embed_type`. Required when using the "projection" `class_embed_type`. class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time embeddings with the class embeddings. mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): Whether to use cross attention with the mid block when using the `UNetMidBlockFlatSimpleCrossAttn`. If `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is None, the `only_cross_attention` value will be used as the value for `mid_block_only_cross_attention`. Else, it will default to `False`. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels: int = 4, center_input_sample: bool = False, flip_sin_to_cos: bool = True, freq_shift: int = 0, down_block_types: Tuple[str] = ( "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat", ), mid_block_type: Optional[str] = "UNetMidBlockFlatCrossAttn", up_block_types: Tuple[str] = ( "UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", ), only_cross_attention: Union[bool, Tuple[bool]] = False, block_out_channels: Tuple[int] = (320, 640, 1280, 1280), layers_per_block: Union[int, Tuple[int]] = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = "silu", norm_num_groups: Optional[int] = 32, norm_eps: float = 1e-5, cross_attention_dim: Union[int, Tuple[int]] = 1280, encoder_hid_dim: Optional[int] = None, attention_head_dim: Union[int, Tuple[int]] = 8, dual_cross_attention: bool = False, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, addition_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: int = 1.0, time_embedding_type: str = "positional", time_embedding_dim: Optional[int] = None, time_embedding_act_fn: Optional[str] = None, timestep_post_act: Optional[str] = None, time_cond_proj_dim: Optional[int] = None, conv_in_kernel: int = 3, conv_out_kernel: int = 3, projection_class_embeddings_input_dim: Optional[int] = None, class_embeddings_concat: bool = False, mid_block_only_cross_attention: Optional[bool] = None, cross_attention_norm: Optional[str] = None, addition_embed_type_num_heads=64, ): super().__init__() self.sample_size = sample_size # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( "Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`:" f" {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( "Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`:" f" {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): raise ValueError( "Must provide the same number of `only_cross_attention` as `down_block_types`." f" `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." ) if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): raise ValueError( "Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`:" f" {attention_head_dim}. `down_block_types`: {down_block_types}." ) if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): raise ValueError( "Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`:" f" {cross_attention_dim}. `down_block_types`: {down_block_types}." ) if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): raise ValueError( "Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`:" f" {layers_per_block}. `down_block_types`: {down_block_types}." ) # input conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in = LinearMultiDim( in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time if time_embedding_type == "fourier": time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 if time_embed_dim % 2 != 0: raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") self.time_proj = GaussianFourierProjection( time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos ) timestep_input_dim = time_embed_dim elif time_embedding_type == "positional": time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] else: raise ValueError( f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." ) self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, post_act_fn=timestep_post_act, cond_proj_dim=time_cond_proj_dim, ) if encoder_hid_dim is not None: self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) else: self.encoder_hid_proj = None # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) elif class_embed_type == "projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" ) # The projection `class_embed_type` is the same as the timestep `class_embed_type` except # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings # 2. it projects from an arbitrary input dimension. # # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. # As a result, `TimestepEmbedding` can be passed arbitrary vectors. self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) elif class_embed_type == "simple_projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" ) self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) else: self.class_embedding = None if addition_embed_type == "text": if encoder_hid_dim is not None: text_time_embedding_from_dim = encoder_hid_dim else: text_time_embedding_from_dim = cross_attention_dim self.add_embedding = TextTimeEmbedding( text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads ) elif addition_embed_type is not None: raise ValueError(f"addition_embed_type: {addition_embed_type} must be None or 'text'.") if time_embedding_act_fn is None: self.time_embed_act = None elif time_embedding_act_fn == "swish": self.time_embed_act = lambda x: F.silu(x) elif time_embedding_act_fn == "mish": self.time_embed_act = nn.Mish() elif time_embedding_act_fn == "silu": self.time_embed_act = nn.SiLU() elif time_embedding_act_fn == "gelu": self.time_embed_act = nn.GELU() else: raise ValueError(f"Unsupported activation function: {time_embedding_act_fn}") self.down_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([]) if isinstance(only_cross_attention, bool): if mid_block_only_cross_attention is None: mid_block_only_cross_attention = only_cross_attention only_cross_attention = [only_cross_attention] * len(down_block_types) if mid_block_only_cross_attention is None: mid_block_only_cross_attention = False if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) if isinstance(cross_attention_dim, int): cross_attention_dim = (cross_attention_dim,) * len(down_block_types) if isinstance(layers_per_block, int): layers_per_block = [layers_per_block] * len(down_block_types) if class_embeddings_concat: # The time embeddings are concatenated with the class embeddings. The dimension of the # time embeddings passed to the down, middle, and up blocks is twice the dimension of the # regular time embeddings blocks_time_embed_dim = time_embed_dim * 2 else: blocks_time_embed_dim = time_embed_dim # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block[i], in_channels=input_channel, out_channels=output_channel, temb_channels=blocks_time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim[i], attn_num_head_channels=attention_head_dim[i], downsample_padding=downsample_padding, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, cross_attention_norm=cross_attention_norm, ) self.down_blocks.append(down_block) # mid if mid_block_type == "UNetMidBlockFlatCrossAttn": self.mid_block = UNetMidBlockFlatCrossAttn( in_channels=block_out_channels[-1], temb_channels=blocks_time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim[-1], attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) elif mid_block_type == "UNetMidBlockFlatSimpleCrossAttn": self.mid_block = UNetMidBlockFlatSimpleCrossAttn( in_channels=block_out_channels[-1], temb_channels=blocks_time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, cross_attention_dim=cross_attention_dim[-1], attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, only_cross_attention=mid_block_only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif mid_block_type is None: self.mid_block = None else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_attention_head_dim = list(reversed(attention_head_dim)) reversed_layers_per_block = list(reversed(layers_per_block)) reversed_cross_attention_dim = list(reversed(cross_attention_dim)) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=reversed_layers_per_block[i] + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=blocks_time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=reversed_cross_attention_dim[i], attn_num_head_channels=reversed_attention_head_dim[i], dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, cross_attention_norm=cross_attention_norm, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if norm_num_groups is not None: self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps ) if act_fn == "swish": self.conv_act = lambda x: F.silu(x) elif act_fn == "mish": self.conv_act = nn.Mish() elif act_fn == "silu": self.conv_act = nn.SiLU() elif act_fn == "gelu": self.conv_act = nn.GELU() else: raise ValueError(f"Unsupported activation function: {act_fn}") else: self.conv_norm_out = None self.conv_act = None conv_out_padding = (conv_out_kernel - 1) // 2 self.conv_out = LinearMultiDim( block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding ) @property def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.processor for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Parameters: `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor of **all** `Attention` layers. In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.: """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ self.set_attn_processor(AttnProcessor()) def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_sliceable_layers * [1] slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)): module.gradient_checkpointing = value def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet2DConditionOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Returns: [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): logger.info("Forward upsample size to force interpolation output size.") forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, 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=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # `Timesteps` does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=sample.dtype) emb = self.time_embedding(t_emb, timestep_cond) if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) # `Timesteps` does not contain any weights and will always return f32 tensors # there might be better ways to encapsulate this. class_labels = class_labels.to(dtype=sample.dtype) class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) if self.config.class_embeddings_concat: emb = torch.cat([emb, class_emb], dim=-1) else: emb = emb + class_emb if self.config.addition_embed_type == "text": aug_emb = self.add_embedding(encoder_hidden_states) emb = emb + aug_emb if self.time_embed_act is not None: emb = self.time_embed_act(emb) if self.encoder_hid_proj is not None: encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) # 2. pre-process sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples if down_block_additional_residuals is not None: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) if mid_block_additional_residual is not None: sample = sample + mid_block_additional_residual # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, upsample_size=upsample_size, attention_mask=attention_mask, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size ) # 6. post-process if self.conv_norm_out: sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if not return_dict: return (sample,) return UNet2DConditionOutput(sample=sample) class LinearMultiDim(nn.Linear): def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs): in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features) if out_features is None: out_features = in_features out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features) self.in_features_multidim = in_features self.out_features_multidim = out_features super().__init__(np.array(in_features).prod(), np.array(out_features).prod()) def forward(self, input_tensor, *args, **kwargs): shape = input_tensor.shape n_dim = len(self.in_features_multidim) input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features) output_tensor = super().forward(input_tensor) output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim) return output_tensor class ResnetBlockFlat(nn.Module): def __init__( self, *, in_channels, out_channels=None, dropout=0.0, temb_channels=512, groups=32, groups_out=None, pre_norm=True, eps=1e-6, time_embedding_norm="default", use_in_shortcut=None, second_dim=4, **kwargs, ): super().__init__() self.pre_norm = pre_norm self.pre_norm = True in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels) self.in_channels_prod = np.array(in_channels).prod() self.channels_multidim = in_channels if out_channels is not None: out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels) out_channels_prod = np.array(out_channels).prod() self.out_channels_multidim = out_channels else: out_channels_prod = self.in_channels_prod self.out_channels_multidim = self.channels_multidim self.time_embedding_norm = time_embedding_norm if groups_out is None: groups_out = groups self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True) self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0) if temb_channels is not None: self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod) else: self.time_emb_proj = None self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True) self.dropout = torch.nn.Dropout(dropout) self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0) self.nonlinearity = nn.SiLU() self.use_in_shortcut = ( self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut ) self.conv_shortcut = None if self.use_in_shortcut: self.conv_shortcut = torch.nn.Conv2d( self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0 ) def forward(self, input_tensor, temb): shape = input_tensor.shape n_dim = len(self.channels_multidim) input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1) input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1) hidden_states = input_tensor hidden_states = self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv1(hidden_states) if temb is not None: temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] hidden_states = hidden_states + temb hidden_states = self.norm2(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) if self.conv_shortcut is not None: input_tensor = self.conv_shortcut(input_tensor) output_tensor = input_tensor + hidden_states output_tensor = output_tensor.view(*shape[0:-n_dim], -1) output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim) return output_tensor # Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim class DownBlockFlat(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_downsample=True, downsample_padding=1, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlockFlat( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ LinearMultiDim( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, temb=None): output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states # Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim class CrossAttnDownBlockFlat(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, downsample_padding=1, add_downsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlockFlat( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ LinearMultiDim( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): # TODO(Patrick, William) - attention mask is not used output_states = () for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, use_reentrant=False, )[0] else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states # Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim class UpBlockFlat(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlockFlat( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states # Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim class CrossAttnUpBlockFlat(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, add_upsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlockFlat( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None, cross_attention_kwargs=None, upsample_size=None, attention_mask=None, ): # TODO(Patrick, William) - attention mask is not used for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, use_reentrant=False, )[0] else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states # Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat class UNetMidBlockFlatCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, dual_cross_attention=False, use_linear_projection=False, upcast_attention=False, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlockFlat( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] attentions = [] for _ in range(num_layers): if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, in_channels // attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, in_channels // attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlockFlat( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] hidden_states = resnet(hidden_states, temb) return hidden_states # Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat class UNetMidBlockFlatSimpleCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, skip_time_act=False, only_cross_attention=False, cross_attention_norm=None, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) self.num_heads = in_channels // self.attn_num_head_channels # there is always at least one resnet resnets = [ ResnetBlockFlat( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ] attentions = [] for _ in range(num_layers): processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=in_channels, cross_attention_dim=in_channels, heads=self.num_heads, dim_head=attn_num_head_channels, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) resnets.append( ResnetBlockFlat( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): # attn hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) # resnet hidden_states = resnet(hidden_states, temb) return hidden_states ================================================ FILE: diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py ================================================ import inspect from typing import Callable, List, Optional, Union import PIL.Image import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import logging from ..pipeline_utils import DiffusionPipeline from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline logger = logging.get_logger(__name__) # pylint: disable=invalid-name class VersatileDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionMegaSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ tokenizer: CLIPTokenizer image_feature_extractor: CLIPImageProcessor text_encoder: CLIPTextModel image_encoder: CLIPVisionModel image_unet: UNet2DConditionModel text_unet: UNet2DConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers def __init__( self, tokenizer: CLIPTokenizer, image_feature_extractor: CLIPImageProcessor, text_encoder: CLIPTextModel, image_encoder: CLIPVisionModel, image_unet: UNet2DConditionModel, text_unet: UNet2DConditionModel, vae: AutoencoderKL, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( tokenizer=tokenizer, image_feature_extractor=image_feature_extractor, text_encoder=text_encoder, image_encoder=image_encoder, image_unet=image_unet, text_unet=text_unet, vae=vae, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) @torch.no_grad() def image_variation( self, image: Union[torch.FloatTensor, PIL.Image.Image], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`): The image prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import VersatileDiffusionPipeline >>> import torch >>> import requests >>> from io import BytesIO >>> from PIL import Image >>> # let's download an initial image >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" >>> response = requests.get(url) >>> image = Image.open(BytesIO(response.content)).convert("RGB") >>> pipe = VersatileDiffusionPipeline.from_pretrained( ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> image = pipe.image_variation(image, generator=generator).images[0] >>> image.save("./car_variation.png") ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys() components = {name: component for name, component in self.components.items() if name in expected_components} return VersatileDiffusionImageVariationPipeline(**components)( image=image, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, negative_prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, eta=eta, generator=generator, latents=latents, output_type=output_type, return_dict=return_dict, callback=callback, callback_steps=callback_steps, ) @torch.no_grad() def text_to_image( self, prompt: Union[str, List[str]], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import VersatileDiffusionPipeline >>> import torch >>> pipe = VersatileDiffusionPipeline.from_pretrained( ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> image = pipe.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0] >>> image.save("./astronaut.png") ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys() components = {name: component for name, component in self.components.items() if name in expected_components} temp_pipeline = VersatileDiffusionTextToImagePipeline(**components) output = temp_pipeline( prompt=prompt, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, negative_prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, eta=eta, generator=generator, latents=latents, output_type=output_type, return_dict=return_dict, callback=callback, callback_steps=callback_steps, ) # swap the attention blocks back to the original state temp_pipeline._swap_unet_attention_blocks() return output @torch.no_grad() def dual_guided( self, prompt: Union[PIL.Image.Image, List[PIL.Image.Image]], image: Union[str, List[str]], text_to_image_strength: float = 0.5, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import VersatileDiffusionPipeline >>> import torch >>> import requests >>> from io import BytesIO >>> from PIL import Image >>> # let's download an initial image >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" >>> response = requests.get(url) >>> image = Image.open(BytesIO(response.content)).convert("RGB") >>> text = "a red car in the sun" >>> pipe = VersatileDiffusionPipeline.from_pretrained( ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> text_to_image_strength = 0.75 >>> image = pipe.dual_guided( ... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator ... ).images[0] >>> image.save("./car_variation.png") ``` Returns: [`~pipelines.stable_diffusion.ImagePipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys() components = {name: component for name, component in self.components.items() if name in expected_components} temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components) output = temp_pipeline( prompt=prompt, image=image, text_to_image_strength=text_to_image_strength, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, num_images_per_prompt=num_images_per_prompt, eta=eta, generator=generator, latents=latents, output_type=output_type, return_dict=return_dict, callback=callback, callback_steps=callback_steps, ) temp_pipeline._revert_dual_attention() return output ================================================ FILE: diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Tuple, Union import numpy as np import PIL import torch import torch.utils.checkpoint from transformers import ( CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection, ) from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput from .modeling_text_unet import UNetFlatConditionModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqvae ([`VQModel`]): Vector-quantized (VQ) Model to encode and decode images to and from latent representations. bert ([`LDMBertModel`]): Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture. tokenizer (`transformers.BertTokenizer`): Tokenizer of class [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ tokenizer: CLIPTokenizer image_feature_extractor: CLIPImageProcessor text_encoder: CLIPTextModelWithProjection image_encoder: CLIPVisionModelWithProjection image_unet: UNet2DConditionModel text_unet: UNetFlatConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers _optional_components = ["text_unet"] def __init__( self, tokenizer: CLIPTokenizer, image_feature_extractor: CLIPImageProcessor, text_encoder: CLIPTextModelWithProjection, image_encoder: CLIPVisionModelWithProjection, image_unet: UNet2DConditionModel, text_unet: UNetFlatConditionModel, vae: AutoencoderKL, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( tokenizer=tokenizer, image_feature_extractor=image_feature_extractor, text_encoder=text_encoder, image_encoder=image_encoder, image_unet=image_unet, text_unet=text_unet, vae=vae, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) if self.text_unet is not None and ( "dual_cross_attention" not in self.image_unet.config or not self.image_unet.config.dual_cross_attention ): # if loading from a universal checkpoint rather than a saved dual-guided pipeline self._convert_to_dual_attention() def remove_unused_weights(self): self.register_modules(text_unet=None) def _convert_to_dual_attention(self): """ Replace image_unet's `Transformer2DModel` blocks with `DualTransformer2DModel` that contains transformer blocks from both `image_unet` and `text_unet` """ for name, module in self.image_unet.named_modules(): if isinstance(module, Transformer2DModel): parent_name, index = name.rsplit(".", 1) index = int(index) image_transformer = self.image_unet.get_submodule(parent_name)[index] text_transformer = self.text_unet.get_submodule(parent_name)[index] config = image_transformer.config dual_transformer = DualTransformer2DModel( num_attention_heads=config.num_attention_heads, attention_head_dim=config.attention_head_dim, in_channels=config.in_channels, num_layers=config.num_layers, dropout=config.dropout, norm_num_groups=config.norm_num_groups, cross_attention_dim=config.cross_attention_dim, attention_bias=config.attention_bias, sample_size=config.sample_size, num_vector_embeds=config.num_vector_embeds, activation_fn=config.activation_fn, num_embeds_ada_norm=config.num_embeds_ada_norm, ) dual_transformer.transformers[0] = image_transformer dual_transformer.transformers[1] = text_transformer self.image_unet.get_submodule(parent_name)[index] = dual_transformer self.image_unet.register_to_config(dual_cross_attention=True) def _revert_dual_attention(self): """ Revert the image_unet `DualTransformer2DModel` blocks back to `Transformer2DModel` with image_unet weights Call this function if you reuse `image_unet` in another pipeline, e.g. `VersatileDiffusionPipeline` """ for name, module in self.image_unet.named_modules(): if isinstance(module, DualTransformer2DModel): parent_name, index = name.rsplit(".", 1) index = int(index) self.image_unet.get_submodule(parent_name)[index] = module.transformers[0] self.image_unet.register_to_config(dual_cross_attention=False) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.image_unet, self.text_unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device with unet->image_unet def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.image_unet, "_hf_hook"): return self.device for module in self.image_unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_text_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not """ def normalize_embeddings(encoder_output): embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state) embeds_pooled = encoder_output.text_embeds embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True) return embeds batch_size = len(prompt) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids if not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = normalize_embeddings(prompt_embeds) # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens = [""] * batch_size max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def _encode_image_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not """ def normalize_embeddings(encoder_output): embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state) embeds = self.image_encoder.visual_projection(embeds) embeds_pooled = embeds[:, 0:1] embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True) return embeds batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings image_input = self.image_feature_extractor(images=prompt, return_tensors="pt") pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype) image_embeddings = self.image_encoder(pixel_values) image_embeddings = normalize_embeddings(image_embeddings) # duplicate image embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = image_embeddings.shape image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1) image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt") pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype) negative_prompt_embeds = self.image_encoder(pixel_values) negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and conditional embeddings into a single batch # to avoid doing two forward passes image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings]) return image_embeddings # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs(self, prompt, image, height, width, callback_steps): if not isinstance(prompt, str) and not isinstance(prompt, PIL.Image.Image) and not isinstance(prompt, list): raise ValueError(f"`prompt` has to be of type `str` `PIL.Image` or `list` but is {type(prompt)}") if not isinstance(image, str) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list): raise ValueError(f"`image` has to be of type `str` `PIL.Image` or `list` but is {type(image)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def set_transformer_params(self, mix_ratio: float = 0.5, condition_types: Tuple = ("text", "image")): for name, module in self.image_unet.named_modules(): if isinstance(module, DualTransformer2DModel): module.mix_ratio = mix_ratio for i, type in enumerate(condition_types): if type == "text": module.condition_lengths[i] = self.text_encoder.config.max_position_embeddings module.transformer_index_for_condition[i] = 1 # use the second (text) transformer else: module.condition_lengths[i] = 257 module.transformer_index_for_condition[i] = 0 # use the first (image) transformer @torch.no_grad() def __call__( self, prompt: Union[PIL.Image.Image, List[PIL.Image.Image]], image: Union[str, List[str]], text_to_image_strength: float = 0.5, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import VersatileDiffusionDualGuidedPipeline >>> import torch >>> import requests >>> from io import BytesIO >>> from PIL import Image >>> # let's download an initial image >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" >>> response = requests.get(url) >>> image = Image.open(BytesIO(response.content)).convert("RGB") >>> text = "a red car in the sun" >>> pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained( ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 ... ) >>> pipe.remove_unused_weights() >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> text_to_image_strength = 0.75 >>> image = pipe( ... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator ... ).images[0] >>> image.save("./car_variation.png") ``` Returns: [`~pipelines.stable_diffusion.ImagePipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ # 0. Default height and width to unet height = height or self.image_unet.config.sample_size * self.vae_scale_factor width = width or self.image_unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, image, height, width, callback_steps) # 2. Define call parameters prompt = [prompt] if not isinstance(prompt, list) else prompt image = [image] if not isinstance(image, list) else image batch_size = len(prompt) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompts prompt_embeds = self._encode_text_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance) image_embeddings = self._encode_image_prompt(image, device, num_images_per_prompt, do_classifier_free_guidance) dual_prompt_embeddings = torch.cat([prompt_embeds, image_embeddings], dim=1) prompt_types = ("text", "image") # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.image_unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, dual_prompt_embeddings.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Combine the attention blocks of the image and text UNets self.set_transformer_params(text_to_image_strength, prompt_types) # 8. Denoising loop for i, t in enumerate(self.progress_bar(timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=dual_prompt_embeddings).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Union import numpy as np import PIL import torch import torch.utils.checkpoint from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name class VersatileDiffusionImageVariationPipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqvae ([`VQModel`]): Vector-quantized (VQ) Model to encode and decode images to and from latent representations. bert ([`LDMBertModel`]): Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture. tokenizer (`transformers.BertTokenizer`): Tokenizer of class [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ image_feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection image_unet: UNet2DConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers def __init__( self, image_feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection, image_unet: UNet2DConditionModel, vae: AutoencoderKL, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( image_feature_extractor=image_feature_extractor, image_encoder=image_encoder, image_unet=image_unet, vae=vae, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.image_unet, self.text_unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device with unet->image_unet def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.image_unet, "_hf_hook"): return self.device for module in self.image_unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). """ def normalize_embeddings(encoder_output): embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state) embeds = self.image_encoder.visual_projection(embeds) embeds_pooled = embeds[:, 0:1] embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True) return embeds if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4: prompt = list(prompt) batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings image_input = self.image_feature_extractor(images=prompt, return_tensors="pt") pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype) image_embeddings = self.image_encoder(pixel_values) image_embeddings = normalize_embeddings(image_embeddings) # duplicate image embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = image_embeddings.shape image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1) image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_images: List[str] if negative_prompt is None: uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, PIL.Image.Image): uncond_images = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_images = negative_prompt uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt") pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype) negative_prompt_embeds = self.image_encoder(pixel_values) negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and conditional embeddings into a single batch # to avoid doing two forward passes image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings]) return image_embeddings # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs def check_inputs(self, image, height, width, callback_steps): if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`): The image prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import VersatileDiffusionImageVariationPipeline >>> import torch >>> import requests >>> from io import BytesIO >>> from PIL import Image >>> # let's download an initial image >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" >>> response = requests.get(url) >>> image = Image.open(BytesIO(response.content)).convert("RGB") >>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained( ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> image = pipe(image, generator=generator).images[0] >>> image.save("./car_variation.png") ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.image_unet.config.sample_size * self.vae_scale_factor width = width or self.image_unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(image, height, width, callback_steps) # 2. Define call parameters batch_size = 1 if isinstance(image, PIL.Image.Image) else len(image) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt image_embeddings = self._encode_prompt( image, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.image_unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, image_embeddings.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop for i, t in enumerate(self.progress_bar(timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py ================================================ # Copyright 2023 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. import inspect import warnings from typing import Callable, List, Optional, Union import torch import torch.utils.checkpoint from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL, Transformer2DModel, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput from .modeling_text_unet import UNetFlatConditionModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name class VersatileDiffusionTextToImagePipeline(DiffusionPipeline): r""" This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Parameters: vqvae ([`VQModel`]): Vector-quantized (VQ) Model to encode and decode images to and from latent representations. bert ([`LDMBertModel`]): Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture. tokenizer (`transformers.BertTokenizer`): Tokenizer of class [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ tokenizer: CLIPTokenizer image_feature_extractor: CLIPImageProcessor text_encoder: CLIPTextModelWithProjection image_unet: UNet2DConditionModel text_unet: UNetFlatConditionModel vae: AutoencoderKL scheduler: KarrasDiffusionSchedulers _optional_components = ["text_unet"] def __init__( self, tokenizer: CLIPTokenizer, text_encoder: CLIPTextModelWithProjection, image_unet: UNet2DConditionModel, text_unet: UNetFlatConditionModel, vae: AutoencoderKL, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, image_unet=image_unet, text_unet=text_unet, vae=vae, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) if self.text_unet is not None: self._swap_unet_attention_blocks() def _swap_unet_attention_blocks(self): """ Swap the `Transformer2DModel` blocks between the image and text UNets """ for name, module in self.image_unet.named_modules(): if isinstance(module, Transformer2DModel): parent_name, index = name.rsplit(".", 1) index = int(index) self.image_unet.get_submodule(parent_name)[index], self.text_unet.get_submodule(parent_name)[index] = ( self.text_unet.get_submodule(parent_name)[index], self.image_unet.get_submodule(parent_name)[index], ) def remove_unused_weights(self): self.register_modules(text_unet=None) def enable_sequential_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. """ if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.image_unet, self.text_unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device with unet->image_unet def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module hooks. """ if not hasattr(self.image_unet, "_hf_hook"): return self.device for module in self.image_unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). """ def normalize_embeddings(encoder_output): embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state) embeds_pooled = encoder_output.text_embeds embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True) return embeds batch_size = len(prompt) if isinstance(prompt, list) else 1 text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids if not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = normalize_embeddings(prompt_embeds) # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Examples: ```py >>> from diffusers import VersatileDiffusionTextToImagePipeline >>> import torch >>> pipe = VersatileDiffusionTextToImagePipeline.from_pretrained( ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 ... ) >>> pipe.remove_unused_weights() >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0] >>> image.save("./astronaut.png") ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ # 0. Default height and width to unet height = height or self.image_unet.config.sample_size * self.vae_scale_factor width = width or self.image_unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps) # 2. Define call parameters batch_size = 1 if isinstance(prompt, str) else len(prompt) device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.image_unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop for i, t in enumerate(self.progress_bar(timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) if not return_dict: return (image,) return ImagePipelineOutput(images=image) ================================================ FILE: diffusers/pipelines/vq_diffusion/__init__.py ================================================ from ...utils import is_torch_available, is_transformers_available if is_transformers_available() and is_torch_available(): from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline ================================================ FILE: diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py ================================================ # Copyright 2023 Microsoft and 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 Callable, List, Optional, Tuple, Union import torch from transformers import CLIPTextModel, CLIPTokenizer from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin, Transformer2DModel, VQModel from ...schedulers import VQDiffusionScheduler from ...utils import logging from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin): """ Utility class for storing learned text embeddings for classifier free sampling """ @register_to_config def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None): super().__init__() self.learnable = learnable if self.learnable: assert hidden_size is not None, "learnable=True requires `hidden_size` to be set" assert length is not None, "learnable=True requires `length` to be set" embeddings = torch.zeros(length, hidden_size) else: embeddings = None self.embeddings = torch.nn.Parameter(embeddings) class VQDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using VQ Diffusion This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vqvae ([`VQModel`]): Vector Quantized Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. VQ Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). transformer ([`Transformer2DModel`]): Conditional transformer to denoise the encoded image latents. scheduler ([`VQDiffusionScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. """ vqvae: VQModel text_encoder: CLIPTextModel tokenizer: CLIPTokenizer transformer: Transformer2DModel learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings scheduler: VQDiffusionScheduler def __init__( self, vqvae: VQModel, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, transformer: Transformer2DModel, scheduler: VQDiffusionScheduler, learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings, ): super().__init__() self.register_modules( vqvae=vqvae, transformer=transformer, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler, learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings, ) def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance): batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ) text_input_ids = text_inputs.input_ids if text_input_ids.shape[-1] > self.tokenizer.model_max_length: removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] prompt_embeds = self.text_encoder(text_input_ids.to(self.device))[0] # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion. # While CLIP does normalize the pooled output of the text transformer when combining # the image and text embeddings, CLIP does not directly normalize the last hidden state. # # CLIP normalizing the pooled output. # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053 prompt_embeds = prompt_embeds / prompt_embeds.norm(dim=-1, keepdim=True) # duplicate text embeddings for each generation per prompt prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) if do_classifier_free_guidance: if self.learned_classifier_free_sampling_embeddings.learnable: negative_prompt_embeds = self.learned_classifier_free_sampling_embeddings.embeddings negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0).repeat(batch_size, 1, 1) else: uncond_tokens = [""] * batch_size max_length = text_input_ids.shape[-1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # See comment for normalizing text embeddings negative_prompt_embeds = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1, keepdim=True) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], num_inference_steps: int = 100, guidance_scale: float = 5.0, truncation_rate: float = 1.0, num_images_per_prompt: int = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, ) -> Union[ImagePipelineOutput, Tuple]: """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)): Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to zero. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor` of shape (batch), *optional*): Pre-generated noisy latents to be used as inputs for image generation. Must be valid embedding indices. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated of completely masked latent pixels. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput `] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. """ if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") batch_size = batch_size * num_images_per_prompt do_classifier_free_guidance = guidance_scale > 1.0 prompt_embeds = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) # get the initial completely masked latents unless the user supplied it latents_shape = (batch_size, self.transformer.num_latent_pixels) if latents is None: mask_class = self.transformer.num_vector_embeds - 1 latents = torch.full(latents_shape, mask_class).to(self.device) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any(): raise ValueError( "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0," f" {self.transformer.num_vector_embeds - 1} (inclusive)." ) latents = latents.to(self.device) # set timesteps self.scheduler.set_timesteps(num_inference_steps, device=self.device) timesteps_tensor = self.scheduler.timesteps.to(self.device) sample = latents for i, t in enumerate(self.progress_bar(timesteps_tensor)): # expand the sample if we are doing classifier free guidance latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample # predict the un-noised image # model_output == `log_p_x_0` model_output = self.transformer(latent_model_input, encoder_hidden_states=prompt_embeds, timestep=t).sample if do_classifier_free_guidance: model_output_uncond, model_output_text = model_output.chunk(2) model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond) model_output -= torch.logsumexp(model_output, dim=1, keepdim=True) model_output = self.truncate(model_output, truncation_rate) # remove `log(0)`'s (`-inf`s) model_output = model_output.clamp(-70) # compute the previous noisy sample x_t -> x_t-1 sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample # call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, sample) embedding_channels = self.vqvae.config.vq_embed_dim embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels) embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape) image = self.vqvae.decode(embeddings, force_not_quantize=True).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image,) return ImagePipelineOutput(images=image) def truncate(self, log_p_x_0: torch.FloatTensor, truncation_rate: float) -> torch.FloatTensor: """ Truncates log_p_x_0 such that for each column vector, the total cumulative probability is `truncation_rate` The lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to zero. """ sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True) sorted_p_x_0 = torch.exp(sorted_log_p_x_0) keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate # Ensure that at least the largest probability is not zeroed out all_true = torch.full_like(keep_mask[:, 0:1, :], True) keep_mask = torch.cat((all_true, keep_mask), dim=1) keep_mask = keep_mask[:, :-1, :] keep_mask = keep_mask.gather(1, indices.argsort(1)) rv = log_p_x_0.clone() rv[~keep_mask] = -torch.inf # -inf = log(0) return rv ================================================ FILE: diffusers/schedulers/README.md ================================================ # Schedulers For more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers/overview). ================================================ FILE: diffusers/schedulers/__init__.py ================================================ # Copyright 2023 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 ..utils import ( OptionalDependencyNotAvailable, is_flax_available, is_scipy_available, is_torch_available, is_torchsde_available, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_pt_objects import * # noqa F403 else: from .scheduling_ddim import DDIMScheduler from .scheduling_ddim_inverse import DDIMInverseScheduler from .scheduling_ddpm import DDPMScheduler from .scheduling_deis_multistep import DEISMultistepScheduler from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler from .scheduling_euler_discrete import EulerDiscreteScheduler from .scheduling_heun_discrete import HeunDiscreteScheduler from .scheduling_ipndm import IPNDMScheduler from .scheduling_k_dpm_2_ancestral_discrete import KDPM2AncestralDiscreteScheduler from .scheduling_k_dpm_2_discrete import KDPM2DiscreteScheduler from .scheduling_karras_ve import KarrasVeScheduler from .scheduling_pndm import PNDMScheduler from .scheduling_repaint import RePaintScheduler from .scheduling_sde_ve import ScoreSdeVeScheduler from .scheduling_sde_vp import ScoreSdeVpScheduler from .scheduling_unclip import UnCLIPScheduler from .scheduling_unipc_multistep import UniPCMultistepScheduler from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin from .scheduling_vq_diffusion import VQDiffusionScheduler try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_flax_objects import * # noqa F403 else: from .scheduling_ddim_flax import FlaxDDIMScheduler from .scheduling_ddpm_flax import FlaxDDPMScheduler from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler from .scheduling_pndm_flax import FlaxPNDMScheduler from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler from .scheduling_utils_flax import ( FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left, ) try: if not (is_torch_available() and is_scipy_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_scipy_objects import * # noqa F403 else: from .scheduling_lms_discrete import LMSDiscreteScheduler try: if not (is_torch_available() and is_torchsde_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403 else: from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler ================================================ FILE: diffusers/schedulers/scheduling_ddim.py ================================================ # Copyright 2023 Stanford University Team and 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. # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion # and https://github.com/hojonathanho/diffusion import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM class DDIMSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DDIMScheduler(SchedulerMixin, ConfigMixin): """ Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2010.02502 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. clip_sample (`bool`, default `True`): option to clip predicted sample for numerical stability. clip_sample_range (`float`, default `1.0`): the maximum magnitude for sample clipping. Valid only when `clip_sample=True`. set_alpha_to_one (`bool`, default `True`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the value of alpha at step 0. steps_offset (`int`, default `0`): an offset added to the inference steps. You can use a combination of `offset=1` and `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in stable diffusion. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`. sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True`. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", skip_type = 'uniform', trained_betas: Optional[Union[np.ndarray, List[float]]] = None, clip_sample: bool = True, set_alpha_to_one: bool = True, steps_offset: int = 0, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, clip_sample_range: float = 1.0, sample_max_value: float = 1.0, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) self.skip_type = skip_type # At every step in ddim, we are looking into the previous alphas_cumprod # For the final step, there is no previous alphas_cumprod because we are already at 0 # `set_alpha_to_one` decides whether we set this parameter simply to one or # whether we use the final alpha of the "non-previous" one. self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # setable values self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def _get_variance(self, timestep, prev_timestep): alpha_prod_t = self.alphas_cumprod[timestep] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) return variance # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ if num_inference_steps > self.config.num_train_timesteps: raise ValueError( f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" f" maximal {self.config.num_train_timesteps} timesteps." ) self.num_inference_steps = num_inference_steps if self.skip_type == 'uniform': step_ratio = (self.config.num_train_timesteps-1) / (self.num_inference_steps-1) timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64) #print(timesteps) elif self.skip_type == 'quad': step_ratio = (self.config.num_train_timesteps-1) / (self.num_inference_steps-1)**2 timesteps = (np.arange(0, num_inference_steps)**2 * step_ratio).round()[::-1].copy().astype(np.int64) #print(timesteps) else: raise NotImplementedError(f"skip_type {self.skip_type} is not implemented") self.timesteps = torch.from_numpy(timesteps).to(device) self.timesteps += self.config.steps_offset def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, eta: float = 0.0, use_clipped_model_output: bool = False, generator=None, variance_noise: Optional[torch.FloatTensor] = None, return_dict: bool = True, ) -> Union[DDIMSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. eta (`float`): weight of noise for added noise in diffusion step. use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would coincide with the one provided as input and `use_clipped_model_output` will have not effect. generator: random number generator. variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we can directly provide the noise for the variance itself. This is useful for methods such as CycleDiffusion. (https://arxiv.org/abs/2210.05559) return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class Returns: [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf # Ideally, read DDIM paper in-detail understanding # Notation ( -> # - pred_noise_t -> e_theta(x_t, t) # - pred_original_sample -> f_theta(x_t, t) or x_0 # - std_dev_t -> sigma_t # - eta -> η # - pred_sample_direction -> "direction pointing to x_t" # - pred_prev_sample -> "x_t-1" # 1. get previous step value (=t-1) prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps # 2. compute alphas, betas alpha_prod_t = self.alphas_cumprod[timestep] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf if self.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) pred_epsilon = model_output elif self.config.prediction_type == "sample": pred_original_sample = model_output pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) elif self.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction`" ) # 4. Clip or threshold "predicted x_0" if self.config.thresholding: pred_original_sample = self._threshold_sample(pred_original_sample) elif self.config.clip_sample: pred_original_sample = pred_original_sample.clamp( -self.config.clip_sample_range, self.config.clip_sample_range ) # 5. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) variance = self._get_variance(timestep, prev_timestep) std_dev_t = eta * variance ** (0.5) if use_clipped_model_output: # the pred_epsilon is always re-derived from the clipped x_0 in Glide pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction if eta > 0: if variance_noise is not None and generator is not None: raise ValueError( "Cannot pass both generator and variance_noise. Please make sure that either `generator` or" " `variance_noise` stays `None`." ) if variance_noise is None: variance_noise = randn_tensor( model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype ) variance = std_dev_t * variance_noise prev_sample = prev_sample + variance if not return_dict: return (prev_sample,) return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity def get_velocity( self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as sample alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype) timesteps = timesteps.to(sample.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(sample.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample return velocity def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_ddim_flax.py ================================================ # Copyright 2023 Stanford University Team and 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. # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion # and https://github.com/hojonathanho/diffusion from dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax.numpy as jnp from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import ( CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, add_noise_common, get_velocity_common, ) @flax.struct.dataclass class DDIMSchedulerState: common: CommonSchedulerState final_alpha_cumprod: jnp.ndarray # setable values init_noise_sigma: jnp.ndarray timesteps: jnp.ndarray num_inference_steps: Optional[int] = None @classmethod def create( cls, common: CommonSchedulerState, final_alpha_cumprod: jnp.ndarray, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, ): return cls( common=common, final_alpha_cumprod=final_alpha_cumprod, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) @dataclass class FlaxDDIMSchedulerOutput(FlaxSchedulerOutput): state: DDIMSchedulerState class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin): """ Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2010.02502 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. clip_sample (`bool`, default `True`): option to clip predicted sample between -1 and 1 for numerical stability. set_alpha_to_one (`bool`, default `True`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the value of alpha at step 0. steps_offset (`int`, default `0`): an offset added to the inference steps. You can use a combination of `offset=1` and `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in stable diffusion. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`. `v-prediction` is not supported for this scheduler. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. """ _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers] dtype: jnp.dtype @property def has_state(self): return True @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[jnp.ndarray] = None, set_alpha_to_one: bool = True, steps_offset: int = 0, prediction_type: str = "epsilon", dtype: jnp.dtype = jnp.float32, ): self.dtype = dtype def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState: if common is None: common = CommonSchedulerState.create(self) # At every step in ddim, we are looking into the previous alphas_cumprod # For the final step, there is no previous alphas_cumprod because we are already at 0 # `set_alpha_to_one` decides whether we set this parameter simply to one or # whether we use the final alpha of the "non-previous" one. final_alpha_cumprod = ( jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0] ) # standard deviation of the initial noise distribution init_noise_sigma = jnp.array(1.0, dtype=self.dtype) timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] return DDIMSchedulerState.create( common=common, final_alpha_cumprod=final_alpha_cumprod, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) def scale_model_input( self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None ) -> jnp.ndarray: """ Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. sample (`jnp.ndarray`): input sample timestep (`int`, optional): current timestep Returns: `jnp.ndarray`: scaled input sample """ return sample def set_timesteps( self, state: DDIMSchedulerState, num_inference_steps: int, shape: Tuple = () ) -> DDIMSchedulerState: """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`DDIMSchedulerState`): the `FlaxDDIMScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ step_ratio = self.config.num_train_timesteps // num_inference_steps # creates integer timesteps by multiplying by ratio # rounding to avoid issues when num_inference_step is power of 3 timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1] + self.config.steps_offset return state.replace( num_inference_steps=num_inference_steps, timesteps=timesteps, ) def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep): alpha_prod_t = state.common.alphas_cumprod[timestep] alpha_prod_t_prev = jnp.where( prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod ) beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) return variance def step( self, state: DDIMSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, eta: float = 0.0, return_dict: bool = True, ) -> Union[FlaxDDIMSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`DDIMSchedulerState`): the `FlaxDDIMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than FlaxDDIMSchedulerOutput class Returns: [`FlaxDDIMSchedulerOutput`] or `tuple`: [`FlaxDDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf # Ideally, read DDIM paper in-detail understanding # Notation ( -> # - pred_noise_t -> e_theta(x_t, t) # - pred_original_sample -> f_theta(x_t, t) or x_0 # - std_dev_t -> sigma_t # - eta -> η # - pred_sample_direction -> "direction pointing to x_t" # - pred_prev_sample -> "x_t-1" # 1. get previous step value (=t-1) prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps alphas_cumprod = state.common.alphas_cumprod final_alpha_cumprod = state.final_alpha_cumprod # 2. compute alphas, betas alpha_prod_t = alphas_cumprod[timestep] alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], final_alpha_cumprod) beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf if self.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) pred_epsilon = model_output elif self.config.prediction_type == "sample": pred_original_sample = model_output pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) elif self.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction`" ) # 4. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) variance = self._get_variance(state, timestep, prev_timestep) std_dev_t = eta * variance ** (0.5) # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction if not return_dict: return (prev_sample, state) return FlaxDDIMSchedulerOutput(prev_sample=prev_sample, state=state) def add_noise( self, state: DDIMSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: return add_noise_common(state.common, original_samples, noise, timesteps) def get_velocity( self, state: DDIMSchedulerState, sample: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: return get_velocity_common(state.common, sample, noise, timesteps) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_ddim_inverse.py ================================================ # Copyright 2023 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. # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion # and https://github.com/hojonathanho/diffusion import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.schedulers.scheduling_utils import SchedulerMixin from diffusers.utils import BaseOutput, deprecate @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM class DDIMSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DDIMInverseScheduler(SchedulerMixin, ConfigMixin): """ DDIMInverseScheduler is the reverse scheduler of [`DDIMScheduler`]. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2010.02502 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. clip_sample (`bool`, default `True`): option to clip predicted sample for numerical stability. clip_sample_range (`float`, default `1.0`): the maximum magnitude for sample clipping. Valid only when `clip_sample=True`. set_alpha_to_zero (`bool`, default `True`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `0`, otherwise it uses the value of alpha at step `num_train_timesteps - 1`. steps_offset (`int`, default `0`): an offset added to the inference steps. You can use a combination of `offset=1` and `set_alpha_to_zero=False`, to make the last step use step `num_train_timesteps - 1` for the previous alpha product. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) """ order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, clip_sample: bool = True, set_alpha_to_zero: bool = True, steps_offset: int = 0, prediction_type: str = "epsilon", clip_sample_range: float = 1.0, **kwargs, ): if kwargs.get("set_alpha_to_one", None) is not None: deprecation_message = ( "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead." ) deprecate("set_alpha_to_one", "1.0.0", deprecation_message, standard_warn=False) set_alpha_to_zero = kwargs["set_alpha_to_one"] if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # At every step in inverted ddim, we are looking into the next alphas_cumprod # For the final step, there is no next alphas_cumprod, and the index is out of bounds # `set_alpha_to_zero` decides whether we set this parameter simply to zero # in this case, self.step() just output the predicted noise # or whether we use the final alpha of the "non-previous" one. self.final_alpha_cumprod = torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1] # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # setable values self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps).copy().astype(np.int64)) # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ if num_inference_steps > self.config.num_train_timesteps: raise ValueError( f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" f" maximal {self.config.num_train_timesteps} timesteps." ) self.num_inference_steps = num_inference_steps step_ratio = self.config.num_train_timesteps // self.num_inference_steps # creates integer timesteps by multiplying by ratio # casting to int to avoid issues when num_inference_step is power of 3 timesteps = (np.arange(0, num_inference_steps) * step_ratio).round().copy().astype(np.int64) self.timesteps = torch.from_numpy(timesteps).to(device) self.timesteps += self.config.steps_offset def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, eta: float = 0.0, use_clipped_model_output: bool = False, variance_noise: Optional[torch.FloatTensor] = None, return_dict: bool = True, ) -> Union[DDIMSchedulerOutput, Tuple]: # 1. get previous step value (=t+1) prev_timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps # 2. compute alphas, betas # change original implementation to exactly match noise levels for analogous forward process alpha_prod_t = self.alphas_cumprod[timestep] alpha_prod_t_prev = ( self.alphas_cumprod[prev_timestep] if prev_timestep < self.config.num_train_timesteps else self.final_alpha_cumprod ) beta_prod_t = 1 - alpha_prod_t # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf if self.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) pred_epsilon = model_output elif self.config.prediction_type == "sample": pred_original_sample = model_output pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) elif self.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction`" ) # 4. Clip or threshold "predicted x_0" if self.config.clip_sample: pred_original_sample = pred_original_sample.clamp( -self.config.clip_sample_range, self.config.clip_sample_range ) # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * pred_epsilon # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction if not return_dict: return (prev_sample, pred_original_sample) return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_ddpm.py ================================================ # Copyright 2023 UC Berkeley Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin @dataclass class DDPMSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DDPMScheduler(SchedulerMixin, ConfigMixin): """ Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and Langevin dynamics sampling. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2006.11239 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, `squaredcos_cap_v2` or `sigmoid`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. variance_type (`str`): options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. clip_sample (`bool`, default `True`): option to clip predicted sample for numerical stability. clip_sample_range (`float`, default `1.0`): the maximum magnitude for sample clipping. Valid only when `clip_sample=True`. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`. sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True`. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, variance_type: str = "fixed_small", clip_sample: bool = True, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, clip_sample_range: float = 1.0, sample_max_value: float = 1.0, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) elif beta_schedule == "sigmoid": # GeoDiff sigmoid schedule betas = torch.linspace(-6, 6, num_train_timesteps) self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) self.one = torch.tensor(1.0) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # setable values self.custom_timesteps = False self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) self.variance_type = variance_type def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def set_timesteps( self, num_inference_steps: Optional[int] = None, device: Union[str, torch.device] = None, timesteps: Optional[List[int]] = None, ): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`Optional[int]`): the number of diffusion steps used when generating samples with a pre-trained model. If passed, then `timesteps` must be `None`. device (`str` or `torch.device`, optional): the device to which the timesteps are moved to. custom_timesteps (`List[int]`, optional): custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default timestep spacing strategy of equal spacing between timesteps is used. If passed, `num_inference_steps` must be `None`. """ if num_inference_steps is not None and timesteps is not None: raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.") if timesteps is not None: for i in range(1, len(timesteps)): if timesteps[i] >= timesteps[i - 1]: raise ValueError("`custom_timesteps` must be in descending order.") if timesteps[0] >= self.config.num_train_timesteps: raise ValueError( f"`timesteps` must start before `self.config.train_timesteps`:" f" {self.config.num_train_timesteps}." ) timesteps = np.array(timesteps, dtype=np.int64) self.custom_timesteps = True else: if num_inference_steps > self.config.num_train_timesteps: raise ValueError( f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" f" maximal {self.config.num_train_timesteps} timesteps." ) self.num_inference_steps = num_inference_steps step_ratio = self.config.num_train_timesteps // self.num_inference_steps timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64) self.custom_timesteps = False self.timesteps = torch.from_numpy(timesteps).to(device) def _get_variance(self, t, predicted_variance=None, variance_type=None): prev_t = self.previous_timestep(t) alpha_prod_t = self.alphas_cumprod[t] alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # and sample from it to get previous sample # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t # we always take the log of variance, so clamp it to ensure it's not 0 variance = torch.clamp(variance, min=1e-20) if variance_type is None: variance_type = self.config.variance_type # hacks - were probably added for training stability if variance_type == "fixed_small": variance = variance # for rl-diffuser https://arxiv.org/abs/2205.09991 elif variance_type == "fixed_small_log": variance = torch.log(variance) variance = torch.exp(0.5 * variance) elif variance_type == "fixed_large": variance = current_beta_t elif variance_type == "fixed_large_log": # Glide max_log variance = torch.log(current_beta_t) elif variance_type == "learned": return predicted_variance elif variance_type == "learned_range": min_log = torch.log(variance) max_log = torch.log(current_beta_t) frac = (predicted_variance + 1) / 2 variance = frac * max_log + (1 - frac) * min_log return variance def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, generator=None, return_dict: bool = True, ) -> Union[DDPMSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class Returns: [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ t = timestep prev_t = self.previous_timestep(t) if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]: model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1) else: predicted_variance = None # 1. compute alphas, betas alpha_prod_t = self.alphas_cumprod[t] alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev current_alpha_t = alpha_prod_t / alpha_prod_t_prev current_beta_t = 1 - current_alpha_t # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if self.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) elif self.config.prediction_type == "sample": pred_original_sample = model_output elif self.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or" " `v_prediction` for the DDPMScheduler." ) # 3. Clip or threshold "predicted x_0" if self.config.thresholding: pred_original_sample = self._threshold_sample(pred_original_sample) elif self.config.clip_sample: pred_original_sample = pred_original_sample.clamp( -self.config.clip_sample_range, self.config.clip_sample_range ) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise variance = 0 if t > 0: device = model_output.device variance_noise = randn_tensor( model_output.shape, generator=generator, device=device, dtype=model_output.dtype ) if self.variance_type == "fixed_small_log": variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise elif self.variance_type == "learned_range": variance = self._get_variance(t, predicted_variance=predicted_variance) variance = torch.exp(0.5 * variance) * variance_noise else: variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise pred_prev_sample = pred_prev_sample + variance if not return_dict: return (pred_prev_sample,) return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample) def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def get_velocity( self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as sample alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype) timesteps = timesteps.to(sample.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(sample.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample return velocity def __len__(self): return self.config.num_train_timesteps def previous_timestep(self, timestep): if self.custom_timesteps: index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0] if index == self.timesteps.shape[0] - 1: prev_t = torch.tensor(-1) else: prev_t = self.timesteps[index + 1] else: num_inference_steps = ( self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps ) prev_t = timestep - self.config.num_train_timesteps // num_inference_steps return prev_t ================================================ FILE: diffusers/schedulers/scheduling_ddpm_flax.py ================================================ # Copyright 2023 UC Berkeley Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim from dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax import jax.numpy as jnp from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import ( CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, add_noise_common, get_velocity_common, ) @flax.struct.dataclass class DDPMSchedulerState: common: CommonSchedulerState # setable values init_noise_sigma: jnp.ndarray timesteps: jnp.ndarray num_inference_steps: Optional[int] = None @classmethod def create(cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray): return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps) @dataclass class FlaxDDPMSchedulerOutput(FlaxSchedulerOutput): state: DDPMSchedulerState class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin): """ Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and Langevin dynamics sampling. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2006.11239 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. variance_type (`str`): options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. clip_sample (`bool`, default `True`): option to clip predicted sample between -1 and 1 for numerical stability. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`. `v-prediction` is not supported for this scheduler. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. """ _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers] dtype: jnp.dtype @property def has_state(self): return True @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[jnp.ndarray] = None, variance_type: str = "fixed_small", clip_sample: bool = True, prediction_type: str = "epsilon", dtype: jnp.dtype = jnp.float32, ): self.dtype = dtype def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDPMSchedulerState: if common is None: common = CommonSchedulerState.create(self) # standard deviation of the initial noise distribution init_noise_sigma = jnp.array(1.0, dtype=self.dtype) timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] return DDPMSchedulerState.create( common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) def scale_model_input( self, state: DDPMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None ) -> jnp.ndarray: """ Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. sample (`jnp.ndarray`): input sample timestep (`int`, optional): current timestep Returns: `jnp.ndarray`: scaled input sample """ return sample def set_timesteps( self, state: DDPMSchedulerState, num_inference_steps: int, shape: Tuple = () ) -> DDPMSchedulerState: """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`DDIMSchedulerState`): the `FlaxDDPMScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ step_ratio = self.config.num_train_timesteps // num_inference_steps # creates integer timesteps by multiplying by ratio # rounding to avoid issues when num_inference_step is power of 3 timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1] return state.replace( num_inference_steps=num_inference_steps, timesteps=timesteps, ) def _get_variance(self, state: DDPMSchedulerState, t, predicted_variance=None, variance_type=None): alpha_prod_t = state.common.alphas_cumprod[t] alpha_prod_t_prev = jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype)) # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # and sample from it to get previous sample # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t] if variance_type is None: variance_type = self.config.variance_type # hacks - were probably added for training stability if variance_type == "fixed_small": variance = jnp.clip(variance, a_min=1e-20) # for rl-diffuser https://arxiv.org/abs/2205.09991 elif variance_type == "fixed_small_log": variance = jnp.log(jnp.clip(variance, a_min=1e-20)) elif variance_type == "fixed_large": variance = state.common.betas[t] elif variance_type == "fixed_large_log": # Glide max_log variance = jnp.log(state.common.betas[t]) elif variance_type == "learned": return predicted_variance elif variance_type == "learned_range": min_log = variance max_log = state.common.betas[t] frac = (predicted_variance + 1) / 2 variance = frac * max_log + (1 - frac) * min_log return variance def step( self, state: DDPMSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, key: Optional[jax.random.KeyArray] = None, return_dict: bool = True, ) -> Union[FlaxDDPMSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`DDPMSchedulerState`): the `FlaxDDPMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. key (`jax.random.KeyArray`): a PRNG key. return_dict (`bool`): option for returning tuple rather than FlaxDDPMSchedulerOutput class Returns: [`FlaxDDPMSchedulerOutput`] or `tuple`: [`FlaxDDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ t = timestep if key is None: key = jax.random.PRNGKey(0) if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]: model_output, predicted_variance = jnp.split(model_output, sample.shape[1], axis=1) else: predicted_variance = None # 1. compute alphas, betas alpha_prod_t = state.common.alphas_cumprod[t] alpha_prod_t_prev = jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype)) beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if self.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) elif self.config.prediction_type == "sample": pred_original_sample = model_output elif self.config.prediction_type == "v_prediction": pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` " " for the FlaxDDPMScheduler." ) # 3. Clip "predicted x_0" if self.config.clip_sample: pred_original_sample = jnp.clip(pred_original_sample, -1, 1) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * state.common.betas[t]) / beta_prod_t current_sample_coeff = state.common.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise def random_variance(): split_key = jax.random.split(key, num=1) noise = jax.random.normal(split_key, shape=model_output.shape, dtype=self.dtype) return (self._get_variance(state, t, predicted_variance=predicted_variance) ** 0.5) * noise variance = jnp.where(t > 0, random_variance(), jnp.zeros(model_output.shape, dtype=self.dtype)) pred_prev_sample = pred_prev_sample + variance if not return_dict: return (pred_prev_sample, state) return FlaxDDPMSchedulerOutput(prev_sample=pred_prev_sample, state=state) def add_noise( self, state: DDPMSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: return add_noise_common(state.common, original_samples, noise, timesteps) def get_velocity( self, state: DDPMSchedulerState, sample: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: return get_velocity_common(state.common, sample, noise, timesteps) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_deis_multistep.py ================================================ # Copyright 2023 FLAIR Lab and 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. # DISCLAIMER: check https://arxiv.org/abs/2204.13902 and https://github.com/qsh-zh/deis for more info # The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DEISMultistepScheduler(SchedulerMixin, ConfigMixin): """ DEIS (https://arxiv.org/abs/2204.13902) is a fast high order solver for diffusion ODEs. We slightly modify the polynomial fitting formula in log-rho space instead of the original linear t space in DEIS paper. The modification enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver. More variants of DEIS can be found in https://github.com/qsh-zh/deis. Currently, we support the log-rho multistep DEIS. We recommend to use `solver_order=2 / 3` while `solver_order=1` reduces to DDIM. We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set `thresholding=True` to use the dynamic thresholding. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. solver_order (`int`, default `2`): the order of DEIS; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`, or `v-prediction`. thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True` algorithm_type (`str`, default `deis`): the algorithm type for the solver. current we support multistep deis, we will add other variants of DEIS in the future lower_order_final (`bool`, default `True`): whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically find this trick can stabilize the sampling of DEIS for steps < 15, especially for steps <= 10. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[np.ndarray] = None, solver_order: int = 2, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, algorithm_type: str = "deis", solver_type: str = "logrho", lower_order_final: bool = True, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # Currently we only support VP-type noise schedule self.alpha_t = torch.sqrt(self.alphas_cumprod) self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # settings for DEIS if algorithm_type not in ["deis"]: if algorithm_type in ["dpmsolver", "dpmsolver++"]: self.register_to_config(algorithm_type="deis") else: raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") if solver_type not in ["logrho"]: if solver_type in ["midpoint", "heun", "bh1", "bh2"]: self.register_to_config(solver_type="logrho") else: raise NotImplementedError(f"solver type {solver_type} does is not implemented for {self.__class__}") # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy() self.timesteps = torch.from_numpy(timesteps) self.model_outputs = [None] * solver_order self.lower_order_nums = 0 def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ timesteps = ( np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1) .round()[::-1][:-1] .copy() .astype(np.int64) ) # when num_inference_steps == num_train_timesteps, we can end up with # duplicates in timesteps. _, unique_indices = np.unique(timesteps, return_index=True) timesteps = timesteps[np.sort(unique_indices)] self.timesteps = torch.from_numpy(timesteps).to(device) self.num_inference_steps = len(timesteps) self.model_outputs = [ None, ] * self.config.solver_order self.lower_order_nums = 0 # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample def convert_model_output( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor ) -> torch.FloatTensor: """ Convert the model output to the corresponding type that the algorithm DEIS needs. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the converted model output. """ if self.config.prediction_type == "epsilon": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * model_output) / alpha_t elif self.config.prediction_type == "sample": x0_pred = model_output elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = alpha_t * sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DEISMultistepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) if self.config.algorithm_type == "deis": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] return (sample - alpha_t * x0_pred) / sigma_t else: raise NotImplementedError("only support log-rho multistep deis now") def deis_first_order_update( self, model_output: torch.FloatTensor, timestep: int, prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the first-order DEIS (equivalent to DDIM). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep] alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep] sigma_t, _ = self.sigma_t[prev_timestep], self.sigma_t[timestep] h = lambda_t - lambda_s if self.config.algorithm_type == "deis": x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output else: raise NotImplementedError("only support log-rho multistep deis now") return x_t def multistep_deis_second_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the second-order multistep DEIS. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] m0, m1 = model_output_list[-1], model_output_list[-2] alpha_t, alpha_s0, alpha_s1 = self.alpha_t[t], self.alpha_t[s0], self.alpha_t[s1] sigma_t, sigma_s0, sigma_s1 = self.sigma_t[t], self.sigma_t[s0], self.sigma_t[s1] rho_t, rho_s0, rho_s1 = sigma_t / alpha_t, sigma_s0 / alpha_s0, sigma_s1 / alpha_s1 if self.config.algorithm_type == "deis": def ind_fn(t, b, c): # Integrate[(log(t) - log(c)) / (log(b) - log(c)), {t}] return t * (-np.log(c) + np.log(t) - 1) / (np.log(b) - np.log(c)) coef1 = ind_fn(rho_t, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s0, rho_s1) coef2 = ind_fn(rho_t, rho_s1, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s0) x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1) return x_t else: raise NotImplementedError("only support log-rho multistep deis now") def multistep_deis_third_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the third-order multistep DEIS. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] alpha_t, alpha_s0, alpha_s1, alpha_s2 = self.alpha_t[t], self.alpha_t[s0], self.alpha_t[s1], self.alpha_t[s2] sigma_t, sigma_s0, sigma_s1, simga_s2 = self.sigma_t[t], self.sigma_t[s0], self.sigma_t[s1], self.sigma_t[s2] rho_t, rho_s0, rho_s1, rho_s2 = ( sigma_t / alpha_t, sigma_s0 / alpha_s0, sigma_s1 / alpha_s1, simga_s2 / alpha_s2, ) if self.config.algorithm_type == "deis": def ind_fn(t, b, c, d): # Integrate[(log(t) - log(c))(log(t) - log(d)) / (log(b) - log(c))(log(b) - log(d)), {t}] numerator = t * ( np.log(c) * (np.log(d) - np.log(t) + 1) - np.log(d) * np.log(t) + np.log(d) + np.log(t) ** 2 - 2 * np.log(t) + 2 ) denominator = (np.log(b) - np.log(c)) * (np.log(b) - np.log(d)) return numerator / denominator coef1 = ind_fn(rho_t, rho_s0, rho_s1, rho_s2) - ind_fn(rho_s0, rho_s0, rho_s1, rho_s2) coef2 = ind_fn(rho_t, rho_s1, rho_s2, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s2, rho_s0) coef3 = ind_fn(rho_t, rho_s2, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s2, rho_s0, rho_s1) x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1 + coef3 * m2) return x_t else: raise NotImplementedError("only support log-rho multistep deis now") def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the multistep DEIS. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero() if len(step_index) == 0: step_index = len(self.timesteps) - 1 else: step_index = step_index.item() prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] lower_order_final = ( (step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15 ) lower_order_second = ( (step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15 ) model_output = self.convert_model_output(model_output, timestep, sample) for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.model_outputs[-1] = model_output if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: prev_sample = self.deis_first_order_update(model_output, timestep, prev_timestep, sample) elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: timestep_list = [self.timesteps[step_index - 1], timestep] prev_sample = self.multistep_deis_second_order_update( self.model_outputs, timestep_list, prev_timestep, sample ) else: timestep_list = [self.timesteps[step_index - 2], self.timesteps[step_index - 1], timestep] prev_sample = self.multistep_deis_third_order_update( self.model_outputs, timestep_list, prev_timestep, sample ) if self.lower_order_nums < self.config.solver_order: self.lower_order_nums += 1 if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_dpmsolver_multistep.py ================================================ # Copyright 2023 TSAIL Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin): """ DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality samples, and it can generate quite good samples even in only 10 steps. For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095 Currently, we support the multistep DPM-Solver for both noise prediction models and data prediction models. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). We also support the SDE variant of DPM-Solver and DPM-Solver++, which is a fast SDE solver for the reverse diffusion SDE. Currently we only support the first-order and second-order solvers. We recommend using the second-order `sde-dpmsolver++`. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. solver_order (`int`, default `2`): the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++`. algorithm_type (`str`, default `dpmsolver++`): the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++` or `sde-dpmsolver` or `sde-dpmsolver++`. The `dpmsolver` type implements the algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling (e.g. stable-diffusion). solver_type (`str`, default `midpoint`): the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are slightly better, so we recommend to use the `midpoint` type. lower_order_final (`bool`, default `True`): whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10. use_karras_sigmas (`bool`, *optional*, defaults to `False`): This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. lambda_min_clipped (`float`, default `-inf`): the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for cosine (squaredcos_cap_v2) noise schedule. variance_type (`str`, *optional*): Set to "learned" or "learned_range" for diffusion models that predict variance. For example, OpenAI's guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on diffusion ODEs. whether the model's output contains the predicted Gaussian variance. For example, OpenAI's guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on diffusion ODEs. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, solver_order: int = 2, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, algorithm_type: str = "dpmsolver++", solver_type: str = "midpoint", lower_order_final: bool = True, use_karras_sigmas: Optional[bool] = False, lambda_min_clipped: float = -float("inf"), variance_type: Optional[str] = None, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # Currently we only support VP-type noise schedule self.alpha_t = torch.sqrt(self.alphas_cumprod) self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # settings for DPM-Solver if algorithm_type not in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]: if algorithm_type == "deis": self.register_to_config(algorithm_type="dpmsolver++") else: raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") if solver_type not in ["midpoint", "heun"]: if solver_type in ["logrho", "bh1", "bh2"]: self.register_to_config(solver_type="midpoint") else: raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy() self.timesteps = torch.from_numpy(timesteps) self.model_outputs = [None] * solver_order self.lower_order_nums = 0 self.use_karras_sigmas = use_karras_sigmas def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ # Clipping the minimum of all lambda(t) for numerical stability. # This is critical for cosine (squaredcos_cap_v2) noise schedule. clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped) timesteps = ( np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1) .round()[::-1][:-1] .copy() .astype(np.int64) ) if self.use_karras_sigmas: sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) log_sigmas = np.log(sigmas) sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round() timesteps = np.flip(timesteps).copy().astype(np.int64) # when num_inference_steps == num_train_timesteps, we can end up with # duplicates in timesteps. _, unique_indices = np.unique(timesteps, return_index=True) timesteps = timesteps[np.sort(unique_indices)] self.timesteps = torch.from_numpy(timesteps).to(device) self.num_inference_steps = len(timesteps) self.model_outputs = [ None, ] * self.config.solver_order self.lower_order_nums = 0 # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t def _sigma_to_t(self, sigma, log_sigmas): # get log sigma log_sigma = np.log(sigma) # get distribution dists = log_sigma - log_sigmas[:, np.newaxis] # get sigmas range low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = log_sigmas[low_idx] high = log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = np.clip(w, 0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.reshape(sigma.shape) return t # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor: """Constructs the noise schedule of Karras et al. (2022).""" sigma_min: float = in_sigmas[-1].item() sigma_max: float = in_sigmas[0].item() rho = 7.0 # 7.0 is the value used in the paper ramp = np.linspace(0, 1, num_inference_steps) 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 sigmas def convert_model_output( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor ) -> torch.FloatTensor: """ Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model. So we need to first convert the model output to the corresponding type to match the algorithm. Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or DPM-Solver++ for both noise prediction model and data prediction model. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the converted model output. """ # DPM-Solver++ needs to solve an integral of the data prediction model. if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: if self.config.prediction_type == "epsilon": # DPM-Solver and DPM-Solver++ only need the "mean" output. if self.config.variance_type in ["learned", "learned_range"]: model_output = model_output[:, :3] alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * model_output) / alpha_t elif self.config.prediction_type == "sample": x0_pred = model_output elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = alpha_t * sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DPMSolverMultistepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) return x0_pred # DPM-Solver needs to solve an integral of the noise prediction model. elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]: if self.config.prediction_type == "epsilon": # DPM-Solver and DPM-Solver++ only need the "mean" output. if self.config.variance_type in ["learned", "learned_range"]: epsilon = model_output[:, :3] else: epsilon = model_output elif self.config.prediction_type == "sample": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = (sample - alpha_t * model_output) / sigma_t elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = alpha_t * model_output + sigma_t * sample else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DPMSolverMultistepScheduler." ) if self.config.thresholding: alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * epsilon) / alpha_t x0_pred = self._threshold_sample(x0_pred) epsilon = (sample - alpha_t * x0_pred) / sigma_t return epsilon def dpm_solver_first_order_update( self, model_output: torch.FloatTensor, timestep: int, prev_timestep: int, sample: torch.FloatTensor, noise: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: """ One step for the first-order DPM-Solver (equivalent to DDIM). See https://arxiv.org/abs/2206.00927 for the detailed derivation. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep] alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep] sigma_t, sigma_s = self.sigma_t[prev_timestep], self.sigma_t[timestep] h = lambda_t - lambda_s if self.config.algorithm_type == "dpmsolver++": x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output elif self.config.algorithm_type == "dpmsolver": x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output elif self.config.algorithm_type == "sde-dpmsolver++": assert noise is not None x_t = ( (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise ) elif self.config.algorithm_type == "sde-dpmsolver": assert noise is not None x_t = ( (alpha_t / alpha_s) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * model_output + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise ) return x_t def multistep_dpm_solver_second_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, noise: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: """ One step for the second-order multistep DPM-Solver. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] m0, m1 = model_output_list[-1], model_output_list[-2] lambda_t, lambda_s0, lambda_s1 = self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1] alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 r0 = h_0 / h D0, D1 = m0, (1.0 / r0) * (m0 - m1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2211.01095 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 ) elif self.config.algorithm_type == "sde-dpmsolver++": assert noise is not None if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1 + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise ) elif self.config.algorithm_type == "sde-dpmsolver": assert noise is not None if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s0) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * (torch.exp(h) - 1.0)) * D1 + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s0) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 - 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise ) return x_t def multistep_dpm_solver_third_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the third-order multistep DPM-Solver. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] lambda_t, lambda_s0, lambda_s1, lambda_s2 = ( self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1], self.lambda_t[s2], ) alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 r0, r1 = h_0 / h, h_1 / h D0 = m0 D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2 ) return x_t def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, generator=None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the multistep DPM-Solver. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero() if len(step_index) == 0: step_index = len(self.timesteps) - 1 else: step_index = step_index.item() prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] lower_order_final = ( (step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15 ) lower_order_second = ( (step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15 ) model_output = self.convert_model_output(model_output, timestep, sample) for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.model_outputs[-1] = model_output if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: noise = randn_tensor( model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype ) else: noise = None if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: prev_sample = self.dpm_solver_first_order_update( model_output, timestep, prev_timestep, sample, noise=noise ) elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: timestep_list = [self.timesteps[step_index - 1], timestep] prev_sample = self.multistep_dpm_solver_second_order_update( self.model_outputs, timestep_list, prev_timestep, sample, noise=noise ) else: timestep_list = [self.timesteps[step_index - 2], self.timesteps[step_index - 1], timestep] prev_sample = self.multistep_dpm_solver_third_order_update( self.model_outputs, timestep_list, prev_timestep, sample ) if self.lower_order_nums < self.config.solver_order: self.lower_order_nums += 1 if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_dpmsolver_multistep_flax.py ================================================ # Copyright 2023 TSAIL Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver from dataclasses import dataclass from typing import List, Optional, Tuple, Union import flax import jax import jax.numpy as jnp from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import ( CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, add_noise_common, ) @flax.struct.dataclass class DPMSolverMultistepSchedulerState: common: CommonSchedulerState alpha_t: jnp.ndarray sigma_t: jnp.ndarray lambda_t: jnp.ndarray # setable values init_noise_sigma: jnp.ndarray timesteps: jnp.ndarray num_inference_steps: Optional[int] = None # running values model_outputs: Optional[jnp.ndarray] = None lower_order_nums: Optional[jnp.int32] = None prev_timestep: Optional[jnp.int32] = None cur_sample: Optional[jnp.ndarray] = None @classmethod def create( cls, common: CommonSchedulerState, alpha_t: jnp.ndarray, sigma_t: jnp.ndarray, lambda_t: jnp.ndarray, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, ): return cls( common=common, alpha_t=alpha_t, sigma_t=sigma_t, lambda_t=lambda_t, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) @dataclass class FlaxDPMSolverMultistepSchedulerOutput(FlaxSchedulerOutput): state: DPMSolverMultistepSchedulerState class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin): """ DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality samples, and it can generate quite good samples even in only 10 steps. For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095 Currently, we support the multistep DPM-Solver for both noise prediction models and data prediction models. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. solver_order (`int`, default `2`): the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`, or `v-prediction`. thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++`. algorithm_type (`str`, default `dpmsolver++`): the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++`. The `dpmsolver` type implements the algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` with `solver_order=2` for guided sampling (e.g. stable-diffusion). solver_type (`str`, default `midpoint`): the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are slightly better, so we recommend to use the `midpoint` type. lower_order_final (`bool`, default `True`): whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. """ _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers] dtype: jnp.dtype @property def has_state(self): return True @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[jnp.ndarray] = None, solver_order: int = 2, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, algorithm_type: str = "dpmsolver++", solver_type: str = "midpoint", lower_order_final: bool = True, dtype: jnp.dtype = jnp.float32, ): self.dtype = dtype def create_state(self, common: Optional[CommonSchedulerState] = None) -> DPMSolverMultistepSchedulerState: if common is None: common = CommonSchedulerState.create(self) # Currently we only support VP-type noise schedule alpha_t = jnp.sqrt(common.alphas_cumprod) sigma_t = jnp.sqrt(1 - common.alphas_cumprod) lambda_t = jnp.log(alpha_t) - jnp.log(sigma_t) # settings for DPM-Solver if self.config.algorithm_type not in ["dpmsolver", "dpmsolver++"]: raise NotImplementedError(f"{self.config.algorithm_type} does is not implemented for {self.__class__}") if self.config.solver_type not in ["midpoint", "heun"]: raise NotImplementedError(f"{self.config.solver_type} does is not implemented for {self.__class__}") # standard deviation of the initial noise distribution init_noise_sigma = jnp.array(1.0, dtype=self.dtype) timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] return DPMSolverMultistepSchedulerState.create( common=common, alpha_t=alpha_t, sigma_t=sigma_t, lambda_t=lambda_t, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) def set_timesteps( self, state: DPMSolverMultistepSchedulerState, num_inference_steps: int, shape: Tuple ) -> DPMSolverMultistepSchedulerState: """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`DPMSolverMultistepSchedulerState`): the `FlaxDPMSolverMultistepScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. shape (`Tuple`): the shape of the samples to be generated. """ timesteps = ( jnp.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1) .round()[::-1][:-1] .astype(jnp.int32) ) # initial running values model_outputs = jnp.zeros((self.config.solver_order,) + shape, dtype=self.dtype) lower_order_nums = jnp.int32(0) prev_timestep = jnp.int32(-1) cur_sample = jnp.zeros(shape, dtype=self.dtype) return state.replace( num_inference_steps=num_inference_steps, timesteps=timesteps, model_outputs=model_outputs, lower_order_nums=lower_order_nums, prev_timestep=prev_timestep, cur_sample=cur_sample, ) def convert_model_output( self, state: DPMSolverMultistepSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, ) -> jnp.ndarray: """ Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model. So we need to first convert the model output to the corresponding type to match the algorithm. Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or DPM-Solver++ for both noise prediction model and data prediction model. Args: model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. Returns: `jnp.ndarray`: the converted model output. """ # DPM-Solver++ needs to solve an integral of the data prediction model. if self.config.algorithm_type == "dpmsolver++": if self.config.prediction_type == "epsilon": alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep] x0_pred = (sample - sigma_t * model_output) / alpha_t elif self.config.prediction_type == "sample": x0_pred = model_output elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep] x0_pred = alpha_t * sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, " " or `v_prediction` for the FlaxDPMSolverMultistepScheduler." ) if self.config.thresholding: # Dynamic thresholding in https://arxiv.org/abs/2205.11487 dynamic_max_val = jnp.percentile( jnp.abs(x0_pred), self.config.dynamic_thresholding_ratio, axis=tuple(range(1, x0_pred.ndim)) ) dynamic_max_val = jnp.maximum( dynamic_max_val, self.config.sample_max_value * jnp.ones_like(dynamic_max_val) ) x0_pred = jnp.clip(x0_pred, -dynamic_max_val, dynamic_max_val) / dynamic_max_val return x0_pred # DPM-Solver needs to solve an integral of the noise prediction model. elif self.config.algorithm_type == "dpmsolver": if self.config.prediction_type == "epsilon": return model_output elif self.config.prediction_type == "sample": alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep] epsilon = (sample - alpha_t * model_output) / sigma_t return epsilon elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep] epsilon = alpha_t * model_output + sigma_t * sample return epsilon else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, " " or `v_prediction` for the FlaxDPMSolverMultistepScheduler." ) def dpm_solver_first_order_update( self, state: DPMSolverMultistepSchedulerState, model_output: jnp.ndarray, timestep: int, prev_timestep: int, sample: jnp.ndarray, ) -> jnp.ndarray: """ One step for the first-order DPM-Solver (equivalent to DDIM). See https://arxiv.org/abs/2206.00927 for the detailed derivation. Args: model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. Returns: `jnp.ndarray`: the sample tensor at the previous timestep. """ t, s0 = prev_timestep, timestep m0 = model_output lambda_t, lambda_s = state.lambda_t[t], state.lambda_t[s0] alpha_t, alpha_s = state.alpha_t[t], state.alpha_t[s0] sigma_t, sigma_s = state.sigma_t[t], state.sigma_t[s0] h = lambda_t - lambda_s if self.config.algorithm_type == "dpmsolver++": x_t = (sigma_t / sigma_s) * sample - (alpha_t * (jnp.exp(-h) - 1.0)) * m0 elif self.config.algorithm_type == "dpmsolver": x_t = (alpha_t / alpha_s) * sample - (sigma_t * (jnp.exp(h) - 1.0)) * m0 return x_t def multistep_dpm_solver_second_order_update( self, state: DPMSolverMultistepSchedulerState, model_output_list: jnp.ndarray, timestep_list: List[int], prev_timestep: int, sample: jnp.ndarray, ) -> jnp.ndarray: """ One step for the second-order multistep DPM-Solver. Args: model_output_list (`List[jnp.ndarray]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. Returns: `jnp.ndarray`: the sample tensor at the previous timestep. """ t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] m0, m1 = model_output_list[-1], model_output_list[-2] lambda_t, lambda_s0, lambda_s1 = state.lambda_t[t], state.lambda_t[s0], state.lambda_t[s1] alpha_t, alpha_s0 = state.alpha_t[t], state.alpha_t[s0] sigma_t, sigma_s0 = state.sigma_t[t], state.sigma_t[s0] h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 r0 = h_0 / h D0, D1 = m0, (1.0 / r0) * (m0 - m1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2211.01095 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (jnp.exp(-h) - 1.0)) * D0 - 0.5 * (alpha_t * (jnp.exp(-h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (jnp.exp(-h) - 1.0)) * D0 + (alpha_t * ((jnp.exp(-h) - 1.0) / h + 1.0)) * D1 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (jnp.exp(h) - 1.0)) * D0 - 0.5 * (sigma_t * (jnp.exp(h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (jnp.exp(h) - 1.0)) * D0 - (sigma_t * ((jnp.exp(h) - 1.0) / h - 1.0)) * D1 ) return x_t def multistep_dpm_solver_third_order_update( self, state: DPMSolverMultistepSchedulerState, model_output_list: jnp.ndarray, timestep_list: List[int], prev_timestep: int, sample: jnp.ndarray, ) -> jnp.ndarray: """ One step for the third-order multistep DPM-Solver. Args: model_output_list (`List[jnp.ndarray]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. Returns: `jnp.ndarray`: the sample tensor at the previous timestep. """ t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] lambda_t, lambda_s0, lambda_s1, lambda_s2 = ( state.lambda_t[t], state.lambda_t[s0], state.lambda_t[s1], state.lambda_t[s2], ) alpha_t, alpha_s0 = state.alpha_t[t], state.alpha_t[s0] sigma_t, sigma_s0 = state.sigma_t[t], state.sigma_t[s0] h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 r0, r1 = h_0 / h, h_1 / h D0 = m0 D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (jnp.exp(-h) - 1.0)) * D0 + (alpha_t * ((jnp.exp(-h) - 1.0) / h + 1.0)) * D1 - (alpha_t * ((jnp.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (jnp.exp(h) - 1.0)) * D0 - (sigma_t * ((jnp.exp(h) - 1.0) / h - 1.0)) * D1 - (sigma_t * ((jnp.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2 ) return x_t def step( self, state: DPMSolverMultistepSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, return_dict: bool = True, ) -> Union[FlaxDPMSolverMultistepSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by DPM-Solver. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`DPMSolverMultistepSchedulerState`): the `FlaxDPMSolverMultistepScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than FlaxDPMSolverMultistepSchedulerOutput class Returns: [`FlaxDPMSolverMultistepSchedulerOutput`] or `tuple`: [`FlaxDPMSolverMultistepSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) (step_index,) = jnp.where(state.timesteps == timestep, size=1) step_index = step_index[0] prev_timestep = jax.lax.select(step_index == len(state.timesteps) - 1, 0, state.timesteps[step_index + 1]) model_output = self.convert_model_output(state, model_output, timestep, sample) model_outputs_new = jnp.roll(state.model_outputs, -1, axis=0) model_outputs_new = model_outputs_new.at[-1].set(model_output) state = state.replace( model_outputs=model_outputs_new, prev_timestep=prev_timestep, cur_sample=sample, ) def step_1(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray: return self.dpm_solver_first_order_update( state, state.model_outputs[-1], state.timesteps[step_index], state.prev_timestep, state.cur_sample, ) def step_23(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray: def step_2(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray: timestep_list = jnp.array([state.timesteps[step_index - 1], state.timesteps[step_index]]) return self.multistep_dpm_solver_second_order_update( state, state.model_outputs, timestep_list, state.prev_timestep, state.cur_sample, ) def step_3(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray: timestep_list = jnp.array( [ state.timesteps[step_index - 2], state.timesteps[step_index - 1], state.timesteps[step_index], ] ) return self.multistep_dpm_solver_third_order_update( state, state.model_outputs, timestep_list, state.prev_timestep, state.cur_sample, ) step_2_output = step_2(state) step_3_output = step_3(state) if self.config.solver_order == 2: return step_2_output elif self.config.lower_order_final and len(state.timesteps) < 15: return jax.lax.select( state.lower_order_nums < 2, step_2_output, jax.lax.select( step_index == len(state.timesteps) - 2, step_2_output, step_3_output, ), ) else: return jax.lax.select( state.lower_order_nums < 2, step_2_output, step_3_output, ) step_1_output = step_1(state) step_23_output = step_23(state) if self.config.solver_order == 1: prev_sample = step_1_output elif self.config.lower_order_final and len(state.timesteps) < 15: prev_sample = jax.lax.select( state.lower_order_nums < 1, step_1_output, jax.lax.select( step_index == len(state.timesteps) - 1, step_1_output, step_23_output, ), ) else: prev_sample = jax.lax.select( state.lower_order_nums < 1, step_1_output, step_23_output, ) state = state.replace( lower_order_nums=jnp.minimum(state.lower_order_nums + 1, self.config.solver_order), ) if not return_dict: return (prev_sample, state) return FlaxDPMSolverMultistepSchedulerOutput(prev_sample=prev_sample, state=state) def scale_model_input( self, state: DPMSolverMultistepSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None ) -> jnp.ndarray: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: state (`DPMSolverMultistepSchedulerState`): the `FlaxDPMSolverMultistepScheduler` state data class instance. sample (`jnp.ndarray`): input sample timestep (`int`, optional): current timestep Returns: `jnp.ndarray`: scaled input sample """ return sample def add_noise( self, state: DPMSolverMultistepSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: return add_noise_common(state.common, original_samples, noise, timesteps) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py ================================================ # Copyright 2023 TSAIL Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin): """ DPMSolverMultistepInverseScheduler is the reverse scheduler of [`DPMSolverMultistepScheduler`]. We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. solver_order (`int`, default `2`): the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++`. algorithm_type (`str`, default `dpmsolver++`): the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++` or `sde-dpmsolver` or `sde-dpmsolver++`. The `dpmsolver` type implements the algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling (e.g. stable-diffusion). solver_type (`str`, default `midpoint`): the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are slightly better, so we recommend to use the `midpoint` type. lower_order_final (`bool`, default `True`): whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10. use_karras_sigmas (`bool`, *optional*, defaults to `False`): This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. lambda_min_clipped (`float`, default `-inf`): the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for cosine (squaredcos_cap_v2) noise schedule. variance_type (`str`, *optional*): Set to "learned" or "learned_range" for diffusion models that predict variance. For example, OpenAI's guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on diffusion ODEs. whether the model's output contains the predicted Gaussian variance. For example, OpenAI's guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on diffusion ODEs. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, solver_order: int = 2, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, algorithm_type: str = "dpmsolver++", solver_type: str = "midpoint", lower_order_final: bool = True, use_karras_sigmas: Optional[bool] = False, lambda_min_clipped: float = -float("inf"), variance_type: Optional[str] = None, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # Currently we only support VP-type noise schedule self.alpha_t = torch.sqrt(self.alphas_cumprod) self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # settings for DPM-Solver if algorithm_type not in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]: if algorithm_type == "deis": self.register_to_config(algorithm_type="dpmsolver++") else: raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") if solver_type not in ["midpoint", "heun"]: if solver_type in ["logrho", "bh1", "bh2"]: self.register_to_config(solver_type="midpoint") else: raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32).copy() self.timesteps = torch.from_numpy(timesteps) self.model_outputs = [None] * solver_order self.lower_order_nums = 0 self.use_karras_sigmas = use_karras_sigmas def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ # Clipping the minimum of all lambda(t) for numerical stability. # This is critical for cosine (squaredcos_cap_v2) noise schedule. clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.lambda_min_clipped) self.noisiest_timestep = self.config.num_train_timesteps - 1 - clipped_idx timesteps = ( np.linspace(0, self.noisiest_timestep, num_inference_steps + 1).round()[:-1].copy().astype(np.int64) ) if self.use_karras_sigmas: sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) log_sigmas = np.log(sigmas) sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round() timesteps = timesteps.copy().astype(np.int64) # when num_inference_steps == num_train_timesteps, we can end up with # duplicates in timesteps. _, unique_indices = np.unique(timesteps, return_index=True) timesteps = timesteps[np.sort(unique_indices)] self.timesteps = torch.from_numpy(timesteps).to(device) self.num_inference_steps = len(timesteps) self.model_outputs = [ None, ] * self.config.solver_order self.lower_order_nums = 0 # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t def _sigma_to_t(self, sigma, log_sigmas): # get log sigma log_sigma = np.log(sigma) # get distribution dists = log_sigma - log_sigmas[:, np.newaxis] # get sigmas range low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = log_sigmas[low_idx] high = log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = np.clip(w, 0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.reshape(sigma.shape) return t # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor: """Constructs the noise schedule of Karras et al. (2022).""" sigma_min: float = in_sigmas[-1].item() sigma_max: float = in_sigmas[0].item() rho = 7.0 # 7.0 is the value used in the paper ramp = np.linspace(0, 1, num_inference_steps) 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 sigmas # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.convert_model_output def convert_model_output( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor ) -> torch.FloatTensor: """ Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model. So we need to first convert the model output to the corresponding type to match the algorithm. Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or DPM-Solver++ for both noise prediction model and data prediction model. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the converted model output. """ # DPM-Solver++ needs to solve an integral of the data prediction model. if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: if self.config.prediction_type == "epsilon": # DPM-Solver and DPM-Solver++ only need the "mean" output. if self.config.variance_type in ["learned", "learned_range"]: model_output = model_output[:, :3] alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * model_output) / alpha_t elif self.config.prediction_type == "sample": x0_pred = model_output elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = alpha_t * sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DPMSolverMultistepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) return x0_pred # DPM-Solver needs to solve an integral of the noise prediction model. elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]: if self.config.prediction_type == "epsilon": # DPM-Solver and DPM-Solver++ only need the "mean" output. if self.config.variance_type in ["learned", "learned_range"]: epsilon = model_output[:, :3] else: epsilon = model_output elif self.config.prediction_type == "sample": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = (sample - alpha_t * model_output) / sigma_t elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = alpha_t * model_output + sigma_t * sample else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DPMSolverMultistepScheduler." ) if self.config.thresholding: alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * epsilon) / alpha_t x0_pred = self._threshold_sample(x0_pred) epsilon = (sample - alpha_t * x0_pred) / sigma_t return epsilon # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.dpm_solver_first_order_update def dpm_solver_first_order_update( self, model_output: torch.FloatTensor, timestep: int, prev_timestep: int, sample: torch.FloatTensor, noise: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: """ One step for the first-order DPM-Solver (equivalent to DDIM). See https://arxiv.org/abs/2206.00927 for the detailed derivation. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep] alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep] sigma_t, sigma_s = self.sigma_t[prev_timestep], self.sigma_t[timestep] h = lambda_t - lambda_s if self.config.algorithm_type == "dpmsolver++": x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output elif self.config.algorithm_type == "dpmsolver": x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output elif self.config.algorithm_type == "sde-dpmsolver++": assert noise is not None x_t = ( (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise ) elif self.config.algorithm_type == "sde-dpmsolver": assert noise is not None x_t = ( (alpha_t / alpha_s) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * model_output + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise ) return x_t # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update def multistep_dpm_solver_second_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, noise: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: """ One step for the second-order multistep DPM-Solver. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] m0, m1 = model_output_list[-1], model_output_list[-2] lambda_t, lambda_s0, lambda_s1 = self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1] alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 r0 = h_0 / h D0, D1 = m0, (1.0 / r0) * (m0 - m1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2211.01095 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 ) elif self.config.algorithm_type == "sde-dpmsolver++": assert noise is not None if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s0 * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1 + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise ) elif self.config.algorithm_type == "sde-dpmsolver": assert noise is not None if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s0) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * (torch.exp(h) - 1.0)) * D1 + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s0) * sample - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 - 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise ) return x_t # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update def multistep_dpm_solver_third_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the third-order multistep DPM-Solver. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] lambda_t, lambda_s0, lambda_s1, lambda_s2 = ( self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1], self.lambda_t[s2], ) alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 r0, r1 = h_0 / h, h_1 / h D0 = m0 D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ( (sigma_t / sigma_s0) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations x_t = ( (alpha_t / alpha_s0) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2 ) return x_t def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, generator=None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the multistep DPM-Solver. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero() if len(step_index) == 0: step_index = len(self.timesteps) - 1 else: step_index = step_index.item() prev_timestep = ( self.noisiest_timestep if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] ) lower_order_final = ( (step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15 ) lower_order_second = ( (step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15 ) model_output = self.convert_model_output(model_output, timestep, sample) for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.model_outputs[-1] = model_output if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: noise = randn_tensor( model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype ) else: noise = None if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: prev_sample = self.dpm_solver_first_order_update( model_output, timestep, prev_timestep, sample, noise=noise ) elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: timestep_list = [self.timesteps[step_index - 1], timestep] prev_sample = self.multistep_dpm_solver_second_order_update( self.model_outputs, timestep_list, prev_timestep, sample, noise=noise ) else: timestep_list = [self.timesteps[step_index - 2], self.timesteps[step_index - 1], timestep] prev_sample = self.multistep_dpm_solver_third_order_update( self.model_outputs, timestep_list, prev_timestep, sample ) if self.lower_order_nums < self.config.solver_order: self.lower_order_nums += 1 if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_dpmsolver_sde.py ================================================ # Copyright 2023 Katherine Crowson, The HuggingFace Team and hlky. 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. import math from typing import List, Optional, Tuple, Union import numpy as np import torch import torchsde from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput class BatchedBrownianTree: """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" def __init__(self, x, t0, t1, seed=None, **kwargs): t0, t1, self.sign = self.sort(t0, t1) w0 = kwargs.get("w0", torch.zeros_like(x)) if seed is None: seed = torch.randint(0, 2**63 - 1, []).item() self.batched = True try: assert len(seed) == x.shape[0] w0 = w0[0] except TypeError: seed = [seed] self.batched = False self.trees = [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) w = torch.stack([tree(t0, t1) for tree in self.trees]) * (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): 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) 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() # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin): """ Implements Stochastic Sampler (Algorithm 2) from Karras et al. (2022). Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/41b4cb6df0506694a7776af31349acf082bf6091/k_diffusion/sampling.py#L543 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) use_karras_sigmas (`bool`, *optional*, defaults to `False`): This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. noise_sampler_seed (`int`, *optional*, defaults to `None`): The random seed to use for the noise sampler. If `None`, a random seed will be generated. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 2 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.00085, # sensible defaults beta_end: float = 0.012, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, prediction_type: str = "epsilon", use_karras_sigmas: Optional[bool] = False, noise_sampler_seed: Optional[int] = None, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # set all values self.set_timesteps(num_train_timesteps, None, num_train_timesteps) self.use_karras_sigmas = use_karras_sigmas self.noise_sampler = None self.noise_sampler_seed = noise_sampler_seed # Copied from diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler.index_for_timestep def index_for_timestep(self, timestep, schedule_timesteps=None): if schedule_timesteps is None: schedule_timesteps = self.timesteps indices = (schedule_timesteps == timestep).nonzero() if self.state_in_first_order: pos = -1 else: pos = 0 return indices[pos].item() def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], ) -> torch.FloatTensor: """ Args: Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ step_index = self.index_for_timestep(timestep) sigma = self.sigmas[step_index] sigma_input = sigma if self.state_in_first_order else self.mid_point_sigma sample = sample / ((sigma_input**2 + 1) ** 0.5) return sample def set_timesteps( self, num_inference_steps: int, device: Union[str, torch.device] = None, num_train_timesteps: Optional[int] = None, ): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) log_sigmas = np.log(sigmas) sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) if self.use_karras_sigmas: sigmas = self._convert_to_karras(in_sigmas=sigmas) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]) second_order_timesteps = self._second_order_timesteps(sigmas, log_sigmas) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) sigmas = torch.from_numpy(sigmas).to(device=device) self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]]) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() timesteps = torch.from_numpy(timesteps) second_order_timesteps = torch.from_numpy(second_order_timesteps) timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)]) timesteps[1::2] = second_order_timesteps if str(device).startswith("mps"): # mps does not support float64 self.timesteps = timesteps.to(device, dtype=torch.float32) else: self.timesteps = timesteps.to(device=device) # empty first order variables self.sample = None self.mid_point_sigma = None def _second_order_timesteps(self, sigmas, log_sigmas): def sigma_fn(_t): return np.exp(-_t) def t_fn(_sigma): return -np.log(_sigma) midpoint_ratio = 0.5 t = t_fn(sigmas) delta_time = np.diff(t) t_proposed = t[:-1] + delta_time * midpoint_ratio sig_proposed = sigma_fn(t_proposed) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sig_proposed]) return timesteps # copied from diffusers.schedulers.scheduling_euler_discrete._sigma_to_t def _sigma_to_t(self, sigma, log_sigmas): # get log sigma log_sigma = np.log(sigma) # get distribution dists = log_sigma - log_sigmas[:, np.newaxis] # get sigmas range low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = log_sigmas[low_idx] high = log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = np.clip(w, 0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.reshape(sigma.shape) return t # copied from diffusers.schedulers.scheduling_euler_discrete._convert_to_karras def _convert_to_karras(self, in_sigmas: torch.FloatTensor) -> torch.FloatTensor: """Constructs the noise schedule of Karras et al. (2022).""" sigma_min: float = in_sigmas[-1].item() sigma_max: float = in_sigmas[0].item() rho = 7.0 # 7.0 is the value used in the paper ramp = np.linspace(0, 1, self.num_inference_steps) 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 sigmas @property def state_in_first_order(self): return self.sample is None def step( self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: Union[float, torch.FloatTensor], sample: Union[torch.FloatTensor, np.ndarray], return_dict: bool = True, s_noise: float = 1.0, ) -> Union[SchedulerOutput, Tuple]: """ Args: Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). model_output (Union[torch.FloatTensor, np.ndarray]): Direct output from learned diffusion model. timestep (Union[float, torch.FloatTensor]): Current discrete timestep in the diffusion chain. sample (Union[torch.FloatTensor, np.ndarray]): Current instance of sample being created by diffusion process. return_dict (bool, optional): Option for returning tuple rather than SchedulerOutput class. Defaults to True. s_noise (float, optional): Scaling factor for the noise added to the sample. Defaults to 1.0. Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ step_index = self.index_for_timestep(timestep) # Create a noise sampler if it hasn't been created yet if self.noise_sampler is None: min_sigma, max_sigma = self.sigmas[self.sigmas > 0].min(), self.sigmas.max() self.noise_sampler = BrownianTreeNoiseSampler(sample, min_sigma, max_sigma, self.noise_sampler_seed) # Define functions to compute sigma and t from each other def sigma_fn(_t: torch.FloatTensor) -> torch.FloatTensor: return _t.neg().exp() def t_fn(_sigma: torch.FloatTensor) -> torch.FloatTensor: return _sigma.log().neg() if self.state_in_first_order: sigma = self.sigmas[step_index] sigma_next = self.sigmas[step_index + 1] else: # 2nd order sigma = self.sigmas[step_index - 1] sigma_next = self.sigmas[step_index] # Set the midpoint and step size for the current step midpoint_ratio = 0.5 t, t_next = t_fn(sigma), t_fn(sigma_next) delta_time = t_next - t t_proposed = t + delta_time * midpoint_ratio # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed) pred_original_sample = sample - sigma_input * model_output elif self.config.prediction_type == "v_prediction": sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed) pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + ( sample / (sigma_input**2 + 1) ) elif self.config.prediction_type == "sample": raise NotImplementedError("prediction_type not implemented yet: sample") else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) if sigma_next == 0: derivative = (sample - pred_original_sample) / sigma dt = sigma_next - sigma prev_sample = sample + derivative * dt else: if self.state_in_first_order: t_next = t_proposed else: sample = self.sample sigma_from = sigma_fn(t) sigma_to = sigma_fn(t_next) sigma_up = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5) sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 ancestral_t = t_fn(sigma_down) prev_sample = (sigma_fn(ancestral_t) / sigma_fn(t)) * sample - ( t - ancestral_t ).expm1() * pred_original_sample prev_sample = prev_sample + self.noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * sigma_up if self.state_in_first_order: # store for 2nd order step self.sample = sample self.mid_point_sigma = sigma_fn(t_next) else: # free for "first order mode" self.sample = None self.mid_point_sigma = None if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) # Copied from diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_dpmsolver_singlestep.py ================================================ # Copyright 2023 TSAIL Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin): """ DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality samples, and it can generate quite good samples even in only 10 steps. For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095 Currently, we support the singlestep DPM-Solver for both noise prediction models and data prediction models. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. solver_order (`int`, default `2`): the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`, or `v-prediction`. thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++`. algorithm_type (`str`, default `dpmsolver++`): the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++`. The `dpmsolver` type implements the algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` with `solver_order=2` for guided sampling (e.g. stable-diffusion). solver_type (`str`, default `midpoint`): the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are slightly better, so we recommend to use the `midpoint` type. lower_order_final (`bool`, default `True`): whether to use lower-order solvers in the final steps. For singlestep schedulers, we recommend to enable this to use up all the function evaluations. lambda_min_clipped (`float`, default `-inf`): the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for cosine (squaredcos_cap_v2) noise schedule. variance_type (`str`, *optional*): Set to "learned" or "learned_range" for diffusion models that predict variance. For example, OpenAI's guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on diffusion ODEs. whether the model's output contains the predicted Gaussian variance. For example, OpenAI's guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on diffusion ODEs. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[np.ndarray] = None, solver_order: int = 2, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, algorithm_type: str = "dpmsolver++", solver_type: str = "midpoint", lower_order_final: bool = True, lambda_min_clipped: float = -float("inf"), variance_type: Optional[str] = None, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # Currently we only support VP-type noise schedule self.alpha_t = torch.sqrt(self.alphas_cumprod) self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # settings for DPM-Solver if algorithm_type not in ["dpmsolver", "dpmsolver++"]: if algorithm_type == "deis": self.register_to_config(algorithm_type="dpmsolver++") else: raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") if solver_type not in ["midpoint", "heun"]: if solver_type in ["logrho", "bh1", "bh2"]: self.register_to_config(solver_type="midpoint") else: raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy() self.timesteps = torch.from_numpy(timesteps) self.model_outputs = [None] * solver_order self.sample = None self.order_list = self.get_order_list(num_train_timesteps) def get_order_list(self, num_inference_steps: int) -> List[int]: """ Computes the solver order at each time step. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ steps = num_inference_steps order = self.config.solver_order if self.config.lower_order_final: if order == 3: if steps % 3 == 0: orders = [1, 2, 3] * (steps // 3 - 1) + [1, 2] + [1] elif steps % 3 == 1: orders = [1, 2, 3] * (steps // 3) + [1] else: orders = [1, 2, 3] * (steps // 3) + [1, 2] elif order == 2: if steps % 2 == 0: orders = [1, 2] * (steps // 2) else: orders = [1, 2] * (steps // 2) + [1] elif order == 1: orders = [1] * steps else: if order == 3: orders = [1, 2, 3] * (steps // 3) elif order == 2: orders = [1, 2] * (steps // 2) elif order == 1: orders = [1] * steps return orders def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps # Clipping the minimum of all lambda(t) for numerical stability. # This is critical for cosine (squaredcos_cap_v2) noise schedule. clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped) timesteps = ( np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1) .round()[::-1][:-1] .copy() .astype(np.int64) ) self.timesteps = torch.from_numpy(timesteps).to(device) self.model_outputs = [None] * self.config.solver_order self.sample = None self.orders = self.get_order_list(num_inference_steps) # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample def convert_model_output( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor ) -> torch.FloatTensor: """ Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model. So we need to first convert the model output to the corresponding type to match the algorithm. Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or DPM-Solver++ for both noise prediction model and data prediction model. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the converted model output. """ # DPM-Solver++ needs to solve an integral of the data prediction model. if self.config.algorithm_type == "dpmsolver++": if self.config.prediction_type == "epsilon": # DPM-Solver and DPM-Solver++ only need the "mean" output. if self.config.variance_type in ["learned_range"]: model_output = model_output[:, :3] alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * model_output) / alpha_t elif self.config.prediction_type == "sample": x0_pred = model_output elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = alpha_t * sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DPMSolverSinglestepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) return x0_pred # DPM-Solver needs to solve an integral of the noise prediction model. elif self.config.algorithm_type == "dpmsolver": if self.config.prediction_type == "epsilon": # DPM-Solver and DPM-Solver++ only need the "mean" output. if self.config.variance_type in ["learned_range"]: model_output = model_output[:, :3] return model_output elif self.config.prediction_type == "sample": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = (sample - alpha_t * model_output) / sigma_t return epsilon elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = alpha_t * model_output + sigma_t * sample return epsilon else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the DPMSolverSinglestepScheduler." ) def dpm_solver_first_order_update( self, model_output: torch.FloatTensor, timestep: int, prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the first-order DPM-Solver (equivalent to DDIM). See https://arxiv.org/abs/2206.00927 for the detailed derivation. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep] alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep] sigma_t, sigma_s = self.sigma_t[prev_timestep], self.sigma_t[timestep] h = lambda_t - lambda_s if self.config.algorithm_type == "dpmsolver++": x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output elif self.config.algorithm_type == "dpmsolver": x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output return x_t def singlestep_dpm_solver_second_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the second-order singlestep DPM-Solver. It computes the solution at time `prev_timestep` from the time `timestep_list[-2]`. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] m0, m1 = model_output_list[-1], model_output_list[-2] lambda_t, lambda_s0, lambda_s1 = self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1] alpha_t, alpha_s1 = self.alpha_t[t], self.alpha_t[s1] sigma_t, sigma_s1 = self.sigma_t[t], self.sigma_t[s1] h, h_0 = lambda_t - lambda_s1, lambda_s0 - lambda_s1 r0 = h_0 / h D0, D1 = m1, (1.0 / r0) * (m0 - m1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2211.01095 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s1) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s1) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s1) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1 ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s1) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 ) return x_t def singlestep_dpm_solver_third_order_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, ) -> torch.FloatTensor: """ One step for the third-order singlestep DPM-Solver. It computes the solution at time `prev_timestep` from the time `timestep_list[-3]`. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] lambda_t, lambda_s0, lambda_s1, lambda_s2 = ( self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1], self.lambda_t[s2], ) alpha_t, alpha_s2 = self.alpha_t[t], self.alpha_t[s2] sigma_t, sigma_s2 = self.sigma_t[t], self.sigma_t[s2] h, h_0, h_1 = lambda_t - lambda_s2, lambda_s0 - lambda_s2, lambda_s1 - lambda_s2 r0, r1 = h_0 / h, h_1 / h D0 = m2 D1_0, D1_1 = (1.0 / r1) * (m1 - m2), (1.0 / r0) * (m0 - m2) D1 = (r0 * D1_0 - r1 * D1_1) / (r0 - r1) D2 = 2.0 * (D1_1 - D1_0) / (r0 - r1) if self.config.algorithm_type == "dpmsolver++": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (sigma_t / sigma_s2) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1_1 ) elif self.config.solver_type == "heun": x_t = ( (sigma_t / sigma_s2) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * D0 + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2 ) elif self.config.algorithm_type == "dpmsolver": # See https://arxiv.org/abs/2206.00927 for detailed derivations if self.config.solver_type == "midpoint": x_t = ( (alpha_t / alpha_s2) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1_1 ) elif self.config.solver_type == "heun": x_t = ( (alpha_t / alpha_s2) * sample - (sigma_t * (torch.exp(h) - 1.0)) * D0 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2 ) return x_t def singlestep_dpm_solver_update( self, model_output_list: List[torch.FloatTensor], timestep_list: List[int], prev_timestep: int, sample: torch.FloatTensor, order: int, ) -> torch.FloatTensor: """ One step for the singlestep DPM-Solver. Args: model_output_list (`List[torch.FloatTensor]`): direct outputs from learned diffusion model at current and latter timesteps. timestep (`int`): current and latter discrete timestep in the diffusion chain. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. order (`int`): the solver order at this step. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ if order == 1: return self.dpm_solver_first_order_update(model_output_list[-1], timestep_list[-1], prev_timestep, sample) elif order == 2: return self.singlestep_dpm_solver_second_order_update( model_output_list, timestep_list, prev_timestep, sample ) elif order == 3: return self.singlestep_dpm_solver_third_order_update( model_output_list, timestep_list, prev_timestep, sample ) else: raise ValueError(f"Order must be 1, 2, 3, got {order}") def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the singlestep DPM-Solver. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero() if len(step_index) == 0: step_index = len(self.timesteps) - 1 else: step_index = step_index.item() prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] model_output = self.convert_model_output(model_output, timestep, sample) for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.model_outputs[-1] = model_output order = self.order_list[step_index] # For single-step solvers, we use the initial value at each time with order = 1. if order == 1: self.sample = sample timestep_list = [self.timesteps[step_index - i] for i in range(order - 1, 0, -1)] + [timestep] prev_sample = self.singlestep_dpm_solver_update( self.model_outputs, timestep_list, prev_timestep, self.sample, order ) if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_euler_ancestral_discrete.py ================================================ # Copyright 2023 Katherine Crowson and 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. import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, logging, randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete class EulerAncestralDiscreteSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin): """ Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, prediction_type: str = "epsilon", ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) self.sigmas = torch.from_numpy(sigmas) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy() self.timesteps = torch.from_numpy(timesteps) self.is_scale_input_called = False def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor] ) -> torch.FloatTensor: """ Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. Args: sample (`torch.FloatTensor`): input sample timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain Returns: `torch.FloatTensor`: scaled input sample """ if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero().item() sigma = self.sigmas[step_index] sample = sample / ((sigma**2 + 1) ** 0.5) self.is_scale_input_called = True return sample def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) self.sigmas = torch.from_numpy(sigmas).to(device=device) if str(device).startswith("mps"): # mps does not support float64 self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) else: self.timesteps = torch.from_numpy(timesteps).to(device=device) def step( self, model_output: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], sample: torch.FloatTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`float`): current timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. generator (`torch.Generator`, optional): Random number generator. return_dict (`bool`): option for returning tuple rather than EulerAncestralDiscreteSchedulerOutput class Returns: [`~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if ( isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor) ): raise ValueError( ( "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" " one of the `scheduler.timesteps` as a timestep." ), ) if not self.is_scale_input_called: logger.warning( "The `scale_model_input` function should be called before `step` to ensure correct denoising. " "See `StableDiffusionPipeline` for a usage example." ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero().item() sigma = self.sigmas[step_index] # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": pred_original_sample = sample - sigma * model_output elif self.config.prediction_type == "v_prediction": # * c_out + input * c_skip pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1)) elif self.config.prediction_type == "sample": raise NotImplementedError("prediction_type not implemented yet: sample") else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) sigma_from = self.sigmas[step_index] sigma_to = self.sigmas[step_index + 1] sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5 sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 # 2. Convert to an ODE derivative derivative = (sample - pred_original_sample) / sigma dt = sigma_down - sigma prev_sample = sample + derivative * dt device = model_output.device noise = randn_tensor(model_output.shape, dtype=model_output.dtype, device=device, generator=generator) prev_sample = prev_sample + noise * sigma_up if not return_dict: return (prev_sample,) return EulerAncestralDiscreteSchedulerOutput( prev_sample=prev_sample, pred_original_sample=pred_original_sample ) # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_euler_discrete.py ================================================ # Copyright 2023 Katherine Crowson and 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. import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, logging, randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete class EulerDiscreteSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin): """ Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `"epsilon"`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) interpolation_type (`str`, default `"linear"`, optional): interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be one of [`"linear"`, `"log_linear"`]. use_karras_sigmas (`bool`, *optional*, defaults to `False`): This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, prediction_type: str = "epsilon", interpolation_type: str = "linear", use_karras_sigmas: Optional[bool] = False, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) self.sigmas = torch.from_numpy(sigmas) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy() self.timesteps = torch.from_numpy(timesteps) self.is_scale_input_called = False self.use_karras_sigmas = use_karras_sigmas def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor] ) -> torch.FloatTensor: """ Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. Args: sample (`torch.FloatTensor`): input sample timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain Returns: `torch.FloatTensor`: scaled input sample """ if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero().item() sigma = self.sigmas[step_index] sample = sample / ((sigma**2 + 1) ** 0.5) self.is_scale_input_called = True return sample def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) log_sigmas = np.log(sigmas) if self.config.interpolation_type == "linear": sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) elif self.config.interpolation_type == "log_linear": sigmas = torch.linspace(np.log(sigmas[-1]), np.log(sigmas[0]), num_inference_steps + 1).exp() else: raise ValueError( f"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either" " 'linear' or 'log_linear'" ) if self.use_karras_sigmas: sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) self.sigmas = torch.from_numpy(sigmas).to(device=device) if str(device).startswith("mps"): # mps does not support float64 self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) else: self.timesteps = torch.from_numpy(timesteps).to(device=device) def _sigma_to_t(self, sigma, log_sigmas): # get log sigma log_sigma = np.log(sigma) # get distribution dists = log_sigma - log_sigmas[:, np.newaxis] # get sigmas range low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = log_sigmas[low_idx] high = log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = np.clip(w, 0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.reshape(sigma.shape) return t # Copied from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17 def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor: """Constructs the noise schedule of Karras et al. (2022).""" sigma_min: float = in_sigmas[-1].item() sigma_max: float = in_sigmas[0].item() rho = 7.0 # 7.0 is the value used in the paper ramp = np.linspace(0, 1, num_inference_steps) 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 sigmas def step( self, model_output: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], sample: torch.FloatTensor, s_churn: float = 0.0, s_tmin: float = 0.0, s_tmax: float = float("inf"), s_noise: float = 1.0, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[EulerDiscreteSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`float`): current timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. s_churn (`float`) s_tmin (`float`) s_tmax (`float`) s_noise (`float`) generator (`torch.Generator`, optional): Random number generator. return_dict (`bool`): option for returning tuple rather than EulerDiscreteSchedulerOutput class Returns: [`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if ( isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor) ): raise ValueError( ( "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" " one of the `scheduler.timesteps` as a timestep." ), ) if not self.is_scale_input_called: logger.warning( "The `scale_model_input` function should be called before `step` to ensure correct denoising. " "See `StableDiffusionPipeline` for a usage example." ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero().item() sigma = self.sigmas[step_index] gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0 noise = randn_tensor( model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator ) eps = noise * s_noise sigma_hat = sigma * (gamma + 1) if gamma > 0: sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5 # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise # NOTE: "original_sample" should not be an expected prediction_type but is left in for # backwards compatibility if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample": pred_original_sample = model_output elif self.config.prediction_type == "epsilon": pred_original_sample = sample - sigma_hat * model_output elif self.config.prediction_type == "v_prediction": # * c_out + input * c_skip pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1)) else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) # 2. Convert to an ODE derivative derivative = (sample - pred_original_sample) / sigma_hat dt = self.sigmas[step_index + 1] - sigma_hat prev_sample = sample + derivative * dt if not return_dict: return (prev_sample,) return EulerDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_heun_discrete.py ================================================ # Copyright 2023 Katherine Crowson, The HuggingFace Team and hlky. 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. import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin): """ Implements Algorithm 2 (Heun steps) from Karras et al. (2022). for discrete beta schedules. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L90 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf). use_karras_sigmas (`bool`, *optional*, defaults to `False`): This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 2 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.00085, # sensible defaults beta_end: float = 0.012, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, prediction_type: str = "epsilon", use_karras_sigmas: Optional[bool] = False, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # set all values self.set_timesteps(num_train_timesteps, None, num_train_timesteps) self.use_karras_sigmas = use_karras_sigmas def index_for_timestep(self, timestep, schedule_timesteps=None): if schedule_timesteps is None: schedule_timesteps = self.timesteps indices = (schedule_timesteps == timestep).nonzero() if self.state_in_first_order: pos = -1 else: pos = 0 return indices[pos].item() def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], ) -> torch.FloatTensor: """ Args: Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ step_index = self.index_for_timestep(timestep) sigma = self.sigmas[step_index] sample = sample / ((sigma**2 + 1) ** 0.5) return sample def set_timesteps( self, num_inference_steps: int, device: Union[str, torch.device] = None, num_train_timesteps: Optional[int] = None, ): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) log_sigmas = np.log(sigmas) sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) if self.use_karras_sigmas: sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) sigmas = torch.from_numpy(sigmas).to(device=device) self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]]) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() timesteps = torch.from_numpy(timesteps) timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)]) if str(device).startswith("mps"): # mps does not support float64 self.timesteps = timesteps.to(device, dtype=torch.float32) else: self.timesteps = timesteps.to(device=device) # empty dt and derivative self.prev_derivative = None self.dt = None # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t def _sigma_to_t(self, sigma, log_sigmas): # get log sigma log_sigma = np.log(sigma) # get distribution dists = log_sigma - log_sigmas[:, np.newaxis] # get sigmas range low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = log_sigmas[low_idx] high = log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = np.clip(w, 0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.reshape(sigma.shape) return t # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor: """Constructs the noise schedule of Karras et al. (2022).""" sigma_min: float = in_sigmas[-1].item() sigma_max: float = in_sigmas[0].item() rho = 7.0 # 7.0 is the value used in the paper ramp = np.linspace(0, 1, num_inference_steps) 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 sigmas @property def state_in_first_order(self): return self.dt is None def step( self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: Union[float, torch.FloatTensor], sample: Union[torch.FloatTensor, np.ndarray], return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Args: Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor` or `np.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ step_index = self.index_for_timestep(timestep) if self.state_in_first_order: sigma = self.sigmas[step_index] sigma_next = self.sigmas[step_index + 1] else: # 2nd order / Heun's method sigma = self.sigmas[step_index - 1] sigma_next = self.sigmas[step_index] # currently only gamma=0 is supported. This usually works best anyways. # We can support gamma in the future but then need to scale the timestep before # passing it to the model which requires a change in API gamma = 0 sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": sigma_input = sigma_hat if self.state_in_first_order else sigma_next pred_original_sample = sample - sigma_input * model_output elif self.config.prediction_type == "v_prediction": sigma_input = sigma_hat if self.state_in_first_order else sigma_next pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + ( sample / (sigma_input**2 + 1) ) elif self.config.prediction_type == "sample": raise NotImplementedError("prediction_type not implemented yet: sample") else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) if self.state_in_first_order: # 2. Convert to an ODE derivative for 1st order derivative = (sample - pred_original_sample) / sigma_hat # 3. delta timestep dt = sigma_next - sigma_hat # store for 2nd order step self.prev_derivative = derivative self.dt = dt self.sample = sample else: # 2. 2nd order / Heun's method derivative = (sample - pred_original_sample) / sigma_next derivative = (self.prev_derivative + derivative) / 2 # 3. take prev timestep & sample dt = self.dt sample = self.sample # free dt and derivative # Note, this puts the scheduler in "first order mode" self.prev_derivative = None self.dt = None self.sample = None prev_sample = sample + derivative * dt if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_ipndm.py ================================================ # Copyright 2023 Zhejiang University Team and 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. import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import SchedulerMixin, SchedulerOutput class IPNDMScheduler(SchedulerMixin, ConfigMixin): """ Improved Pseudo numerical methods for diffusion models (iPNDM) ported from @crowsonkb's amazing k-diffusion [library](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296) [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2202.09778 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. """ order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, trained_betas: Optional[Union[np.ndarray, List[float]]] = None ): # set `betas`, `alphas`, `timesteps` self.set_timesteps(num_train_timesteps) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # For now we only support F-PNDM, i.e. the runge-kutta method # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf # mainly at formula (9), (12), (13) and the Algorithm 2. self.pndm_order = 4 # running values self.ets = [] def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ self.num_inference_steps = num_inference_steps steps = torch.linspace(1, 0, num_inference_steps + 1)[:-1] steps = torch.cat([steps, torch.tensor([0.0])]) if self.config.trained_betas is not None: self.betas = torch.tensor(self.config.trained_betas, dtype=torch.float32) else: self.betas = torch.sin(steps * math.pi / 2) ** 2 self.alphas = (1.0 - self.betas**2) ** 0.5 timesteps = (torch.atan2(self.betas, self.alphas) / math.pi * 2)[:-1] self.timesteps = timesteps.to(device) self.ets = [] def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple times to approximate the solution. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) timestep_index = (self.timesteps == timestep).nonzero().item() prev_timestep_index = timestep_index + 1 ets = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index] self.ets.append(ets) if len(self.ets) == 1: ets = self.ets[-1] elif len(self.ets) == 2: ets = (3 * self.ets[-1] - self.ets[-2]) / 2 elif len(self.ets) == 3: ets = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12 else: ets = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4]) prev_sample = self._get_prev_sample(sample, timestep_index, prev_timestep_index, ets) if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample def _get_prev_sample(self, sample, timestep_index, prev_timestep_index, ets): alpha = self.alphas[timestep_index] sigma = self.betas[timestep_index] next_alpha = self.alphas[prev_timestep_index] next_sigma = self.betas[prev_timestep_index] pred = (sample - sigma * ets) / max(alpha, 1e-8) prev_sample = next_alpha * pred + ets * next_sigma return prev_sample def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py ================================================ # Copyright 2023 Katherine Crowson, The HuggingFace Team and hlky. 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. import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin): """ Scheduler created by @crowsonkb in [k_diffusion](https://github.com/crowsonkb/k-diffusion), see: https://github.com/crowsonkb/k-diffusion/blob/5b3af030dd83e0297272d861c19477735d0317ec/k_diffusion/sampling.py#L188 Scheduler inspired by DPM-Solver-2 and Algorthim 2 from Karras et al. (2022). [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 2 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.00085, # sensible defaults beta_end: float = 0.012, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, prediction_type: str = "epsilon", ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # set all values self.set_timesteps(num_train_timesteps, None, num_train_timesteps) # Copied from diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler.index_for_timestep def index_for_timestep(self, timestep, schedule_timesteps=None): if schedule_timesteps is None: schedule_timesteps = self.timesteps indices = (schedule_timesteps == timestep).nonzero() if self.state_in_first_order: pos = -1 else: pos = 0 return indices[pos].item() def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], ) -> torch.FloatTensor: """ Args: Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ step_index = self.index_for_timestep(timestep) if self.state_in_first_order: sigma = self.sigmas[step_index] else: sigma = self.sigmas_interpol[step_index - 1] sample = sample / ((sigma**2 + 1) ** 0.5) return sample def set_timesteps( self, num_inference_steps: int, device: Union[str, torch.device] = None, num_train_timesteps: Optional[int] = None, ): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) self.log_sigmas = torch.from_numpy(np.log(sigmas)).to(device) sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) sigmas = torch.from_numpy(sigmas).to(device=device) # compute up and down sigmas sigmas_next = sigmas.roll(-1) sigmas_next[-1] = 0.0 sigmas_up = (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5 sigmas_down = (sigmas_next**2 - sigmas_up**2) ** 0.5 sigmas_down[-1] = 0.0 # compute interpolated sigmas sigmas_interpol = sigmas.log().lerp(sigmas_down.log(), 0.5).exp() sigmas_interpol[-2:] = 0.0 # set sigmas self.sigmas = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]]) self.sigmas_interpol = torch.cat( [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]] ) self.sigmas_up = torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]]) self.sigmas_down = torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]]) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() if str(device).startswith("mps"): # mps does not support float64 timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) else: timesteps = torch.from_numpy(timesteps).to(device) timesteps_interpol = self.sigma_to_t(sigmas_interpol).to(device, dtype=timesteps.dtype) interleaved_timesteps = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]), dim=-1).flatten() self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps]) self.sample = None def sigma_to_t(self, sigma): # get log sigma log_sigma = sigma.log() # get distribution dists = log_sigma - self.log_sigmas[:, None] # get sigmas range low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = self.log_sigmas[low_idx] high = self.log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = w.clamp(0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.view(sigma.shape) return t @property def state_in_first_order(self): return self.sample is None def step( self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: Union[float, torch.FloatTensor], sample: Union[torch.FloatTensor, np.ndarray], generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Args: Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor` or `np.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ step_index = self.index_for_timestep(timestep) if self.state_in_first_order: sigma = self.sigmas[step_index] sigma_interpol = self.sigmas_interpol[step_index] sigma_up = self.sigmas_up[step_index] sigma_down = self.sigmas_down[step_index - 1] else: # 2nd order / KPDM2's method sigma = self.sigmas[step_index - 1] sigma_interpol = self.sigmas_interpol[step_index - 1] sigma_up = self.sigmas_up[step_index - 1] sigma_down = self.sigmas_down[step_index - 1] # currently only gamma=0 is supported. This usually works best anyways. # We can support gamma in the future but then need to scale the timestep before # passing it to the model which requires a change in API gamma = 0 sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now device = model_output.device noise = randn_tensor(model_output.shape, dtype=model_output.dtype, device=device, generator=generator) # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol pred_original_sample = sample - sigma_input * model_output elif self.config.prediction_type == "v_prediction": sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + ( sample / (sigma_input**2 + 1) ) elif self.config.prediction_type == "sample": raise NotImplementedError("prediction_type not implemented yet: sample") else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) if self.state_in_first_order: # 2. Convert to an ODE derivative for 1st order derivative = (sample - pred_original_sample) / sigma_hat # 3. delta timestep dt = sigma_interpol - sigma_hat # store for 2nd order step self.sample = sample self.dt = dt prev_sample = sample + derivative * dt else: # DPM-Solver-2 # 2. Convert to an ODE derivative for 2nd order derivative = (sample - pred_original_sample) / sigma_interpol # 3. delta timestep dt = sigma_down - sigma_hat sample = self.sample self.sample = None prev_sample = sample + derivative * dt prev_sample = prev_sample + noise * sigma_up if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) # Copied from diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_k_dpm_2_discrete.py ================================================ # Copyright 2023 Katherine Crowson, The HuggingFace Team and hlky. 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. import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin): """ Scheduler created by @crowsonkb in [k_diffusion](https://github.com/crowsonkb/k-diffusion), see: https://github.com/crowsonkb/k-diffusion/blob/5b3af030dd83e0297272d861c19477735d0317ec/k_diffusion/sampling.py#L188 Scheduler inspired by DPM-Solver-2 and Algorthim 2 from Karras et al. (2022). [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 2 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.00085, # sensible defaults beta_end: float = 0.012, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, prediction_type: str = "epsilon", ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # set all values self.set_timesteps(num_train_timesteps, None, num_train_timesteps) # Copied from diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler.index_for_timestep def index_for_timestep(self, timestep, schedule_timesteps=None): if schedule_timesteps is None: schedule_timesteps = self.timesteps indices = (schedule_timesteps == timestep).nonzero() if self.state_in_first_order: pos = -1 else: pos = 0 return indices[pos].item() def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], ) -> torch.FloatTensor: """ Args: Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ step_index = self.index_for_timestep(timestep) if self.state_in_first_order: sigma = self.sigmas[step_index] else: sigma = self.sigmas_interpol[step_index] sample = sample / ((sigma**2 + 1) ** 0.5) return sample def set_timesteps( self, num_inference_steps: int, device: Union[str, torch.device] = None, num_train_timesteps: Optional[int] = None, ): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) self.log_sigmas = torch.from_numpy(np.log(sigmas)).to(device) sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) sigmas = torch.from_numpy(sigmas).to(device=device) # interpolate sigmas sigmas_interpol = sigmas.log().lerp(sigmas.roll(1).log(), 0.5).exp() self.sigmas = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]]) self.sigmas_interpol = torch.cat( [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]] ) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() if str(device).startswith("mps"): # mps does not support float64 timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) else: timesteps = torch.from_numpy(timesteps).to(device) # interpolate timesteps timesteps_interpol = self.sigma_to_t(sigmas_interpol).to(device, dtype=timesteps.dtype) interleaved_timesteps = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]), dim=-1).flatten() self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps]) self.sample = None def sigma_to_t(self, sigma): # get log sigma log_sigma = sigma.log() # get distribution dists = log_sigma - self.log_sigmas[:, None] # get sigmas range low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = self.log_sigmas[low_idx] high = self.log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = w.clamp(0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.view(sigma.shape) return t @property def state_in_first_order(self): return self.sample is None def step( self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: Union[float, torch.FloatTensor], sample: Union[torch.FloatTensor, np.ndarray], return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Args: Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor` or `np.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ step_index = self.index_for_timestep(timestep) if self.state_in_first_order: sigma = self.sigmas[step_index] sigma_interpol = self.sigmas_interpol[step_index + 1] sigma_next = self.sigmas[step_index + 1] else: # 2nd order / KDPM2's method sigma = self.sigmas[step_index - 1] sigma_interpol = self.sigmas_interpol[step_index] sigma_next = self.sigmas[step_index] # currently only gamma=0 is supported. This usually works best anyways. # We can support gamma in the future but then need to scale the timestep before # passing it to the model which requires a change in API gamma = 0 sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol pred_original_sample = sample - sigma_input * model_output elif self.config.prediction_type == "v_prediction": sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + ( sample / (sigma_input**2 + 1) ) elif self.config.prediction_type == "sample": raise NotImplementedError("prediction_type not implemented yet: sample") else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) if self.state_in_first_order: # 2. Convert to an ODE derivative for 1st order derivative = (sample - pred_original_sample) / sigma_hat # 3. delta timestep dt = sigma_interpol - sigma_hat # store for 2nd order step self.sample = sample else: # DPM-Solver-2 # 2. Convert to an ODE derivative for 2nd order derivative = (sample - pred_original_sample) / sigma_interpol # 3. delta timestep dt = sigma_next - sigma_hat sample = self.sample self.sample = None prev_sample = sample + derivative * dt if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) # Copied from diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_karras_ve.py ================================================ # Copyright 2023 NVIDIA and 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 dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import SchedulerMixin @dataclass class KarrasVeOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. derivative (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Derivative of predicted original image sample (x_0). pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor derivative: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None class KarrasVeScheduler(SchedulerMixin, ConfigMixin): """ Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and the VE column of Table 1 from [1] for reference. [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://arxiv.org/abs/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper. Args: sigma_min (`float`): minimum noise magnitude sigma_max (`float`): maximum noise magnitude s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. s_churn (`float`): the parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100]. s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity). A reasonable range is [0, 10]. s_max (`float`): the end value of the sigma range where we add noise. A reasonable range is [0.2, 80]. """ order = 2 @register_to_config def __init__( self, sigma_min: float = 0.02, sigma_max: float = 100, s_noise: float = 1.007, s_churn: float = 80, s_min: float = 0.05, s_max: float = 50, ): # standard deviation of the initial noise distribution self.init_noise_sigma = sigma_max # setable values self.num_inference_steps: int = None self.timesteps: np.IntTensor = None self.schedule: torch.FloatTensor = None # sigma(t_i) def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ self.num_inference_steps = num_inference_steps timesteps = np.arange(0, self.num_inference_steps)[::-1].copy() self.timesteps = torch.from_numpy(timesteps).to(device) schedule = [ ( self.config.sigma_max**2 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1)) ) for i in self.timesteps ] self.schedule = torch.tensor(schedule, dtype=torch.float32, device=device) def add_noise_to_input( self, sample: torch.FloatTensor, sigma: float, generator: Optional[torch.Generator] = None ) -> Tuple[torch.FloatTensor, float]: """ Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a higher noise level sigma_hat = sigma_i + gamma_i*sigma_i. TODO Args: """ if self.config.s_min <= sigma <= self.config.s_max: gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1) else: gamma = 0 # sample eps ~ N(0, S_noise^2 * I) eps = self.config.s_noise * randn_tensor(sample.shape, generator=generator).to(sample.device) sigma_hat = sigma + gamma * sigma sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps) return sample_hat, sigma_hat def step( self, model_output: torch.FloatTensor, sigma_hat: float, sigma_prev: float, sample_hat: torch.FloatTensor, return_dict: bool = True, ) -> Union[KarrasVeOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.FloatTensor`): TODO return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class KarrasVeOutput: updated sample in the diffusion chain and derivative (TODO double check). Returns: [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] or `tuple`: [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ pred_original_sample = sample_hat + sigma_hat * model_output derivative = (sample_hat - pred_original_sample) / sigma_hat sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative if not return_dict: return (sample_prev, derivative) return KarrasVeOutput( prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample ) def step_correct( self, model_output: torch.FloatTensor, sigma_hat: float, sigma_prev: float, sample_hat: torch.FloatTensor, sample_prev: torch.FloatTensor, derivative: torch.FloatTensor, return_dict: bool = True, ) -> Union[KarrasVeOutput, Tuple]: """ Correct the predicted sample based on the output model_output of the network. TODO complete description Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.FloatTensor`): TODO sample_prev (`torch.FloatTensor`): TODO derivative (`torch.FloatTensor`): TODO return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class Returns: prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO """ pred_original_sample = sample_prev + sigma_prev * model_output derivative_corr = (sample_prev - pred_original_sample) / sigma_prev sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr) if not return_dict: return (sample_prev, derivative) return KarrasVeOutput( prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample ) def add_noise(self, original_samples, noise, timesteps): raise NotImplementedError() ================================================ FILE: diffusers/schedulers/scheduling_karras_ve_flax.py ================================================ # Copyright 2023 NVIDIA and 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 dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax.numpy as jnp from jax import random from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .scheduling_utils_flax import FlaxSchedulerMixin @flax.struct.dataclass class KarrasVeSchedulerState: # setable values num_inference_steps: Optional[int] = None timesteps: Optional[jnp.ndarray] = None schedule: Optional[jnp.ndarray] = None # sigma(t_i) @classmethod def create(cls): return cls() @dataclass class FlaxKarrasVeOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. derivative (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Derivative of predicted original image sample (x_0). state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. """ prev_sample: jnp.ndarray derivative: jnp.ndarray state: KarrasVeSchedulerState class FlaxKarrasVeScheduler(FlaxSchedulerMixin, ConfigMixin): """ Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and the VE column of Table 1 from [1] for reference. [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://arxiv.org/abs/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper. Args: sigma_min (`float`): minimum noise magnitude sigma_max (`float`): maximum noise magnitude s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. s_churn (`float`): the parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100]. s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity). A reasonable range is [0, 10]. s_max (`float`): the end value of the sigma range where we add noise. A reasonable range is [0.2, 80]. """ @property def has_state(self): return True @register_to_config def __init__( self, sigma_min: float = 0.02, sigma_max: float = 100, s_noise: float = 1.007, s_churn: float = 80, s_min: float = 0.05, s_max: float = 50, ): pass def create_state(self): return KarrasVeSchedulerState.create() def set_timesteps( self, state: KarrasVeSchedulerState, num_inference_steps: int, shape: Tuple = () ) -> KarrasVeSchedulerState: """ Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ timesteps = jnp.arange(0, num_inference_steps)[::-1].copy() schedule = [ ( self.config.sigma_max**2 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1)) ) for i in timesteps ] return state.replace( num_inference_steps=num_inference_steps, schedule=jnp.array(schedule, dtype=jnp.float32), timesteps=timesteps, ) def add_noise_to_input( self, state: KarrasVeSchedulerState, sample: jnp.ndarray, sigma: float, key: random.KeyArray, ) -> Tuple[jnp.ndarray, float]: """ Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a higher noise level sigma_hat = sigma_i + gamma_i*sigma_i. TODO Args: """ if self.config.s_min <= sigma <= self.config.s_max: gamma = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1) else: gamma = 0 # sample eps ~ N(0, S_noise^2 * I) key = random.split(key, num=1) eps = self.config.s_noise * random.normal(key=key, shape=sample.shape) sigma_hat = sigma + gamma * sigma sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps) return sample_hat, sigma_hat def step( self, state: KarrasVeSchedulerState, model_output: jnp.ndarray, sigma_hat: float, sigma_prev: float, sample_hat: jnp.ndarray, return_dict: bool = True, ) -> Union[FlaxKarrasVeOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class Returns: [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] or `tuple`: Updated sample in the diffusion chain and derivative. [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ pred_original_sample = sample_hat + sigma_hat * model_output derivative = (sample_hat - pred_original_sample) / sigma_hat sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative if not return_dict: return (sample_prev, derivative, state) return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state) def step_correct( self, state: KarrasVeSchedulerState, model_output: jnp.ndarray, sigma_hat: float, sigma_prev: float, sample_hat: jnp.ndarray, sample_prev: jnp.ndarray, derivative: jnp.ndarray, return_dict: bool = True, ) -> Union[FlaxKarrasVeOutput, Tuple]: """ Correct the predicted sample based on the output model_output of the network. TODO complete description Args: state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO derivative (`torch.FloatTensor` or `np.ndarray`): TODO return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class Returns: prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO """ pred_original_sample = sample_prev + sigma_prev * model_output derivative_corr = (sample_prev - pred_original_sample) / sigma_prev sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr) if not return_dict: return (sample_prev, derivative, state) return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state) def add_noise(self, state: KarrasVeSchedulerState, original_samples, noise, timesteps): raise NotImplementedError() ================================================ FILE: diffusers/schedulers/scheduling_lms_discrete.py ================================================ # Copyright 2023 Katherine Crowson and 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. import math import warnings from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from scipy import integrate from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete class LMSDiscreteSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin): """ Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. use_karras_sigmas (`bool`, *optional*, defaults to `False`): This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, use_karras_sigmas: Optional[bool] = False, prediction_type: str = "epsilon", ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) self.sigmas = torch.from_numpy(sigmas) # standard deviation of the initial noise distribution self.init_noise_sigma = self.sigmas.max() # setable values self.num_inference_steps = None self.use_karras_sigmas = use_karras_sigmas self.set_timesteps(num_train_timesteps, None) self.derivatives = [] self.is_scale_input_called = False def scale_model_input( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor] ) -> torch.FloatTensor: """ Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm. Args: sample (`torch.FloatTensor`): input sample timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain Returns: `torch.FloatTensor`: scaled input sample """ if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero().item() sigma = self.sigmas[step_index] sample = sample / ((sigma**2 + 1) ** 0.5) self.is_scale_input_called = True return sample def get_lms_coefficient(self, order, t, current_order): """ Compute a linear multistep coefficient. Args: order (TODO): t (TODO): current_order (TODO): """ def lms_derivative(tau): prod = 1.0 for k in range(order): if current_order == k: continue prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k]) return prod integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0] return integrated_coeff def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) log_sigmas = np.log(sigmas) sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) if self.use_karras_sigmas: sigmas = self._convert_to_karras(in_sigmas=sigmas) timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]) sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) self.sigmas = torch.from_numpy(sigmas).to(device=device) if str(device).startswith("mps"): # mps does not support float64 self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) else: self.timesteps = torch.from_numpy(timesteps).to(device=device) self.derivatives = [] # copied from diffusers.schedulers.scheduling_euler_discrete._sigma_to_t def _sigma_to_t(self, sigma, log_sigmas): # get log sigma log_sigma = np.log(sigma) # get distribution dists = log_sigma - log_sigmas[:, np.newaxis] # get sigmas range low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) high_idx = low_idx + 1 low = log_sigmas[low_idx] high = log_sigmas[high_idx] # interpolate sigmas w = (low - log_sigma) / (low - high) w = np.clip(w, 0, 1) # transform interpolation to time range t = (1 - w) * low_idx + w * high_idx t = t.reshape(sigma.shape) return t # copied from diffusers.schedulers.scheduling_euler_discrete._convert_to_karras def _convert_to_karras(self, in_sigmas: torch.FloatTensor) -> torch.FloatTensor: """Constructs the noise schedule of Karras et al. (2022).""" sigma_min: float = in_sigmas[-1].item() sigma_max: float = in_sigmas[0].item() rho = 7.0 # 7.0 is the value used in the paper ramp = np.linspace(0, 1, self.num_inference_steps) 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 sigmas def step( self, model_output: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], sample: torch.FloatTensor, order: int = 4, return_dict: bool = True, ) -> Union[LMSDiscreteSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`float`): current timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. order: coefficient for multi-step inference. return_dict (`bool`): option for returning tuple rather than LMSDiscreteSchedulerOutput class Returns: [`~schedulers.scheduling_utils.LMSDiscreteSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.LMSDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if not self.is_scale_input_called: warnings.warn( "The `scale_model_input` function should be called before `step` to ensure correct denoising. " "See `StableDiffusionPipeline` for a usage example." ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero().item() sigma = self.sigmas[step_index] # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": pred_original_sample = sample - sigma * model_output elif self.config.prediction_type == "v_prediction": # * c_out + input * c_skip pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1)) elif self.config.prediction_type == "sample": pred_original_sample = model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) # 2. Convert to an ODE derivative derivative = (sample - pred_original_sample) / sigma self.derivatives.append(derivative) if len(self.derivatives) > order: self.derivatives.pop(0) # 3. Compute linear multistep coefficients order = min(step_index + 1, order) lms_coeffs = [self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)] # 4. Compute previous sample based on the derivatives path prev_sample = sample + sum( coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(self.derivatives)) ) if not return_dict: return (prev_sample,) return LMSDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): # mps does not support float64 schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) timesteps = timesteps.to(original_samples.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(original_samples.device) timesteps = timesteps.to(original_samples.device) step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(original_samples.shape): sigma = sigma.unsqueeze(-1) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_lms_discrete_flax.py ================================================ # Copyright 2023 Katherine Crowson and 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 dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax.numpy as jnp from scipy import integrate from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import ( CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left, ) @flax.struct.dataclass class LMSDiscreteSchedulerState: common: CommonSchedulerState # setable values init_noise_sigma: jnp.ndarray timesteps: jnp.ndarray sigmas: jnp.ndarray num_inference_steps: Optional[int] = None # running values derivatives: Optional[jnp.ndarray] = None @classmethod def create( cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, sigmas: jnp.ndarray ): return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps, sigmas=sigmas) @dataclass class FlaxLMSSchedulerOutput(FlaxSchedulerOutput): state: LMSDiscreteSchedulerState class FlaxLMSDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin): """ Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. """ _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers] dtype: jnp.dtype @property def has_state(self): return True @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[jnp.ndarray] = None, prediction_type: str = "epsilon", dtype: jnp.dtype = jnp.float32, ): self.dtype = dtype def create_state(self, common: Optional[CommonSchedulerState] = None) -> LMSDiscreteSchedulerState: if common is None: common = CommonSchedulerState.create(self) timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] sigmas = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5 # standard deviation of the initial noise distribution init_noise_sigma = sigmas.max() return LMSDiscreteSchedulerState.create( common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps, sigmas=sigmas, ) def scale_model_input(self, state: LMSDiscreteSchedulerState, sample: jnp.ndarray, timestep: int) -> jnp.ndarray: """ Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm. Args: state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. timestep (`int`): current discrete timestep in the diffusion chain. Returns: `jnp.ndarray`: scaled input sample """ (step_index,) = jnp.where(state.timesteps == timestep, size=1) step_index = step_index[0] sigma = state.sigmas[step_index] sample = sample / ((sigma**2 + 1) ** 0.5) return sample def get_lms_coefficient(self, state: LMSDiscreteSchedulerState, order, t, current_order): """ Compute a linear multistep coefficient. Args: order (TODO): t (TODO): current_order (TODO): """ def lms_derivative(tau): prod = 1.0 for k in range(order): if current_order == k: continue prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k]) return prod integrated_coeff = integrate.quad(lms_derivative, state.sigmas[t], state.sigmas[t + 1], epsrel=1e-4)[0] return integrated_coeff def set_timesteps( self, state: LMSDiscreteSchedulerState, num_inference_steps: int, shape: Tuple = () ) -> LMSDiscreteSchedulerState: """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ timesteps = jnp.linspace(self.config.num_train_timesteps - 1, 0, num_inference_steps, dtype=self.dtype) low_idx = jnp.floor(timesteps).astype(jnp.int32) high_idx = jnp.ceil(timesteps).astype(jnp.int32) frac = jnp.mod(timesteps, 1.0) sigmas = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5 sigmas = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx] sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)]) timesteps = timesteps.astype(jnp.int32) # initial running values derivatives = jnp.zeros((0,) + shape, dtype=self.dtype) return state.replace( timesteps=timesteps, sigmas=sigmas, num_inference_steps=num_inference_steps, derivatives=derivatives, ) def step( self, state: LMSDiscreteSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, order: int = 4, return_dict: bool = True, ) -> Union[FlaxLMSSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. order: coefficient for multi-step inference. return_dict (`bool`): option for returning tuple rather than FlaxLMSSchedulerOutput class Returns: [`FlaxLMSSchedulerOutput`] or `tuple`: [`FlaxLMSSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) sigma = state.sigmas[timestep] # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": pred_original_sample = sample - sigma * model_output elif self.config.prediction_type == "v_prediction": # * c_out + input * c_skip pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1)) else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" ) # 2. Convert to an ODE derivative derivative = (sample - pred_original_sample) / sigma state = state.replace(derivatives=jnp.append(state.derivatives, derivative)) if len(state.derivatives) > order: state = state.replace(derivatives=jnp.delete(state.derivatives, 0)) # 3. Compute linear multistep coefficients order = min(timestep + 1, order) lms_coeffs = [self.get_lms_coefficient(state, order, timestep, curr_order) for curr_order in range(order)] # 4. Compute previous sample based on the derivatives path prev_sample = sample + sum( coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(state.derivatives)) ) if not return_dict: return (prev_sample, state) return FlaxLMSSchedulerOutput(prev_sample=prev_sample, state=state) def add_noise( self, state: LMSDiscreteSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: sigma = state.sigmas[timesteps].flatten() sigma = broadcast_to_shape_from_left(sigma, noise.shape) noisy_samples = original_samples + noise * sigma return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_pndm.py ================================================ # Copyright 2023 Zhejiang University Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class PNDMScheduler(SchedulerMixin, ConfigMixin): """ Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques, namely Runge-Kutta method and a linear multi-step method. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2202.09778 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. skip_prk_steps (`bool`): allows the scheduler to skip the Runge-Kutta steps that are defined in the original paper as being required before plms steps; defaults to `False`. set_alpha_to_one (`bool`, default `False`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the value of alpha at step 0. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) steps_offset (`int`, default `0`): an offset added to the inference steps. You can use a combination of `offset=1` and `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in stable diffusion. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, skip_prk_steps: bool = False, set_alpha_to_one: bool = False, prediction_type: str = "epsilon", steps_offset: int = 0, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # For now we only support F-PNDM, i.e. the runge-kutta method # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf # mainly at formula (9), (12), (13) and the Algorithm 2. self.pndm_order = 4 # running values self.cur_model_output = 0 self.counter = 0 self.cur_sample = None self.ets = [] # setable values self.num_inference_steps = None self._timesteps = np.arange(0, num_train_timesteps)[::-1].copy() self.prk_timesteps = None self.plms_timesteps = None self.timesteps = None def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ self.num_inference_steps = num_inference_steps step_ratio = self.config.num_train_timesteps // self.num_inference_steps # creates integer timesteps by multiplying by ratio # casting to int to avoid issues when num_inference_step is power of 3 self._timesteps = (np.arange(0, num_inference_steps) * step_ratio).round() self._timesteps += self.config.steps_offset if self.config.skip_prk_steps: # for some models like stable diffusion the prk steps can/should be skipped to # produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation # is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51 self.prk_timesteps = np.array([]) self.plms_timesteps = np.concatenate([self._timesteps[:-1], self._timesteps[-2:-1], self._timesteps[-1:]])[ ::-1 ].copy() else: prk_timesteps = np.array(self._timesteps[-self.pndm_order :]).repeat(2) + np.tile( np.array([0, self.config.num_train_timesteps // num_inference_steps // 2]), self.pndm_order ) self.prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1].copy() self.plms_timesteps = self._timesteps[:-3][ ::-1 ].copy() # we copy to avoid having negative strides which are not supported by torch.from_numpy timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64) self.timesteps = torch.from_numpy(timesteps).to(device) self.ets = [] self.counter = 0 self.cur_model_output = 0 def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.counter < len(self.prk_timesteps) and not self.config.skip_prk_steps: return self.step_prk(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict) else: return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict) def step_prk( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the solution to the differential equation. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2 prev_timestep = timestep - diff_to_prev timestep = self.prk_timesteps[self.counter // 4 * 4] if self.counter % 4 == 0: self.cur_model_output += 1 / 6 * model_output self.ets.append(model_output) self.cur_sample = sample elif (self.counter - 1) % 4 == 0: self.cur_model_output += 1 / 3 * model_output elif (self.counter - 2) % 4 == 0: self.cur_model_output += 1 / 3 * model_output elif (self.counter - 3) % 4 == 0: model_output = self.cur_model_output + 1 / 6 * model_output self.cur_model_output = 0 # cur_sample should not be `None` cur_sample = self.cur_sample if self.cur_sample is not None else sample prev_sample = self._get_prev_sample(cur_sample, timestep, prev_timestep, model_output) self.counter += 1 if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def step_plms( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple times to approximate the solution. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if not self.config.skip_prk_steps and len(self.ets) < 3: raise ValueError( f"{self.__class__} can only be run AFTER scheduler has been run " "in 'prk' mode for at least 12 iterations " "See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py " "for more information." ) prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps if self.counter != 1: self.ets = self.ets[-3:] self.ets.append(model_output) else: prev_timestep = timestep timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps if len(self.ets) == 1 and self.counter == 0: model_output = model_output self.cur_sample = sample elif len(self.ets) == 1 and self.counter == 1: model_output = (model_output + self.ets[-1]) / 2 sample = self.cur_sample self.cur_sample = None elif len(self.ets) == 2: model_output = (3 * self.ets[-1] - self.ets[-2]) / 2 elif len(self.ets) == 3: model_output = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12 else: model_output = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4]) prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output) self.counter += 1 if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample def _get_prev_sample(self, sample, timestep, prev_timestep, model_output): # See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf # this function computes x_(t−δ) using the formula of (9) # Note that x_t needs to be added to both sides of the equation # Notation ( -> # alpha_prod_t -> α_t # alpha_prod_t_prev -> α_(t−δ) # beta_prod_t -> (1 - α_t) # beta_prod_t_prev -> (1 - α_(t−δ)) # sample -> x_t # model_output -> e_θ(x_t, t) # prev_sample -> x_(t−δ) alpha_prod_t = self.alphas_cumprod[timestep] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev if self.config.prediction_type == "v_prediction": model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample elif self.config.prediction_type != "epsilon": raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `v_prediction`" ) # corresponds to (α_(t−δ) - α_t) divided by # denominator of x_t in formula (9) and plus 1 # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) = # sqrt(α_(t−δ)) / sqrt(α_t)) sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5) # corresponds to denominator of e_θ(x_t, t) in formula (9) model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + ( alpha_prod_t * beta_prod_t * alpha_prod_t_prev ) ** (0.5) # full formula (9) prev_sample = ( sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff ) return prev_sample # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_pndm_flax.py ================================================ # Copyright 2023 Zhejiang University Team and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim from dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax import jax.numpy as jnp from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import ( CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, add_noise_common, ) @flax.struct.dataclass class PNDMSchedulerState: common: CommonSchedulerState final_alpha_cumprod: jnp.ndarray # setable values init_noise_sigma: jnp.ndarray timesteps: jnp.ndarray num_inference_steps: Optional[int] = None prk_timesteps: Optional[jnp.ndarray] = None plms_timesteps: Optional[jnp.ndarray] = None # running values cur_model_output: Optional[jnp.ndarray] = None counter: Optional[jnp.int32] = None cur_sample: Optional[jnp.ndarray] = None ets: Optional[jnp.ndarray] = None @classmethod def create( cls, common: CommonSchedulerState, final_alpha_cumprod: jnp.ndarray, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, ): return cls( common=common, final_alpha_cumprod=final_alpha_cumprod, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) @dataclass class FlaxPNDMSchedulerOutput(FlaxSchedulerOutput): state: PNDMSchedulerState class FlaxPNDMScheduler(FlaxSchedulerMixin, ConfigMixin): """ Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques, namely Runge-Kutta method and a linear multi-step method. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2202.09778 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. skip_prk_steps (`bool`): allows the scheduler to skip the Runge-Kutta steps that are defined in the original paper as being required before plms steps; defaults to `False`. set_alpha_to_one (`bool`, default `False`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the value of alpha at step 0. steps_offset (`int`, default `0`): an offset added to the inference steps. You can use a combination of `offset=1` and `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in stable diffusion. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. """ _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers] dtype: jnp.dtype pndm_order: int @property def has_state(self): return True @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[jnp.ndarray] = None, skip_prk_steps: bool = False, set_alpha_to_one: bool = False, steps_offset: int = 0, prediction_type: str = "epsilon", dtype: jnp.dtype = jnp.float32, ): self.dtype = dtype # For now we only support F-PNDM, i.e. the runge-kutta method # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf # mainly at formula (9), (12), (13) and the Algorithm 2. self.pndm_order = 4 def create_state(self, common: Optional[CommonSchedulerState] = None) -> PNDMSchedulerState: if common is None: common = CommonSchedulerState.create(self) # At every step in ddim, we are looking into the previous alphas_cumprod # For the final step, there is no previous alphas_cumprod because we are already at 0 # `set_alpha_to_one` decides whether we set this parameter simply to one or # whether we use the final alpha of the "non-previous" one. final_alpha_cumprod = ( jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0] ) # standard deviation of the initial noise distribution init_noise_sigma = jnp.array(1.0, dtype=self.dtype) timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] return PNDMSchedulerState.create( common=common, final_alpha_cumprod=final_alpha_cumprod, init_noise_sigma=init_noise_sigma, timesteps=timesteps, ) def set_timesteps(self, state: PNDMSchedulerState, num_inference_steps: int, shape: Tuple) -> PNDMSchedulerState: """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. shape (`Tuple`): the shape of the samples to be generated. """ step_ratio = self.config.num_train_timesteps // num_inference_steps # creates integer timesteps by multiplying by ratio # rounding to avoid issues when num_inference_step is power of 3 _timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round() + self.config.steps_offset if self.config.skip_prk_steps: # for some models like stable diffusion the prk steps can/should be skipped to # produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation # is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51 prk_timesteps = jnp.array([], dtype=jnp.int32) plms_timesteps = jnp.concatenate([_timesteps[:-1], _timesteps[-2:-1], _timesteps[-1:]])[::-1] else: prk_timesteps = _timesteps[-self.pndm_order :].repeat(2) + jnp.tile( jnp.array([0, self.config.num_train_timesteps // num_inference_steps // 2], dtype=jnp.int32), self.pndm_order, ) prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1] plms_timesteps = _timesteps[:-3][::-1] timesteps = jnp.concatenate([prk_timesteps, plms_timesteps]) # initial running values cur_model_output = jnp.zeros(shape, dtype=self.dtype) counter = jnp.int32(0) cur_sample = jnp.zeros(shape, dtype=self.dtype) ets = jnp.zeros((4,) + shape, dtype=self.dtype) return state.replace( timesteps=timesteps, num_inference_steps=num_inference_steps, prk_timesteps=prk_timesteps, plms_timesteps=plms_timesteps, cur_model_output=cur_model_output, counter=counter, cur_sample=cur_sample, ets=ets, ) def scale_model_input( self, state: PNDMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None ) -> jnp.ndarray: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. sample (`jnp.ndarray`): input sample timestep (`int`, optional): current timestep Returns: `jnp.ndarray`: scaled input sample """ return sample def step( self, state: PNDMSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, return_dict: bool = True, ) -> Union[FlaxPNDMSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class Returns: [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if self.config.skip_prk_steps: prev_sample, state = self.step_plms(state, model_output, timestep, sample) else: prk_prev_sample, prk_state = self.step_prk(state, model_output, timestep, sample) plms_prev_sample, plms_state = self.step_plms(state, model_output, timestep, sample) cond = state.counter < len(state.prk_timesteps) prev_sample = jax.lax.select(cond, prk_prev_sample, plms_prev_sample) state = state.replace( cur_model_output=jax.lax.select(cond, prk_state.cur_model_output, plms_state.cur_model_output), ets=jax.lax.select(cond, prk_state.ets, plms_state.ets), cur_sample=jax.lax.select(cond, prk_state.cur_sample, plms_state.cur_sample), counter=jax.lax.select(cond, prk_state.counter, plms_state.counter), ) if not return_dict: return (prev_sample, state) return FlaxPNDMSchedulerOutput(prev_sample=prev_sample, state=state) def step_prk( self, state: PNDMSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, ) -> Union[FlaxPNDMSchedulerOutput, Tuple]: """ Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the solution to the differential equation. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class Returns: [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) diff_to_prev = jnp.where( state.counter % 2, 0, self.config.num_train_timesteps // state.num_inference_steps // 2 ) prev_timestep = timestep - diff_to_prev timestep = state.prk_timesteps[state.counter // 4 * 4] model_output = jax.lax.select( (state.counter % 4) != 3, model_output, # remainder 0, 1, 2 state.cur_model_output + 1 / 6 * model_output, # remainder 3 ) state = state.replace( cur_model_output=jax.lax.select_n( state.counter % 4, state.cur_model_output + 1 / 6 * model_output, # remainder 0 state.cur_model_output + 1 / 3 * model_output, # remainder 1 state.cur_model_output + 1 / 3 * model_output, # remainder 2 jnp.zeros_like(state.cur_model_output), # remainder 3 ), ets=jax.lax.select( (state.counter % 4) == 0, state.ets.at[0:3].set(state.ets[1:4]).at[3].set(model_output), # remainder 0 state.ets, # remainder 1, 2, 3 ), cur_sample=jax.lax.select( (state.counter % 4) == 0, sample, # remainder 0 state.cur_sample, # remainder 1, 2, 3 ), ) cur_sample = state.cur_sample prev_sample = self._get_prev_sample(state, cur_sample, timestep, prev_timestep, model_output) state = state.replace(counter=state.counter + 1) return (prev_sample, state) def step_plms( self, state: PNDMSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, ) -> Union[FlaxPNDMSchedulerOutput, Tuple]: """ Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple times to approximate the solution. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class Returns: [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) # NOTE: There is no way to check in the jitted runtime if the prk mode was ran before prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps prev_timestep = jnp.where(prev_timestep > 0, prev_timestep, 0) # Reference: # if state.counter != 1: # state.ets.append(model_output) # else: # prev_timestep = timestep # timestep = timestep + self.config.num_train_timesteps // state.num_inference_steps prev_timestep = jnp.where(state.counter == 1, timestep, prev_timestep) timestep = jnp.where( state.counter == 1, timestep + self.config.num_train_timesteps // state.num_inference_steps, timestep ) # Reference: # if len(state.ets) == 1 and state.counter == 0: # model_output = model_output # state.cur_sample = sample # elif len(state.ets) == 1 and state.counter == 1: # model_output = (model_output + state.ets[-1]) / 2 # sample = state.cur_sample # state.cur_sample = None # elif len(state.ets) == 2: # model_output = (3 * state.ets[-1] - state.ets[-2]) / 2 # elif len(state.ets) == 3: # model_output = (23 * state.ets[-1] - 16 * state.ets[-2] + 5 * state.ets[-3]) / 12 # else: # model_output = (1 / 24) * (55 * state.ets[-1] - 59 * state.ets[-2] + 37 * state.ets[-3] - 9 * state.ets[-4]) state = state.replace( ets=jax.lax.select( state.counter != 1, state.ets.at[0:3].set(state.ets[1:4]).at[3].set(model_output), # counter != 1 state.ets, # counter 1 ), cur_sample=jax.lax.select( state.counter != 1, sample, # counter != 1 state.cur_sample, # counter 1 ), ) state = state.replace( cur_model_output=jax.lax.select_n( jnp.clip(state.counter, 0, 4), model_output, # counter 0 (model_output + state.ets[-1]) / 2, # counter 1 (3 * state.ets[-1] - state.ets[-2]) / 2, # counter 2 (23 * state.ets[-1] - 16 * state.ets[-2] + 5 * state.ets[-3]) / 12, # counter 3 (1 / 24) * (55 * state.ets[-1] - 59 * state.ets[-2] + 37 * state.ets[-3] - 9 * state.ets[-4]), # counter >= 4 ), ) sample = state.cur_sample model_output = state.cur_model_output prev_sample = self._get_prev_sample(state, sample, timestep, prev_timestep, model_output) state = state.replace(counter=state.counter + 1) return (prev_sample, state) def _get_prev_sample(self, state: PNDMSchedulerState, sample, timestep, prev_timestep, model_output): # See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf # this function computes x_(t−δ) using the formula of (9) # Note that x_t needs to be added to both sides of the equation # Notation ( -> # alpha_prod_t -> α_t # alpha_prod_t_prev -> α_(t−δ) # beta_prod_t -> (1 - α_t) # beta_prod_t_prev -> (1 - α_(t−δ)) # sample -> x_t # model_output -> e_θ(x_t, t) # prev_sample -> x_(t−δ) alpha_prod_t = state.common.alphas_cumprod[timestep] alpha_prod_t_prev = jnp.where( prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod ) beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev if self.config.prediction_type == "v_prediction": model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample elif self.config.prediction_type != "epsilon": raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `v_prediction`" ) # corresponds to (α_(t−δ) - α_t) divided by # denominator of x_t in formula (9) and plus 1 # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) = # sqrt(α_(t−δ)) / sqrt(α_t)) sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5) # corresponds to denominator of e_θ(x_t, t) in formula (9) model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + ( alpha_prod_t * beta_prod_t * alpha_prod_t_prev ) ** (0.5) # full formula (9) prev_sample = ( sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff ) return prev_sample def add_noise( self, state: PNDMSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray, ) -> jnp.ndarray: return add_noise_common(state.common, original_samples, noise, timesteps) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_repaint.py ================================================ # Copyright 2023 ETH Zurich Computer Vision Lab and 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. import math from dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import SchedulerMixin @dataclass class RePaintSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: torch.FloatTensor # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class RePaintScheduler(SchedulerMixin, ConfigMixin): """ RePaint is a schedule for DDPM inpainting inside a given mask. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, `squaredcos_cap_v2` or `sigmoid`. eta (`float`): The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and 1.0 is DDPM scheduler respectively. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. variance_type (`str`): options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. clip_sample (`bool`, default `True`): option to clip predicted sample between -1 and 1 for numerical stability. """ order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", eta: float = 0.0, trained_betas: Optional[np.ndarray] = None, clip_sample: bool = True, ): if trained_betas is not None: self.betas = torch.from_numpy(trained_betas) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) elif beta_schedule == "sigmoid": # GeoDiff sigmoid schedule betas = torch.linspace(-6, 6, num_train_timesteps) self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) self.one = torch.tensor(1.0) self.final_alpha_cumprod = torch.tensor(1.0) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # setable values self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) self.eta = eta def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def set_timesteps( self, num_inference_steps: int, jump_length: int = 10, jump_n_sample: int = 10, device: Union[str, torch.device] = None, ): num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps) self.num_inference_steps = num_inference_steps timesteps = [] jumps = {} for j in range(0, num_inference_steps - jump_length, jump_length): jumps[j] = jump_n_sample - 1 t = num_inference_steps while t >= 1: t = t - 1 timesteps.append(t) if jumps.get(t, 0) > 0: jumps[t] = jumps[t] - 1 for _ in range(jump_length): t = t + 1 timesteps.append(t) timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps) self.timesteps = torch.from_numpy(timesteps).to(device) def _get_variance(self, t): prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps alpha_prod_t = self.alphas_cumprod[t] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev # For t > 0, compute predicted variance βt (see formula (6) and (7) from # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add # variance to pred_sample # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf # without eta. # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t] variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) return variance def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, original_image: torch.FloatTensor, mask: torch.FloatTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[RePaintSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. original_image (`torch.FloatTensor`): the original image to inpaint on. mask (`torch.FloatTensor`): the mask where 0.0 values define which part of the original image to inpaint (change). generator (`torch.Generator`, *optional*): random number generator. return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class Returns: [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ t = timestep prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps # 1. compute alphas, betas alpha_prod_t = self.alphas_cumprod[t] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5 # 3. Clip "predicted x_0" if self.config.clip_sample: pred_original_sample = torch.clamp(pred_original_sample, -1, 1) # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we # substitute formula (7) in the algorithm coming from DDPM paper # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper. # DDIM schedule gives the same results as DDPM with eta = 1.0 # Noise is being reused in 7. and 8., but no impact on quality has # been observed. # 5. Add noise device = model_output.device noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype) std_dev_t = self.eta * self._get_variance(timestep) ** 0.5 variance = 0 if t > 0 and self.eta > 0: variance = std_dev_t * noise # 6. compute "direction pointing to x_t" of formula (12) # from https://arxiv.org/pdf/2010.02502.pdf pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part if not return_dict: return ( pred_prev_sample, pred_original_sample, ) return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample) def undo_step(self, sample, timestep, generator=None): n = self.config.num_train_timesteps // self.num_inference_steps for i in range(n): beta = self.betas[timestep + i] if sample.device.type == "mps": # randn does not work reproducibly on mps noise = randn_tensor(sample.shape, dtype=sample.dtype, generator=generator) noise = noise.to(sample.device) else: noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype) # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise return sample def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.") def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_sde_ve.py ================================================ # Copyright 2023 Google Brain and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from dataclasses import dataclass from typing import Optional, Tuple, Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import SchedulerMixin, SchedulerOutput @dataclass class SdeVeOutput(BaseOutput): """ Output class for the ScoreSdeVeScheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sample_mean (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps. """ prev_sample: torch.FloatTensor prev_sample_mean: torch.FloatTensor class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin): """ The variance exploding stochastic differential equation (SDE) scheduler. For more information, see the original paper: https://arxiv.org/abs/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. snr (`float`): coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (`float`): initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the distribution of the data. sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model. sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to epsilon. correct_steps (`int`): number of correction steps performed on a produced sample. """ order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 2000, snr: float = 0.15, sigma_min: float = 0.01, sigma_max: float = 1348.0, sampling_eps: float = 1e-5, correct_steps: int = 1, ): # standard deviation of the initial noise distribution self.init_noise_sigma = sigma_max # setable values self.timesteps = None self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps) def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def set_timesteps( self, num_inference_steps: int, sampling_eps: float = None, device: Union[str, torch.device] = None ): """ Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation). """ sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps, device=device) def set_sigmas( self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None ): """ Sets the noise scales used for the diffusion chain. Supporting function to be run before inference. The sigmas control the weight of the `drift` and `diffusion` components of sample update. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. sigma_min (`float`, optional): initial noise scale value (overrides value given at Scheduler instantiation). sigma_max (`float`, optional): final noise scale value (overrides value given at Scheduler instantiation). sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation). """ sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps if self.timesteps is None: self.set_timesteps(num_inference_steps, sampling_eps) self.sigmas = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps) self.discrete_sigmas = torch.exp(torch.linspace(math.log(sigma_min), math.log(sigma_max), num_inference_steps)) self.sigmas = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]) def get_adjacent_sigma(self, timesteps, t): return torch.where( timesteps == 0, torch.zeros_like(t.to(timesteps.device)), self.discrete_sigmas[timesteps - 1].to(timesteps.device), ) def step_pred( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[SdeVeOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.timesteps is None: raise ValueError( "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" ) timestep = timestep * torch.ones( sample.shape[0], device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0]) timesteps = (timestep * (len(self.timesteps) - 1)).long() # mps requires indices to be in the same device, so we use cpu as is the default with cuda timesteps = timesteps.to(self.discrete_sigmas.device) sigma = self.discrete_sigmas[timesteps].to(sample.device) adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep).to(sample.device) drift = torch.zeros_like(sample) diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5 # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x) # also equation 47 shows the analog from SDE models to ancestral sampling methods diffusion = diffusion.flatten() while len(diffusion.shape) < len(sample.shape): diffusion = diffusion.unsqueeze(-1) drift = drift - diffusion**2 * model_output # equation 6: sample noise for the diffusion term of noise = randn_tensor( sample.shape, layout=sample.layout, generator=generator, device=sample.device, dtype=sample.dtype ) prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep # TODO is the variable diffusion the correct scaling term for the noise? prev_sample = prev_sample_mean + diffusion * noise # add impact of diffusion field g if not return_dict: return (prev_sample, prev_sample_mean) return SdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean) def step_correct( self, model_output: torch.FloatTensor, sample: torch.FloatTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Correct the predicted sample based on the output model_output of the network. This is often run repeatedly after making the prediction for the previous timestep. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.timesteps is None: raise ValueError( "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" ) # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z" # sample noise for correction noise = randn_tensor(sample.shape, layout=sample.layout, generator=generator).to(sample.device) # compute step size from the model_output, the noise, and the snr grad_norm = torch.norm(model_output.reshape(model_output.shape[0], -1), dim=-1).mean() noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean() step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2 step_size = step_size * torch.ones(sample.shape[0]).to(sample.device) # self.repeat_scalar(step_size, sample.shape[0]) # compute corrected sample: model_output term and noise term step_size = step_size.flatten() while len(step_size.shape) < len(sample.shape): step_size = step_size.unsqueeze(-1) prev_sample_mean = sample + step_size * model_output prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.FloatTensor, ) -> torch.FloatTensor: # Make sure sigmas and timesteps have the same device and dtype as original_samples timesteps = timesteps.to(original_samples.device) sigmas = self.discrete_sigmas.to(original_samples.device)[timesteps] noise = torch.randn_like(original_samples) * sigmas[:, None, None, None] noisy_samples = noise + original_samples return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_sde_ve_flax.py ================================================ # Copyright 2023 Google Brain and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch from dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax.numpy as jnp from jax import random from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left @flax.struct.dataclass class ScoreSdeVeSchedulerState: # setable values timesteps: Optional[jnp.ndarray] = None discrete_sigmas: Optional[jnp.ndarray] = None sigmas: Optional[jnp.ndarray] = None @classmethod def create(cls): return cls() @dataclass class FlaxSdeVeOutput(FlaxSchedulerOutput): """ Output class for the ScoreSdeVeScheduler's step function output. Args: state (`ScoreSdeVeSchedulerState`): prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sample_mean (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps. """ state: ScoreSdeVeSchedulerState prev_sample: jnp.ndarray prev_sample_mean: Optional[jnp.ndarray] = None class FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin): """ The variance exploding stochastic differential equation (SDE) scheduler. For more information, see the original paper: https://arxiv.org/abs/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. snr (`float`): coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (`float`): initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the distribution of the data. sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model. sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to epsilon. correct_steps (`int`): number of correction steps performed on a produced sample. """ @property def has_state(self): return True @register_to_config def __init__( self, num_train_timesteps: int = 2000, snr: float = 0.15, sigma_min: float = 0.01, sigma_max: float = 1348.0, sampling_eps: float = 1e-5, correct_steps: int = 1, ): pass def create_state(self): state = ScoreSdeVeSchedulerState.create() return self.set_sigmas( state, self.config.num_train_timesteps, self.config.sigma_min, self.config.sigma_max, self.config.sampling_eps, ) def set_timesteps( self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, shape: Tuple = (), sampling_eps: float = None ) -> ScoreSdeVeSchedulerState: """ Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation). """ sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps timesteps = jnp.linspace(1, sampling_eps, num_inference_steps) return state.replace(timesteps=timesteps) def set_sigmas( self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None, ) -> ScoreSdeVeSchedulerState: """ Sets the noise scales used for the diffusion chain. Supporting function to be run before inference. The sigmas control the weight of the `drift` and `diffusion` components of sample update. Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. sigma_min (`float`, optional): initial noise scale value (overrides value given at Scheduler instantiation). sigma_max (`float`, optional): final noise scale value (overrides value given at Scheduler instantiation). sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation). """ sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps if state.timesteps is None: state = self.set_timesteps(state, num_inference_steps, sampling_eps) discrete_sigmas = jnp.exp(jnp.linspace(jnp.log(sigma_min), jnp.log(sigma_max), num_inference_steps)) sigmas = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]) return state.replace(discrete_sigmas=discrete_sigmas, sigmas=sigmas) def get_adjacent_sigma(self, state, timesteps, t): return jnp.where(timesteps == 0, jnp.zeros_like(t), state.discrete_sigmas[timesteps - 1]) def step_pred( self, state: ScoreSdeVeSchedulerState, model_output: jnp.ndarray, timestep: int, sample: jnp.ndarray, key: random.KeyArray, return_dict: bool = True, ) -> Union[FlaxSdeVeOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class Returns: [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.timesteps is None: raise ValueError( "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" ) timestep = timestep * jnp.ones( sample.shape[0], ) timesteps = (timestep * (len(state.timesteps) - 1)).long() sigma = state.discrete_sigmas[timesteps] adjacent_sigma = self.get_adjacent_sigma(state, timesteps, timestep) drift = jnp.zeros_like(sample) diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5 # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x) # also equation 47 shows the analog from SDE models to ancestral sampling methods diffusion = diffusion.flatten() diffusion = broadcast_to_shape_from_left(diffusion, sample.shape) drift = drift - diffusion**2 * model_output # equation 6: sample noise for the diffusion term of key = random.split(key, num=1) noise = random.normal(key=key, shape=sample.shape) prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep # TODO is the variable diffusion the correct scaling term for the noise? prev_sample = prev_sample_mean + diffusion * noise # add impact of diffusion field g if not return_dict: return (prev_sample, prev_sample_mean, state) return FlaxSdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean, state=state) def step_correct( self, state: ScoreSdeVeSchedulerState, model_output: jnp.ndarray, sample: jnp.ndarray, key: random.KeyArray, return_dict: bool = True, ) -> Union[FlaxSdeVeOutput, Tuple]: """ Correct the predicted sample based on the output model_output of the network. This is often run repeatedly after making the prediction for the previous timestep. Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class Returns: [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if state.timesteps is None: raise ValueError( "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" ) # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z" # sample noise for correction key = random.split(key, num=1) noise = random.normal(key=key, shape=sample.shape) # compute step size from the model_output, the noise, and the snr grad_norm = jnp.linalg.norm(model_output) noise_norm = jnp.linalg.norm(noise) step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2 step_size = step_size * jnp.ones(sample.shape[0]) # compute corrected sample: model_output term and noise term step_size = step_size.flatten() step_size = broadcast_to_shape_from_left(step_size, sample.shape) prev_sample_mean = sample + step_size * model_output prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise if not return_dict: return (prev_sample, state) return FlaxSdeVeOutput(prev_sample=prev_sample, state=state) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_sde_vp.py ================================================ # Copyright 2023 Google Brain and 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. # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin): """ The variance preserving stochastic differential equation (SDE) scheduler. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more information, see the original paper: https://arxiv.org/abs/2011.13456 UNDER CONSTRUCTION """ order = 1 @register_to_config def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3): self.sigmas = None self.discrete_sigmas = None self.timesteps = None def set_timesteps(self, num_inference_steps, device: Union[str, torch.device] = None): self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps, device=device) def step_pred(self, score, x, t, generator=None): if self.timesteps is None: raise ValueError( "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score log_mean_coeff = ( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff)) std = std.flatten() while len(std.shape) < len(score.shape): std = std.unsqueeze(-1) score = -score / std # compute dt = -1.0 / len(self.timesteps) beta_t = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) beta_t = beta_t.flatten() while len(beta_t.shape) < len(x.shape): beta_t = beta_t.unsqueeze(-1) drift = -0.5 * beta_t * x diffusion = torch.sqrt(beta_t) drift = drift - diffusion**2 * score x_mean = x + drift * dt # add noise noise = randn_tensor(x.shape, layout=x.layout, generator=generator, device=x.device, dtype=x.dtype) x = x_mean + diffusion * math.sqrt(-dt) * noise return x, x_mean def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_unclip.py ================================================ # Copyright 2023 Kakao Brain and 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. import math from dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import SchedulerMixin @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP class UnCLIPSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. """ prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class UnCLIPScheduler(SchedulerMixin, ConfigMixin): """ NOTE: do not use this scheduler. The DDPM scheduler has been updated to support the changes made here. This scheduler will be removed and replaced with DDPM. This is a modified DDPM Scheduler specifically for the karlo unCLIP model. This scheduler has some minor variations in how it calculates the learned range variance and dynamically re-calculates betas based off the timesteps it is skipping. The scheduler also uses a slightly different step ratio when computing timesteps to use for inference. See [`~DDPMScheduler`] for more information on DDPM scheduling Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. variance_type (`str`): options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small_log` or `learned_range`. clip_sample (`bool`, default `True`): option to clip predicted sample between `-clip_sample_range` and `clip_sample_range` for numerical stability. clip_sample_range (`float`, default `1.0`): The range to clip the sample between. See `clip_sample`. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process) or `sample` (directly predicting the noisy sample`) """ @register_to_config def __init__( self, num_train_timesteps: int = 1000, variance_type: str = "fixed_small_log", clip_sample: bool = True, clip_sample_range: Optional[float] = 1.0, prediction_type: str = "epsilon", beta_schedule: str = "squaredcos_cap_v2", ): if beta_schedule != "squaredcos_cap_v2": raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'") self.betas = betas_for_alpha_bar(num_train_timesteps) self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) self.one = torch.tensor(1.0) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 # setable values self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) self.variance_type = variance_type def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.FloatTensor`: scaled input sample """ return sample def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Note that this scheduler uses a slightly different step ratio than the other diffusers schedulers. The different step ratio is to mimic the original karlo implementation and does not affect the quality or accuracy of the results. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. """ self.num_inference_steps = num_inference_steps step_ratio = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1) timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64) self.timesteps = torch.from_numpy(timesteps).to(device) def _get_variance(self, t, prev_timestep=None, predicted_variance=None, variance_type=None): if prev_timestep is None: prev_timestep = t - 1 alpha_prod_t = self.alphas_cumprod[t] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev if prev_timestep == t - 1: beta = self.betas[t] else: beta = 1 - alpha_prod_t / alpha_prod_t_prev # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # and sample from it to get previous sample # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample variance = beta_prod_t_prev / beta_prod_t * beta if variance_type is None: variance_type = self.config.variance_type # hacks - were probably added for training stability if variance_type == "fixed_small_log": variance = torch.log(torch.clamp(variance, min=1e-20)) variance = torch.exp(0.5 * variance) elif variance_type == "learned_range": # NOTE difference with DDPM scheduler min_log = variance.log() max_log = beta.log() frac = (predicted_variance + 1) / 2 variance = frac * max_log + (1 - frac) * min_log return variance def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, prev_timestep: Optional[int] = None, generator=None, return_dict: bool = True, ) -> Union[UnCLIPSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. prev_timestep (`int`, *optional*): The previous timestep to predict the previous sample at. Used to dynamically compute beta. If not given, `t-1` is used and the pre-computed beta is used. generator: random number generator. return_dict (`bool`): option for returning tuple rather than UnCLIPSchedulerOutput class Returns: [`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ t = timestep if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range": model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1) else: predicted_variance = None # 1. compute alphas, betas if prev_timestep is None: prev_timestep = t - 1 alpha_prod_t = self.alphas_cumprod[t] alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one beta_prod_t = 1 - alpha_prod_t beta_prod_t_prev = 1 - alpha_prod_t_prev if prev_timestep == t - 1: beta = self.betas[t] alpha = self.alphas[t] else: beta = 1 - alpha_prod_t / alpha_prod_t_prev alpha = 1 - beta # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if self.config.prediction_type == "epsilon": pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) elif self.config.prediction_type == "sample": pred_original_sample = model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`" " for the UnCLIPScheduler." ) # 3. Clip "predicted x_0" if self.config.clip_sample: pred_original_sample = torch.clamp( pred_original_sample, -self.config.clip_sample_range, self.config.clip_sample_range ) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * beta) / beta_prod_t current_sample_coeff = alpha ** (0.5) * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise variance = 0 if t > 0: variance_noise = randn_tensor( model_output.shape, dtype=model_output.dtype, generator=generator, device=model_output.device ) variance = self._get_variance( t, predicted_variance=predicted_variance, prev_timestep=prev_timestep, ) if self.variance_type == "fixed_small_log": variance = variance elif self.variance_type == "learned_range": variance = (0.5 * variance).exp() else: raise ValueError( f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`" " for the UnCLIPScheduler." ) variance = variance * variance_noise pred_prev_sample = pred_prev_sample + variance if not return_dict: return (pred_prev_sample,) return UnCLIPSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample) ================================================ FILE: diffusers/schedulers/scheduling_unipc_multistep.py ================================================ # Copyright 2023 TSAIL Team and 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. # DISCLAIMER: check https://arxiv.org/abs/2302.04867 and https://github.com/wl-zhao/UniPC for more info # The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput def betas_for_alpha_bar(num_diffusion_timesteps, 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 torch.tensor(betas, dtype=torch.float32) class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin): """ UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders. UniPC is by desinged model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction model and data prediction model. The corrector UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy. For more details, see the original paper: https://arxiv.org/abs/2302.04867 Currently, we support the multistep UniPC for both noise prediction models and data prediction models. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. solver_order (`int`, default `2`): the order of UniPC, also the p in UniPC-p; can be any positive integer. Note that the effective order of accuracy is `solver_order + 1` due to the UniC. We recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://arxiv.org/abs/2205.11487). sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`. predict_x0 (`bool`, default `True`): whether to use the updating algrithm on the predicted x0. See https://arxiv.org/abs/2211.01095 for details solver_type (`str`, default `bh2`): the solver type of UniPC. We recommend use `bh1` for unconditional sampling when steps < 10, and use `bh2` otherwise. lower_order_final (`bool`, default `True`): whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10. disable_corrector (`list`, default `[]`): decide which step to disable the corrector. For large guidance scale, the misalignment between the `epsilon_theta(x_t, c)`and `epsilon_theta(x_t^c, c)` might influence the convergence. This can be mitigated by disable the corrector at the first few steps (e.g., disable_corrector=[0]) solver_p (`SchedulerMixin`, default `None`): can be any other scheduler. If specified, the algorithm will become solver_p + UniC. """ _compatibles = [e.name for e in KarrasDiffusionSchedulers] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end: float = 0.02, beta_schedule: str = "linear", trained_betas: Optional[Union[np.ndarray, List[float]]] = None, solver_order: int = 2, prediction_type: str = "epsilon", thresholding: bool = False, dynamic_thresholding_ratio: float = 0.995, sample_max_value: float = 1.0, predict_x0: bool = True, solver_type: str = "bh2", lower_order_final: bool = True, disable_corrector: List[int] = [], solver_p: SchedulerMixin = None, ): if trained_betas is not None: self.betas = torch.tensor(trained_betas, dtype=torch.float32) elif beta_schedule == "linear": self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. self.betas = ( torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule self.betas = betas_for_alpha_bar(num_train_timesteps) else: raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") self.alphas = 1.0 - self.betas self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) # Currently we only support VP-type noise schedule self.alpha_t = torch.sqrt(self.alphas_cumprod) self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) # standard deviation of the initial noise distribution self.init_noise_sigma = 1.0 if solver_type not in ["bh1", "bh2"]: if solver_type in ["midpoint", "heun", "logrho"]: self.register_to_config(solver_type="bh1") else: raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") self.predict_x0 = predict_x0 # setable values self.num_inference_steps = None timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy() self.timesteps = torch.from_numpy(timesteps) self.model_outputs = [None] * solver_order self.timestep_list = [None] * solver_order self.lower_order_nums = 0 self.disable_corrector = disable_corrector self.solver_p = solver_p self.last_sample = None def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, optional): the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ timesteps = ( np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1) .round()[::-1][:-1] .copy() .astype(np.int64) ) # when num_inference_steps == num_train_timesteps, we can end up with # duplicates in timesteps. _, unique_indices = np.unique(timesteps, return_index=True) timesteps = timesteps[np.sort(unique_indices)] self.timesteps = torch.from_numpy(timesteps).to(device) self.num_inference_steps = len(timesteps) self.model_outputs = [ None, ] * self.config.solver_order self.lower_order_nums = 0 self.last_sample = None if self.solver_p: self.solver_p.set_timesteps(self.num_inference_steps, device=device) # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: """ "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://arxiv.org/abs/2205.11487 """ dtype = sample.dtype batch_size, channels, height, width = sample.shape if dtype not in (torch.float32, torch.float64): sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half # Flatten sample for doing quantile calculation along each image sample = sample.reshape(batch_size, channels * height * width) abs_sample = sample.abs() # "a certain percentile absolute pixel value" s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) s = torch.clamp( s, min=1, max=self.config.sample_max_value ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" sample = sample.reshape(batch_size, channels, height, width) sample = sample.to(dtype) return sample def convert_model_output( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor ) -> torch.FloatTensor: r""" Convert the model output to the corresponding type that the algorithm PC needs. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. Returns: `torch.FloatTensor`: the converted model output. """ if self.predict_x0: if self.config.prediction_type == "epsilon": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = (sample - sigma_t * model_output) / alpha_t elif self.config.prediction_type == "sample": x0_pred = model_output elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] x0_pred = alpha_t * sample - sigma_t * model_output else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the UniPCMultistepScheduler." ) if self.config.thresholding: x0_pred = self._threshold_sample(x0_pred) return x0_pred else: if self.config.prediction_type == "epsilon": return model_output elif self.config.prediction_type == "sample": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = (sample - alpha_t * model_output) / sigma_t return epsilon elif self.config.prediction_type == "v_prediction": alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] epsilon = alpha_t * model_output + sigma_t * sample return epsilon else: raise ValueError( f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" " `v_prediction` for the UniPCMultistepScheduler." ) def multistep_uni_p_bh_update( self, model_output: torch.FloatTensor, prev_timestep: int, sample: torch.FloatTensor, order: int, ) -> torch.FloatTensor: """ One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. Args: model_output (`torch.FloatTensor`): direct outputs from learned diffusion model at the current timestep. prev_timestep (`int`): previous discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. order (`int`): the order of UniP at this step, also the p in UniPC-p. Returns: `torch.FloatTensor`: the sample tensor at the previous timestep. """ timestep_list = self.timestep_list model_output_list = self.model_outputs s0, t = self.timestep_list[-1], prev_timestep m0 = model_output_list[-1] x = sample if self.solver_p: x_t = self.solver_p.step(model_output, s0, x).prev_sample return x_t lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0] alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] h = lambda_t - lambda_s0 device = sample.device rks = [] D1s = [] for i in range(1, order): si = timestep_list[-(i + 1)] mi = model_output_list[-(i + 1)] lambda_si = self.lambda_t[si] rk = (lambda_si - lambda_s0) / h rks.append(rk) D1s.append((mi - m0) / rk) rks.append(1.0) rks = torch.tensor(rks, device=device) R = [] b = [] hh = -h if self.predict_x0 else h 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.config.solver_type == "bh1": B_h = hh elif self.config.solver_type == "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=device) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) # (B, K) # for order 2, we use a simplified version if order == 2: rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device) else: rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) else: D1s = None if self.predict_x0: x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 if D1s is not None: pred_res = torch.einsum("k,bkchw->bchw", rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - alpha_t * B_h * pred_res else: x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 if D1s is not None: pred_res = torch.einsum("k,bkchw->bchw", rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - sigma_t * B_h * pred_res x_t = x_t.to(x.dtype) return x_t def multistep_uni_c_bh_update( self, this_model_output: torch.FloatTensor, this_timestep: int, last_sample: torch.FloatTensor, this_sample: torch.FloatTensor, order: int, ) -> torch.FloatTensor: """ One step for the UniC (B(h) version). Args: this_model_output (`torch.FloatTensor`): the model outputs at `x_t` this_timestep (`int`): the current timestep `t` last_sample (`torch.FloatTensor`): the generated sample before the last predictor: `x_{t-1}` this_sample (`torch.FloatTensor`): the generated sample after the last predictor: `x_{t}` order (`int`): the `p` of UniC-p at this step. Note that the effective order of accuracy should be order + 1 Returns: `torch.FloatTensor`: the corrected sample tensor at the current timestep. """ timestep_list = self.timestep_list model_output_list = self.model_outputs s0, t = timestep_list[-1], this_timestep m0 = model_output_list[-1] x = last_sample x_t = this_sample model_t = this_model_output lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0] alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] h = lambda_t - lambda_s0 device = this_sample.device rks = [] D1s = [] for i in range(1, order): si = timestep_list[-(i + 1)] mi = model_output_list[-(i + 1)] lambda_si = self.lambda_t[si] rk = (lambda_si - lambda_s0) / h rks.append(rk) D1s.append((mi - m0) / rk) rks.append(1.0) rks = torch.tensor(rks, device=device) R = [] b = [] hh = -h if self.predict_x0 else h 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.config.solver_type == "bh1": B_h = hh elif self.config.solver_type == "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=device) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) else: D1s = None # for order 1, we use a simplified version if order == 1: rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device) else: rhos_c = torch.linalg.solve(R, b) if self.predict_x0: x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 if D1s is not None: corr_res = torch.einsum("k,bkchw->bchw", rhos_c[:-1], D1s) else: corr_res = 0 D1_t = model_t - m0 x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t) else: x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 if D1s is not None: corr_res = torch.einsum("k,bkchw->bchw", rhos_c[:-1], D1s) else: corr_res = 0 D1_t = model_t - m0 x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t) x_t = x_t.to(x.dtype) return x_t def step( self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ Step function propagating the sample with the multistep UniPC. Args: model_output (`torch.FloatTensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class Returns: [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) step_index = (self.timesteps == timestep).nonzero() if len(step_index) == 0: step_index = len(self.timesteps) - 1 else: step_index = step_index.item() use_corrector = ( step_index > 0 and step_index - 1 not in self.disable_corrector and self.last_sample is not None ) model_output_convert = self.convert_model_output(model_output, timestep, sample) if use_corrector: sample = self.multistep_uni_c_bh_update( this_model_output=model_output_convert, this_timestep=timestep, last_sample=self.last_sample, this_sample=sample, order=self.this_order, ) # now prepare to run the predictor prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] for i in range(self.config.solver_order - 1): self.model_outputs[i] = self.model_outputs[i + 1] self.timestep_list[i] = self.timestep_list[i + 1] self.model_outputs[-1] = model_output_convert self.timestep_list[-1] = timestep if self.config.lower_order_final: this_order = min(self.config.solver_order, len(self.timesteps) - step_index) else: this_order = self.config.solver_order self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep assert self.this_order > 0 self.last_sample = sample prev_sample = self.multistep_uni_p_bh_update( model_output=model_output, # pass the original non-converted model output, in case solver-p is used prev_timestep=prev_timestep, sample=sample, order=self.this_order, ) if self.lower_order_nums < self.config.solver_order: self.lower_order_nums += 1 if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=prev_sample) def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: """ Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.FloatTensor`): input sample Returns: `torch.FloatTensor`: scaled input sample """ return sample # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise def add_noise( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, ) -> torch.FloatTensor: # Make sure alphas_cumprod and timestep have same device and dtype as original_samples alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) timesteps = timesteps.to(original_samples.device) sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def __len__(self): return self.config.num_train_timesteps ================================================ FILE: diffusers/schedulers/scheduling_utils.py ================================================ # Copyright 2023 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. import importlib import os from dataclasses import dataclass from enum import Enum from typing import Any, Dict, Optional, Union import torch from ..utils import BaseOutput SCHEDULER_CONFIG_NAME = "scheduler_config.json" # NOTE: We make this type an enum because it simplifies usage in docs and prevents # circular imports when used for `_compatibles` within the schedulers module. # When it's used as a type in pipelines, it really is a Union because the actual # scheduler instance is passed in. class KarrasDiffusionSchedulers(Enum): DDIMScheduler = 1 DDPMScheduler = 2 PNDMScheduler = 3 LMSDiscreteScheduler = 4 EulerDiscreteScheduler = 5 HeunDiscreteScheduler = 6 EulerAncestralDiscreteScheduler = 7 DPMSolverMultistepScheduler = 8 DPMSolverSinglestepScheduler = 9 KDPM2DiscreteScheduler = 10 KDPM2AncestralDiscreteScheduler = 11 DEISMultistepScheduler = 12 UniPCMultistepScheduler = 13 DPMSolverSDEScheduler = 14 @dataclass class SchedulerOutput(BaseOutput): """ Base class for the scheduler's step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. """ prev_sample: torch.FloatTensor class SchedulerMixin: """ Mixin containing common functions for the schedulers. Class attributes: - **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that `from_config` can be used from a class different than the one used to save the config (should be overridden by parent class). """ config_name = SCHEDULER_CONFIG_NAME _compatibles = [] has_compatibles = True @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Dict[str, Any] = None, subfolder: Optional[str] = None, return_unused_kwargs=False, **kwargs, ): r""" Instantiate a Scheduler class from a pre-defined JSON configuration file inside a directory or Hub repo. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing the schedluer configurations saved using [`~SchedulerMixin.save_pretrained`], e.g., `./my_model_directory/`. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. return_unused_kwargs (`bool`, *optional*, defaults to `False`): Whether kwargs that are not consumed by the Python class should be returned or not. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to use this method in a firewalled environment. """ config, kwargs, commit_hash = cls.load_config( pretrained_model_name_or_path=pretrained_model_name_or_path, subfolder=subfolder, return_unused_kwargs=True, return_commit_hash=True, **kwargs, ) return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs) def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs): """ Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the [`~SchedulerMixin.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the configuration JSON file will be saved (will be created if it does not exist). """ self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs) @property def compatibles(self): """ Returns all schedulers that are compatible with this scheduler Returns: `List[SchedulerMixin]`: List of compatible schedulers """ return self._get_compatibles() @classmethod def _get_compatibles(cls): compatible_classes_str = list(set([cls.__name__] + cls._compatibles)) diffusers_library = importlib.import_module(__name__.split(".")[0]) compatible_classes = [ getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c) ] return compatible_classes ================================================ FILE: diffusers/schedulers/scheduling_utils_flax.py ================================================ # Copyright 2023 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. import importlib import math import os from dataclasses import dataclass from enum import Enum from typing import Any, Dict, Optional, Tuple, Union import flax import jax.numpy as jnp from ..utils import BaseOutput SCHEDULER_CONFIG_NAME = "scheduler_config.json" # NOTE: We make this type an enum because it simplifies usage in docs and prevents # circular imports when used for `_compatibles` within the schedulers module. # When it's used as a type in pipelines, it really is a Union because the actual # scheduler instance is passed in. class FlaxKarrasDiffusionSchedulers(Enum): FlaxDDIMScheduler = 1 FlaxDDPMScheduler = 2 FlaxPNDMScheduler = 3 FlaxLMSDiscreteScheduler = 4 FlaxDPMSolverMultistepScheduler = 5 @dataclass class FlaxSchedulerOutput(BaseOutput): """ Base class for the scheduler's step function output. Args: prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. """ prev_sample: jnp.ndarray class FlaxSchedulerMixin: """ Mixin containing common functions for the schedulers. Class attributes: - **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that `from_config` can be used from a class different than the one used to save the config (should be overridden by parent class). """ config_name = SCHEDULER_CONFIG_NAME ignore_for_config = ["dtype"] _compatibles = [] has_compatibles = True @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Dict[str, Any] = None, subfolder: Optional[str] = None, return_unused_kwargs=False, **kwargs, ): r""" Instantiate a Scheduler class from a pre-defined JSON-file. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~SchedulerMixin.save_pretrained`], e.g., `./my_model_directory/`. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. return_unused_kwargs (`bool`, *optional*, defaults to `False`): Whether kwargs that are not consumed by the Python class should be returned or not. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(`bool`, *optional*, defaults to `False`): Whether or not to only look at local files (i.e., do not try to download the model). use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to use this method in a firewalled environment. """ config, kwargs = cls.load_config( pretrained_model_name_or_path=pretrained_model_name_or_path, subfolder=subfolder, return_unused_kwargs=True, **kwargs, ) scheduler, unused_kwargs = cls.from_config(config, return_unused_kwargs=True, **kwargs) if hasattr(scheduler, "create_state") and getattr(scheduler, "has_state", False): state = scheduler.create_state() if return_unused_kwargs: return scheduler, state, unused_kwargs return scheduler, state def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs): """ Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the [`~FlaxSchedulerMixin.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the configuration JSON file will be saved (will be created if it does not exist). """ self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs) @property def compatibles(self): """ Returns all schedulers that are compatible with this scheduler Returns: `List[SchedulerMixin]`: List of compatible schedulers """ return self._get_compatibles() @classmethod def _get_compatibles(cls): compatible_classes_str = list(set([cls.__name__] + cls._compatibles)) diffusers_library = importlib.import_module(__name__.split(".")[0]) compatible_classes = [ getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c) ] return compatible_classes def broadcast_to_shape_from_left(x: jnp.ndarray, shape: Tuple[int]) -> jnp.ndarray: assert len(shape) >= x.ndim return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(shape) - x.ndim)), shape) def betas_for_alpha_bar(num_diffusion_timesteps: int, max_beta=0.999, dtype=jnp.float32) -> jnp.ndarray: """ 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]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. Returns: betas (`jnp.ndarray`): the betas used by the scheduler to step the model outputs """ def alpha_bar(time_step): return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 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 jnp.array(betas, dtype=dtype) @flax.struct.dataclass class CommonSchedulerState: alphas: jnp.ndarray betas: jnp.ndarray alphas_cumprod: jnp.ndarray @classmethod def create(cls, scheduler): config = scheduler.config if config.trained_betas is not None: betas = jnp.asarray(config.trained_betas, dtype=scheduler.dtype) elif config.beta_schedule == "linear": betas = jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype) elif config.beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. betas = ( jnp.linspace( config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype ) ** 2 ) elif config.beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule betas = betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype) else: raise NotImplementedError( f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}" ) alphas = 1.0 - betas alphas_cumprod = jnp.cumprod(alphas, axis=0) return cls( alphas=alphas, betas=betas, alphas_cumprod=alphas_cumprod, ) def get_sqrt_alpha_prod( state: CommonSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray ): alphas_cumprod = state.alphas_cumprod sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 sqrt_alpha_prod = sqrt_alpha_prod.flatten() sqrt_alpha_prod = broadcast_to_shape_from_left(sqrt_alpha_prod, original_samples.shape) sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() sqrt_one_minus_alpha_prod = broadcast_to_shape_from_left(sqrt_one_minus_alpha_prod, original_samples.shape) return sqrt_alpha_prod, sqrt_one_minus_alpha_prod def add_noise_common( state: CommonSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray ): sqrt_alpha_prod, sqrt_one_minus_alpha_prod = get_sqrt_alpha_prod(state, original_samples, noise, timesteps) noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples def get_velocity_common(state: CommonSchedulerState, sample: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray): sqrt_alpha_prod, sqrt_one_minus_alpha_prod = get_sqrt_alpha_prod(state, sample, noise, timesteps) velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample return velocity ================================================ FILE: diffusers/schedulers/scheduling_vq_diffusion.py ================================================ # Copyright 2023 Microsoft and 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 dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .scheduling_utils import SchedulerMixin @dataclass class VQDiffusionSchedulerOutput(BaseOutput): """ Output class for the scheduler's step function output. Args: prev_sample (`torch.LongTensor` of shape `(batch size, num latent pixels)`): Computed sample x_{t-1} of previous timestep. `prev_sample` should be used as next model input in the denoising loop. """ prev_sample: torch.LongTensor def index_to_log_onehot(x: torch.LongTensor, num_classes: int) -> torch.FloatTensor: """ Convert batch of vector of class indices into batch of log onehot vectors Args: x (`torch.LongTensor` of shape `(batch size, vector length)`): Batch of class indices num_classes (`int`): number of classes to be used for the onehot vectors Returns: `torch.FloatTensor` of shape `(batch size, num classes, vector length)`: Log onehot vectors """ x_onehot = F.one_hot(x, num_classes) x_onehot = x_onehot.permute(0, 2, 1) log_x = torch.log(x_onehot.float().clamp(min=1e-30)) return log_x def gumbel_noised(logits: torch.FloatTensor, generator: Optional[torch.Generator]) -> torch.FloatTensor: """ Apply gumbel noise to `logits` """ uniform = torch.rand(logits.shape, device=logits.device, generator=generator) gumbel_noise = -torch.log(-torch.log(uniform + 1e-30) + 1e-30) noised = gumbel_noise + logits return noised def alpha_schedules(num_diffusion_timesteps: int, alpha_cum_start=0.99999, alpha_cum_end=0.000009): """ Cumulative and non-cumulative alpha schedules. See section 4.1. """ att = ( np.arange(0, num_diffusion_timesteps) / (num_diffusion_timesteps - 1) * (alpha_cum_end - alpha_cum_start) + alpha_cum_start ) att = np.concatenate(([1], att)) at = att[1:] / att[:-1] att = np.concatenate((att[1:], [1])) return at, att def gamma_schedules(num_diffusion_timesteps: int, gamma_cum_start=0.000009, gamma_cum_end=0.99999): """ Cumulative and non-cumulative gamma schedules. See section 4.1. """ ctt = ( np.arange(0, num_diffusion_timesteps) / (num_diffusion_timesteps - 1) * (gamma_cum_end - gamma_cum_start) + gamma_cum_start ) ctt = np.concatenate(([0], ctt)) one_minus_ctt = 1 - ctt one_minus_ct = one_minus_ctt[1:] / one_minus_ctt[:-1] ct = 1 - one_minus_ct ctt = np.concatenate((ctt[1:], [0])) return ct, ctt class VQDiffusionScheduler(SchedulerMixin, ConfigMixin): """ The VQ-diffusion transformer outputs predicted probabilities of the initial unnoised image. The VQ-diffusion scheduler converts the transformer's output into a sample for the unnoised image at the previous diffusion timestep. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2111.14822 Args: num_vec_classes (`int`): The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked latent pixel. num_train_timesteps (`int`): Number of diffusion steps used to train the model. alpha_cum_start (`float`): The starting cumulative alpha value. alpha_cum_end (`float`): The ending cumulative alpha value. gamma_cum_start (`float`): The starting cumulative gamma value. gamma_cum_end (`float`): The ending cumulative gamma value. """ order = 1 @register_to_config def __init__( self, num_vec_classes: int, num_train_timesteps: int = 100, alpha_cum_start: float = 0.99999, alpha_cum_end: float = 0.000009, gamma_cum_start: float = 0.000009, gamma_cum_end: float = 0.99999, ): self.num_embed = num_vec_classes # By convention, the index for the mask class is the last class index self.mask_class = self.num_embed - 1 at, att = alpha_schedules(num_train_timesteps, alpha_cum_start=alpha_cum_start, alpha_cum_end=alpha_cum_end) ct, ctt = gamma_schedules(num_train_timesteps, gamma_cum_start=gamma_cum_start, gamma_cum_end=gamma_cum_end) num_non_mask_classes = self.num_embed - 1 bt = (1 - at - ct) / num_non_mask_classes btt = (1 - att - ctt) / num_non_mask_classes at = torch.tensor(at.astype("float64")) bt = torch.tensor(bt.astype("float64")) ct = torch.tensor(ct.astype("float64")) log_at = torch.log(at) log_bt = torch.log(bt) log_ct = torch.log(ct) att = torch.tensor(att.astype("float64")) btt = torch.tensor(btt.astype("float64")) ctt = torch.tensor(ctt.astype("float64")) log_cumprod_at = torch.log(att) log_cumprod_bt = torch.log(btt) log_cumprod_ct = torch.log(ctt) self.log_at = log_at.float() self.log_bt = log_bt.float() self.log_ct = log_ct.float() self.log_cumprod_at = log_cumprod_at.float() self.log_cumprod_bt = log_cumprod_bt.float() self.log_cumprod_ct = log_cumprod_ct.float() # setable values self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): """ Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`): device to place the timesteps and the diffusion process parameters (alpha, beta, gamma) on. """ self.num_inference_steps = num_inference_steps timesteps = np.arange(0, self.num_inference_steps)[::-1].copy() self.timesteps = torch.from_numpy(timesteps).to(device) self.log_at = self.log_at.to(device) self.log_bt = self.log_bt.to(device) self.log_ct = self.log_ct.to(device) self.log_cumprod_at = self.log_cumprod_at.to(device) self.log_cumprod_bt = self.log_cumprod_bt.to(device) self.log_cumprod_ct = self.log_cumprod_ct.to(device) def step( self, model_output: torch.FloatTensor, timestep: torch.long, sample: torch.LongTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[VQDiffusionSchedulerOutput, Tuple]: """ Predict the sample at the previous timestep via the reverse transition distribution i.e. Equation (11). See the docstring for `self.q_posterior` for more in depth docs on how Equation (11) is computed. Args: log_p_x_0: (`torch.FloatTensor` of shape `(batch size, num classes - 1, num latent pixels)`): The log probabilities for the predicted classes of the initial latent pixels. Does not include a prediction for the masked class as the initial unnoised image cannot be masked. t (`torch.long`): The timestep that determines which transition matrices are used. x_t: (`torch.LongTensor` of shape `(batch size, num latent pixels)`): The classes of each latent pixel at time `t` generator: (`torch.Generator` or None): RNG for the noise applied to p(x_{t-1} | x_t) before it is sampled from. return_dict (`bool`): option for returning tuple rather than VQDiffusionSchedulerOutput class Returns: [`~schedulers.scheduling_utils.VQDiffusionSchedulerOutput`] or `tuple`: [`~schedulers.scheduling_utils.VQDiffusionSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ if timestep == 0: log_p_x_t_min_1 = model_output else: log_p_x_t_min_1 = self.q_posterior(model_output, sample, timestep) log_p_x_t_min_1 = gumbel_noised(log_p_x_t_min_1, generator) x_t_min_1 = log_p_x_t_min_1.argmax(dim=1) if not return_dict: return (x_t_min_1,) return VQDiffusionSchedulerOutput(prev_sample=x_t_min_1) def q_posterior(self, log_p_x_0, x_t, t): """ Calculates the log probabilities for the predicted classes of the image at timestep `t-1`. I.e. Equation (11). Instead of directly computing equation (11), we use Equation (5) to restate Equation (11) in terms of only forward probabilities. Equation (11) stated in terms of forward probabilities via Equation (5): Where: - the sum is over x_0 = {C_0 ... C_{k-1}} (classes for x_0) p(x_{t-1} | x_t) = sum( q(x_t | x_{t-1}) * q(x_{t-1} | x_0) * p(x_0) / q(x_t | x_0) ) Args: log_p_x_0: (`torch.FloatTensor` of shape `(batch size, num classes - 1, num latent pixels)`): The log probabilities for the predicted classes of the initial latent pixels. Does not include a prediction for the masked class as the initial unnoised image cannot be masked. x_t: (`torch.LongTensor` of shape `(batch size, num latent pixels)`): The classes of each latent pixel at time `t` t (torch.Long): The timestep that determines which transition matrix is used. Returns: `torch.FloatTensor` of shape `(batch size, num classes, num latent pixels)`: The log probabilities for the predicted classes of the image at timestep `t-1`. I.e. Equation (11). """ log_onehot_x_t = index_to_log_onehot(x_t, self.num_embed) log_q_x_t_given_x_0 = self.log_Q_t_transitioning_to_known_class( t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=True ) log_q_t_given_x_t_min_1 = self.log_Q_t_transitioning_to_known_class( t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=False ) # p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) # . . . # . . . # . . . # p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) q = log_p_x_0 - log_q_x_t_given_x_0 # sum_0 = p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}), ... , # sum_n = p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) q_log_sum_exp = torch.logsumexp(q, dim=1, keepdim=True) # p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0 ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n # . . . # . . . # . . . # p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0 ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n q = q - q_log_sum_exp # (p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1} # . . . # . . . # . . . # (p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1} # c_cumulative_{t-1} ... c_cumulative_{t-1} q = self.apply_cumulative_transitions(q, t - 1) # ((p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_0 ... ((p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_n # . . . # . . . # . . . # ((p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_0 ... ((p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_n # c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0 ... c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0 log_p_x_t_min_1 = q + log_q_t_given_x_t_min_1 + q_log_sum_exp # For each column, there are two possible cases. # # Where: # - sum(p_n(x_0))) is summing over all classes for x_0 # - C_i is the class transitioning from (not to be confused with c_t and c_cumulative_t being used for gamma's) # - C_j is the class transitioning to # # 1. x_t is masked i.e. x_t = c_k # # Simplifying the expression, the column vector is: # . # . # . # (c_t / c_cumulative_t) * (a_cumulative_{t-1} * p_n(x_0 = C_i | x_t) + b_cumulative_{t-1} * sum(p_n(x_0))) # . # . # . # (c_cumulative_{t-1} / c_cumulative_t) * sum(p_n(x_0)) # # From equation (11) stated in terms of forward probabilities, the last row is trivially verified. # # For the other rows, we can state the equation as ... # # (c_t / c_cumulative_t) * [b_cumulative_{t-1} * p(x_0=c_0) + ... + (a_cumulative_{t-1} + b_cumulative_{t-1}) * p(x_0=C_i) + ... + b_cumulative_{k-1} * p(x_0=c_{k-1})] # # This verifies the other rows. # # 2. x_t is not masked # # Simplifying the expression, there are two cases for the rows of the column vector, where C_j = C_i and where C_j != C_i: # . # . # . # C_j != C_i: b_t * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / b_cumulative_t) * p_n(x_0 = C_i) + ... + (b_cumulative_{t-1} / (a_cumulative_t + b_cumulative_t)) * p_n(c_0=C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1})) # . # . # . # C_j = C_i: (a_t + b_t) * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / (a_cumulative_t + b_cumulative_t)) * p_n(x_0 = C_i = C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1})) # . # . # . # 0 # # The last row is trivially verified. The other rows can be verified by directly expanding equation (11) stated in terms of forward probabilities. return log_p_x_t_min_1 def log_Q_t_transitioning_to_known_class( self, *, t: torch.int, x_t: torch.LongTensor, log_onehot_x_t: torch.FloatTensor, cumulative: bool ): """ Returns the log probabilities of the rows from the (cumulative or non-cumulative) transition matrix for each latent pixel in `x_t`. See equation (7) for the complete non-cumulative transition matrix. The complete cumulative transition matrix is the same structure except the parameters (alpha, beta, gamma) are the cumulative analogs. Args: t (torch.Long): The timestep that determines which transition matrix is used. x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`): The classes of each latent pixel at time `t`. log_onehot_x_t (`torch.FloatTensor` of shape `(batch size, num classes, num latent pixels)`): The log one-hot vectors of `x_t` cumulative (`bool`): If cumulative is `False`, we use the single step transition matrix `t-1`->`t`. If cumulative is `True`, we use the cumulative transition matrix `0`->`t`. Returns: `torch.FloatTensor` of shape `(batch size, num classes - 1, num latent pixels)`: Each _column_ of the returned matrix is a _row_ of log probabilities of the complete probability transition matrix. When non cumulative, returns `self.num_classes - 1` rows because the initial latent pixel cannot be masked. Where: - `q_n` is the probability distribution for the forward process of the `n`th latent pixel. - C_0 is a class of a latent pixel embedding - C_k is the class of the masked latent pixel non-cumulative result (omitting logarithms): ``` q_0(x_t | x_{t-1} = C_0) ... q_n(x_t | x_{t-1} = C_0) . . . . . . . . . q_0(x_t | x_{t-1} = C_k) ... q_n(x_t | x_{t-1} = C_k) ``` cumulative result (omitting logarithms): ``` q_0_cumulative(x_t | x_0 = C_0) ... q_n_cumulative(x_t | x_0 = C_0) . . . . . . . . . q_0_cumulative(x_t | x_0 = C_{k-1}) ... q_n_cumulative(x_t | x_0 = C_{k-1}) ``` """ if cumulative: a = self.log_cumprod_at[t] b = self.log_cumprod_bt[t] c = self.log_cumprod_ct[t] else: a = self.log_at[t] b = self.log_bt[t] c = self.log_ct[t] if not cumulative: # The values in the onehot vector can also be used as the logprobs for transitioning # from masked latent pixels. If we are not calculating the cumulative transitions, # we need to save these vectors to be re-appended to the final matrix so the values # aren't overwritten. # # `P(x_t!=mask|x_{t-1=mask}) = 0` and 0 will be the value of the last row of the onehot vector # if x_t is not masked # # `P(x_t=mask|x_{t-1=mask}) = 1` and 1 will be the value of the last row of the onehot vector # if x_t is masked log_onehot_x_t_transitioning_from_masked = log_onehot_x_t[:, -1, :].unsqueeze(1) # `index_to_log_onehot` will add onehot vectors for masked pixels, # so the default one hot matrix has one too many rows. See the doc string # for an explanation of the dimensionality of the returned matrix. log_onehot_x_t = log_onehot_x_t[:, :-1, :] # this is a cheeky trick to produce the transition probabilities using log one-hot vectors. # # Don't worry about what values this sets in the columns that mark transitions # to masked latent pixels. They are overwrote later with the `mask_class_mask`. # # Looking at the below logspace formula in non-logspace, each value will evaluate to either # `1 * a + b = a + b` where `log_Q_t` has the one hot value in the column # or # `0 * a + b = b` where `log_Q_t` has the 0 values in the column. # # See equation 7 for more details. log_Q_t = (log_onehot_x_t + a).logaddexp(b) # The whole column of each masked pixel is `c` mask_class_mask = x_t == self.mask_class mask_class_mask = mask_class_mask.unsqueeze(1).expand(-1, self.num_embed - 1, -1) log_Q_t[mask_class_mask] = c if not cumulative: log_Q_t = torch.cat((log_Q_t, log_onehot_x_t_transitioning_from_masked), dim=1) return log_Q_t def apply_cumulative_transitions(self, q, t): bsz = q.shape[0] a = self.log_cumprod_at[t] b = self.log_cumprod_bt[t] c = self.log_cumprod_ct[t] num_latent_pixels = q.shape[2] c = c.expand(bsz, 1, num_latent_pixels) q = (q + a).logaddexp(b) q = torch.cat((q, c), dim=1) return q ================================================ FILE: diffusers/training_utils.py ================================================ import contextlib import copy import os import random from typing import Any, Dict, Iterable, Optional, Union import numpy as np import torch from .utils import deprecate, is_transformers_available if is_transformers_available(): import transformers def enable_full_determinism(seed: int): """ Helper function for reproducible behavior during distributed training. See - https://pytorch.org/docs/stable/notes/randomness.html for pytorch """ # set seed first set_seed(seed) # Enable PyTorch deterministic mode. This potentially requires either the environment # variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set, # depending on the CUDA version, so we set them both here os.environ["CUDA_LAUNCH_BLOCKING"] = "1" os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" torch.use_deterministic_algorithms(True) # Enable CUDNN deterministic mode torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def set_seed(seed: int): """ Args: Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`. seed (`int`): The seed to set. """ random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # ^^ safe to call this function even if cuda is not available # Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14 class EMAModel: """ Exponential Moving Average of models weights """ def __init__( self, parameters: Iterable[torch.nn.Parameter], decay: float = 0.9999, min_decay: float = 0.0, update_after_step: int = 0, use_ema_warmup: bool = False, inv_gamma: Union[float, int] = 1.0, power: Union[float, int] = 2 / 3, model_cls: Optional[Any] = None, model_config: Dict[str, Any] = None, **kwargs, ): """ Args: parameters (Iterable[torch.nn.Parameter]): The parameters to track. decay (float): The decay factor for the exponential moving average. min_decay (float): The minimum decay factor for the exponential moving average. update_after_step (int): The number of steps to wait before starting to update the EMA weights. use_ema_warmup (bool): Whether to use EMA warmup. inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. Only used if `use_ema_warmup` is True. power (float): Exponential factor of EMA warmup. Default: 2/3. Only used if `use_ema_warmup` is True. device (Optional[Union[str, torch.device]]): The device to store the EMA weights on. If None, the EMA weights will be stored on CPU. @crowsonkb's notes on EMA Warmup: If gamma=1 and power=1, implements a simple average. 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), 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). """ if isinstance(parameters, torch.nn.Module): deprecation_message = ( "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. " "Please pass the parameters of the module instead." ) deprecate( "passing a `torch.nn.Module` to `ExponentialMovingAverage`", "1.0.0", deprecation_message, standard_warn=False, ) parameters = parameters.parameters() # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility use_ema_warmup = True if kwargs.get("max_value", None) is not None: deprecation_message = "The `max_value` argument is deprecated. Please use `decay` instead." deprecate("max_value", "1.0.0", deprecation_message, standard_warn=False) decay = kwargs["max_value"] if kwargs.get("min_value", None) is not None: deprecation_message = "The `min_value` argument is deprecated. Please use `min_decay` instead." deprecate("min_value", "1.0.0", deprecation_message, standard_warn=False) min_decay = kwargs["min_value"] parameters = list(parameters) self.shadow_params = [p.clone().detach() for p in parameters] if kwargs.get("device", None) is not None: deprecation_message = "The `device` argument is deprecated. Please use `to` instead." deprecate("device", "1.0.0", deprecation_message, standard_warn=False) self.to(device=kwargs["device"]) self.temp_stored_params = None self.decay = decay self.min_decay = min_decay self.update_after_step = update_after_step self.use_ema_warmup = use_ema_warmup self.inv_gamma = inv_gamma self.power = power self.optimization_step = 0 self.cur_decay_value = None # set in `step()` self.model_cls = model_cls self.model_config = model_config @classmethod def from_pretrained(cls, path, model_cls) -> "EMAModel": _, ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True) model = model_cls.from_pretrained(path) ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config) ema_model.load_state_dict(ema_kwargs) return ema_model def save_pretrained(self, path): if self.model_cls is None: raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__.") if self.model_config is None: raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__.") model = self.model_cls.from_config(self.model_config) state_dict = self.state_dict() state_dict.pop("shadow_params", None) model.register_to_config(**state_dict) self.copy_to(model.parameters()) model.save_pretrained(path) def get_decay(self, optimization_step: int) -> float: """ Compute the decay factor for the exponential moving average. """ step = max(0, optimization_step - self.update_after_step - 1) if step <= 0: return 0.0 if self.use_ema_warmup: cur_decay_value = 1 - (1 + step / self.inv_gamma) ** -self.power else: cur_decay_value = (1 + step) / (10 + step) cur_decay_value = min(cur_decay_value, self.decay) # make sure decay is not smaller than min_decay cur_decay_value = max(cur_decay_value, self.min_decay) return cur_decay_value @torch.no_grad() def step(self, parameters: Iterable[torch.nn.Parameter]): if isinstance(parameters, torch.nn.Module): deprecation_message = ( "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. " "Please pass the parameters of the module instead." ) deprecate( "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`", "1.0.0", deprecation_message, standard_warn=False, ) parameters = parameters.parameters() parameters = list(parameters) self.optimization_step += 1 # Compute the decay factor for the exponential moving average. decay = self.decay #self.get_decay(self.optimization_step) self.cur_decay_value = decay one_minus_decay = 1 - decay context_manager = contextlib.nullcontext if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled(): import deepspeed for s_param, param in zip(self.shadow_params, parameters): if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled(): context_manager = deepspeed.zero.GatheredParameters(param, modifier_rank=None) with context_manager(): if param.requires_grad: #s_param.sub_(one_minus_decay * (s_param - param)) s_param.data = one_minus_decay * param.data + decay * s_param.data else: s_param.copy_(param) def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None: """ Copy current averaged parameters into given collection of parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored moving averages. If `None`, the parameters with which this `ExponentialMovingAverage` was initialized will be used. """ parameters = list(parameters) for s_param, param in zip(self.shadow_params, parameters): param.data.copy_(s_param.to(param.device).data) def to(self, device=None, dtype=None) -> None: r"""Move internal buffers of the ExponentialMovingAverage to `device`. Args: device: like `device` argument to `torch.Tensor.to` """ # .to() on the tensors handles None correctly self.shadow_params = [ p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) for p in self.shadow_params ] def state_dict(self) -> dict: r""" Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during checkpointing to save the ema state dict. """ # Following PyTorch conventions, references to tensors are returned: # "returns a reference to the state and not its copy!" - # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict return { "decay": self.decay, "min_decay": self.min_decay, "optimization_step": self.optimization_step, "update_after_step": self.update_after_step, "use_ema_warmup": self.use_ema_warmup, "inv_gamma": self.inv_gamma, "power": self.power, "shadow_params": self.shadow_params, } def store(self, parameters: Iterable[torch.nn.Parameter]) -> None: r""" Args: Save the current parameters for restoring later. parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.temp_stored_params = [param.detach().cpu().clone() for param in parameters] def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None: r""" Args: 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. parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. If `None`, the parameters with which this `ExponentialMovingAverage` was initialized will be used. """ if self.temp_stored_params is None: raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`") for c_param, param in zip(self.temp_stored_params, parameters): param.data.copy_(c_param.data) # Better memory-wise. self.temp_stored_params = None def load_state_dict(self, state_dict: dict) -> None: r""" Args: Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the ema state dict. state_dict (dict): EMA state. Should be an object returned from a call to :meth:`state_dict`. """ # deepcopy, to be consistent with module API state_dict = copy.deepcopy(state_dict) self.decay = state_dict.get("decay", self.decay) if self.decay < 0.0 or self.decay > 1.0: raise ValueError("Decay must be between 0 and 1") self.min_decay = state_dict.get("min_decay", self.min_decay) if not isinstance(self.min_decay, float): raise ValueError("Invalid min_decay") self.optimization_step = state_dict.get("optimization_step", self.optimization_step) if not isinstance(self.optimization_step, int): raise ValueError("Invalid optimization_step") self.update_after_step = state_dict.get("update_after_step", self.update_after_step) if not isinstance(self.update_after_step, int): raise ValueError("Invalid update_after_step") self.use_ema_warmup = state_dict.get("use_ema_warmup", self.use_ema_warmup) if not isinstance(self.use_ema_warmup, bool): raise ValueError("Invalid use_ema_warmup") self.inv_gamma = state_dict.get("inv_gamma", self.inv_gamma) if not isinstance(self.inv_gamma, (float, int)): raise ValueError("Invalid inv_gamma") self.power = state_dict.get("power", self.power) if not isinstance(self.power, (float, int)): raise ValueError("Invalid power") shadow_params = state_dict.get("shadow_params", None) if shadow_params is not None: self.shadow_params = shadow_params if not isinstance(self.shadow_params, list): raise ValueError("shadow_params must be a list") if not all(isinstance(p, torch.Tensor) for p in self.shadow_params): raise ValueError("shadow_params must all be Tensors") ================================================ FILE: diffusers/utils/__init__.py ================================================ # Copyright 2023 The HuggingFace Inc. 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. import os from packaging import version from .. import __version__ from .accelerate_utils import apply_forward_hook from .constants import ( CONFIG_NAME, DEPRECATED_REVISION_ARGS, DIFFUSERS_CACHE, DIFFUSERS_DYNAMIC_MODULE_NAME, FLAX_WEIGHTS_NAME, HF_MODULES_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME, TEXT_ENCODER_TARGET_MODULES, WEIGHTS_NAME, ) from .deprecation_utils import deprecate from .doc_utils import replace_example_docstring from .dynamic_modules_utils import get_class_from_dynamic_module from .hub_utils import ( HF_HUB_OFFLINE, _add_variant, _get_model_file, extract_commit_hash, http_user_agent, ) from .import_utils import ( BACKENDS_MAPPING, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, USE_JAX, USE_TF, USE_TORCH, DummyObject, OptionalDependencyNotAvailable, is_accelerate_available, is_accelerate_version, is_bs4_available, is_flax_available, is_ftfy_available, is_inflect_available, is_k_diffusion_available, is_k_diffusion_version, is_librosa_available, is_note_seq_available, is_omegaconf_available, is_onnx_available, is_safetensors_available, is_scipy_available, is_tensorboard_available, is_tf_available, is_torch_available, is_torch_version, is_torchsde_available, is_transformers_available, is_transformers_version, is_unidecode_available, is_wandb_available, is_xformers_available, requires_backends, ) from .logging import get_logger from .outputs import BaseOutput from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil from .torch_utils import is_compiled_module, randn_tensor if is_torch_available(): from .testing_utils import ( floats_tensor, load_hf_numpy, load_image, load_numpy, load_pt, nightly, parse_flag_from_env, print_tensor_test, require_torch_2, require_torch_gpu, skip_mps, slow, torch_all_close, torch_device, ) from .torch_utils import maybe_allow_in_graph from .testing_utils import export_to_video logger = get_logger(__name__) def check_min_version(min_version): if version.parse(__version__) < version.parse(min_version): if "dev" in min_version: error_message = ( "This example requires a source install from HuggingFace diffusers (see " "`https://huggingface.co/docs/diffusers/installation#install-from-source`)," ) else: error_message = f"This example requires a minimum version of {min_version}," error_message += f" but the version found is {__version__}.\n" raise ImportError(error_message) ================================================ FILE: diffusers/utils/accelerate_utils.py ================================================ # Copyright 2023 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. """ Accelerate utilities: Utilities related to accelerate """ from packaging import version from .import_utils import is_accelerate_available if is_accelerate_available(): import accelerate def apply_forward_hook(method): """ Decorator that applies a registered CpuOffload hook to an arbitrary function rather than `forward`. This is useful for cases where a PyTorch module provides functions other than `forward` that should trigger a move to the appropriate acceleration device. This is the case for `encode` and `decode` in [`AutoencoderKL`]. This decorator looks inside the internal `_hf_hook` property to find a registered offload hook. :param method: The method to decorate. This method should be a method of a PyTorch module. """ if not is_accelerate_available(): return method accelerate_version = version.parse(accelerate.__version__).base_version if version.parse(accelerate_version) < version.parse("0.17.0"): return method def wrapper(self, *args, **kwargs): if hasattr(self, "_hf_hook") and hasattr(self._hf_hook, "pre_forward"): self._hf_hook.pre_forward(self) return method(self, *args, **kwargs) return wrapper ================================================ FILE: diffusers/utils/constants.py ================================================ # Copyright 2023 The HuggingFace Inc. 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. import os from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home default_cache_path = HUGGINGFACE_HUB_CACHE CONFIG_NAME = "config.json" WEIGHTS_NAME = "diffusion_pytorch_model.bin" FLAX_WEIGHTS_NAME = "diffusion_flax_model.msgpack" ONNX_WEIGHTS_NAME = "model.onnx" SAFETENSORS_WEIGHTS_NAME = "diffusion_pytorch_model.safetensors" ONNX_EXTERNAL_WEIGHTS_NAME = "weights.pb" HUGGINGFACE_CO_RESOLVE_ENDPOINT = "https://huggingface.co" DIFFUSERS_CACHE = default_cache_path DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules" HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules")) DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"] TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj", "k_proj", "out_proj"] ================================================ FILE: diffusers/utils/deprecation_utils.py ================================================ import inspect import warnings from typing import Any, Dict, Optional, Union from packaging import version def deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn=True, stacklevel=2): from .. import __version__ deprecated_kwargs = take_from values = () if not isinstance(args[0], tuple): args = (args,) for attribute, version_name, message in args: if version.parse(version.parse(__version__).base_version) >= version.parse(version_name): raise ValueError( f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'" f" version {__version__} is >= {version_name}" ) warning = None if isinstance(deprecated_kwargs, dict) and attribute in deprecated_kwargs: values += (deprecated_kwargs.pop(attribute),) warning = f"The `{attribute}` argument is deprecated and will be removed in version {version_name}." elif hasattr(deprecated_kwargs, attribute): values += (getattr(deprecated_kwargs, attribute),) warning = f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}." elif deprecated_kwargs is None: warning = f"`{attribute}` is deprecated and will be removed in version {version_name}." if warning is not None: warning = warning + " " if standard_warn else "" warnings.warn(warning + message, FutureWarning, stacklevel=stacklevel) if isinstance(deprecated_kwargs, dict) and len(deprecated_kwargs) > 0: call_frame = inspect.getouterframes(inspect.currentframe())[1] filename = call_frame.filename line_number = call_frame.lineno function = call_frame.function key, value = next(iter(deprecated_kwargs.items())) raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`") if len(values) == 0: return elif len(values) == 1: return values[0] return values ================================================ FILE: diffusers/utils/doc_utils.py ================================================ # Copyright 2023 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. """ Doc utilities: Utilities related to documentation """ import re def replace_example_docstring(example_docstring): def docstring_decorator(fn): func_doc = fn.__doc__ lines = func_doc.split("\n") i = 0 while i < len(lines) and re.search(r"^\s*Examples?:\s*$", lines[i]) is None: i += 1 if i < len(lines): lines[i] = example_docstring func_doc = "\n".join(lines) else: raise ValueError( f"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, " f"current docstring is:\n{func_doc}" ) fn.__doc__ = func_doc return fn return docstring_decorator ================================================ FILE: diffusers/utils/dummy_flax_and_transformers_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject): _backends = ["flax", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) class FlaxStableDiffusionImg2ImgPipeline(metaclass=DummyObject): _backends = ["flax", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) class FlaxStableDiffusionInpaintPipeline(metaclass=DummyObject): _backends = ["flax", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) class FlaxStableDiffusionPipeline(metaclass=DummyObject): _backends = ["flax", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax", "transformers"]) ================================================ FILE: diffusers/utils/dummy_flax_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class FlaxControlNetModel(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxModelMixin(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxUNet2DConditionModel(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxAutoencoderKL(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxDiffusionPipeline(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxDDIMScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxDDPMScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxDPMSolverMultistepScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxKarrasVeScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxLMSDiscreteScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxPNDMScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxSchedulerMixin(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) class FlaxScoreSdeVeScheduler(metaclass=DummyObject): _backends = ["flax"] def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["flax"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["flax"]) ================================================ FILE: diffusers/utils/dummy_note_seq_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class MidiProcessor(metaclass=DummyObject): _backends = ["note_seq"] def __init__(self, *args, **kwargs): requires_backends(self, ["note_seq"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["note_seq"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["note_seq"]) ================================================ FILE: diffusers/utils/dummy_onnx_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class OnnxRuntimeModel(metaclass=DummyObject): _backends = ["onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["onnx"]) ================================================ FILE: diffusers/utils/dummy_pt_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class AutoencoderKL(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class ControlNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class ModelMixin(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class PriorTransformer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class T5FilmDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class Transformer2DModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class UNet1DModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class UNet2DConditionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class UNet2DModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class UNet3DConditionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class VQModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) def get_constant_schedule(*args, **kwargs): requires_backends(get_constant_schedule, ["torch"]) def get_constant_schedule_with_warmup(*args, **kwargs): requires_backends(get_constant_schedule_with_warmup, ["torch"]) def get_cosine_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_schedule_with_warmup, ["torch"]) def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"]) def get_linear_schedule_with_warmup(*args, **kwargs): requires_backends(get_linear_schedule_with_warmup, ["torch"]) def get_polynomial_decay_schedule_with_warmup(*args, **kwargs): requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"]) def get_scheduler(*args, **kwargs): requires_backends(get_scheduler, ["torch"]) class AudioPipelineOutput(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DanceDiffusionPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DDIMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DDPMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DiffusionPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DiTPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class ImagePipelineOutput(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class KarrasVePipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class LDMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class LDMSuperResolutionPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class PNDMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class RePaintPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class ScoreSdeVePipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DDIMInverseScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DDIMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DDPMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DEISMultistepScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DPMSolverMultistepInverseScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DPMSolverMultistepScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class DPMSolverSinglestepScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class EulerAncestralDiscreteScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class EulerDiscreteScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class HeunDiscreteScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class IPNDMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class KarrasVeScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class KDPM2DiscreteScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class PNDMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class RePaintScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class SchedulerMixin(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class ScoreSdeVeScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class UnCLIPScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class UniPCMultistepScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class VQDiffusionScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) class EMAModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) ================================================ FILE: diffusers/utils/dummy_torch_and_librosa_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class AudioDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "librosa"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "librosa"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "librosa"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "librosa"]) class Mel(metaclass=DummyObject): _backends = ["torch", "librosa"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "librosa"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "librosa"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "librosa"]) ================================================ FILE: diffusers/utils/dummy_torch_and_scipy_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class LMSDiscreteScheduler(metaclass=DummyObject): _backends = ["torch", "scipy"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "scipy"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "scipy"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "scipy"]) ================================================ FILE: diffusers/utils/dummy_torch_and_torchsde_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class DPMSolverSDEScheduler(metaclass=DummyObject): _backends = ["torch", "torchsde"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "torchsde"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "torchsde"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "torchsde"]) ================================================ FILE: diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class StableDiffusionKDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers", "k_diffusion"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "k_diffusion"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "k_diffusion"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "k_diffusion"]) ================================================ FILE: diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class OnnxStableDiffusionImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers", "onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) class OnnxStableDiffusionInpaintPipeline(metaclass=DummyObject): _backends = ["torch", "transformers", "onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) class OnnxStableDiffusionInpaintPipelineLegacy(metaclass=DummyObject): _backends = ["torch", "transformers", "onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) class OnnxStableDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers", "onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) class OnnxStableDiffusionUpscalePipeline(metaclass=DummyObject): _backends = ["torch", "transformers", "onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) class StableDiffusionOnnxPipeline(metaclass=DummyObject): _backends = ["torch", "transformers", "onnx"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers", "onnx"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers", "onnx"]) ================================================ FILE: diffusers/utils/dummy_torch_and_transformers_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class AltDiffusionImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class AltDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class AudioLDMPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class CycleDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class IFImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class IFImg2ImgSuperResolutionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class IFInpaintingPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class IFInpaintingSuperResolutionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class IFPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class IFSuperResolutionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class LDMTextToImagePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class PaintByExamplePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class SemanticStableDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionAttendAndExcitePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionControlNetImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionControlNetInpaintPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionControlNetPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionDepth2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionDiffEditPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionImageVariationPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionInpaintPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionInpaintPipelineLegacy(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionInstructPix2PixPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionLatentUpscalePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionModelEditingPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionPanoramaPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionPipelineSafe(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionPix2PixZeroPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionSAGPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableDiffusionUpscalePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableUnCLIPImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class StableUnCLIPPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class TextToVideoSDPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class TextToVideoZeroPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class UnCLIPImageVariationPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class UnCLIPPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class VersatileDiffusionDualGuidedPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class VersatileDiffusionImageVariationPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class VersatileDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class VersatileDiffusionTextToImagePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) class VQDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) ================================================ FILE: diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py ================================================ # This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class SpectrogramDiffusionPipeline(metaclass=DummyObject): _backends = ["transformers", "torch", "note_seq"] def __init__(self, *args, **kwargs): requires_backends(self, ["transformers", "torch", "note_seq"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["transformers", "torch", "note_seq"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["transformers", "torch", "note_seq"]) ================================================ FILE: diffusers/utils/dynamic_modules_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """Utilities to dynamically load objects from the Hub.""" import importlib import inspect import json import os import re import shutil import sys from distutils.version import StrictVersion from pathlib import Path from typing import Dict, Optional, Union from urllib import request from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info from .. import __version__ from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging COMMUNITY_PIPELINES_URL = ( "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py" ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_diffusers_versions(): url = "https://pypi.org/pypi/diffusers/json" releases = json.loads(request.urlopen(url).read())["releases"].keys() return sorted(releases, key=StrictVersion) def init_hf_modules(): """ Creates the cache directory for modules with an init, and adds it to the Python path. """ # This function has already been executed if HF_MODULES_CACHE already is in the Python path. if HF_MODULES_CACHE in sys.path: return sys.path.append(HF_MODULES_CACHE) os.makedirs(HF_MODULES_CACHE, exist_ok=True) init_path = Path(HF_MODULES_CACHE) / "__init__.py" if not init_path.exists(): init_path.touch() def create_dynamic_module(name: Union[str, os.PathLike]): """ Creates a dynamic module in the cache directory for modules. """ init_hf_modules() dynamic_module_path = Path(HF_MODULES_CACHE) / name # If the parent module does not exist yet, recursively create it. if not dynamic_module_path.parent.exists(): create_dynamic_module(dynamic_module_path.parent) os.makedirs(dynamic_module_path, exist_ok=True) init_path = dynamic_module_path / "__init__.py" if not init_path.exists(): init_path.touch() def get_relative_imports(module_file): """ Get the list of modules that are relatively imported in a module file. Args: module_file (`str` or `os.PathLike`): The module file to inspect. """ with open(module_file, "r", encoding="utf-8") as f: content = f.read() # Imports of the form `import .xxx` relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE) # Imports of the form `from .xxx import yyy` relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE) # Unique-ify return list(set(relative_imports)) def get_relative_import_files(module_file): """ Get the list of all files that are needed for a given module. Note that this function recurses through the relative imports (if a imports b and b imports c, it will return module files for b and c). Args: module_file (`str` or `os.PathLike`): The module file to inspect. """ no_change = False files_to_check = [module_file] all_relative_imports = [] # Let's recurse through all relative imports while not no_change: new_imports = [] for f in files_to_check: new_imports.extend(get_relative_imports(f)) module_path = Path(module_file).parent new_import_files = [str(module_path / m) for m in new_imports] new_import_files = [f for f in new_import_files if f not in all_relative_imports] files_to_check = [f"{f}.py" for f in new_import_files] no_change = len(new_import_files) == 0 all_relative_imports.extend(files_to_check) return all_relative_imports def check_imports(filename): """ Check if the current Python environment contains all the libraries that are imported in a file. """ with open(filename, "r", encoding="utf-8") as f: content = f.read() # Imports of the form `import xxx` imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE) # Imports of the form `from xxx import yyy` imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE) # Only keep the top-level module imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")] # Unique-ify and test we got them all imports = list(set(imports)) missing_packages = [] for imp in imports: try: importlib.import_module(imp) except ImportError: missing_packages.append(imp) if len(missing_packages) > 0: raise ImportError( "This modeling file requires the following packages that were not found in your environment: " f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`" ) return get_relative_imports(filename) def get_class_in_module(class_name, module_path): """ Import a module on the cache directory for modules and extract a class from it. """ module_path = module_path.replace(os.path.sep, ".") module = importlib.import_module(module_path) if class_name is None: return find_pipeline_class(module) return getattr(module, class_name) def find_pipeline_class(loaded_module): """ Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class inheriting from `DiffusionPipeline`. """ from ..pipelines import DiffusionPipeline cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass)) pipeline_class = None for cls_name, cls in cls_members.items(): if ( cls_name != DiffusionPipeline.__name__ and issubclass(cls, DiffusionPipeline) and cls.__module__.split(".")[0] != "diffusers" ): if pipeline_class is not None: raise ValueError( f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:" f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in" f" {loaded_module}." ) pipeline_class = cls return pipeline_class def get_cached_module_file( pretrained_model_name_or_path: Union[str, os.PathLike], module_file: str, cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, resume_download: bool = False, proxies: Optional[Dict[str, str]] = None, use_auth_token: Optional[Union[bool, str]] = None, revision: Optional[str] = None, local_files_only: bool = False, ): """ Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached Transformers module. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. module_file (`str`): The name of the module file containing the class to look for. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Returns: `str`: The path to the module inside the cache. """ # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file. pretrained_model_name_or_path = str(pretrained_model_name_or_path) module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file) if os.path.isfile(module_file_or_url): resolved_module_file = module_file_or_url submodule = "local" elif pretrained_model_name_or_path.count("/") == 0: available_versions = get_diffusers_versions() # cut ".dev0" latest_version = "v" + ".".join(__version__.split(".")[:3]) # retrieve github version that matches if revision is None: revision = latest_version if latest_version[1:] in available_versions else "main" logger.info(f"Defaulting to latest_version: {revision}.") elif revision in available_versions: revision = f"v{revision}" elif revision == "main": revision = revision else: raise ValueError( f"`custom_revision`: {revision} does not exist. Please make sure to choose one of" f" {', '.join(available_versions + ['main'])}." ) # community pipeline on GitHub github_url = COMMUNITY_PIPELINES_URL.format(revision=revision, pipeline=pretrained_model_name_or_path) try: resolved_module_file = cached_download( github_url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=False, ) submodule = "git" module_file = pretrained_model_name_or_path + ".py" except EnvironmentError: logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.") raise else: try: # Load from URL or cache if already cached resolved_module_file = hf_hub_download( pretrained_model_name_or_path, module_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, ) submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/"))) except EnvironmentError: logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.") raise # Check we have all the requirements in our environment modules_needed = check_imports(resolved_module_file) # Now we move the module inside our cached dynamic modules. full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule create_dynamic_module(full_submodule) submodule_path = Path(HF_MODULES_CACHE) / full_submodule if submodule == "local" or submodule == "git": # We always copy local files (we could hash the file to see if there was a change, and give them the name of # that hash, to only copy when there is a modification but it seems overkill for now). # The only reason we do the copy is to avoid putting too many folders in sys.path. shutil.copy(resolved_module_file, submodule_path / module_file) for module_needed in modules_needed: module_needed = f"{module_needed}.py" shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed) else: # Get the commit hash # TODO: we will get this info in the etag soon, so retrieve it from there and not here. if isinstance(use_auth_token, str): token = use_auth_token elif use_auth_token is True: token = HfFolder.get_token() else: token = None commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the # benefit of versioning. submodule_path = submodule_path / commit_hash full_submodule = full_submodule + os.path.sep + commit_hash create_dynamic_module(full_submodule) if not (submodule_path / module_file).exists(): shutil.copy(resolved_module_file, submodule_path / module_file) # Make sure we also have every file with relative for module_needed in modules_needed: if not (submodule_path / module_needed).exists(): get_cached_module_file( pretrained_model_name_or_path, f"{module_needed}.py", cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, use_auth_token=use_auth_token, revision=revision, local_files_only=local_files_only, ) return os.path.join(full_submodule, module_file) def get_class_from_dynamic_module( pretrained_model_name_or_path: Union[str, os.PathLike], module_file: str, class_name: Optional[str] = None, cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, resume_download: bool = False, proxies: Optional[Dict[str, str]] = None, use_auth_token: Optional[Union[bool, str]] = None, revision: Optional[str] = None, local_files_only: bool = False, **kwargs, ): """ Extracts a class from a module file, present in the local folder or repository of a model. Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should therefore only be called on trusted repos. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. module_file (`str`): The name of the module file containing the class to look for. class_name (`str`): The name of the class to import in the module. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. use_auth_token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). Returns: `type`: The class, dynamically imported from the module. Examples: ```python # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this # module. cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel") ```""" # And lastly we get the class inside our newly created module final_module = get_cached_module_file( pretrained_model_name_or_path, module_file, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, use_auth_token=use_auth_token, revision=revision, local_files_only=local_files_only, ) return get_class_in_module(class_name, final_module.replace(".py", "")) ================================================ FILE: diffusers/utils/hub_utils.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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 os import re import sys import traceback import warnings from pathlib import Path from typing import Dict, Optional, Union from uuid import uuid4 from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami from huggingface_hub.file_download import REGEX_COMMIT_HASH from huggingface_hub.utils import ( EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError, is_jinja_available, ) from packaging import version from requests import HTTPError from .. import __version__ from .constants import ( DEPRECATED_REVISION_ARGS, DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, ) from .import_utils import ( ENV_VARS_TRUE_VALUES, _flax_version, _jax_version, _onnxruntime_version, _torch_version, is_flax_available, is_onnx_available, is_torch_available, ) from .logging import get_logger logger = get_logger(__name__) MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "model_card_template.md" SESSION_ID = uuid4().hex HF_HUB_OFFLINE = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES HUGGINGFACE_CO_TELEMETRY = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/" def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str: """ Formats a user-agent string with basic info about a request. """ ua = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}" if DISABLE_TELEMETRY or HF_HUB_OFFLINE: return ua + "; telemetry/off" if is_torch_available(): ua += f"; torch/{_torch_version}" if is_flax_available(): ua += f"; jax/{_jax_version}" ua += f"; flax/{_flax_version}" if is_onnx_available(): ua += f"; onnxruntime/{_onnxruntime_version}" # CI will set this value to True if os.environ.get("DIFFUSERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: ua += "; is_ci/true" if isinstance(user_agent, dict): ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()) elif isinstance(user_agent, str): ua += "; " + user_agent return ua def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): if token is None: token = HfFolder.get_token() if organization is None: username = whoami(token)["name"] return f"{username}/{model_id}" else: return f"{organization}/{model_id}" def create_model_card(args, model_name): if not is_jinja_available(): raise ValueError( "Modelcard rendering is based on Jinja templates." " Please make sure to have `jinja` installed before using `create_model_card`." " To install it, please run `pip install Jinja2`." ) if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]: return hub_token = args.hub_token if hasattr(args, "hub_token") else None repo_name = get_full_repo_name(model_name, token=hub_token) model_card = ModelCard.from_template( card_data=ModelCardData( # Card metadata object that will be converted to YAML block language="en", license="apache-2.0", library_name="diffusers", tags=[], datasets=args.dataset_name, metrics=[], ), template_path=MODEL_CARD_TEMPLATE_PATH, model_name=model_name, repo_name=repo_name, dataset_name=args.dataset_name if hasattr(args, "dataset_name") else None, learning_rate=args.learning_rate, train_batch_size=args.train_batch_size, eval_batch_size=args.eval_batch_size, gradient_accumulation_steps=( args.gradient_accumulation_steps if hasattr(args, "gradient_accumulation_steps") else None ), adam_beta1=args.adam_beta1 if hasattr(args, "adam_beta1") else None, adam_beta2=args.adam_beta2 if hasattr(args, "adam_beta2") else None, adam_weight_decay=args.adam_weight_decay if hasattr(args, "adam_weight_decay") else None, adam_epsilon=args.adam_epsilon if hasattr(args, "adam_epsilon") else None, lr_scheduler=args.lr_scheduler if hasattr(args, "lr_scheduler") else None, lr_warmup_steps=args.lr_warmup_steps if hasattr(args, "lr_warmup_steps") else None, ema_inv_gamma=args.ema_inv_gamma if hasattr(args, "ema_inv_gamma") else None, ema_power=args.ema_power if hasattr(args, "ema_power") else None, ema_max_decay=args.ema_max_decay if hasattr(args, "ema_max_decay") else None, mixed_precision=args.mixed_precision, ) card_path = os.path.join(args.output_dir, "README.md") model_card.save(card_path) def extract_commit_hash(resolved_file: Optional[str], commit_hash: Optional[str] = None): """ Extracts the commit hash from a resolved filename toward a cache file. """ if resolved_file is None or commit_hash is not None: return commit_hash resolved_file = str(Path(resolved_file).as_posix()) search = re.search(r"snapshots/([^/]+)/", resolved_file) if search is None: return None commit_hash = search.groups()[0] return commit_hash if REGEX_COMMIT_HASH.match(commit_hash) else None # Old default cache path, potentially to be migrated. # This logic was more or less taken from `transformers`, with the following differences: # - Diffusers doesn't use custom environment variables to specify the cache path. # - There is no need to migrate the cache format, just move the files to the new location. hf_cache_home = os.path.expanduser( os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface")) ) old_diffusers_cache = os.path.join(hf_cache_home, "diffusers") def move_cache(old_cache_dir: Optional[str] = None, new_cache_dir: Optional[str] = None) -> None: if new_cache_dir is None: new_cache_dir = DIFFUSERS_CACHE if old_cache_dir is None: old_cache_dir = old_diffusers_cache old_cache_dir = Path(old_cache_dir).expanduser() new_cache_dir = Path(new_cache_dir).expanduser() for old_blob_path in old_cache_dir.glob("**/blobs/*"): if old_blob_path.is_file() and not old_blob_path.is_symlink(): new_blob_path = new_cache_dir / old_blob_path.relative_to(old_cache_dir) new_blob_path.parent.mkdir(parents=True, exist_ok=True) os.replace(old_blob_path, new_blob_path) try: os.symlink(new_blob_path, old_blob_path) except OSError: logger.warning( "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." ) # At this point, old_cache_dir contains symlinks to the new cache (it can still be used). cache_version_file = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt") if not os.path.isfile(cache_version_file): cache_version = 0 else: with open(cache_version_file) as f: try: cache_version = int(f.read()) except ValueError: cache_version = 0 if cache_version < 1: old_cache_is_not_empty = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0 if old_cache_is_not_empty: logger.warning( "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your " "existing cached models. This is a one-time operation, you can interrupt it or run it " "later by calling `diffusers.utils.hub_utils.move_cache()`." ) try: move_cache() except Exception as e: trace = "\n".join(traceback.format_tb(e.__traceback__)) logger.error( f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease " "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole " "message and we will do our best to help." ) if cache_version < 1: try: os.makedirs(DIFFUSERS_CACHE, exist_ok=True) with open(cache_version_file, "w") as f: f.write("1") except Exception: logger.warning( f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure " "the directory exists and can be written to." ) def _add_variant(weights_name: str, variant: Optional[str] = None) -> str: if variant is not None: splits = weights_name.split(".") splits = splits[:-1] + [variant] + splits[-1:] weights_name = ".".join(splits) return weights_name def _get_model_file( pretrained_model_name_or_path, *, weights_name, subfolder, cache_dir, force_download, proxies, resume_download, local_files_only, use_auth_token, user_agent, revision, commit_hash=None, ): pretrained_model_name_or_path = str(pretrained_model_name_or_path) if os.path.isfile(pretrained_model_name_or_path): return pretrained_model_name_or_path elif os.path.isdir(pretrained_model_name_or_path): if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)): # Load from a PyTorch checkpoint model_file = os.path.join(pretrained_model_name_or_path, weights_name) return model_file elif subfolder is not None and os.path.isfile( os.path.join(pretrained_model_name_or_path, subfolder, weights_name) ): model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name) return model_file else: raise EnvironmentError( f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." ) else: # 1. First check if deprecated way of loading from branches is used if ( revision in DEPRECATED_REVISION_ARGS and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME) and version.parse(version.parse(__version__).base_version) >= version.parse("0.18.0") ): try: model_file = hf_hub_download( pretrained_model_name_or_path, filename=_add_variant(weights_name, revision), cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, subfolder=subfolder, revision=revision or commit_hash, ) warnings.warn( f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.", FutureWarning, ) return model_file except: # noqa: E722 warnings.warn( f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.", FutureWarning, ) try: # 2. Load model file as usual model_file = hf_hub_download( pretrained_model_name_or_path, filename=weights_name, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, subfolder=subfolder, revision=revision or commit_hash, ) return model_file except RepositoryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier " "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a " "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli " "login`." ) except RevisionNotFoundError: raise EnvironmentError( f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for " "this model name. Check the model page at " f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." ) except EntryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." ) except HTTPError as err: raise EnvironmentError( f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" ) except ValueError: raise EnvironmentError( f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it" f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a" f" directory containing a file named {weights_name} or" " \nCheckout your internet connection or see how to run the library in" " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." ) except EnvironmentError: raise EnvironmentError( f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from " "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " f"containing a file named {weights_name}" ) ================================================ FILE: diffusers/utils/import_utils.py ================================================ # Copyright 2023 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. """ Import utilities: Utilities related to imports and our lazy inits. """ import importlib.util import operator as op import os import sys from collections import OrderedDict from typing import Union from huggingface_hub.utils import is_jinja_available # noqa: F401 from packaging import version from packaging.version import Version, parse from . import logging # The package importlib_metadata is in a different place, depending on the python version. if sys.version_info < (3, 8): import importlib_metadata else: import importlib.metadata as importlib_metadata logger = logging.get_logger(__name__) # pylint: disable=invalid-name ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"} ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"}) USE_TF = os.environ.get("USE_TF", "AUTO").upper() USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper() USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper() USE_SAFETENSORS = os.environ.get("USE_SAFETENSORS", "AUTO").upper() STR_OPERATION_TO_FUNC = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt} _torch_version = "N/A" if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES: _torch_available = importlib.util.find_spec("torch") is not None if _torch_available: try: _torch_version = importlib_metadata.version("torch") logger.info(f"PyTorch version {_torch_version} available.") except importlib_metadata.PackageNotFoundError: _torch_available = False else: logger.info("Disabling PyTorch because USE_TORCH is set") _torch_available = False _tf_version = "N/A" if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES: _tf_available = importlib.util.find_spec("tensorflow") is not None if _tf_available: candidates = ( "tensorflow", "tensorflow-cpu", "tensorflow-gpu", "tf-nightly", "tf-nightly-cpu", "tf-nightly-gpu", "intel-tensorflow", "intel-tensorflow-avx512", "tensorflow-rocm", "tensorflow-macos", "tensorflow-aarch64", ) _tf_version = None # For the metadata, we have to look for both tensorflow and tensorflow-cpu for pkg in candidates: try: _tf_version = importlib_metadata.version(pkg) break except importlib_metadata.PackageNotFoundError: pass _tf_available = _tf_version is not None if _tf_available: if version.parse(_tf_version) < version.parse("2"): logger.info(f"TensorFlow found but with version {_tf_version}. Diffusers requires version 2 minimum.") _tf_available = False else: logger.info(f"TensorFlow version {_tf_version} available.") else: logger.info("Disabling Tensorflow because USE_TORCH is set") _tf_available = False _jax_version = "N/A" _flax_version = "N/A" if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES: _flax_available = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("flax") is not None if _flax_available: try: _jax_version = importlib_metadata.version("jax") _flax_version = importlib_metadata.version("flax") logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.") except importlib_metadata.PackageNotFoundError: _flax_available = False else: _flax_available = False if USE_SAFETENSORS in ENV_VARS_TRUE_AND_AUTO_VALUES: _safetensors_available = importlib.util.find_spec("safetensors") is not None if _safetensors_available: try: _safetensors_version = importlib_metadata.version("safetensors") logger.info(f"Safetensors version {_safetensors_version} available.") except importlib_metadata.PackageNotFoundError: _safetensors_available = False else: logger.info("Disabling Safetensors because USE_TF is set") _safetensors_available = False _transformers_available = importlib.util.find_spec("transformers") is not None try: _transformers_version = importlib_metadata.version("transformers") logger.debug(f"Successfully imported transformers version {_transformers_version}") except importlib_metadata.PackageNotFoundError: _transformers_available = False _inflect_available = importlib.util.find_spec("inflect") is not None try: _inflect_version = importlib_metadata.version("inflect") logger.debug(f"Successfully imported inflect version {_inflect_version}") except importlib_metadata.PackageNotFoundError: _inflect_available = False _unidecode_available = importlib.util.find_spec("unidecode") is not None try: _unidecode_version = importlib_metadata.version("unidecode") logger.debug(f"Successfully imported unidecode version {_unidecode_version}") except importlib_metadata.PackageNotFoundError: _unidecode_available = False _onnxruntime_version = "N/A" _onnx_available = importlib.util.find_spec("onnxruntime") is not None if _onnx_available: candidates = ( "onnxruntime", "onnxruntime-gpu", "ort_nightly_gpu", "onnxruntime-directml", "onnxruntime-openvino", "ort_nightly_directml", "onnxruntime-rocm", "onnxruntime-training", ) _onnxruntime_version = None # For the metadata, we have to look for both onnxruntime and onnxruntime-gpu for pkg in candidates: try: _onnxruntime_version = importlib_metadata.version(pkg) break except importlib_metadata.PackageNotFoundError: pass _onnx_available = _onnxruntime_version is not None if _onnx_available: logger.debug(f"Successfully imported onnxruntime version {_onnxruntime_version}") # (sayakpaul): importlib.util.find_spec("opencv-python") returns None even when it's installed. # _opencv_available = importlib.util.find_spec("opencv-python") is not None try: candidates = ( "opencv-python", "opencv-contrib-python", "opencv-python-headless", "opencv-contrib-python-headless", ) _opencv_version = None for pkg in candidates: try: _opencv_version = importlib_metadata.version(pkg) break except importlib_metadata.PackageNotFoundError: pass _opencv_available = _opencv_version is not None if _opencv_available: logger.debug(f"Successfully imported cv2 version {_opencv_version}") except importlib_metadata.PackageNotFoundError: _opencv_available = False _scipy_available = importlib.util.find_spec("scipy") is not None try: _scipy_version = importlib_metadata.version("scipy") logger.debug(f"Successfully imported scipy version {_scipy_version}") except importlib_metadata.PackageNotFoundError: _scipy_available = False _librosa_available = importlib.util.find_spec("librosa") is not None try: _librosa_version = importlib_metadata.version("librosa") logger.debug(f"Successfully imported librosa version {_librosa_version}") except importlib_metadata.PackageNotFoundError: _librosa_available = False _accelerate_available = importlib.util.find_spec("accelerate") is not None try: _accelerate_version = importlib_metadata.version("accelerate") logger.debug(f"Successfully imported accelerate version {_accelerate_version}") except importlib_metadata.PackageNotFoundError: _accelerate_available = False _xformers_available = importlib.util.find_spec("xformers") is not None try: _xformers_version = importlib_metadata.version("xformers") if _torch_available: import torch if version.Version(torch.__version__) < version.Version("1.12"): raise ValueError("PyTorch should be >= 1.12") logger.debug(f"Successfully imported xformers version {_xformers_version}") except importlib_metadata.PackageNotFoundError: _xformers_available = False _k_diffusion_available = importlib.util.find_spec("k_diffusion") is not None try: _k_diffusion_version = importlib_metadata.version("k_diffusion") logger.debug(f"Successfully imported k-diffusion version {_k_diffusion_version}") except importlib_metadata.PackageNotFoundError: _k_diffusion_available = False _note_seq_available = importlib.util.find_spec("note_seq") is not None try: _note_seq_version = importlib_metadata.version("note_seq") logger.debug(f"Successfully imported note-seq version {_note_seq_version}") except importlib_metadata.PackageNotFoundError: _note_seq_available = False _wandb_available = importlib.util.find_spec("wandb") is not None try: _wandb_version = importlib_metadata.version("wandb") logger.debug(f"Successfully imported wandb version {_wandb_version }") except importlib_metadata.PackageNotFoundError: _wandb_available = False _omegaconf_available = importlib.util.find_spec("omegaconf") is not None try: _omegaconf_version = importlib_metadata.version("omegaconf") logger.debug(f"Successfully imported omegaconf version {_omegaconf_version}") except importlib_metadata.PackageNotFoundError: _omegaconf_available = False _tensorboard_available = importlib.util.find_spec("tensorboard") try: _tensorboard_version = importlib_metadata.version("tensorboard") logger.debug(f"Successfully imported tensorboard version {_tensorboard_version}") except importlib_metadata.PackageNotFoundError: _tensorboard_available = False _compel_available = importlib.util.find_spec("compel") try: _compel_version = importlib_metadata.version("compel") logger.debug(f"Successfully imported compel version {_compel_version}") except importlib_metadata.PackageNotFoundError: _compel_available = False _ftfy_available = importlib.util.find_spec("ftfy") is not None try: _ftfy_version = importlib_metadata.version("ftfy") logger.debug(f"Successfully imported ftfy version {_ftfy_version}") except importlib_metadata.PackageNotFoundError: _ftfy_available = False _bs4_available = importlib.util.find_spec("bs4") is not None try: # importlib metadata under different name _bs4_version = importlib_metadata.version("beautifulsoup4") logger.debug(f"Successfully imported ftfy version {_bs4_version}") except importlib_metadata.PackageNotFoundError: _bs4_available = False _torchsde_available = importlib.util.find_spec("torchsde") is not None try: _torchsde_version = importlib_metadata.version("torchsde") logger.debug(f"Successfully imported torchsde version {_torchsde_version}") except importlib_metadata.PackageNotFoundError: _torchsde_available = False def is_torch_available(): return _torch_available def is_safetensors_available(): return _safetensors_available def is_tf_available(): return _tf_available def is_flax_available(): return _flax_available def is_transformers_available(): return _transformers_available def is_inflect_available(): return _inflect_available def is_unidecode_available(): return _unidecode_available def is_onnx_available(): return _onnx_available def is_opencv_available(): return _opencv_available def is_scipy_available(): return _scipy_available def is_librosa_available(): return _librosa_available def is_xformers_available(): return _xformers_available def is_accelerate_available(): return _accelerate_available def is_k_diffusion_available(): return _k_diffusion_available def is_note_seq_available(): return _note_seq_available def is_wandb_available(): return _wandb_available def is_omegaconf_available(): return _omegaconf_available def is_tensorboard_available(): return _tensorboard_available def is_compel_available(): return _compel_available def is_ftfy_available(): return _ftfy_available def is_bs4_available(): return _bs4_available def is_torchsde_available(): return _torchsde_available # docstyle-ignore FLAX_IMPORT_ERROR = """ {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the installation page: https://github.com/google/flax and follow the ones that match your environment. """ # docstyle-ignore INFLECT_IMPORT_ERROR = """ {0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install inflect` """ # docstyle-ignore PYTORCH_IMPORT_ERROR = """ {0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment. """ # docstyle-ignore ONNX_IMPORT_ERROR = """ {0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip install onnxruntime` """ # docstyle-ignore OPENCV_IMPORT_ERROR = """ {0} requires the OpenCV library but it was not found in your environment. You can install it with pip: `pip install opencv-python` """ # docstyle-ignore SCIPY_IMPORT_ERROR = """ {0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install scipy` """ # docstyle-ignore LIBROSA_IMPORT_ERROR = """ {0} requires the librosa library but it was not found in your environment. Checkout the instructions on the installation page: https://librosa.org/doc/latest/install.html and follow the ones that match your environment. """ # docstyle-ignore TRANSFORMERS_IMPORT_ERROR = """ {0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip install transformers` """ # docstyle-ignore UNIDECODE_IMPORT_ERROR = """ {0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install Unidecode` """ # docstyle-ignore K_DIFFUSION_IMPORT_ERROR = """ {0} requires the k-diffusion library but it was not found in your environment. You can install it with pip: `pip install k-diffusion` """ # docstyle-ignore NOTE_SEQ_IMPORT_ERROR = """ {0} requires the note-seq library but it was not found in your environment. You can install it with pip: `pip install note-seq` """ # docstyle-ignore WANDB_IMPORT_ERROR = """ {0} requires the wandb library but it was not found in your environment. You can install it with pip: `pip install wandb` """ # docstyle-ignore OMEGACONF_IMPORT_ERROR = """ {0} requires the omegaconf library but it was not found in your environment. You can install it with pip: `pip install omegaconf` """ # docstyle-ignore TENSORBOARD_IMPORT_ERROR = """ {0} requires the tensorboard library but it was not found in your environment. You can install it with pip: `pip install tensorboard` """ # docstyle-ignore COMPEL_IMPORT_ERROR = """ {0} requires the compel library but it was not found in your environment. You can install it with pip: `pip install compel` """ # docstyle-ignore BS4_IMPORT_ERROR = """ {0} requires the Beautiful Soup library but it was not found in your environment. You can install it with pip: `pip install beautifulsoup4`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore FTFY_IMPORT_ERROR = """ {0} requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TORCHSDE_IMPORT_ERROR = """ {0} requires the torchsde library but it was not found in your environment. You can install it with pip: `pip install torchsde` """ BACKENDS_MAPPING = OrderedDict( [ ("bs4", (is_bs4_available, BS4_IMPORT_ERROR)), ("flax", (is_flax_available, FLAX_IMPORT_ERROR)), ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)), ("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)), ("opencv", (is_opencv_available, OPENCV_IMPORT_ERROR)), ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)), ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)), ("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)), ("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)), ("librosa", (is_librosa_available, LIBROSA_IMPORT_ERROR)), ("k_diffusion", (is_k_diffusion_available, K_DIFFUSION_IMPORT_ERROR)), ("note_seq", (is_note_seq_available, NOTE_SEQ_IMPORT_ERROR)), ("wandb", (is_wandb_available, WANDB_IMPORT_ERROR)), ("omegaconf", (is_omegaconf_available, OMEGACONF_IMPORT_ERROR)), ("tensorboard", (_tensorboard_available, TENSORBOARD_IMPORT_ERROR)), ("compel", (_compel_available, COMPEL_IMPORT_ERROR)), ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)), ("torchsde", (_torchsde_available, TORCHSDE_IMPORT_ERROR)), ] ) def requires_backends(obj, backends): if not isinstance(backends, (list, tuple)): backends = [backends] name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__ checks = (BACKENDS_MAPPING[backend] for backend in backends) failed = [msg.format(name) for available, msg in checks if not available()] if failed: raise ImportError("".join(failed)) if name in [ "VersatileDiffusionTextToImagePipeline", "VersatileDiffusionPipeline", "VersatileDiffusionDualGuidedPipeline", "StableDiffusionImageVariationPipeline", "UnCLIPPipeline", ] and is_transformers_version("<", "4.25.0"): raise ImportError( f"You need to install `transformers>=4.25` in order to use {name}: \n```\n pip install" " --upgrade transformers \n```" ) if name in ["StableDiffusionDepth2ImgPipeline", "StableDiffusionPix2PixZeroPipeline"] and is_transformers_version( "<", "4.26.0" ): raise ImportError( f"You need to install `transformers>=4.26` in order to use {name}: \n```\n pip install" " --upgrade transformers \n```" ) class DummyObject(type): """ Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by `requires_backend` each time a user tries to access any method of that class. """ def __getattr__(cls, key): if key.startswith("_"): return super().__getattr__(cls, key) requires_backends(cls, cls._backends) # This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319 def compare_versions(library_or_version: Union[str, Version], operation: str, requirement_version: str): """ Args: Compares a library version to some requirement using a given operation. library_or_version (`str` or `packaging.version.Version`): A library name or a version to check. operation (`str`): A string representation of an operator, such as `">"` or `"<="`. requirement_version (`str`): The version to compare the library version against """ if operation not in STR_OPERATION_TO_FUNC.keys(): raise ValueError(f"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}") operation = STR_OPERATION_TO_FUNC[operation] if isinstance(library_or_version, str): library_or_version = parse(importlib_metadata.version(library_or_version)) return operation(library_or_version, parse(requirement_version)) # This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L338 def is_torch_version(operation: str, version: str): """ Args: Compares the current PyTorch version to a given reference with an operation. operation (`str`): A string representation of an operator, such as `">"` or `"<="` version (`str`): A string version of PyTorch """ return compare_versions(parse(_torch_version), operation, version) def is_transformers_version(operation: str, version: str): """ Args: Compares the current Transformers version to a given reference with an operation. operation (`str`): A string representation of an operator, such as `">"` or `"<="` version (`str`): A version string """ if not _transformers_available: return False return compare_versions(parse(_transformers_version), operation, version) def is_accelerate_version(operation: str, version: str): """ Args: Compares the current Accelerate version to a given reference with an operation. operation (`str`): A string representation of an operator, such as `">"` or `"<="` version (`str`): A version string """ if not _accelerate_available: return False return compare_versions(parse(_accelerate_version), operation, version) def is_k_diffusion_version(operation: str, version: str): """ Args: Compares the current k-diffusion version to a given reference with an operation. operation (`str`): A string representation of an operator, such as `">"` or `"<="` version (`str`): A version string """ if not _k_diffusion_available: return False return compare_versions(parse(_k_diffusion_version), operation, version) class OptionalDependencyNotAvailable(BaseException): """An error indicating that an optional dependency of Diffusers was not found in the environment.""" ================================================ FILE: diffusers/utils/logging.py ================================================ # coding=utf-8 # Copyright 2023 Optuna, Hugging Face # # 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. """ Logging utilities.""" import logging import os import sys import threading from logging import ( CRITICAL, # NOQA DEBUG, # NOQA ERROR, # NOQA FATAL, # NOQA INFO, # NOQA NOTSET, # NOQA WARN, # NOQA WARNING, # NOQA ) from typing import Optional from tqdm import auto as tqdm_lib _lock = threading.Lock() _default_handler: Optional[logging.Handler] = None log_levels = { "debug": logging.DEBUG, "info": logging.INFO, "warning": logging.WARNING, "error": logging.ERROR, "critical": logging.CRITICAL, } _default_log_level = logging.WARNING _tqdm_active = True def _get_default_logging_level(): """ If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is not - fall back to `_default_log_level` """ env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None) if env_level_str: if env_level_str in log_levels: return log_levels[env_level_str] else: logging.getLogger().warning( f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, " f"has to be one of: { ', '.join(log_levels.keys()) }" ) return _default_log_level def _get_library_name() -> str: return __name__.split(".")[0] def _get_library_root_logger() -> logging.Logger: return logging.getLogger(_get_library_name()) def _configure_library_root_logger() -> None: global _default_handler with _lock: if _default_handler: # This library has already configured the library root logger. return _default_handler = logging.StreamHandler() # Set sys.stderr as stream. _default_handler.flush = sys.stderr.flush # Apply our default configuration to the library root logger. library_root_logger = _get_library_root_logger() library_root_logger.addHandler(_default_handler) library_root_logger.setLevel(_get_default_logging_level()) library_root_logger.propagate = False def _reset_library_root_logger() -> None: global _default_handler with _lock: if not _default_handler: return library_root_logger = _get_library_root_logger() library_root_logger.removeHandler(_default_handler) library_root_logger.setLevel(logging.NOTSET) _default_handler = None def get_log_levels_dict(): return log_levels def get_logger(name: Optional[str] = None) -> logging.Logger: """ Return a logger with the specified name. This function is not supposed to be directly accessed unless you are writing a custom diffusers module. """ if name is None: name = _get_library_name() _configure_library_root_logger() return logging.getLogger(name) def get_verbosity() -> int: """ Return the current level for the 🤗 Diffusers' root logger as an int. Returns: `int`: The logging level. 🤗 Diffusers has following logging levels: - 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL` - 40: `diffusers.logging.ERROR` - 30: `diffusers.logging.WARNING` or `diffusers.logging.WARN` - 20: `diffusers.logging.INFO` - 10: `diffusers.logging.DEBUG` """ _configure_library_root_logger() return _get_library_root_logger().getEffectiveLevel() def set_verbosity(verbosity: int) -> None: """ Set the verbosity level for the 🤗 Diffusers' root logger. Args: verbosity (`int`): Logging level, e.g., one of: - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL` - `diffusers.logging.ERROR` - `diffusers.logging.WARNING` or `diffusers.logging.WARN` - `diffusers.logging.INFO` - `diffusers.logging.DEBUG` """ _configure_library_root_logger() _get_library_root_logger().setLevel(verbosity) def set_verbosity_info(): """Set the verbosity to the `INFO` level.""" return set_verbosity(INFO) def set_verbosity_warning(): """Set the verbosity to the `WARNING` level.""" return set_verbosity(WARNING) def set_verbosity_debug(): """Set the verbosity to the `DEBUG` level.""" return set_verbosity(DEBUG) def set_verbosity_error(): """Set the verbosity to the `ERROR` level.""" return set_verbosity(ERROR) def disable_default_handler() -> None: """Disable the default handler of the HuggingFace Diffusers' root logger.""" _configure_library_root_logger() assert _default_handler is not None _get_library_root_logger().removeHandler(_default_handler) def enable_default_handler() -> None: """Enable the default handler of the HuggingFace Diffusers' root logger.""" _configure_library_root_logger() assert _default_handler is not None _get_library_root_logger().addHandler(_default_handler) def add_handler(handler: logging.Handler) -> None: """adds a handler to the HuggingFace Diffusers' root logger.""" _configure_library_root_logger() assert handler is not None _get_library_root_logger().addHandler(handler) def remove_handler(handler: logging.Handler) -> None: """removes given handler from the HuggingFace Diffusers' root logger.""" _configure_library_root_logger() assert handler is not None and handler not in _get_library_root_logger().handlers _get_library_root_logger().removeHandler(handler) def disable_propagation() -> None: """ Disable propagation of the library log outputs. Note that log propagation is disabled by default. """ _configure_library_root_logger() _get_library_root_logger().propagate = False def enable_propagation() -> None: """ Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent double logging if the root logger has been configured. """ _configure_library_root_logger() _get_library_root_logger().propagate = True def enable_explicit_format() -> None: """ Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows: ``` [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE ``` All handlers currently bound to the root logger are affected by this method. """ handlers = _get_library_root_logger().handlers for handler in handlers: formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s") handler.setFormatter(formatter) def reset_format() -> None: """ Resets the formatting for HuggingFace Diffusers' loggers. All handlers currently bound to the root logger are affected by this method. """ handlers = _get_library_root_logger().handlers for handler in handlers: handler.setFormatter(None) def warning_advice(self, *args, **kwargs): """ This method is identical to `logger.warning()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this warning will not be printed """ no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False) if no_advisory_warnings: return self.warning(*args, **kwargs) logging.Logger.warning_advice = warning_advice class EmptyTqdm: """Dummy tqdm which doesn't do anything.""" def __init__(self, *args, **kwargs): # pylint: disable=unused-argument self._iterator = args[0] if args else None def __iter__(self): return iter(self._iterator) def __getattr__(self, _): """Return empty function.""" def empty_fn(*args, **kwargs): # pylint: disable=unused-argument return return empty_fn def __enter__(self): return self def __exit__(self, type_, value, traceback): return class _tqdm_cls: def __call__(self, *args, **kwargs): if _tqdm_active: return tqdm_lib.tqdm(*args, **kwargs) else: return EmptyTqdm(*args, **kwargs) def set_lock(self, *args, **kwargs): self._lock = None if _tqdm_active: return tqdm_lib.tqdm.set_lock(*args, **kwargs) def get_lock(self): if _tqdm_active: return tqdm_lib.tqdm.get_lock() tqdm = _tqdm_cls() def is_progress_bar_enabled() -> bool: """Return a boolean indicating whether tqdm progress bars are enabled.""" global _tqdm_active return bool(_tqdm_active) def enable_progress_bar(): """Enable tqdm progress bar.""" global _tqdm_active _tqdm_active = True def disable_progress_bar(): """Disable tqdm progress bar.""" global _tqdm_active _tqdm_active = False ================================================ FILE: diffusers/utils/model_card_template.md ================================================ --- {{ card_data }} --- # {{ model_name | default("Diffusion Model") }} ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `{{ dataset_name }}` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: {{ learning_rate }} - train_batch_size: {{ train_batch_size }} - eval_batch_size: {{ eval_batch_size }} - gradient_accumulation_steps: {{ gradient_accumulation_steps }} - optimizer: AdamW with betas=({{ adam_beta1 }}, {{ adam_beta2 }}), weight_decay={{ adam_weight_decay }} and epsilon={{ adam_epsilon }} - lr_scheduler: {{ lr_scheduler }} - lr_warmup_steps: {{ lr_warmup_steps }} - ema_inv_gamma: {{ ema_inv_gamma }} - ema_inv_gamma: {{ ema_power }} - ema_inv_gamma: {{ ema_max_decay }} - mixed_precision: {{ mixed_precision }} ### Training results 📈 [TensorBoard logs](https://huggingface.co/{{ repo_name }}/tensorboard?#scalars) ================================================ FILE: diffusers/utils/outputs.py ================================================ # Copyright 2023 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. """ Generic utilities """ from collections import OrderedDict from dataclasses import fields from typing import Any, Tuple import numpy as np from .import_utils import is_torch_available def is_tensor(x): """ Tests if `x` is a `torch.Tensor` or `np.ndarray`. """ if is_torch_available(): import torch if isinstance(x, torch.Tensor): return True return isinstance(x, np.ndarray) class BaseOutput(OrderedDict): """ Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular python dictionary. You can't unpack a `BaseOutput` directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple before. """ def __post_init__(self): class_fields = fields(self) # Safety and consistency checks if not len(class_fields): raise ValueError(f"{self.__class__.__name__} has no fields.") first_field = getattr(self, class_fields[0].name) other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:]) if other_fields_are_none and isinstance(first_field, dict): for key, value in first_field.items(): self[key] = value else: for field in class_fields: v = getattr(self, field.name) if v is not None: self[field.name] = v def __delitem__(self, *args, **kwargs): raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.") def setdefault(self, *args, **kwargs): raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.") def pop(self, *args, **kwargs): raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.") def update(self, *args, **kwargs): raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.") def __getitem__(self, k): if isinstance(k, str): inner_dict = dict(self.items()) return inner_dict[k] else: return self.to_tuple()[k] def __setattr__(self, name, value): if name in self.keys() and value is not None: # Don't call self.__setitem__ to avoid recursion errors super().__setitem__(name, value) super().__setattr__(name, value) def __setitem__(self, key, value): # Will raise a KeyException if needed super().__setitem__(key, value) # Don't call self.__setattr__ to avoid recursion errors super().__setattr__(key, value) def to_tuple(self) -> Tuple[Any]: """ Convert self to a tuple containing all the attributes/keys that are not `None`. """ return tuple(self[k] for k in self.keys()) ================================================ FILE: diffusers/utils/pil_utils.py ================================================ import PIL.Image import PIL.ImageOps from packaging import version from PIL import Image if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): PIL_INTERPOLATION = { "linear": PIL.Image.Resampling.BILINEAR, "bilinear": PIL.Image.Resampling.BILINEAR, "bicubic": PIL.Image.Resampling.BICUBIC, "lanczos": PIL.Image.Resampling.LANCZOS, "nearest": PIL.Image.Resampling.NEAREST, } else: PIL_INTERPOLATION = { "linear": PIL.Image.LINEAR, "bilinear": PIL.Image.BILINEAR, "bicubic": PIL.Image.BICUBIC, "lanczos": PIL.Image.LANCZOS, "nearest": PIL.Image.NEAREST, } def pt_to_pil(images): images = (images / 2 + 0.5).clamp(0, 1) images = images.cpu().permute(0, 2, 3, 1).float().numpy() images = numpy_to_pil(images) return images def numpy_to_pil(images): """ Convert a numpy image or a batch of images to a PIL image. """ if images.ndim == 3: images = images[None, ...] images = (images * 255).round().astype("uint8") if images.shape[-1] == 1: # special case for grayscale (single channel) images pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images] else: pil_images = [Image.fromarray(image) for image in images] return pil_images ================================================ FILE: diffusers/utils/testing_utils.py ================================================ import inspect import logging import os import random import re import tempfile import unittest import urllib.parse from distutils.util import strtobool from io import BytesIO, StringIO from pathlib import Path from typing import List, Optional, Union import numpy as np import PIL.Image import PIL.ImageOps import requests from packaging import version from .import_utils import ( BACKENDS_MAPPING, is_compel_available, is_flax_available, is_note_seq_available, is_onnx_available, is_opencv_available, is_torch_available, is_torch_version, is_torchsde_available, ) from .logging import get_logger global_rng = random.Random() logger = get_logger(__name__) if is_torch_available(): import torch if "DIFFUSERS_TEST_DEVICE" in os.environ: torch_device = os.environ["DIFFUSERS_TEST_DEVICE"] available_backends = ["cuda", "cpu", "mps"] if torch_device not in available_backends: raise ValueError( f"unknown torch backend for diffusers tests: {torch_device}. Available backends are:" f" {available_backends}" ) logger.info(f"torch_device overrode to {torch_device}") else: torch_device = "cuda" if torch.cuda.is_available() else "cpu" is_torch_higher_equal_than_1_12 = version.parse( version.parse(torch.__version__).base_version ) >= version.parse("1.12") if is_torch_higher_equal_than_1_12: # Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details mps_backend_registered = hasattr(torch.backends, "mps") torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device def torch_all_close(a, b, *args, **kwargs): if not is_torch_available(): raise ValueError("PyTorch needs to be installed to use this function.") if not torch.allclose(a, b, *args, **kwargs): assert False, f"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}." return True def print_tensor_test(tensor, filename="test_corrections.txt", expected_tensor_name="expected_slice"): test_name = os.environ.get("PYTEST_CURRENT_TEST") if not torch.is_tensor(tensor): tensor = torch.from_numpy(tensor) tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "") # format is usually: # expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161]) output_str = tensor_str.replace("tensor", f"{expected_tensor_name} = np.array") test_file, test_class, test_fn = test_name.split("::") test_fn = test_fn.split()[0] with open(filename, "a") as f: print(";".join([test_file, test_class, test_fn, output_str]), file=f) def get_tests_dir(append_path=None): """ Args: append_path: optional path to append to the tests dir path Return: The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is joined after the `tests` dir the former is provided. """ # this function caller's __file__ caller__file__ = inspect.stack()[1][1] tests_dir = os.path.abspath(os.path.dirname(caller__file__)) while not tests_dir.endswith("tests"): tests_dir = os.path.dirname(tests_dir) if append_path: return os.path.join(tests_dir, append_path) else: return tests_dir def parse_flag_from_env(key, default=False): try: value = os.environ[key] except KeyError: # KEY isn't set, default to `default`. _value = default else: # KEY is set, convert it to True or False. try: _value = strtobool(value) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(f"If set, {key} must be yes or no.") return _value _run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False) _run_nightly_tests = parse_flag_from_env("RUN_NIGHTLY", default=False) def floats_tensor(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.random() * scale) return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous() def slow(test_case): """ Decorator marking a test as slow. Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them. """ return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case) def nightly(test_case): """ Decorator marking a test that runs nightly in the diffusers CI. Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them. """ return unittest.skipUnless(_run_nightly_tests, "test is nightly")(test_case) def require_torch(test_case): """ Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed. """ return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case) def require_torch_2(test_case): """ Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed. """ return unittest.skipUnless(is_torch_available() and is_torch_version(">=", "2.0.0"), "test requires PyTorch 2")( test_case ) def require_torch_gpu(test_case): """Decorator marking a test that requires CUDA and PyTorch.""" return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")( test_case ) def skip_mps(test_case): """Decorator marking a test to skip if torch_device is 'mps'""" return unittest.skipUnless(torch_device != "mps", "test requires non 'mps' device")(test_case) def require_flax(test_case): """ Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed """ return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case) def require_compel(test_case): """ Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when the library is not installed. """ return unittest.skipUnless(is_compel_available(), "test requires compel")(test_case) def require_onnxruntime(test_case): """ Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed. """ return unittest.skipUnless(is_onnx_available(), "test requires onnxruntime")(test_case) def require_note_seq(test_case): """ Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed. """ return unittest.skipUnless(is_note_seq_available(), "test requires note_seq")(test_case) def require_torchsde(test_case): """ Decorator marking a test that requires torchsde. These tests are skipped when torchsde isn't installed. """ return unittest.skipUnless(is_torchsde_available(), "test requires torchsde")(test_case) def load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -> np.ndarray: if isinstance(arry, str): # local_path = "/home/patrick_huggingface_co/" if local_path is not None: # local_path can be passed to correct images of tests return os.path.join(local_path, "/".join([arry.split("/")[-5], arry.split("/")[-2], arry.split("/")[-1]])) elif arry.startswith("http://") or arry.startswith("https://"): response = requests.get(arry) response.raise_for_status() arry = np.load(BytesIO(response.content)) elif os.path.isfile(arry): arry = np.load(arry) else: raise ValueError( f"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path" ) elif isinstance(arry, np.ndarray): pass else: raise ValueError( "Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a" " ndarray." ) return arry def load_pt(url: str): response = requests.get(url) response.raise_for_status() arry = torch.load(BytesIO(response.content)) return arry def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image: """ Args: Loads `image` to a PIL Image. image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. Returns: `PIL.Image.Image`: A PIL Image. """ if isinstance(image, str): if image.startswith("http://") or image.startswith("https://"): image = PIL.Image.open(requests.get(image, stream=True).raw) elif os.path.isfile(image): image = PIL.Image.open(image) else: raise ValueError( f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path" ) elif isinstance(image, PIL.Image.Image): image = image else: raise ValueError( "Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image." ) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") return image def preprocess_image(image: PIL.Image, batch_size: int): w, h = image.size w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = image.resize((w, h), resample=PIL.Image.LANCZOS) image = np.array(image).astype(np.float32) / 255.0 image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size) image = torch.from_numpy(image) return 2.0 * image - 1.0 def export_to_video(video_frames: List[np.ndarray], output_video_path: str = None) -> str: if is_opencv_available(): import cv2 else: raise ImportError(BACKENDS_MAPPING["opencv"][1].format("export_to_video")) if output_video_path is None: output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name fourcc = cv2.VideoWriter_fourcc(*"mp4v") h, w, c = video_frames[0].shape video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=8, frameSize=(w, h)) for i in range(len(video_frames)): img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR) video_writer.write(img) return output_video_path def load_hf_numpy(path) -> np.ndarray: if not path.startswith("http://") or path.startswith("https://"): path = os.path.join( "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main", urllib.parse.quote(path) ) return load_numpy(path) # --- pytest conf functions --- # # to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once pytest_opt_registered = {} def pytest_addoption_shared(parser): """ This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there. It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest` option. """ option = "--make-reports" if option not in pytest_opt_registered: parser.addoption( option, action="store", default=False, help="generate report files. The value of this option is used as a prefix to report names", ) pytest_opt_registered[option] = 1 def pytest_terminal_summary_main(tr, id): """ Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current directory. The report files are prefixed with the test suite name. This function emulates --duration and -rA pytest arguments. This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined there. Args: - tr: `terminalreporter` passed from `conftest.py` - id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other. NB: this functions taps into a private _pytest API and while unlikely, it could break should pytest do internal changes - also it calls default internal methods of terminalreporter which can be hijacked by various `pytest-` plugins and interfere. """ from _pytest.config import create_terminal_writer if not len(id): id = "tests" config = tr.config orig_writer = config.get_terminal_writer() orig_tbstyle = config.option.tbstyle orig_reportchars = tr.reportchars dir = "reports" Path(dir).mkdir(parents=True, exist_ok=True) report_files = { k: f"{dir}/{id}_{k}.txt" for k in [ "durations", "errors", "failures_long", "failures_short", "failures_line", "passes", "stats", "summary_short", "warnings", ] } # custom durations report # note: there is no need to call pytest --durations=XX to get this separate report # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66 dlist = [] for replist in tr.stats.values(): for rep in replist: if hasattr(rep, "duration"): dlist.append(rep) if dlist: dlist.sort(key=lambda x: x.duration, reverse=True) with open(report_files["durations"], "w") as f: durations_min = 0.05 # sec f.write("slowest durations\n") for i, rep in enumerate(dlist): if rep.duration < durations_min: f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted") break f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n") def summary_failures_short(tr): # expecting that the reports were --tb=long (default) so we chop them off here to the last frame reports = tr.getreports("failed") if not reports: return tr.write_sep("=", "FAILURES SHORT STACK") for rep in reports: msg = tr._getfailureheadline(rep) tr.write_sep("_", msg, red=True, bold=True) # chop off the optional leading extra frames, leaving only the last one longrepr = re.sub(r".*_ _ _ (_ ){10,}_ _ ", "", rep.longreprtext, 0, re.M | re.S) tr._tw.line(longrepr) # note: not printing out any rep.sections to keep the report short # use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814 # note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g. # pytest-instafail does that) # report failures with line/short/long styles config.option.tbstyle = "auto" # full tb with open(report_files["failures_long"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.summary_failures() # config.option.tbstyle = "short" # short tb with open(report_files["failures_short"], "w") as f: tr._tw = create_terminal_writer(config, f) summary_failures_short(tr) config.option.tbstyle = "line" # one line per error with open(report_files["failures_line"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.summary_failures() with open(report_files["errors"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.summary_errors() with open(report_files["warnings"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.summary_warnings() # normal warnings tr.summary_warnings() # final warnings tr.reportchars = "wPpsxXEf" # emulate -rA (used in summary_passes() and short_test_summary()) with open(report_files["passes"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.summary_passes() with open(report_files["summary_short"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.short_test_summary() with open(report_files["stats"], "w") as f: tr._tw = create_terminal_writer(config, f) tr.summary_stats() # restore: tr._tw = orig_writer tr.reportchars = orig_reportchars config.option.tbstyle = orig_tbstyle class CaptureLogger: """ Args: Context manager to capture `logging` streams logger: 'logging` logger object Returns: The captured output is available via `self.out` Example: ```python >>> from diffusers import logging >>> from diffusers.testing_utils import CaptureLogger >>> msg = "Testing 1, 2, 3" >>> logging.set_verbosity_info() >>> logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py") >>> with CaptureLogger(logger) as cl: ... logger.info(msg) >>> assert cl.out, msg + "\n" ``` """ def __init__(self, logger): self.logger = logger self.io = StringIO() self.sh = logging.StreamHandler(self.io) self.out = "" def __enter__(self): self.logger.addHandler(self.sh) return self def __exit__(self, *exc): self.logger.removeHandler(self.sh) self.out = self.io.getvalue() def __repr__(self): return f"captured: {self.out}\n" ================================================ FILE: diffusers/utils/torch_utils.py ================================================ # Copyright 2023 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. """ PyTorch utilities: Utilities related to PyTorch """ from typing import List, Optional, Tuple, Union from . import logging from .import_utils import is_torch_available, is_torch_version if is_torch_available(): import torch logger = logging.get_logger(__name__) # pylint: disable=invalid-name try: from torch._dynamo import allow_in_graph as maybe_allow_in_graph except (ImportError, ModuleNotFoundError): def maybe_allow_in_graph(cls): return cls def randn_tensor( shape: Union[Tuple, List], generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None, device: Optional["torch.device"] = None, dtype: Optional["torch.dtype"] = None, layout: Optional["torch.layout"] = None, ): """This is a helper function that allows to create random tensors on the desired `device` with the desired `dtype`. When passing a list of generators one can seed each batched size individually. If CPU generators are passed the tensor will always be created on CPU. """ # device on which tensor is created defaults to device rand_device = device batch_size = shape[0] layout = layout or torch.strided device = device or torch.device("cpu") if generator is not None: gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type if gen_device_type != device.type and gen_device_type == "cpu": rand_device = "cpu" if device != "mps": logger.info( f"The passed generator was created on 'cpu' even though a tensor on {device} was expected." f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably" f" slighly speed up this function by passing a generator that was created on the {device} device." ) elif gen_device_type != device.type and gen_device_type == "cuda": raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.") if isinstance(generator, list): shape = (1,) + shape[1:] latents = [ torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout) for i in range(batch_size) ] latents = torch.cat(latents, dim=0).to(device) else: latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device) return latents def is_compiled_module(module): """Check whether the module was compiled with torch.compile()""" if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"): return False return isinstance(module, torch._dynamo.eval_frame.OptimizedModule) ================================================ FILE: fid_score.py ================================================ """Calculates the Frechet Inception Distance (FID) to evalulate GANs The FID metric calculates the distance between two distributions of images. Typically, we have summary statistics (mean & covariance matrix) of one of these distributions, while the 2nd distribution is given by a GAN. When run as a stand-alone program, it compares the distribution of images that are stored as PNG/JPEG at a specified location with a distribution given by summary statistics (in pickle format). The FID is calculated by assuming that X_1 and X_2 are the activations of the pool_3 layer of the inception net for generated samples and real world samples respectively. See --help to see further details. Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead of Tensorflow Copyright 2018 Institute of Bioinformatics, JKU Linz 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 os import pathlib from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser import numpy as np import torch import torchvision.transforms as TF from PIL import Image from scipy import linalg from torch.nn.functional import adaptive_avg_pool2d try: from tqdm import tqdm except ImportError: # If tqdm is not available, provide a mock version of it def tqdm(x): return x from inception import InceptionV3 parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument('--batch-size', type=int, default=50, help='Batch size to use') parser.add_argument('--dataset_name', type=str, default=None) parser.add_argument('--num-workers', type=int, help=('Number of processes to use for data loading. ' 'Defaults to `min(8, num_cpus)`')) parser.add_argument('--device', type=str, default=None, help='Device to use. Like cuda, cuda:0 or cpu') parser.add_argument('--dims', type=int, default=2048, choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), help=('Dimensionality of Inception features to use. ' 'By default, uses pool3 features')) parser.add_argument('--num_samples', type=int, default=None, help=('Number of samples for FID estimation')) parser.add_argument('--res', type=int, default=None, help=('Resolutions of samples for FID estimation')) parser.add_argument('--save-stats', action='store_true', help=('Generate an npz archive from a directory of samples. ' 'The first path is used as input and the second as output.')) parser.add_argument('path', type=str, nargs=2, help=('Paths to the generated images or ' 'to .npz statistic files')) IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm', 'tif', 'tiff', 'webp'} class ImagePathDataset(torch.utils.data.Dataset): def __init__(self, files, transforms=None): self.files = files self.transforms = transforms def __len__(self): return len(self.files) def __getitem__(self, i): path = self.files[i] img = Image.open(path).convert('RGB') if self.transforms is not None: img = self.transforms(img) return img def get_activations(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, res=None, dataset_name=None): """Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : Batch size of images for the model to process at once. Make sure that the number of samples is a multiple of the batch size, otherwise some samples are ignored. This behavior is retained to match the original FID score implementation. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- A numpy array of dimension (num images, dims) that contains the activations of the given tensor when feeding inception with the query tensor. """ model.eval() if batch_size > len(files): print(('Warning: batch size is bigger than the data size. ' 'Setting batch size to data size')) batch_size = len(files) if res is None: trans = TF.ToTensor() else: if dataset_name == 'celeba': from datasets import Crop cx = 89 cy = 121 x1 = cy - 64 x2 = cy + 64 y1 = cx - 64 y2 = cx + 64 trans = TF.Compose([ Crop(x1, x2, y1, y2), TF.Resize(res), TF.ToTensor(), ]) else: trans = TF.Compose([ TF.Resize(res), TF.CenterCrop(res), TF.ToTensor() ]) dataset = ImagePathDataset(files, transforms=trans) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers) pred_arr = np.empty((len(files), dims)) start_idx = 0 for batch in tqdm(dataloader): batch = batch.to(device) with torch.no_grad(): pred = model(batch)[0] # If model output is not scalar, apply global spatial average pooling. # This happens if you choose a dimensionality not equal 2048. if pred.size(2) != 1 or pred.size(3) != 1: pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) pred = pred.squeeze(3).squeeze(2).cpu().numpy() pred_arr[start_idx:start_idx + pred.shape[0]] = pred start_idx = start_idx + pred.shape[0] return pred_arr def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of a layer of the inception net (like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations, precalculated on an representative data set. -- sigma1: The covariance matrix over activations for generated samples. -- sigma2: The covariance matrix over activations, precalculated on an representative data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, \ 'Training and test mean vectors have different lengths' assert sigma1.shape == sigma2.shape, \ 'Training and test covariances have different dimensions' diff = mu1 - mu2 # Product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ('fid calculation produces singular product; ' 'adding %s to diagonal of cov estimates') % eps print(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError('Imaginary component {}'.format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean) def calculate_activation_statistics(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, res=None, dataset_name=None): """Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : The images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the hardware. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the inception model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the inception model. """ act = get_activations(files, model, batch_size, dims, device, num_workers, res=res, dataset_name=dataset_name) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma def compute_statistics_of_path(path, model, batch_size, dims, device, num_workers=1, num_samples=None, res=None, dataset_name=None): if path.endswith('.npz'): with np.load(path) as f: m, s = f['mu'][:], f['sigma'][:] else: path = pathlib.Path(path) files = sorted([file for ext in IMAGE_EXTENSIONS for file in path.glob('**/*.{}'.format(ext))]) if num_samples is not None: #import random #files = random.sample(files, num_samples) files = files[:num_samples] print("Found %d files." % len(files)) m, s = calculate_activation_statistics(files, model, batch_size, dims, device, num_workers, res=res, dataset_name=dataset_name) return m, s def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1, num_samples=None, res=None, dataset_name=None): """Calculates the FID of two paths""" for p in paths: if not os.path.exists(p): raise RuntimeError('Invalid path: %s' % p) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) m2, s2 = compute_statistics_of_path(paths[1], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) fid_value = calculate_frechet_distance(m1, s1, m2, s2) return fid_value def save_fid_stats(paths, batch_size, device, dims, num_workers=1, num_samples=None, res=None, dataset_name=None): """Calculates the FID of two paths""" if not os.path.exists(paths[0]): raise RuntimeError('Invalid path: %s' % paths[0]) if os.path.exists(paths[1]): raise RuntimeError('Existing output file: %s' % paths[1]) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) print(f"Saving statistics for {paths[0]}") m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) np.savez_compressed(paths[1], mu=m1, sigma=s1) def main(): args = parser.parse_args() if args.device is None: device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu') else: device = torch.device(args.device) if args.num_workers is None: try: num_cpus = len(os.sched_getaffinity(0)) except AttributeError: # os.sched_getaffinity is not available under Windows, use # os.cpu_count instead (which may not return the *available* number # of CPUs). num_cpus = os.cpu_count() num_workers = min(num_cpus, 8) if num_cpus is not None else 0 else: num_workers = args.num_workers if args.save_stats: save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers, num_samples=args.num_samples, res=args.res, dataset_name=args.dataset_name) return fid_value = calculate_fid_given_paths(args.path, args.batch_size, device, args.dims, num_workers, num_samples=args.num_samples, res = args.res, dataset_name=args.dataset_name) print('FID: ', fid_value) if __name__ == '__main__': main() ================================================ FILE: inception.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torchvision try: from torchvision.models.utils import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url # Inception weights ported to Pytorch from # http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 class InceptionV3(nn.Module): """Pretrained InceptionV3 network returning feature maps""" # Index of default block of inception to return, # corresponds to output of final average pooling DEFAULT_BLOCK_INDEX = 3 # Maps feature dimensionality to their output blocks indices BLOCK_INDEX_BY_DIM = { 64: 0, # First max pooling features 192: 1, # Second max pooling featurs 768: 2, # Pre-aux classifier features 2048: 3 # Final average pooling features } def __init__(self, output_blocks=(DEFAULT_BLOCK_INDEX,), resize_input=True, normalize_input=True, requires_grad=False, use_fid_inception=True): """Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, scales the input from range (0, 1) to the range the pretrained Inception network expects, namely (-1, 1) requires_grad : bool If true, parameters of the model require gradients. Possibly useful for finetuning the network use_fid_inception : bool If true, uses the pretrained Inception model used in Tensorflow's FID implementation. If false, uses the pretrained Inception model available in torchvision. The FID Inception model has different weights and a slightly different structure from torchvision's Inception model. If you want to compute FID scores, you are strongly advised to set this parameter to true to get comparable results. """ super(InceptionV3, self).__init__() self.resize_input = resize_input self.normalize_input = normalize_input self.output_blocks = sorted(output_blocks) self.last_needed_block = max(output_blocks) assert self.last_needed_block <= 3, \ 'Last possible output block index is 3' self.blocks = nn.ModuleList() if use_fid_inception: inception = fid_inception_v3() else: inception = _inception_v3(weights='DEFAULT') # Block 0: input to maxpool1 block0 = [ inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block0)) # Block 1: maxpool1 to maxpool2 if self.last_needed_block >= 1: block1 = [ inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block1)) # Block 2: maxpool2 to aux classifier if self.last_needed_block >= 2: block2 = [ inception.Mixed_5b, inception.Mixed_5c, inception.Mixed_5d, inception.Mixed_6a, inception.Mixed_6b, inception.Mixed_6c, inception.Mixed_6d, inception.Mixed_6e, ] self.blocks.append(nn.Sequential(*block2)) # Block 3: aux classifier to final avgpool if self.last_needed_block >= 3: block3 = [ inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, nn.AdaptiveAvgPool2d(output_size=(1, 1)) ] self.blocks.append(nn.Sequential(*block3)) for param in self.parameters(): param.requires_grad = requires_grad def forward(self, inp): """Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index """ outp = [] x = inp if self.resize_input: x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) if self.normalize_input: x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) for idx, block in enumerate(self.blocks): x = block(x) if idx in self.output_blocks: outp.append(x) if idx == self.last_needed_block: break return outp def _inception_v3(*args, **kwargs): """Wraps `torchvision.models.inception_v3`""" try: version = tuple(map(int, torchvision.__version__.split('.')[:2])) except ValueError: # Just a caution against weird version strings version = (0,) # Skips default weight inititialization if supported by torchvision # version. See https://github.com/mseitzer/pytorch-fid/issues/28. if version >= (0, 6): kwargs['init_weights'] = False # Backwards compatibility: `weights` argument was handled by `pretrained` # argument prior to version 0.13. if version < (0, 13) and 'weights' in kwargs: if kwargs['weights'] == 'DEFAULT': kwargs['pretrained'] = True elif kwargs['weights'] is None: kwargs['pretrained'] = False else: raise ValueError( 'weights=={} not supported in torchvision {}'.format( kwargs['weights'], torchvision.__version__ ) ) del kwargs['weights'] return torchvision.models.inception_v3(*args, **kwargs) def fid_inception_v3(): """Build pretrained Inception model for FID computation The Inception model for FID computation uses a different set of weights and has a slightly different structure than torchvision's Inception. This method first constructs torchvision's Inception and then patches the necessary parts that are different in the FID Inception model. """ inception = _inception_v3(num_classes=1008, aux_logits=False, weights=None) inception.Mixed_5b = FIDInceptionA(192, pool_features=32) inception.Mixed_5c = FIDInceptionA(256, pool_features=64) inception.Mixed_5d = FIDInceptionA(288, pool_features=64) inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) inception.Mixed_7b = FIDInceptionE_1(1280) inception.Mixed_7c = FIDInceptionE_2(2048) state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True) inception.load_state_dict(state_dict) return inception class FIDInceptionA(torchvision.models.inception.InceptionA): """InceptionA block patched for FID computation""" def __init__(self, in_channels, pool_features): super(FIDInceptionA, self).__init__(in_channels, pool_features) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionC(torchvision.models.inception.InceptionC): """InceptionC block patched for FID computation""" def __init__(self, in_channels, channels_7x7): super(FIDInceptionC, self).__init__(in_channels, channels_7x7) def forward(self, x): branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionE_1(torchvision.models.inception.InceptionE): """First InceptionE block patched for FID computation""" def __init__(self, in_channels): super(FIDInceptionE_1, self).__init__(in_channels) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionE_2(torchvision.models.inception.InceptionE): """Second InceptionE block patched for FID computation""" def __init__(self, in_channels): super(FIDInceptionE_2, self).__init__(in_channels) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) # Patch: The FID Inception model uses max pooling instead of average # pooling. This is likely an error in this specific Inception # implementation, as other Inception models use average pooling here # (which matches the description in the paper). branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) ================================================ FILE: ldm_exp/LICENSE ================================================ MIT License Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_exp/README.md ================================================ # Latent Diffusion Models [arXiv](https://arxiv.org/abs/2112.10752) | [BibTeX](#bibtex)

[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)
[Robin Rombach](https://github.com/rromb)\*, [Andreas Blattmann](https://github.com/ablattmann)\*, [Dominik Lorenz](https://github.com/qp-qp)\, [Patrick Esser](https://github.com/pesser), [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)
\* equal contribution

## News ### July 2022 - Inference code and model weights to run our [retrieval-augmented diffusion models](https://arxiv.org/abs/2204.11824) are now available. See [this section](#retrieval-augmented-diffusion-models). ### April 2022 - Thanks to [Katherine Crowson](https://github.com/crowsonkb), classifier-free guidance received a ~2x speedup and the [PLMS sampler](https://arxiv.org/abs/2202.09778) is available. See also [this PR](https://github.com/CompVis/latent-diffusion/pull/51). - Our 1.45B [latent diffusion LAION model](#text-to-image) was integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/multimodalart/latentdiffusion) - More pre-trained LDMs are available: - A 1.45B [model](#text-to-image) trained on the [LAION-400M](https://arxiv.org/abs/2111.02114) database. - A class-conditional model on ImageNet, achieving a FID of 3.6 when using [classifier-free guidance](https://openreview.net/pdf?id=qw8AKxfYbI) Available via a [colab notebook](https://colab.research.google.com/github/CompVis/latent-diffusion/blob/main/scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin]. ## Requirements A suitable [conda](https://conda.io/) environment named `ldm` can be created and activated with: ``` conda env create -f environment.yaml conda activate ldm ``` # Pretrained Models A general list of all available checkpoints is available in via our [model zoo](#model-zoo). If you use any of these models in your work, we are always happy to receive a [citation](#bibtex). ## Retrieval Augmented Diffusion Models ![rdm-figure](assets/rdm-preview.jpg) We include inference code to run our retrieval-augmented diffusion models (RDMs) as described in [https://arxiv.org/abs/2204.11824](https://arxiv.org/abs/2204.11824). To get started, install the additionally required python packages into your `ldm` environment ```shell script pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0 pip install git+https://github.com/arogozhnikov/einops.git ``` and download the trained weights (preliminary ceckpoints): ```bash mkdir -p models/rdm/rdm768x768/ wget -O models/rdm/rdm768x768/model.ckpt https://ommer-lab.com/files/rdm/model.ckpt ``` As these models are conditioned on a set of CLIP image embeddings, our RDMs support different inference modes, which are described in the following. #### RDM with text-prompt only (no explicit retrieval needed) Since CLIP offers a shared image/text feature space, and RDMs learn to cover a neighborhood of a given example during training, we can directly take a CLIP text embedding of a given prompt and condition on it. Run this mode via ``` python scripts/knn2img.py --prompt "a happy bear reading a newspaper, oil on canvas" ``` #### RDM with text-to-image retrieval To be able to run a RDM conditioned on a text-prompt and additionally images retrieved from this prompt, you will also need to download the corresponding retrieval database. We provide two distinct databases extracted from the [Openimages-](https://storage.googleapis.com/openimages/web/index.html) and [ArtBench-](https://github.com/liaopeiyuan/artbench) datasets. Interchanging the databases results in different capabilities of the model as visualized below, although the learned weights are the same in both cases. Download the retrieval-databases which contain the retrieval-datasets ([Openimages](https://storage.googleapis.com/openimages/web/index.html) (~11GB) and [ArtBench](https://github.com/liaopeiyuan/artbench) (~82MB)) compressed into CLIP image embeddings: ```bash mkdir -p data/rdm/retrieval_databases wget -O data/rdm/retrieval_databases/artbench.zip https://ommer-lab.com/files/rdm/artbench_databases.zip wget -O data/rdm/retrieval_databases/openimages.zip https://ommer-lab.com/files/rdm/openimages_database.zip unzip data/rdm/retrieval_databases/artbench.zip -d data/rdm/retrieval_databases/ unzip data/rdm/retrieval_databases/openimages.zip -d data/rdm/retrieval_databases/ ``` We also provide trained [ScaNN](https://github.com/google-research/google-research/tree/master/scann) search indices for ArtBench. Download and extract via ```bash mkdir -p data/rdm/searchers wget -O data/rdm/searchers/artbench.zip https://ommer-lab.com/files/rdm/artbench_searchers.zip unzip data/rdm/searchers/artbench.zip -d data/rdm/searchers ``` Since the index for OpenImages is large (~21 GB), we provide a script to create and save it for usage during sampling. Note however, that sampling with the OpenImages database will not be possible without this index. Run the script via ```bash python scripts/train_searcher.py ``` Retrieval based text-guided sampling with visual nearest neighbors can be started via ``` python scripts/knn2img.py --prompt "a happy pineapple" --use_neighbors --knn ``` Note that the maximum supported number of neighbors is 20. The database can be changed via the cmd parameter ``--database`` which can be `[openimages, artbench-art_nouveau, artbench-baroque, artbench-expressionism, artbench-impressionism, artbench-post_impressionism, artbench-realism, artbench-renaissance, artbench-romanticism, artbench-surrealism, artbench-ukiyo_e]`. For using `--database openimages`, the above script (`scripts/train_searcher.py`) must be executed before. Due to their relatively small size, the artbench datasetbases are best suited for creating more abstract concepts and do not work well for detailed text control. #### Coming Soon - better models - more resolutions - image-to-image retrieval ## Text-to-Image ![text2img-figure](assets/txt2img-preview.png) Download the pre-trained weights (5.7GB) ``` mkdir -p models/ldm/text2img-large/ wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt ``` and sample with ``` python scripts/txt2img.py --prompt "a virus monster is playing guitar, oil on canvas" --ddim_eta 0.0 --n_samples 4 --n_iter 4 --scale 5.0 --ddim_steps 50 ``` This will save each sample individually as well as a grid of size `n_iter` x `n_samples` at the specified output location (default: `outputs/txt2img-samples`). Quality, sampling speed and diversity are best controlled via the `scale`, `ddim_steps` and `ddim_eta` arguments. As a rule of thumb, higher values of `scale` produce better samples at the cost of a reduced output diversity. Furthermore, increasing `ddim_steps` generally also gives higher quality samples, but returns are diminishing for values > 250. Fast sampling (i.e. low values of `ddim_steps`) while retaining good quality can be achieved by using `--ddim_eta 0.0`. Faster sampling (i.e. even lower values of `ddim_steps`) while retaining good quality can be achieved by using `--ddim_eta 0.0` and `--plms` (see [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778)). #### Beyond 256² For certain inputs, simply running the model in a convolutional fashion on larger features than it was trained on can sometimes result in interesting results. To try it out, tune the `H` and `W` arguments (which will be integer-divided by 8 in order to calculate the corresponding latent size), e.g. run ``` python scripts/txt2img.py --prompt "a sunset behind a mountain range, vector image" --ddim_eta 1.0 --n_samples 1 --n_iter 1 --H 384 --W 1024 --scale 5.0 ``` to create a sample of size 384x1024. Note, however, that controllability is reduced compared to the 256x256 setting. The example below was generated using the above command. ![text2img-figure-conv](assets/txt2img-convsample.png) ## Inpainting ![inpainting](assets/inpainting.png) Download the pre-trained weights ``` wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1 ``` and sample with ``` python scripts/inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results ``` `indir` should contain images `*.png` and masks `_mask.png` like the examples provided in `data/inpainting_examples`. ## Class-Conditional ImageNet Available via a [notebook](scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin]. ![class-conditional](assets/birdhouse.png) [colab]: [colab-cin]: ## Unconditional Models We also provide a script for sampling from unconditional LDMs (e.g. LSUN, FFHQ, ...). Start it via ```shell script CUDA_VISIBLE_DEVICES= python scripts/sample_diffusion.py -r models/ldm//model.ckpt -l -n <\#samples> --batch_size -c <\#ddim steps> -e <\#eta> ``` # Train your own LDMs ## Data preparation ### Faces For downloading the CelebA-HQ and FFHQ datasets, proceed as described in the [taming-transformers](https://github.com/CompVis/taming-transformers#celeba-hq) repository. ### LSUN The LSUN datasets can be conveniently downloaded via the script available [here](https://github.com/fyu/lsun). We performed a custom split into training and validation images, and provide the corresponding filenames at [https://ommer-lab.com/files/lsun.zip](https://ommer-lab.com/files/lsun.zip). After downloading, extract them to `./data/lsun`. The beds/cats/churches subsets should also be placed/symlinked at `./data/lsun/bedrooms`/`./data/lsun/cats`/`./data/lsun/churches`, respectively. ### ImageNet The code will try to download (through [Academic Torrents](http://academictorrents.com/)) and prepare ImageNet the first time it is used. However, since ImageNet is quite large, this requires a lot of disk space and time. If you already have ImageNet on your disk, you can speed things up by putting the data into `${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` (which defaults to `~/.cache/autoencoders/data/ILSVRC2012_{split}/data/`), where `{split}` is one of `train`/`validation`. It should have the following structure: ``` ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ ├── n01440764 │ ├── n01440764_10026.JPEG │ ├── n01440764_10027.JPEG │ ├── ... ├── n01443537 │ ├── n01443537_10007.JPEG │ ├── n01443537_10014.JPEG │ ├── ... ├── ... ``` If you haven't extracted the data, you can also place `ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into `${XDG_CACHE}/autoencoders/data/ILSVRC2012_train/` / `${XDG_CACHE}/autoencoders/data/ILSVRC2012_validation/`, which will then be extracted into above structure without downloading it again. Note that this will only happen if neither a folder `${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` nor a file `${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/.ready` exist. Remove them if you want to force running the dataset preparation again. ## Model Training Logs and checkpoints for trained models are saved to `logs/_`. ### Training autoencoder models Configs for training a KL-regularized autoencoder on ImageNet are provided at `configs/autoencoder`. Training can be started by running ``` CUDA_VISIBLE_DEVICES= python main.py --base configs/autoencoder/.yaml -t --gpus 0, ``` where `config_spec` is one of {`autoencoder_kl_8x8x64`(f=32, d=64), `autoencoder_kl_16x16x16`(f=16, d=16), `autoencoder_kl_32x32x4`(f=8, d=4), `autoencoder_kl_64x64x3`(f=4, d=3)}. For training VQ-regularized models, see the [taming-transformers](https://github.com/CompVis/taming-transformers) repository. ### Training LDMs In ``configs/latent-diffusion/`` we provide configs for training LDMs on the LSUN-, CelebA-HQ, FFHQ and ImageNet datasets. Training can be started by running ```shell script CUDA_VISIBLE_DEVICES= python main.py --base configs/latent-diffusion/.yaml -t --gpus 0, ``` where ```` is one of {`celebahq-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),`ffhq-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3), `lsun_bedrooms-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3), `lsun_churches-ldm-vq-4`(f=8, KL-reg. autoencoder, spatial size 32x32x4),`cin-ldm-vq-8`(f=8, VQ-reg. autoencoder, spatial size 32x32x4)}. # Model Zoo ## Pretrained Autoencoding Models ![rec2](assets/reconstruction2.png) All models were trained until convergence (no further substantial improvement in rFID). | Model | rFID vs val | train steps |PSNR | PSIM | Link | Comments |-------------------------|------------|----------------|----------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------| | f=4, VQ (Z=8192, d=3) | 0.58 | 533066 | 27.43 +/- 4.26 | 0.53 +/- 0.21 | https://ommer-lab.com/files/latent-diffusion/vq-f4.zip | | | f=4, VQ (Z=8192, d=3) | 1.06 | 658131 | 25.21 +/- 4.17 | 0.72 +/- 0.26 | https://heibox.uni-heidelberg.de/f/9c6681f64bb94338a069/?dl=1 | no attention | | f=8, VQ (Z=16384, d=4) | 1.14 | 971043 | 23.07 +/- 3.99 | 1.17 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/vq-f8.zip | | | f=8, VQ (Z=256, d=4) | 1.49 | 1608649 | 22.35 +/- 3.81 | 1.26 +/- 0.37 | https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip | | f=16, VQ (Z=16384, d=8) | 5.15 | 1101166 | 20.83 +/- 3.61 | 1.73 +/- 0.43 | https://heibox.uni-heidelberg.de/f/0e42b04e2e904890a9b6/?dl=1 | | | | | | | | | | | f=4, KL | 0.27 | 176991 | 27.53 +/- 4.54 | 0.55 +/- 0.24 | https://ommer-lab.com/files/latent-diffusion/kl-f4.zip | | | f=8, KL | 0.90 | 246803 | 24.19 +/- 4.19 | 1.02 +/- 0.35 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | | | f=16, KL (d=16) | 0.87 | 442998 | 24.08 +/- 4.22 | 1.07 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f16.zip | | | f=32, KL (d=64) | 2.04 | 406763 | 22.27 +/- 3.93 | 1.41 +/- 0.40 | https://ommer-lab.com/files/latent-diffusion/kl-f32.zip | | ### Get the models Running the following script downloads und extracts all available pretrained autoencoding models. ```shell script bash scripts/download_first_stages.sh ``` The first stage models can then be found in `models/first_stage_models/` ## Pretrained LDMs | Datset | Task | Model | FID | IS | Prec | Recall | Link | Comments |---------------------------------|------|--------------|---------------|-----------------|------|------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------| | CelebA-HQ | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=0)| 5.11 (5.11) | 3.29 | 0.72 | 0.49 | https://ommer-lab.com/files/latent-diffusion/celeba.zip | | | FFHQ | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=1)| 4.98 (4.98) | 4.50 (4.50) | 0.73 | 0.50 | https://ommer-lab.com/files/latent-diffusion/ffhq.zip | | | LSUN-Churches | Unconditional Image Synthesis | LDM-KL-8 (400 DDIM steps, eta=0)| 4.02 (4.02) | 2.72 | 0.64 | 0.52 | https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip | | | LSUN-Bedrooms | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=1)| 2.95 (3.0) | 2.22 (2.23)| 0.66 | 0.48 | https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip | | | ImageNet | Class-conditional Image Synthesis | LDM-VQ-8 (200 DDIM steps, eta=1) | 7.77(7.76)* /15.82** | 201.56(209.52)* /78.82** | 0.84* / 0.65** | 0.35* / 0.63** | https://ommer-lab.com/files/latent-diffusion/cin.zip | *: w/ guiding, classifier_scale 10 **: w/o guiding, scores in bracket calculated with script provided by [ADM](https://github.com/openai/guided-diffusion) | | Conceptual Captions | Text-conditional Image Synthesis | LDM-VQ-f4 (100 DDIM steps, eta=0) | 16.79 | 13.89 | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/text2img.zip | finetuned from LAION | | OpenImages | Super-resolution | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip | BSR image degradation | | OpenImages | Layout-to-Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=0) | 32.02 | 15.92 | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip | | | Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip | | | Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip | finetuned on resolution 512x512 | ### Get the models The LDMs listed above can jointly be downloaded and extracted via ```shell script bash scripts/download_models.sh ``` The models can then be found in `models/ldm/`. ## Coming Soon... * More inference scripts for conditional LDMs. * In the meantime, you can play with our colab notebook https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing ## Comments - Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion) and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). Thanks for open-sourcing! - The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). ## BibTeX ``` @misc{rombach2021highresolution, title={High-Resolution Image Synthesis with Latent Diffusion Models}, author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer}, year={2021}, eprint={2112.10752}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{https://doi.org/10.48550/arxiv.2204.11824, doi = {10.48550/ARXIV.2204.11824}, url = {https://arxiv.org/abs/2204.11824}, author = {Blattmann, Andreas and Rombach, Robin and Oktay, Kaan and Ommer, Björn}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Retrieval-Augmented Diffusion Models}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ================================================ FILE: ldm_exp/configs/autoencoder/autoencoder_kl_16x16x16.yaml ================================================ model: base_learning_rate: 4.5e-6 target: ldm.models.autoencoder.AutoencoderKL params: monitor: "val/rec_loss" embed_dim: 16 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 0.000001 disc_weight: 0.5 ddconfig: double_z: True z_channels: 16 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [16] dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 12 wrap: True train: target: ldm.data.imagenet.ImageNetSRTrain params: size: 256 degradation: pil_nearest validation: target: ldm.data.imagenet.ImageNetSRValidation params: size: 256 degradation: pil_nearest lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1000 max_images: 8 increase_log_steps: True trainer: benchmark: True accumulate_grad_batches: 2 ================================================ FILE: ldm_exp/configs/autoencoder/autoencoder_kl_32x32x4.yaml ================================================ model: base_learning_rate: 4.5e-6 target: ldm.models.autoencoder.AutoencoderKL params: monitor: "val/rec_loss" embed_dim: 4 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 0.000001 disc_weight: 0.5 ddconfig: double_z: True z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 12 wrap: True train: target: ldm.data.imagenet.ImageNetSRTrain params: size: 256 degradation: pil_nearest validation: target: ldm.data.imagenet.ImageNetSRValidation params: size: 256 degradation: pil_nearest lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1000 max_images: 8 increase_log_steps: True trainer: benchmark: True accumulate_grad_batches: 2 ================================================ FILE: ldm_exp/configs/autoencoder/autoencoder_kl_64x64x3.yaml ================================================ model: base_learning_rate: 4.5e-6 target: ldm.models.autoencoder.AutoencoderKL params: monitor: "val/rec_loss" embed_dim: 3 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 0.000001 disc_weight: 0.5 ddconfig: double_z: True z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 12 wrap: True train: target: ldm.data.imagenet.ImageNetSRTrain params: size: 256 degradation: pil_nearest validation: target: ldm.data.imagenet.ImageNetSRValidation params: size: 256 degradation: pil_nearest lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1000 max_images: 8 increase_log_steps: True trainer: benchmark: True accumulate_grad_batches: 2 ================================================ FILE: ldm_exp/configs/autoencoder/autoencoder_kl_8x8x64.yaml ================================================ model: base_learning_rate: 4.5e-6 target: ldm.models.autoencoder.AutoencoderKL params: monitor: "val/rec_loss" embed_dim: 64 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 0.000001 disc_weight: 0.5 ddconfig: double_z: True z_channels: 64 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [16,8] dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 12 wrap: True train: target: ldm.data.imagenet.ImageNetSRTrain params: size: 256 degradation: pil_nearest validation: target: ldm.data.imagenet.ImageNetSRValidation params: size: 256 degradation: pil_nearest lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1000 max_images: 8 increase_log_steps: True trainer: benchmark: True accumulate_grad_batches: 2 ================================================ FILE: ldm_exp/configs/latent-diffusion/celebahq-ldm-vq-4.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image image_size: 64 channels: 3 monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: # note: this isn\t actually the resolution but # the downsampling factor, i.e. this corresnponds to # attention on spatial resolution 8,16,32, as the # spatial reolution of the latents is 64 for f4 - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ckpt_path: models/first_stage_models/vq-f4/model.ckpt ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 48 num_workers: 5 wrap: false train: target: taming.data.faceshq.CelebAHQTrain params: size: 256 validation: target: taming.data.faceshq.CelebAHQValidation params: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 5000 max_images: 8 increase_log_steps: False trainer: benchmark: True ================================================ FILE: ldm_exp/configs/latent-diffusion/cin-ldm-vq-f8.yaml ================================================ model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: class_label image_size: 32 channels: 4 cond_stage_trainable: true conditioning_key: crossattn monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 256 attention_resolutions: #note: this isn\t actually the resolution but # the downsampling factor, i.e. this corresnponds to # attention on spatial resolution 8,16,32, as the # spatial reolution of the latents is 32 for f8 - 4 - 2 - 1 num_res_blocks: 2 channel_mult: - 1 - 2 - 4 num_head_channels: 32 use_spatial_transformer: true transformer_depth: 1 context_dim: 512 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 4 n_embed: 16384 ckpt_path: models/first_stage_models/vq-f8/model.ckpt ddconfig: double_z: false z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 32 dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.ClassEmbedder params: embed_dim: 512 key: class_label data: target: main.DataModuleFromConfig params: batch_size: 12 num_workers: 4 wrap: false train: target: ldm.data.imagenet.ImageNetTrain params: config: size: 256 validation: target: ldm.data.imagenet.ImageNetValidation params: config: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 1000 max_images: 8 increase_log_steps: False trainer: benchmark: True ================================================ FILE: ldm_exp/configs/latent-diffusion/cin256-v2.yaml ================================================ model: base_learning_rate: 2e-6 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: class_label image_size: 64 channels: 3 cond_stage_trainable: true conditioning_key: crossattn monitor: val/loss use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 192 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 5 num_heads: 1 use_spatial_transformer: true transformer_depth: 1 context_dim: 512 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.ClassEmbedder params: n_classes: 1001 embed_dim: 512 key: class_label data: target: main.DataModuleFromConfig params: batch_size: 16 num_workers: 4 wrap: false train: target: ldm.data.imagenet.ImageNetTrain params: config: size: 256 validation: target: ldm.data.imagenet.ImageNetValidation params: config: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 500 max_images: 8 increase_log_steps: False ================================================ FILE: ldm_exp/configs/latent-diffusion/ffhq-ldm-vq-4.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image image_size: 64 channels: 3 monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: # note: this isn\t actually the resolution but # the downsampling factor, i.e. this corresnponds to # attention on spatial resolution 8,16,32, as the # spatial reolution of the latents is 64 for f4 - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ckpt_path: configs/first_stage_models/vq-f4/model.yaml ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 42 num_workers: 5 wrap: false train: target: taming.data.faceshq.FFHQTrain params: size: 256 validation: target: taming.data.faceshq.FFHQValidation params: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 5000 max_images: 8 increase_log_steps: False trainer: benchmark: True ================================================ FILE: ldm_exp/configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml ================================================ model: base_learning_rate: 1.0e-05 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image image_size: 64 channels: 3 monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: # note: this isn\t actually the resolution but # the downsampling factor, i.e. this corresnponds to # attention on spatial resolution 8,16,32, as the # spatial reolution of the latents is 64 for f4 - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: ckpt_path: configs/first_stage_models/vq-f4/model.yaml embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 48 num_workers: 5 wrap: false train: target: ldm.data.lsun.LSUNBedroomsTrain params: size: 256 validation: target: ldm.data.lsun.LSUNBedroomsValidation params: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 5000 max_images: 8 increase_log_steps: False trainer: benchmark: True ================================================ FILE: ldm_exp/configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml ================================================ model: base_learning_rate: 5.0e-5 # set to target_lr by starting main.py with '--scale_lr False' target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0155 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 loss_type: l1 first_stage_key: "image" cond_stage_key: "image" image_size: 32 channels: 4 cond_stage_trainable: False concat_mode: False scale_by_std: True monitor: 'val/loss_simple_ema' scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [10000] cycle_lengths: [10000000000000] f_start: [1.e-6] f_max: [1.] f_min: [ 1.] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 192 attention_resolutions: [ 1, 2, 4, 8 ] # 32, 16, 8, 4 num_res_blocks: 2 channel_mult: [ 1,2,2,4,4 ] # 32, 16, 8, 4, 2 num_heads: 8 use_scale_shift_norm: True resblock_updown: True first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: "val/rec_loss" ckpt_path: "models/first_stage_models/kl-f8/model.ckpt" ddconfig: double_z: True z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: "__is_unconditional__" data: target: main.DataModuleFromConfig params: batch_size: 96 num_workers: 5 wrap: False train: target: ldm.data.lsun.LSUNChurchesTrain params: size: 256 validation: target: ldm.data.lsun.LSUNChurchesValidation params: size: 256 lightning: callbacks: image_logger: target: main.ImageLogger params: batch_frequency: 5000 max_images: 8 increase_log_steps: False trainer: benchmark: True ================================================ FILE: ldm_exp/configs/latent-diffusion/txt2img-1p4B-eval.yaml ================================================ model: base_learning_rate: 5.0e-05 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.012 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: caption image_size: 32 channels: 4 cond_stage_trainable: true conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: - 4 - 2 - 1 num_res_blocks: 2 channel_mult: - 1 - 2 - 4 - 4 num_heads: 8 use_spatial_transformer: true transformer_depth: 1 context_dim: 1280 use_checkpoint: true legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss 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 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.BERTEmbedder params: n_embed: 1280 n_layer: 32 ================================================ FILE: ldm_exp/configs/retrieval-augmented-diffusion/768x768.yaml ================================================ model: base_learning_rate: 0.0001 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.015 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: jpg cond_stage_key: nix image_size: 48 channels: 16 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_by_std: false scale_factor: 0.22765929 unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 48 in_channels: 16 out_channels: 16 model_channels: 448 attention_resolutions: - 4 - 2 - 1 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 use_scale_shift_norm: false resblock_updown: false num_head_channels: 32 use_spatial_transformer: true transformer_depth: 1 context_dim: 768 use_checkpoint: true first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: monitor: val/rec_loss embed_dim: 16 ddconfig: double_z: true z_channels: 16 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 16 dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: torch.nn.Identity ================================================ FILE: ldm_exp/environment.yaml ================================================ name: ldm channels: - pytorch - defaults dependencies: - python=3.8.5 - pip=20.3 - cudatoolkit=11.0 - pytorch=1.7.0 - torchvision=0.8.1 - numpy=1.19.2 - pip: - albumentations==0.4.3 - opencv-python==4.1.2.30 - pudb==2019.2 - imageio==2.9.0 - imageio-ffmpeg==0.4.2 - pytorch-lightning==1.4.2 - omegaconf==2.1.1 - test-tube>=0.7.5 - streamlit>=0.73.1 - einops==0.3.0 - torch-fidelity==0.3.0 - transformers==4.3.1 - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers - -e git+https://github.com/openai/CLIP.git@main#egg=clip - -e . ================================================ FILE: ldm_exp/fid_score.py ================================================ """Calculates the Frechet Inception Distance (FID) to evalulate GANs The FID metric calculates the distance between two distributions of images. Typically, we have summary statistics (mean & covariance matrix) of one of these distributions, while the 2nd distribution is given by a GAN. When run as a stand-alone program, it compares the distribution of images that are stored as PNG/JPEG at a specified location with a distribution given by summary statistics (in pickle format). The FID is calculated by assuming that X_1 and X_2 are the activations of the pool_3 layer of the inception net for generated samples and real world samples respectively. See --help to see further details. Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead of Tensorflow Copyright 2018 Institute of Bioinformatics, JKU Linz 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 os import pathlib from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True import numpy as np import torch import torchvision.transforms as TF from PIL import Image from scipy import linalg from torch.nn.functional import adaptive_avg_pool2d try: from tqdm import tqdm except ImportError: # If tqdm is not available, provide a mock version of it def tqdm(x): return x from inception import InceptionV3 parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument('--batch-size', type=int, default=50, help='Batch size to use') parser.add_argument('--dataset_name', type=str, default=None) parser.add_argument('--num-workers', type=int, help=('Number of processes to use for data loading. ' 'Defaults to `min(8, num_cpus)`')) parser.add_argument('--device', type=str, default=None, help='Device to use. Like cuda, cuda:0 or cpu') parser.add_argument('--dims', type=int, default=2048, choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), help=('Dimensionality of Inception features to use. ' 'By default, uses pool3 features')) parser.add_argument('--num_samples', type=int, default=None, help=('Number of samples for FID estimation')) parser.add_argument('--res', type=int, default=None, help=('Resolutions of samples for FID estimation')) parser.add_argument('--save-stats', action='store_true', help=('Generate an npz archive from a directory of samples. ' 'The first path is used as input and the second as output.')) parser.add_argument('path', type=str, nargs=2, help=('Paths to the generated images or ' 'to .npz statistic files')) IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm', 'tif', 'tiff', 'webp', 'JPEG'} class ImagePathDataset(torch.utils.data.Dataset): def __init__(self, files, transforms=None): self.files = files self.transforms = transforms def __len__(self): return len(self.files) def __getitem__(self, i): path = self.files[i] img = Image.open(path).convert('RGB') if self.transforms is not None: img = self.transforms(img) return img def get_activations(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, res=None, dataset_name=None): """Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : Batch size of images for the model to process at once. Make sure that the number of samples is a multiple of the batch size, otherwise some samples are ignored. This behavior is retained to match the original FID score implementation. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- A numpy array of dimension (num images, dims) that contains the activations of the given tensor when feeding inception with the query tensor. """ model.eval() if batch_size > len(files): print(('Warning: batch size is bigger than the data size. ' 'Setting batch size to data size')) batch_size = len(files) if res is None: trans = TF.ToTensor() else: if dataset_name == 'celeba': from datasets import Crop cx = 89 cy = 121 x1 = cy - 64 x2 = cy + 64 y1 = cx - 64 y2 = cx + 64 trans = TF.Compose([ Crop(x1, x2, y1, y2), TF.Resize(res), TF.ToTensor(), ]) else: trans = TF.Compose([ TF.Resize(res), TF.CenterCrop(res), TF.ToTensor() ]) dataset = ImagePathDataset(files, transforms=trans) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers) pred_arr = np.empty((len(files), dims)) start_idx = 0 for batch in tqdm(dataloader): batch = batch.to(device) with torch.no_grad(): pred = model(batch)[0] # If model output is not scalar, apply global spatial average pooling. # This happens if you choose a dimensionality not equal 2048. if pred.size(2) != 1 or pred.size(3) != 1: pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) pred = pred.squeeze(3).squeeze(2).cpu().numpy() pred_arr[start_idx:start_idx + pred.shape[0]] = pred start_idx = start_idx + pred.shape[0] return pred_arr def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of a layer of the inception net (like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations, precalculated on an representative data set. -- sigma1: The covariance matrix over activations for generated samples. -- sigma2: The covariance matrix over activations, precalculated on an representative data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, \ 'Training and test mean vectors have different lengths' assert sigma1.shape == sigma2.shape, \ 'Training and test covariances have different dimensions' diff = mu1 - mu2 # Product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ('fid calculation produces singular product; ' 'adding %s to diagonal of cov estimates') % eps print(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError('Imaginary component {}'.format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean) def calculate_activation_statistics(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, res=None, dataset_name=None): """Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : The images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the hardware. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the inception model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the inception model. """ act = get_activations(files, model, batch_size, dims, device, num_workers, res=res, dataset_name=dataset_name) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma def compute_statistics_of_path(path, model, batch_size, dims, device, num_workers=1, num_samples=None, res=None, dataset_name=None): if path.endswith('.npz'): with np.load(path) as f: m, s = f['mu'][:], f['sigma'][:] else: path = pathlib.Path(path) files = sorted([file for ext in IMAGE_EXTENSIONS for file in path.glob('**/*.{}'.format(ext))]) if num_samples is not None: subfolders = [] for f in os.listdir(path): if os.path.isdir(os.path.join(path, f)): subfolders.append(f) imgs_per_subfolder = num_samples // len(subfolders) files = [] for subfolder in subfolders: subfolder_path = os.path.join(path, subfolder) subfolder_files = sorted([file for ext in IMAGE_EXTENSIONS for file in pathlib.Path(subfolder_path).glob('**/*.{}'.format(ext))]) num_images = min(len(subfolder_files), imgs_per_subfolder) files += subfolder_files[:num_images] print("Found %d files." % len(files)) m, s = calculate_activation_statistics(files, model, batch_size, dims, device, num_workers, res=res, dataset_name=dataset_name) return m, s def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1, num_samples=None, res=None, dataset_name=None): """Calculates the FID of two paths""" for p in paths: if not os.path.exists(p): raise RuntimeError('Invalid path: %s' % p) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) m2, s2 = compute_statistics_of_path(paths[1], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) fid_value = calculate_frechet_distance(m1, s1, m2, s2) return fid_value def save_fid_stats(paths, batch_size, device, dims, num_workers=1, num_samples=None, res=None, dataset_name=None): """Calculates the FID of two paths""" if not os.path.exists(paths[0]): raise RuntimeError('Invalid path: %s' % paths[0]) if os.path.exists(paths[1]): raise RuntimeError('Existing output file: %s' % paths[1]) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) print(f"Saving statistics for {paths[0]}") m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, num_samples=num_samples, res=res, dataset_name=dataset_name) np.savez_compressed(paths[1], mu=m1, sigma=s1) def main(): args = parser.parse_args() if args.device is None: device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu') else: device = torch.device(args.device) if args.num_workers is None: try: num_cpus = len(os.sched_getaffinity(0)) except AttributeError: # os.sched_getaffinity is not available under Windows, use # os.cpu_count instead (which may not return the *available* number # of CPUs). num_cpus = os.cpu_count() num_workers = min(num_cpus, 8) if num_cpus is not None else 0 else: num_workers = args.num_workers if args.save_stats: save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers, num_samples=args.num_samples, res=args.res, dataset_name=args.dataset_name) return fid_value = calculate_fid_given_paths(args.path, args.batch_size, device, args.dims, num_workers, num_samples=args.num_samples, res = args.res, dataset_name=args.dataset_name) print('FID: ', fid_value) if __name__ == '__main__': main() ================================================ FILE: ldm_exp/inception.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torchvision try: from torchvision.models.utils import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url # Inception weights ported to Pytorch from # http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 class InceptionV3(nn.Module): """Pretrained InceptionV3 network returning feature maps""" # Index of default block of inception to return, # corresponds to output of final average pooling DEFAULT_BLOCK_INDEX = 3 # Maps feature dimensionality to their output blocks indices BLOCK_INDEX_BY_DIM = { 64: 0, # First max pooling features 192: 1, # Second max pooling featurs 768: 2, # Pre-aux classifier features 2048: 3 # Final average pooling features } def __init__(self, output_blocks=(DEFAULT_BLOCK_INDEX,), resize_input=True, normalize_input=True, requires_grad=False, use_fid_inception=True): """Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, scales the input from range (0, 1) to the range the pretrained Inception network expects, namely (-1, 1) requires_grad : bool If true, parameters of the model require gradients. Possibly useful for finetuning the network use_fid_inception : bool If true, uses the pretrained Inception model used in Tensorflow's FID implementation. If false, uses the pretrained Inception model available in torchvision. The FID Inception model has different weights and a slightly different structure from torchvision's Inception model. If you want to compute FID scores, you are strongly advised to set this parameter to true to get comparable results. """ super(InceptionV3, self).__init__() self.resize_input = resize_input self.normalize_input = normalize_input self.output_blocks = sorted(output_blocks) self.last_needed_block = max(output_blocks) assert self.last_needed_block <= 3, \ 'Last possible output block index is 3' self.blocks = nn.ModuleList() if use_fid_inception: inception = fid_inception_v3() else: inception = _inception_v3(weights='DEFAULT') # Block 0: input to maxpool1 block0 = [ inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block0)) # Block 1: maxpool1 to maxpool2 if self.last_needed_block >= 1: block1 = [ inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block1)) # Block 2: maxpool2 to aux classifier if self.last_needed_block >= 2: block2 = [ inception.Mixed_5b, inception.Mixed_5c, inception.Mixed_5d, inception.Mixed_6a, inception.Mixed_6b, inception.Mixed_6c, inception.Mixed_6d, inception.Mixed_6e, ] self.blocks.append(nn.Sequential(*block2)) # Block 3: aux classifier to final avgpool if self.last_needed_block >= 3: block3 = [ inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, nn.AdaptiveAvgPool2d(output_size=(1, 1)) ] self.blocks.append(nn.Sequential(*block3)) for param in self.parameters(): param.requires_grad = requires_grad def forward(self, inp): """Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index """ outp = [] x = inp if self.resize_input: x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) if self.normalize_input: x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) for idx, block in enumerate(self.blocks): x = block(x) if idx in self.output_blocks: outp.append(x) if idx == self.last_needed_block: break return outp def _inception_v3(*args, **kwargs): """Wraps `torchvision.models.inception_v3`""" try: version = tuple(map(int, torchvision.__version__.split('.')[:2])) except ValueError: # Just a caution against weird version strings version = (0,) # Skips default weight inititialization if supported by torchvision # version. See https://github.com/mseitzer/pytorch-fid/issues/28. if version >= (0, 6): kwargs['init_weights'] = False # Backwards compatibility: `weights` argument was handled by `pretrained` # argument prior to version 0.13. if version < (0, 13) and 'weights' in kwargs: if kwargs['weights'] == 'DEFAULT': kwargs['pretrained'] = True elif kwargs['weights'] is None: kwargs['pretrained'] = False else: raise ValueError( 'weights=={} not supported in torchvision {}'.format( kwargs['weights'], torchvision.__version__ ) ) del kwargs['weights'] return torchvision.models.inception_v3(*args, **kwargs) def fid_inception_v3(): """Build pretrained Inception model for FID computation The Inception model for FID computation uses a different set of weights and has a slightly different structure than torchvision's Inception. This method first constructs torchvision's Inception and then patches the necessary parts that are different in the FID Inception model. """ inception = _inception_v3(num_classes=1008, aux_logits=False, weights=None) inception.Mixed_5b = FIDInceptionA(192, pool_features=32) inception.Mixed_5c = FIDInceptionA(256, pool_features=64) inception.Mixed_5d = FIDInceptionA(288, pool_features=64) inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) inception.Mixed_7b = FIDInceptionE_1(1280) inception.Mixed_7c = FIDInceptionE_2(2048) state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True) inception.load_state_dict(state_dict) return inception class FIDInceptionA(torchvision.models.inception.InceptionA): """InceptionA block patched for FID computation""" def __init__(self, in_channels, pool_features): super(FIDInceptionA, self).__init__(in_channels, pool_features) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionC(torchvision.models.inception.InceptionC): """InceptionC block patched for FID computation""" def __init__(self, in_channels, channels_7x7): super(FIDInceptionC, self).__init__(in_channels, channels_7x7) def forward(self, x): branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionE_1(torchvision.models.inception.InceptionE): """First InceptionE block patched for FID computation""" def __init__(self, in_channels): super(FIDInceptionE_1, self).__init__(in_channels) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) # Patch: Tensorflow's average pool does not use the padded zero's in # its average calculation branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class FIDInceptionE_2(torchvision.models.inception.InceptionE): """Second InceptionE block patched for FID computation""" def __init__(self, in_channels): super(FIDInceptionE_2, self).__init__(in_channels) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) # Patch: The FID Inception model uses max pooling instead of average # pooling. This is likely an error in this specific Inception # implementation, as other Inception models use average pooling here # (which matches the description in the paper). branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) ================================================ FILE: ldm_exp/ldm/lr_scheduler.py ================================================ import numpy as np class LambdaWarmUpCosineScheduler: """ note: use with a base_lr of 1.0 """ def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): self.lr_warm_up_steps = warm_up_steps self.lr_start = lr_start self.lr_min = lr_min self.lr_max = lr_max self.lr_max_decay_steps = max_decay_steps self.last_lr = 0. self.verbosity_interval = verbosity_interval def schedule(self, n, **kwargs): if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") if n < self.lr_warm_up_steps: lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start self.last_lr = lr return lr else: t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) t = min(t, 1.0) lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( 1 + np.cos(t * np.pi)) self.last_lr = lr return lr def __call__(self, n, **kwargs): return self.schedule(n,**kwargs) class LambdaWarmUpCosineScheduler2: """ supports repeated iterations, configurable via lists note: use with a base_lr of 1.0. """ def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) self.lr_warm_up_steps = warm_up_steps self.f_start = f_start self.f_min = f_min self.f_max = f_max self.cycle_lengths = cycle_lengths self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) self.last_f = 0. self.verbosity_interval = verbosity_interval def find_in_interval(self, n): interval = 0 for cl in self.cum_cycles[1:]: if n <= cl: return interval interval += 1 def schedule(self, n, **kwargs): cycle = self.find_in_interval(n) n = n - self.cum_cycles[cycle] if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") if n < self.lr_warm_up_steps[cycle]: f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] self.last_f = f return f else: t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) t = min(t, 1.0) f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( 1 + np.cos(t * np.pi)) self.last_f = f return f def __call__(self, n, **kwargs): return self.schedule(n, **kwargs) class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): def schedule(self, n, **kwargs): cycle = self.find_in_interval(n) n = n - self.cum_cycles[cycle] if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") if n < self.lr_warm_up_steps[cycle]: f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] self.last_f = f return f else: f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) self.last_f = f return f ================================================ FILE: ldm_exp/ldm/models/autoencoder.py ================================================ import torch import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution from ldm.util import instantiate_from_config class VQModel(pl.LightningModule): def __init__(self, ddconfig, lossconfig, n_embed, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, batch_resize_range=None, scheduler_config=None, lr_g_factor=1.0, remap=None, sane_index_shape=False, # tell vector quantizer to return indices as bhw use_ema=False ): super().__init__() self.embed_dim = embed_dim self.n_embed = n_embed self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, remap=remap, sane_index_shape=sane_index_shape) self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor self.batch_resize_range = batch_resize_range if self.batch_resize_range is not None: print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) self.scheduler_config = scheduler_config self.lr_g_factor = lr_g_factor @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: print(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: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") print(f"Unexpected Keys: {unexpected}") def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self) def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) quant, emb_loss, info = self.quantize(h) return quant, emb_loss, info def encode_to_prequant(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, quant): quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec def decode_code(self, code_b): quant_b = self.quantize.embed_code(code_b) dec = self.decode(quant_b) return dec def forward(self, input, return_pred_indices=False): quant, diff, (_,_,ind) = self.encode(input) dec = self.decode(quant) if return_pred_indices: return dec, diff, ind return dec, diff def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() if self.batch_resize_range is not None: lower_size = self.batch_resize_range[0] upper_size = self.batch_resize_range[1] if self.global_step <= 4: # do the first few batches with max size to avoid later oom new_resize = upper_size else: new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) if new_resize != x.shape[2]: x = F.interpolate(x, size=new_resize, mode="bicubic") x = x.detach() return x def training_step(self, batch, batch_idx, optimizer_idx): # https://github.com/pytorch/pytorch/issues/37142 # try not to fool the heuristics x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) if optimizer_idx == 0: # autoencode aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train", predicted_indices=ind) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) return aeloss if optimizer_idx == 1: # discriminator discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) return discloss def validation_step(self, batch, batch_idx): log_dict = self._validation_step(batch, batch_idx) with self.ema_scope(): log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") return log_dict def _validation_step(self, batch, batch_idx, suffix=""): x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] self.log(f"val{suffix}/rec_loss", rec_loss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) self.log(f"val{suffix}/aeloss", aeloss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) if version.parse(pl.__version__) >= version.parse('1.4.0'): del log_dict_ae[f"val{suffix}/rec_loss"] self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def configure_optimizers(self): lr_d = self.learning_rate lr_g = self.lr_g_factor*self.learning_rate print("lr_d", lr_d) print("lr_g", lr_g) opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quantize.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr_g, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)) if self.scheduler_config is not None: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, { 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, ] return [opt_ae, opt_disc], scheduler return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if only_inputs: log["inputs"] = x return log xrec, _ = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["inputs"] = x log["reconstructions"] = xrec if plot_ema: with self.ema_scope(): xrec_ema, _ = self(x) if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) log["reconstructions_ema"] = xrec_ema return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class VQModelInterface(VQModel): def __init__(self, embed_dim, *args, **kwargs): super().__init__(embed_dim=embed_dim, *args, **kwargs) self.embed_dim = embed_dim def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, h, force_not_quantize=False): # also go through quantization layer if not force_not_quantize: quant, emb_loss, info = self.quantize(h) else: quant = h quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec class AutoencoderKL(pl.LightningModule): def __init__(self, ddconfig, lossconfig, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, ): super().__init__() self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] self.load_state_dict(sd, strict=False) print(f"Restored from {path}") def encode(self, x): h = self.encoder(x) moments = self.quant_conv(h) posterior = DiagonalGaussianDistribution(moments) return posterior def decode(self, z): z = self.post_quant_conv(z) dec = self.decoder(z) return dec def forward(self, input, sample_posterior=True): posterior = self.encode(input) if sample_posterior: z = posterior.sample() else: z = posterior.mode() dec = self.decode(z) return dec, posterior def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() return x def training_step(self, batch, batch_idx, optimizer_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) if optimizer_idx == 0: # train encoder+decoder+logvar aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) return aeloss if optimizer_idx == 1: # train the discriminator discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) return discloss def validation_step(self, batch, batch_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, last_layer=self.get_last_layer(), split="val") discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, last_layer=self.get_last_layer(), split="val") self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def configure_optimizers(self): lr = self.learning_rate opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)) return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight @torch.no_grad() def log_images(self, batch, only_inputs=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if not only_inputs: xrec, posterior = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["samples"] = self.decode(torch.randn_like(posterior.sample())) log["reconstructions"] = xrec log["inputs"] = x return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class IdentityFirstStage(torch.nn.Module): def __init__(self, *args, vq_interface=False, **kwargs): self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff super().__init__() def encode(self, x, *args, **kwargs): return x def decode(self, x, *args, **kwargs): return x def quantize(self, x, *args, **kwargs): if self.vq_interface: return x, None, [None, None, None] return x def forward(self, x, *args, **kwargs): return x ================================================ FILE: ldm_exp/ldm/models/diffusion/__init__.py ================================================ ================================================ FILE: ldm_exp/ldm/models/diffusion/classifier.py ================================================ import os import torch import pytorch_lightning as pl from omegaconf import OmegaConf from torch.nn import functional as F from torch.optim import AdamW from torch.optim.lr_scheduler import LambdaLR from copy import deepcopy from einops import rearrange from glob import glob from natsort import natsorted from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config __models__ = { 'class_label': EncoderUNetModel, 'segmentation': UNetModel } def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class NoisyLatentImageClassifier(pl.LightningModule): def __init__(self, diffusion_path, num_classes, ckpt_path=None, pool='attention', label_key=None, diffusion_ckpt_path=None, scheduler_config=None, weight_decay=1.e-2, log_steps=10, monitor='val/loss', *args, **kwargs): super().__init__(*args, **kwargs) self.num_classes = num_classes # get latest config of diffusion model diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] self.diffusion_config = OmegaConf.load(diffusion_config).model self.diffusion_config.params.ckpt_path = diffusion_ckpt_path self.load_diffusion() self.monitor = monitor self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 self.log_time_interval = self.diffusion_model.num_timesteps // log_steps self.log_steps = log_steps self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ else self.diffusion_model.cond_stage_key assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' if self.label_key not in __models__: raise NotImplementedError() self.load_classifier(ckpt_path, pool) self.scheduler_config = scheduler_config self.use_scheduler = self.scheduler_config is not None self.weight_decay = weight_decay def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") def load_diffusion(self): model = instantiate_from_config(self.diffusion_config) self.diffusion_model = model.eval() self.diffusion_model.train = disabled_train for param in self.diffusion_model.parameters(): param.requires_grad = False def load_classifier(self, ckpt_path, pool): model_config = deepcopy(self.diffusion_config.params.unet_config.params) model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels model_config.out_channels = self.num_classes if self.label_key == 'class_label': model_config.pool = pool self.model = __models__[self.label_key](**model_config) if ckpt_path is not None: print('#####################################################################') print(f'load from ckpt "{ckpt_path}"') print('#####################################################################') self.init_from_ckpt(ckpt_path) @torch.no_grad() def get_x_noisy(self, x, t, noise=None): noise = default(noise, lambda: torch.randn_like(x)) continuous_sqrt_alpha_cumprod = None if self.diffusion_model.use_continuous_noise: continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) # todo: make sure t+1 is correct here return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) def forward(self, x_noisy, t, *args, **kwargs): return self.model(x_noisy, t) @torch.no_grad() def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x @torch.no_grad() def get_conditioning(self, batch, k=None): if k is None: k = self.label_key assert k is not None, 'Needs to provide label key' targets = batch[k].to(self.device) if self.label_key == 'segmentation': targets = rearrange(targets, 'b h w c -> b c h w') for down in range(self.numd): h, w = targets.shape[-2:] targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') # targets = rearrange(targets,'b c h w -> b h w c') return targets def compute_top_k(self, logits, labels, k, reduction="mean"): _, top_ks = torch.topk(logits, k, dim=1) if reduction == "mean": return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() elif reduction == "none": return (top_ks == labels[:, None]).float().sum(dim=-1) def on_train_epoch_start(self): # save some memory self.diffusion_model.model.to('cpu') @torch.no_grad() def write_logs(self, loss, logits, targets): log_prefix = 'train' if self.training else 'val' log = {} log[f"{log_prefix}/loss"] = loss.mean() log[f"{log_prefix}/acc@1"] = self.compute_top_k( logits, targets, k=1, reduction="mean" ) log[f"{log_prefix}/acc@5"] = self.compute_top_k( logits, targets, k=5, reduction="mean" ) self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) def shared_step(self, batch, t=None): x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) targets = self.get_conditioning(batch) if targets.dim() == 4: targets = targets.argmax(dim=1) if t is None: t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() else: t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() x_noisy = self.get_x_noisy(x, t) logits = self(x_noisy, t) loss = F.cross_entropy(logits, targets, reduction='none') self.write_logs(loss.detach(), logits.detach(), targets.detach()) loss = loss.mean() return loss, logits, x_noisy, targets def training_step(self, batch, batch_idx): loss, *_ = self.shared_step(batch) return loss def reset_noise_accs(self): self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} def on_validation_start(self): self.reset_noise_accs() @torch.no_grad() def validation_step(self, batch, batch_idx): loss, *_ = self.shared_step(batch) for t in self.noisy_acc: _, logits, _, targets = self.shared_step(batch, t) self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) return loss def configure_optimizers(self): optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) if self.use_scheduler: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [optimizer], scheduler return optimizer @torch.no_grad() def log_images(self, batch, N=8, *args, **kwargs): log = dict() x = self.get_input(batch, self.diffusion_model.first_stage_key) log['inputs'] = x y = self.get_conditioning(batch) if self.label_key == 'class_label': y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['labels'] = y if ismap(y): log['labels'] = self.diffusion_model.to_rgb(y) for step in range(self.log_steps): current_time = step * self.log_time_interval _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) log[f'inputs@t{current_time}'] = x_noisy pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) pred = rearrange(pred, 'b h w c -> b c h w') log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) for key in log: log[key] = log[key][:N] return log ================================================ FILE: ldm_exp/ldm/models/diffusion/ddim.py ================================================ """SAMPLING ONLY.""" import torch import numpy as np from tqdm import tqdm from functools import partial from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like class DDIMSampler(object): def __init__(self, model, schedule="linear", **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device("cuda"): attr = attr.to(torch.device("cuda")) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.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.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): cbs = conditioning[list(conditioning.keys())[0]].shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) print(f'Data shape for DDIM sampling is {size}, eta {eta}') samples, intermediates = self.ddim_sampling(conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, ) return samples, intermediates @torch.no_grad() def ddim_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None,): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running DDIM Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) img, pred_x0 = outs if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None): b, *_, device = *x.shape, x.device if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 ================================================ FILE: ldm_exp/ldm/models/diffusion/ddpm.py ================================================ """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager from functools import partial from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.distributed import rank_zero_only from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self def uniform_on_device(r1, r2, shape, device): return (r1 - r2) * torch.rand(*shape, device=device) + r2 class DDPM(pl.LightningModule): # classic DDPM with Gaussian diffusion, in image space def __init__(self, unet_config, timesteps=1000, beta_schedule="linear", loss_type="l2", ckpt_path=None, ignore_keys=[], load_only_unet=False, monitor="val/loss", use_ema=True, first_stage_key="image", image_size=256, channels=3, log_every_t=100, clip_denoised=True, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, given_betas=None, original_elbo_weight=0., v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta l_simple_weight=1., conditioning_key=None, parameterization="eps", # all assuming fixed variance schedules scheduler_config=None, use_positional_encodings=False, learn_logvar=False, logvar_init=0., ): super().__init__() assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") self.cond_stage_model = None self.clip_denoised = clip_denoised self.log_every_t = log_every_t self.first_stage_key = first_stage_key self.image_size = image_size # try conv? self.channels = channels self.use_positional_encodings = use_positional_encodings self.model = DiffusionWrapper(unet_config, conditioning_key) count_params(self.model, verbose=True) self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self.model) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") self.use_scheduler = scheduler_config is not None if self.use_scheduler: self.scheduler_config = scheduler_config self.v_posterior = v_posterior self.original_elbo_weight = original_elbo_weight self.l_simple_weight = l_simple_weight if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) self.loss_type = loss_type self.learn_logvar = learn_logvar self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) if self.learn_logvar: self.logvar = nn.Parameter(self.logvar, requires_grad=True) def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if exists(given_betas): 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 = 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))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( 1. - alphas_cumprod) + self.v_posterior * betas # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) if self.parameterization == "eps": lvlb_weights = self.betas ** 2 / ( 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) elif self.parameterization == "x0": lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) else: raise NotImplementedError("mu not supported") # TODO how to choose this term lvlb_weights[0] = lvlb_weights[1] self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) assert not torch.isnan(self.lvlb_weights).all() @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.model.parameters()) self.model_ema.copy_to(self.model) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.model.parameters()) if context is not None: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): """ Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diffusion steps (minus 1). Here, 0 means one step. :return: A tuple (mean, variance, log_variance), all of x_start's shape. """ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, clip_denoised: bool): model_out = self.model(x, t) if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def p_sample_loop(self, shape, return_intermediates=False): device = self.betas.device b = shape[0] img = torch.randn(shape, device=device) intermediates = [img] for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), clip_denoised=self.clip_denoised) if i % self.log_every_t == 0 or i == self.num_timesteps - 1: intermediates.append(img) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, batch_size=16, return_intermediates=False): image_size = self.image_size channels = self.channels return self.p_sample_loop((batch_size, channels, image_size, image_size), return_intermediates=return_intermediates) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) def get_loss(self, pred, target, mean=True): if self.loss_type == 'l1': loss = (target - pred).abs() if mean: loss = loss.mean() elif self.loss_type == 'l2': if mean: loss = torch.nn.functional.mse_loss(target, pred) else: loss = torch.nn.functional.mse_loss(target, pred, reduction='none') else: raise NotImplementedError("unknown loss type '{loss_type}'") return loss def p_losses(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_out = self.model(x_noisy, t) loss_dict = {} if self.parameterization == "eps": target = noise elif self.parameterization == "x0": target = x_start else: raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) log_prefix = 'train' if self.training else 'val' loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) loss_simple = loss.mean() * self.l_simple_weight loss_vlb = (self.lvlb_weights[t] * loss).mean() loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) loss = loss_simple + self.original_elbo_weight * loss_vlb loss_dict.update({f'{log_prefix}/loss': loss}) return loss, loss_dict def forward(self, x, *args, **kwargs): # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() return self.p_losses(x, t, *args, **kwargs) def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x def shared_step(self, batch): x = self.get_input(batch, self.first_stage_key) loss, loss_dict = self(x) return loss, loss_dict def training_step(self, batch, batch_idx): loss, loss_dict = self.shared_step(batch) self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False) if self.use_scheduler: lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) return loss @torch.no_grad() def validation_step(self, batch, batch_idx): _, loss_dict_no_ema = self.shared_step(batch) with self.ema_scope(): _, loss_dict_ema = self.shared_step(batch) loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self.model) def _get_rows_from_list(self, samples): n_imgs_per_row = len(samples) denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid @torch.no_grad() def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): log = dict() x = self.get_input(batch, self.first_stage_key) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) x = x.to(self.device)[:N] log["inputs"] = x # get diffusion row diffusion_row = list() x_start = x[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(x_start) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) diffusion_row.append(x_noisy) log["diffusion_row"] = self._get_rows_from_list(diffusion_row) if sample: # get denoise row with self.ema_scope("Plotting"): samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) log["samples"] = samples log["denoise_row"] = self._get_rows_from_list(denoise_row) if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.learn_logvar: params = params + [self.logvar] opt = torch.optim.AdamW(params, lr=lr) return opt class LatentDiffusion(DDPM): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, cond_stage_forward=None, conditioning_key=None, scale_factor=1.0, scale_by_std=False, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(conditioning_key=conditioning_key, *args, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor else: self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) self.restarted_from_ckpt = True def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def instantiate_first_stage(self, config): model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): param.requires_grad = False def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__": print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior): if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() elif isinstance(encoder_posterior, torch.Tensor): z = encoder_posterior else: raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") return self.scale_factor * z def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): c = self.cond_stage_model.encode(c) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() else: c = self.cond_stage_model(c) else: assert hasattr(self.cond_stage_model, self.cond_stage_forward) c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c def meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None): x = super().get_input(batch, k) if bs is not None: x = x[:bs] x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() if self.model.conditioning_key is not None: if cond_key is None: cond_key = self.cond_stage_key if cond_key != self.first_stage_key: if cond_key in ['caption', 'coordinates_bbox']: xc = batch[cond_key] elif cond_key == 'class_label': xc = batch else: xc = super().get_input(batch, cond_key).to(self.device) else: xc = x if not self.cond_stage_trainable or force_c_encode: if isinstance(xc, dict) or isinstance(xc, list): # import pudb; pudb.set_trace() c = self.get_learned_conditioning(xc) else: c = self.get_learned_conditioning(xc.to(self.device)) else: c = xc if bs is not None: c = c[:bs] if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) ckey = __conditioning_keys__[self.model.conditioning_key] c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} else: c = None xc = None if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) c = {'pos_x': pos_x, 'pos_y': pos_y} out = [z, c] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # same as above but without decorator def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) @torch.no_grad() def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key) loss = self(x, c) return loss def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable: c = self.get_learned_conditioning(c) if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def get_loss_at_t(self, x, c, t, *args, **kwargs): if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable: c = self.get_learned_conditioning(c) if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset def rescale_bbox(bbox): x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) w = min(bbox[2] / crop_coordinates[2], 1 - x0) h = min(bbox[3] / crop_coordinates[3], 1 - y0) return x0, y0, w, h return [rescale_bbox(b) for b in bboxes] def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict pass else: if not isinstance(cond, list): cond = [cond] key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left postions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) print(patch_limits_tknzd[0].shape) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) print(cut_cond.shape) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') print(adapted_cond.shape) adapted_cond = self.get_learned_conditioning(adapted_cond) print(adapted_cond.shape) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) print(adapted_cond.shape) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: x_recon = self.model(x_noisy, t, **cond) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) self.logvar = self.logvar.to(self.device) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) if return_codebook_ids: model_out, logits = model_out if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out else: raise NotImplementedError() if clip_denoised: x_recon.clamp_(-1., 1.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) if return_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 is not None img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T intermediates = [img] if timesteps is None: timesteps = self.num_timesteps if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( range(0, timesteps)) if mask is not None: assert x0 is not None assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match for i in iterator: ts = torch.full((b,), i, device=device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) if callback: callback(i) if img_callback: img_callback(img, i) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.image_size, self.image_size) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.image_size, self.image_size) samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, shape,cond,verbose=False,**kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True,**kwargs) return samples, intermediates @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, **kwargs): use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with self.ema_scope("Plotting"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with self.ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if inpaint: # make a simple center square b, h, w = z.shape[0], z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...] with self.ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask"] = mask # outpaint with self.ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples if plot_progressive_rows: with self.ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.image_size, self.image_size), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.diffusion_model = instantiate_from_config(diff_model_config) self.conditioning_key = conditioning_key assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): if self.conditioning_key is None: out = self.diffusion_model(x, t) elif self.conditioning_key == 'concat': xc = torch.cat([x] + c_concat, dim=1) out = self.diffusion_model(xc, t) elif self.conditioning_key == 'crossattn': cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'hybrid': xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(xc, t, context=cc) elif self.conditioning_key == 'adm': cc = c_crossattn[0] out = self.diffusion_model(x, t, y=cc) else: raise NotImplementedError() return out class Layout2ImgDiffusion(LatentDiffusion): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): logs = super().log_images(batch=batch, N=N, *args, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] mapper = dset.conditional_builders[self.cond_stage_key] bbox_imgs = [] map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) for tknzd_bbox in batch[self.cond_stage_key][:N]: bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) bbox_imgs.append(bboximg) cond_img = torch.stack(bbox_imgs, dim=0) logs['bbox_image'] = cond_img return logs ================================================ FILE: ldm_exp/ldm/models/diffusion/plms.py ================================================ """SAMPLING ONLY.""" import torch import numpy as np from tqdm import tqdm from functools import partial from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like class PLMSSampler(object): def __init__(self, model, schedule="linear", **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device("cuda"): attr = attr.to(torch.device("cuda")) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): if ddim_eta != 0: raise ValueError('ddim_eta must be 0 for PLMS') self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.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.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): cbs = conditioning[list(conditioning.keys())[0]].shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) print(f'Data shape for PLMS sampling is {size}') samples, intermediates = self.plms_sampling(conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, ) return samples, intermediates @torch.no_grad() def plms_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None,): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running PLMS Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) old_eps = [] for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, old_eps=old_eps, t_next=ts_next) img, pred_x0, e_t = outs old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): b, *_, device = *x.shape, x.device def get_model_output(x, t): if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) return e_t alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas def get_x_prev_and_pred_x0(e_t, index): # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 e_t = get_model_output(x, t) if len(old_eps) == 0: # Pseudo Improved Euler (2nd order) x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) e_t_next = get_model_output(x_prev, t_next) e_t_prime = (e_t + e_t_next) / 2 elif len(old_eps) == 1: # 2nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (3 * e_t - old_eps[-1]) / 2 elif len(old_eps) == 2: # 3nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 elif len(old_eps) >= 3: # 4nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) return x_prev, pred_x0, e_t ================================================ FILE: ldm_exp/ldm/modules/__init__.py ================================================ from . import attention ================================================ FILE: ldm_exp/ldm/modules/attention.py ================================================ from inspect import isfunction import math import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from ldm.modules.diffusionmodules.util import checkpoint def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d() if isfunction(d) else d def max_neg_value(t): return -torch.finfo(t.dtype).max def init_(tensor): dim = tensor.shape[-1] std = 1 / math.sqrt(dim) tensor.uniform_(-std, std) return tensor # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) 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.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) 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 Normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) class LinearAttention(nn.Module): def __init__(self, dim, heads=4, dim_head=32): super().__init__() self.heads = heads hidden_dim = dim_head * heads self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) self.to_out = nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): b, c, h, w = x.shape qkv = self.to_qkv(x) q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) k = k.softmax(dim=-1) context = torch.einsum('bhdn,bhen->bhde', k, v) out = torch.einsum('bhde,bhdn->bhen', context, q) out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) return self.to_out(out) class SpatialSelfAttention(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.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 = rearrange(q, 'b c h w -> b (h w) c') k = rearrange(k, 'b c h w -> b c (h w)') w_ = torch.einsum('bij,bjk->bik', q, k) w_ = w_ * (int(c)**(-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = rearrange(v, 'b c h w -> b c (h w)') w_ = rearrange(w_, 'b i j -> b j i') h_ = torch.einsum('bij,bjk->bik', v, w_) h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) h_ = self.proj_out(h_) return x+h_ class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.scale = dim_head ** -0.5 self.heads = heads self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_out = nn.Sequential( nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) ) def forward(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) k = self.to_k(context) v = self.to_v(context) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) sim = einsum('b i d, b j d -> b i j', q, k) * self.scale if exists(mask): mask = rearrange(mask, 'b ... -> b (...)') 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) # attention, what we cannot get enough of attn = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', attn, v) out = rearrange(out, '(b h) n d -> b n (h d)', h=h) 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): super().__init__() self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint def forward(self, x, context=None): x = self.attn1(self.norm1(x)) + x x = self.attn2(self.norm2(x), context=context) + x x = self.ff(self.norm3(x)) + 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 """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None): super().__init__() self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = Normalize(in_channels) self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) for d in range(depth)] ) self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) def forward(self, x, context=None): # note: if no context is given, cross-attention defaults to self-attention b, c, h, w = x.shape x_in = x x = self.norm(x) x = self.proj_in(x) x = rearrange(x, 'b c h w -> b (h w) c') for block in self.transformer_blocks: x = block(x, context=context) x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w) x = self.proj_out(x) return x + x_in ================================================ FILE: ldm_exp/ldm/modules/diffusionmodules/__init__.py ================================================ ================================================ FILE: ldm_exp/ldm/modules/diffusionmodules/model.py ================================================ # pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np from einops import rearrange from ldm.util import instantiate_from_config from ldm.modules.attention import LinearAttention 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 x*torch.sigmoid(x) def Normalize(in_channels, num_groups=32): return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") if self.with_conv: x = self.conv(x) return x class Downsample(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.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: pad = (0,1,0,1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) 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, temb_channels=512): 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.norm1 = Normalize(in_channels) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: self.temb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout) self.conv2 = torch.nn.Conv2d(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 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb): h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] h = self.norm2(h) h = nonlinearity(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 LinAttnBlock(LinearAttention): """to match AttnBlock usage""" def __init__(self, in_channels): super().__init__(dim=in_channels, heads=1, dim_head=in_channels) class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.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) q = q.permute(0,2,1) # b,hw,c k = k.reshape(b,c,h*w) # b,c,hw w_ = torch.bmm(q,k) # 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) w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) h_ = torch.bmm(v,w_) # 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) h_ = self.proj_out(h_) return x+h_ def make_attn(in_channels, attn_type="vanilla"): assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' print(f"making attention of type '{attn_type}' with {in_channels} in_channels") if attn_type == "vanilla": return AttnBlock(in_channels) elif attn_type == "none": return nn.Identity(in_channels) else: return LinAttnBlock(in_channels) 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([ torch.nn.Linear(self.ch, self.temb_ch), torch.nn.Linear(self.temb_ch, self.temb_ch), ]) # downsampling self.conv_in = torch.nn.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 = torch.nn.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", **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 # downsampling self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=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)) 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) # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(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 # 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) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) 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, give_pre_end=False, tanh_out=False, use_linear_attn=False, attn_type="vanilla", **ignorekwargs): 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.give_pre_end = give_pre_end self.tanh_out = tanh_out # compute in_ch_mult, block_in and curr_res at lowest res in_ch_mult = (1,)+tuple(ch_mult) 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) print("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) # 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] for i_block 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)) 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 = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, z): #assert z.shape[1:] == self.z_shape[1:] self.last_z_shape = z.shape # timestep embedding temb = None # z to block_in h = self.conv_in(z) # middle 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](h, 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 if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) if self.tanh_out: h = torch.tanh(h) return h class SimpleDecoder(nn.Module): def __init__(self, in_channels, out_channels, *args, **kwargs): super().__init__() self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), ResnetBlock(in_channels=in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=2 * in_channels, out_channels=4 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=4 * in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), nn.Conv2d(2*in_channels, in_channels, 1), Upsample(in_channels, with_conv=True)]) # end self.norm_out = Normalize(in_channels) self.conv_out = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): for i, layer in enumerate(self.model): if i in [1,2,3]: x = layer(x, None) else: x = layer(x) h = self.norm_out(x) h = nonlinearity(h) x = self.conv_out(h) return x class UpsampleDecoder(nn.Module): def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0): super().__init__() # upsampling self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks block_in = in_channels curr_res = resolution // 2 ** (self.num_resolutions - 1) self.res_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() for i_level in range(self.num_resolutions): res_block = [] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): res_block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out self.res_blocks.append(nn.ModuleList(res_block)) if i_level != self.num_resolutions - 1: self.upsample_blocks.append(Upsample(block_in, True)) curr_res = curr_res * 2 # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # upsampling h = x for k, i_level in enumerate(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.res_blocks[i_level][i_block](h, None) if i_level != self.num_resolutions - 1: h = self.upsample_blocks[k](h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class LatentRescaler(nn.Module): def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): super().__init__() # residual block, interpolate, residual block self.factor = factor self.conv_in = nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1) self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.attn = AttnBlock(mid_channels) self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.conv_out = nn.Conv2d(mid_channels, out_channels, kernel_size=1, ) def forward(self, x): x = self.conv_in(x) for block in self.res_block1: x = block(x, None) x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) x = self.attn(x) for block in self.res_block2: x = block(x, None) x = self.conv_out(x) return x class MergedRescaleEncoder(nn.Module): def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): super().__init__() intermediate_chn = ch * ch_mult[-1] self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, z_channels=intermediate_chn, double_z=False, resolution=resolution, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, out_ch=None) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) def forward(self, x): x = self.encoder(x) x = self.rescaler(x) return x class MergedRescaleDecoder(nn.Module): def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): super().__init__() tmp_chn = z_channels*ch_mult[-1] self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, ch_mult=ch_mult, resolution=resolution, ch=ch) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, out_channels=tmp_chn, depth=rescale_module_depth) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Upsampler(nn.Module): def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): super().__init__() assert out_size >= in_size num_blocks = int(np.log2(out_size//in_size))+1 factor_up = 1.+ (out_size % in_size) print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, out_channels=in_channels) self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, attn_resolutions=[], in_channels=None, ch=in_channels, ch_mult=[ch_mult for _ in range(num_blocks)]) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Resize(nn.Module): def __init__(self, in_channels=None, learned=False, mode="bilinear"): super().__init__() self.with_conv = learned self.mode = mode if self.with_conv: print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") raise NotImplementedError() assert in_channels is not None # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=4, stride=2, padding=1) def forward(self, x, scale_factor=1.0): if scale_factor==1.0: return x else: x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) return x class FirstStagePostProcessor(nn.Module): def __init__(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=False, n_channels=None, dropout=0., pretrained_config=None): super().__init__() if pretrained_config is None: assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.pretrained_model = pretrained_model else: assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.instantiate_pretrained(pretrained_config) self.do_reshape = reshape if n_channels is None: n_channels = self.pretrained_model.encoder.ch self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, stride=1,padding=1) blocks = [] downs = [] ch_in = n_channels for m in ch_mult: blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) ch_in = m * n_channels downs.append(Downsample(ch_in, with_conv=False)) self.model = nn.ModuleList(blocks) self.downsampler = nn.ModuleList(downs) def instantiate_pretrained(self, config): model = instantiate_from_config(config) self.pretrained_model = model.eval() # self.pretrained_model.train = False for param in self.pretrained_model.parameters(): param.requires_grad = False @torch.no_grad() def encode_with_pretrained(self,x): c = self.pretrained_model.encode(x) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() return c def forward(self,x): z_fs = self.encode_with_pretrained(x) z = self.proj_norm(z_fs) z = self.proj(z) z = nonlinearity(z) for submodel, downmodel in zip(self.model,self.downsampler): z = submodel(z,temb=None) z = downmodel(z) if self.do_reshape: z = rearrange(z,'b c h w -> b (h w) c') return z ================================================ FILE: ldm_exp/ldm/modules/diffusionmodules/openaimodel.py ================================================ from abc import abstractmethod from functools import partial import math from typing import Iterable import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F from ldm.modules.diffusionmodules.util import ( checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding, ) from ldm.modules.attention import SpatialTransformer # dummy replace def convert_module_to_f16(x): pass def convert_module_to_f32(x): pass ## go class AttentionPool2d(nn.Module): """ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py """ def __init__( self, spacial_dim: int, embed_dim: int, num_heads_channels: int, output_dim: int = None, ): super().__init__() self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) self.num_heads = embed_dim // num_heads_channels self.attention = QKVAttention(self.num_heads) def forward(self, x): b, c, *_spatial = x.shape x = x.reshape(b, c, -1) # NC(HW) x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) x = self.qkv_proj(x) x = self.attention(x) x = self.c_proj(x) return x[:, :, 0] 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. """ 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, x, emb, context=None): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer): x = layer(x, context) else: x = layer(x) return x 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): 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 = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) def forward(self, x): #assert x.shape[1] == self.channels if self.dims == 3: x = F.interpolate( x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" ) else: x = F.interpolate(x, scale_factor=2, mode="nearest") if self.use_conv: x = self.conv(x) return x class TransposedUpsample(nn.Module): 'Learned 2x upsampling without padding' def __init__(self, channels, out_channels=None, ks=5): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) def forward(self,x): return self.up(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): 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 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, ): 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.in_layers = nn.Sequential( normalization(channels), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 3, padding=1), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims) self.x_upd = Upsample(channels, False, dims) elif down: self.h_upd = Downsample(channels, False, dims) self.x_upd = Downsample(channels, False, dims) else: self.h_upd = self.x_upd = nn.Identity() self.emb_layers = nn.Sequential( nn.SiLU(), linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( dims, channels, self.out_channels, 3, padding=1 ) else: self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) 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 = 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:] scale, shift = th.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class AttentionBlock(nn.Module): """ An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted to the N-d case. https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. """ def __init__( self, channels, num_heads=1, num_head_channels=-1, use_checkpoint=False, use_new_attention_order=False, ): super().__init__() self.channels = channels if num_head_channels == -1: self.num_heads = num_heads else: assert ( channels % num_head_channels == 0 ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" self.num_heads = channels // num_head_channels self.use_checkpoint = use_checkpoint self.norm = normalization(channels) self.qkv = conv_nd(1, channels, channels * 3, 1) if use_new_attention_order: # split qkv before split heads self.attention = QKVAttention(self.num_heads) else: # split heads before split qkv self.attention = QKVAttentionLegacy(self.num_heads) self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) def forward(self, x): return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! #return pt_checkpoint(self._forward, x) # pytorch def _forward(self, x): b, c, *spatial = x.shape x = x.reshape(b, c, -1) qkv = self.qkv(self.norm(x)) h = self.attention(qkv) h = self.proj_out(h) return (x + h).reshape(b, c, *spatial) def count_flops_attn(model, _x, y): """ A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, ) """ b, c, *spatial = y[0].shape num_spatial = int(np.prod(spatial)) # We perform two matmuls with the same number of ops. # The first computes the weight matrix, the second computes # the combination of the value vectors. matmul_ops = 2 * b * (num_spatial ** 2) * c model.total_ops += th.DoubleTensor([matmul_ops]) class QKVAttentionLegacy(nn.Module): """ A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping """ def __init__(self, n_heads): super().__init__() self.n_heads = n_heads def forward(self, qkv): """ Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after attention. """ bs, width, length = qkv.shape assert width % (3 * self.n_heads) == 0 ch = width // (3 * self.n_heads) q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) scale = 1 / math.sqrt(math.sqrt(ch)) weight = th.einsum( "bct,bcs->bts", q * scale, k * scale ) # More stable with f16 than dividing afterwards weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) a = th.einsum("bts,bcs->bct", weight, v) return a.reshape(bs, -1, length) @staticmethod def count_flops(model, _x, y): return count_flops_attn(model, _x, y) class QKVAttention(nn.Module): """ A module which performs QKV attention and splits in a different order. """ def __init__(self, n_heads): super().__init__() self.n_heads = n_heads def forward(self, qkv): """ Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after attention. """ bs, width, length = qkv.shape assert width % (3 * self.n_heads) == 0 ch = width // (3 * self.n_heads) q, k, v = qkv.chunk(3, dim=1) scale = 1 / math.sqrt(math.sqrt(ch)) weight = th.einsum( "bct,bcs->bts", (q * scale).view(bs * self.n_heads, ch, length), (k * scale).view(bs * self.n_heads, ch, length), ) # More stable with f16 than dividing afterwards weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) return a.reshape(bs, -1, length) @staticmethod def count_flops(model, _x, y): return count_flops_attn(model, _x, y) 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 attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :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, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, 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, ): super().__init__() 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.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions 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 = th.float16 if use_fp16 else th.float32 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( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if self.num_classes is not None: self.label_emb = nn.Embedding(num_classes, time_embed_dim) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): 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, ) ] ch = mult * model_channels if ds in attention_resolutions: 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 layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ) ) 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( 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, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) 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 self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(num_res_blocks + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = model_channels * mult if ds in attention_resolutions: 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 layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads_upsample, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ) ) if level and i == num_res_blocks: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( normalization(ch), conv_nd(dims, model_channels, n_embed, 1), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) self.output_blocks.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32) def forward(self, x, timesteps=None, context=None, y=None,**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. """ 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) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape == (x.shape[0],) emb = emb + self.label_emb(y) h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb, context) hs.append(h) h = self.middle_block(h, emb, context) for module in self.output_blocks: h = th.cat([h, hs.pop()], dim=1) h = module(h, emb, context) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) class EncoderUNetModel(nn.Module): """ The half UNet model with attention and timestep embedding. For usage, see UNet. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, use_checkpoint=False, use_fp16=False, num_heads=1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, pool="adaptive", *args, **kwargs ): super().__init__() if num_heads_upsample == -1: num_heads_upsample = num_heads self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): 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, ) ] ch = mult * model_channels if ds in attention_resolutions: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ) ) 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( 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, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.pool = pool if pool == "adaptive": self.out = nn.Sequential( normalization(ch), nn.SiLU(), nn.AdaptiveAvgPool2d((1, 1)), zero_module(conv_nd(dims, ch, out_channels, 1)), nn.Flatten(), ) elif pool == "attention": assert num_head_channels != -1 self.out = nn.Sequential( normalization(ch), nn.SiLU(), AttentionPool2d( (image_size // ds), ch, num_head_channels, out_channels ), ) elif pool == "spatial": self.out = nn.Sequential( nn.Linear(self._feature_size, 2048), nn.ReLU(), nn.Linear(2048, self.out_channels), ) elif pool == "spatial_v2": self.out = nn.Sequential( nn.Linear(self._feature_size, 2048), normalization(2048), nn.SiLU(), nn.Linear(2048, self.out_channels), ) else: raise NotImplementedError(f"Unexpected {pool} pooling") def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) def forward(self, x, timesteps): """ 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. :return: an [N x K] Tensor of outputs. """ emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) results = [] h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb) if self.pool.startswith("spatial"): results.append(h.type(x.dtype).mean(dim=(2, 3))) h = self.middle_block(h, emb) if self.pool.startswith("spatial"): results.append(h.type(x.dtype).mean(dim=(2, 3))) h = th.cat(results, axis=-1) return self.out(h) else: h = h.type(x.dtype) return self.out(h) ================================================ FILE: ldm_exp/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 os import math import torch import torch.nn as nn import numpy as np from einops import repeat from ldm.util import instantiate_from_config 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 = np.clip(betas, a_min=0, a_max=0.999) 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.numpy() 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: print(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: print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') print(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:]) 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(): # 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) / half ).to(device=timesteps.device) 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 normalization(channels): """ Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization. """ return GroupNorm32(32, channels) # PyTorch 1.7 has SiLU, but we support PyTorch 1.5. class SiLU(nn.Module): def forward(self, x): return x * torch.sigmoid(x) class GroupNorm32(nn.GroupNorm): def forward(self, x): return super().forward(x.float()).type(x.dtype) 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 linear(*args, **kwargs): """ Create a linear module. """ return nn.Linear(*args, **kwargs) 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: ldm_exp/ldm/modules/distributions/__init__.py ================================================ ================================================ FILE: ldm_exp/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).to(device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape).to(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: ldm_exp/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 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 not key 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 not key 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: ldm_exp/ldm/modules/encoders/__init__.py ================================================ ================================================ FILE: ldm_exp/ldm/modules/encoders/modules.py ================================================ import torch import torch.nn as nn from functools import partial import clip from einops import rearrange, repeat import kornia from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test class AbstractEncoder(nn.Module): def __init__(self): super().__init__() def encode(self, *args, **kwargs): raise NotImplementedError class ClassEmbedder(nn.Module): def __init__(self, embed_dim, n_classes=1000, key='class'): super().__init__() self.key = key self.embedding = nn.Embedding(n_classes, embed_dim) def forward(self, batch, key=None): if key is None: key = self.key # this is for use in crossattn c = batch[key][:, None] c = self.embedding(c) return c class TransformerEmbedder(AbstractEncoder): """Some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): super().__init__() self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer)) def forward(self, tokens): tokens = tokens.to(self.device) # meh z = self.transformer(tokens, return_embeddings=True) return z def encode(self, x): return self(x) class BERTTokenizer(AbstractEncoder): """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" def __init__(self, device="cuda", vq_interface=True, max_length=77): super().__init__() from transformers import BertTokenizerFast # TODO: add to reuquirements self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") self.device = device self.vq_interface = vq_interface self.max_length = max_length def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) return tokens @torch.no_grad() def encode(self, text): tokens = self(text) if not self.vq_interface: return tokens return None, None, [None, None, tokens] def decode(self, text): return text class BERTEmbedder(AbstractEncoder): """Uses the BERT tokenizr model and add some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, device="cuda",use_tokenizer=True, embedding_dropout=0.0): super().__init__() self.use_tknz_fn = use_tokenizer if self.use_tknz_fn: self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer), emb_dropout=embedding_dropout) def forward(self, text): if self.use_tknz_fn: tokens = self.tknz_fn(text)#.to(self.device) else: tokens = text z = self.transformer(tokens, return_embeddings=True) return z def encode(self, text): # output of length 77 return self(text) class SpatialRescaler(nn.Module): def __init__(self, n_stages=1, method='bilinear', multiplier=0.5, in_channels=3, out_channels=None, bias=False): super().__init__() self.n_stages = n_stages assert self.n_stages >= 0 assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] self.multiplier = multiplier self.interpolator = partial(torch.nn.functional.interpolate, mode=method) self.remap_output = out_channels is not None if self.remap_output: print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) def forward(self,x): for stage in range(self.n_stages): x = self.interpolator(x, scale_factor=self.multiplier) if self.remap_output: x = self.channel_mapper(x) return x def encode(self, x): return self(x) class FrozenCLIPTextEmbedder(nn.Module): """ Uses the CLIP transformer encoder for text. """ def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True): super().__init__() self.model, _ = clip.load(version, jit=False, device="cpu") self.device = device self.max_length = max_length self.n_repeat = n_repeat self.normalize = normalize def freeze(self): self.model = self.model.eval() for param in self.parameters(): param.requires_grad = False def forward(self, text): tokens = clip.tokenize(text).to(self.device) z = self.model.encode_text(tokens) if self.normalize: z = z / torch.linalg.norm(z, dim=1, keepdim=True) return z def encode(self, text): z = self(text) if z.ndim==2: z = z[:, None, :] z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat) return z class FrozenClipImageEmbedder(nn.Module): """ Uses the CLIP image encoder. """ def __init__( self, model, jit=False, device='cuda' if torch.cuda.is_available() else 'cpu', antialias=False, ): super().__init__() self.model, _ = clip.load(name=model, device=device, jit=jit) self.antialias = antialias self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) def preprocess(self, x): # normalize to [0,1] x = kornia.geometry.resize(x, (224, 224), interpolation='bicubic',align_corners=True, antialias=self.antialias) x = (x + 1.) / 2. # renormalize according to clip x = kornia.enhance.normalize(x, self.mean, self.std) return x def forward(self, x): # x is assumed to be in range [-1,1] return self.model.encode_image(self.preprocess(x)) ================================================ FILE: ldm_exp/ldm/modules/image_degradation/__init__.py ================================================ from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light ================================================ FILE: ldm_exp/ldm/modules/image_degradation/bsrgan.py ================================================ # -*- coding: utf-8 -*- """ # -------------------------------------------- # Super-Resolution # -------------------------------------------- # # Kai Zhang (cskaizhang@gmail.com) # https://github.com/cszn # From 2019/03--2021/08 # -------------------------------------------- """ import numpy as np import cv2 import torch from functools import partial import random from scipy import ndimage import scipy import scipy.stats as ss from scipy.interpolate import interp2d from scipy.linalg import orth import albumentations import ldm.modules.image_degradation.utils_image as util def modcrop_np(img, sf): ''' Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image ''' w, h = img.shape[:2] im = np.copy(img) return im[:w - w % sf, :h - h % sf, ...] """ # -------------------------------------------- # anisotropic Gaussian kernels # -------------------------------------------- """ def analytic_kernel(k): """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" k_size = k.shape[0] # Calculate the big kernels size big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) # Loop over the small kernel to fill the big one for r in range(k_size): for c in range(k_size): big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k # Crop the edges of the big kernel to ignore very small values and increase run time of SR crop = k_size // 2 cropped_big_k = big_k[crop:-crop, crop:-crop] # Normalize to 1 return cropped_big_k / cropped_big_k.sum() def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): """ generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1 : [0.1,50], scaling of eigenvalues l2 : [0.1,l1], scaling of eigenvalues If l1 = l2, will get an isotropic Gaussian kernel. Returns: k : kernel """ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) V = np.array([[v[0], v[1]], [v[1], -v[0]]]) D = np.array([[l1, 0], [0, l2]]) Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) return k def gm_blur_kernel(mean, cov, size=15): center = size / 2.0 + 0.5 k = np.zeros([size, size]) for y in range(size): for x in range(size): cy = y - center + 1 cx = x - center + 1 k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) k = k / np.sum(k) return k def shift_pixel(x, sf, upper_left=True): """shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift direction """ h, w = x.shape[:2] shift = (sf - 1) * 0.5 xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) if upper_left: x1 = xv + shift y1 = yv + shift else: x1 = xv - shift y1 = yv - shift x1 = np.clip(x1, 0, w - 1) y1 = np.clip(y1, 0, h - 1) if x.ndim == 2: x = interp2d(xv, yv, x)(x1, y1) if x.ndim == 3: for i in range(x.shape[-1]): x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) return x def blur(x, k): ''' x: image, NxcxHxW k: kernel, Nx1xhxw ''' n, c = x.shape[:2] p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') k = k.repeat(1, c, 1, 1) k = k.view(-1, 1, k.shape[2], k.shape[3]) x = x.view(1, -1, x.shape[2], x.shape[3]) x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) x = x.view(n, c, x.shape[2], x.shape[3]) return x def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): """" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf """ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix lambda_1 = min_var + np.random.rand() * (max_var - min_var) lambda_2 = min_var + np.random.rand() * (max_var - min_var) theta = np.random.rand() * np.pi # random theta noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 # Set COV matrix using Lambdas and Theta LAMBDA = np.diag([lambda_1, lambda_2]) Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) SIGMA = Q @ LAMBDA @ Q.T INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] # Set expectation position (shifting kernel for aligned image) MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) MU = MU[None, None, :, None] # Create meshgrid for Gaussian [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) Z = np.stack([X, Y], 2)[:, :, :, None] # Calcualte Gaussian for every pixel of the kernel ZZ = Z - MU ZZ_t = ZZ.transpose(0, 1, 3, 2) raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) # shift the kernel so it will be centered # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) # Normalize the kernel and return # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) kernel = raw_kernel / np.sum(raw_kernel) return kernel def fspecial_gaussian(hsize, sigma): hsize = [hsize, hsize] siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] std = sigma [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) arg = -(x * x + y * y) / (2 * std * std) h = np.exp(arg) h[h < scipy.finfo(float).eps * h.max()] = 0 sumh = h.sum() if sumh != 0: h = h / sumh return h def fspecial_laplacian(alpha): alpha = max([0, min([alpha, 1])]) h1 = alpha / (alpha + 1) h2 = (1 - alpha) / (alpha + 1) h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] h = np.array(h) return h def fspecial(filter_type, *args, **kwargs): ''' python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py ''' if filter_type == 'gaussian': return fspecial_gaussian(*args, **kwargs) if filter_type == 'laplacian': return fspecial_laplacian(*args, **kwargs) """ # -------------------------------------------- # degradation models # -------------------------------------------- """ def bicubic_degradation(x, sf=3): ''' Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image ''' x = util.imresize_np(x, scale=1 / sf) return x def srmd_degradation(x, k, sf=3): ''' blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' x = bicubic_degradation(x, sf=sf) return x def dpsr_degradation(x, k, sf=3): ''' bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } ''' x = bicubic_degradation(x, sf=sf) x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') return x def classical_degradation(x, k, sf=3): ''' blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) st = 0 return x[st::sf, st::sf, ...] def add_sharpening(img, weight=0.5, radius=50, threshold=10): """USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) K = img + weight * residual K = np.clip(K, 0, 1) return soft_mask * K + (1 - soft_mask) * img def add_blur(img, sf=4): wd2 = 4.0 + sf wd = 2.0 + 0.2 * sf if random.random() < 0.5: l1 = wd2 * random.random() l2 = wd2 * random.random() k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) else: k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') return img def add_resize(img, sf=4): rnum = np.random.rand() if rnum > 0.8: # up sf1 = random.uniform(1, 2) elif rnum < 0.7: # down sf1 = random.uniform(0.5 / sf, 1) else: sf1 = 1.0 img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) return img # def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): # noise_level = random.randint(noise_level1, noise_level2) # rnum = np.random.rand() # if rnum > 0.6: # add color Gaussian noise # img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) # elif rnum < 0.4: # add grayscale Gaussian noise # img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) # else: # add noise # L = noise_level2 / 255. # D = np.diag(np.random.rand(3)) # U = orth(np.random.rand(3, 3)) # conv = np.dot(np.dot(np.transpose(U), D), U) # img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) # img = np.clip(img, 0.0, 1.0) # return img def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) rnum = np.random.rand() if rnum > 0.6: # add color Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: # add grayscale Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: # add noise L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_speckle_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) img = np.clip(img, 0.0, 1.0) rnum = random.random() if rnum > 0.6: img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_Poisson_noise(img): img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = 10 ** (2 * random.random() + 2.0) # [2, 4] if random.random() < 0.5: img = np.random.poisson(img * vals).astype(np.float32) / vals else: img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray img += noise_gray[:, :, np.newaxis] img = np.clip(img, 0.0, 1.0) return img def add_JPEG_noise(img): quality_factor = random.randint(30, 95) img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) img = cv2.imdecode(encimg, 1) img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) return img def random_crop(lq, hq, sf=4, lq_patchsize=64): h, w = lq.shape[:2] rnd_h = random.randint(0, h - lq_patchsize) rnd_w = random.randint(0, w - lq_patchsize) lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] return lq, hq def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') hq = img.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: img = util.imresize_np(img, 1 / 2, True) img = np.clip(img, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_blur(img, sf=sf) elif i == 2: a, b = img.shape[1], img.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') img = img[0::sf, 0::sf, ...] # nearest downsampling img = np.clip(img, 0.0, 1.0) elif i == 3: # downsample3 img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) elif i == 4: # add Gaussian noise img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: img = add_JPEG_noise(img) elif i == 6: # add processed camera sensor noise if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf_ori, lq_patchsize) return img, hq # todo no isp_model? def degradation_bsrgan_variant(image, sf=4, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ image = util.uint2single(image) isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = image.shape[:2] image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = image.shape[:2] hq = image.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: image = util.imresize_np(image, 1 / 2, True) image = np.clip(image, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: image = add_blur(image, sf=sf) elif i == 1: image = add_blur(image, sf=sf) elif i == 2: a, b = image.shape[1], image.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') image = image[0::sf, 0::sf, ...] # nearest downsampling image = np.clip(image, 0.0, 1.0) elif i == 3: # downsample3 image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) image = np.clip(image, 0.0, 1.0) elif i == 4: # add Gaussian noise image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: image = add_JPEG_noise(image) # elif i == 6: # # add processed camera sensor noise # if random.random() < isp_prob and isp_model is not None: # with torch.no_grad(): # img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise image = add_JPEG_noise(image) image = util.single2uint(image) example = {"image":image} return example # TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): """ This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor use_shuffle: the degradation shuffle use_sharp: sharpening the img Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') if use_sharp: img = add_sharpening(img) hq = img.copy() if random.random() < shuffle_prob: shuffle_order = random.sample(range(13), 13) else: shuffle_order = list(range(13)) # local shuffle for noise, JPEG is always the last one shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_resize(img, sf=sf) elif i == 2: img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 3: if random.random() < poisson_prob: img = add_Poisson_noise(img) elif i == 4: if random.random() < speckle_prob: img = add_speckle_noise(img) elif i == 5: if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) elif i == 6: img = add_JPEG_noise(img) elif i == 7: img = add_blur(img, sf=sf) elif i == 8: img = add_resize(img, sf=sf) elif i == 9: img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 10: if random.random() < poisson_prob: img = add_Poisson_noise(img) elif i == 11: if random.random() < speckle_prob: img = add_speckle_noise(img) elif i == 12: if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) else: print('check the shuffle!') # resize to desired size img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), interpolation=random.choice([1, 2, 3])) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf, lq_patchsize) return img, hq if __name__ == '__main__': print("hey") img = util.imread_uint('utils/test.png', 3) print(img) img = util.uint2single(img) print(img) img = img[:448, :448] h = img.shape[0] // 4 print("resizing to", h) sf = 4 deg_fn = partial(degradation_bsrgan_variant, sf=sf) for i in range(20): print(i) img_lq = deg_fn(img) print(img_lq) img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] print(img_lq.shape) print("bicubic", img_lq_bicubic.shape) print(img_hq.shape) lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) util.imsave(img_concat, str(i) + '.png') ================================================ FILE: ldm_exp/ldm/modules/image_degradation/bsrgan_light.py ================================================ # -*- coding: utf-8 -*- import numpy as np import cv2 import torch from functools import partial import random from scipy import ndimage import scipy import scipy.stats as ss from scipy.interpolate import interp2d from scipy.linalg import orth import albumentations import ldm.modules.image_degradation.utils_image as util """ # -------------------------------------------- # Super-Resolution # -------------------------------------------- # # Kai Zhang (cskaizhang@gmail.com) # https://github.com/cszn # From 2019/03--2021/08 # -------------------------------------------- """ def modcrop_np(img, sf): ''' Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image ''' w, h = img.shape[:2] im = np.copy(img) return im[:w - w % sf, :h - h % sf, ...] """ # -------------------------------------------- # anisotropic Gaussian kernels # -------------------------------------------- """ def analytic_kernel(k): """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" k_size = k.shape[0] # Calculate the big kernels size big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) # Loop over the small kernel to fill the big one for r in range(k_size): for c in range(k_size): big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k # Crop the edges of the big kernel to ignore very small values and increase run time of SR crop = k_size // 2 cropped_big_k = big_k[crop:-crop, crop:-crop] # Normalize to 1 return cropped_big_k / cropped_big_k.sum() def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): """ generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1 : [0.1,50], scaling of eigenvalues l2 : [0.1,l1], scaling of eigenvalues If l1 = l2, will get an isotropic Gaussian kernel. Returns: k : kernel """ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) V = np.array([[v[0], v[1]], [v[1], -v[0]]]) D = np.array([[l1, 0], [0, l2]]) Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) return k def gm_blur_kernel(mean, cov, size=15): center = size / 2.0 + 0.5 k = np.zeros([size, size]) for y in range(size): for x in range(size): cy = y - center + 1 cx = x - center + 1 k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) k = k / np.sum(k) return k def shift_pixel(x, sf, upper_left=True): """shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift direction """ h, w = x.shape[:2] shift = (sf - 1) * 0.5 xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) if upper_left: x1 = xv + shift y1 = yv + shift else: x1 = xv - shift y1 = yv - shift x1 = np.clip(x1, 0, w - 1) y1 = np.clip(y1, 0, h - 1) if x.ndim == 2: x = interp2d(xv, yv, x)(x1, y1) if x.ndim == 3: for i in range(x.shape[-1]): x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) return x def blur(x, k): ''' x: image, NxcxHxW k: kernel, Nx1xhxw ''' n, c = x.shape[:2] p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') k = k.repeat(1, c, 1, 1) k = k.view(-1, 1, k.shape[2], k.shape[3]) x = x.view(1, -1, x.shape[2], x.shape[3]) x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) x = x.view(n, c, x.shape[2], x.shape[3]) return x def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): """" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf """ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix lambda_1 = min_var + np.random.rand() * (max_var - min_var) lambda_2 = min_var + np.random.rand() * (max_var - min_var) theta = np.random.rand() * np.pi # random theta noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 # Set COV matrix using Lambdas and Theta LAMBDA = np.diag([lambda_1, lambda_2]) Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) SIGMA = Q @ LAMBDA @ Q.T INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] # Set expectation position (shifting kernel for aligned image) MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) MU = MU[None, None, :, None] # Create meshgrid for Gaussian [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) Z = np.stack([X, Y], 2)[:, :, :, None] # Calcualte Gaussian for every pixel of the kernel ZZ = Z - MU ZZ_t = ZZ.transpose(0, 1, 3, 2) raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) # shift the kernel so it will be centered # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) # Normalize the kernel and return # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) kernel = raw_kernel / np.sum(raw_kernel) return kernel def fspecial_gaussian(hsize, sigma): hsize = [hsize, hsize] siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] std = sigma [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) arg = -(x * x + y * y) / (2 * std * std) h = np.exp(arg) h[h < scipy.finfo(float).eps * h.max()] = 0 sumh = h.sum() if sumh != 0: h = h / sumh return h def fspecial_laplacian(alpha): alpha = max([0, min([alpha, 1])]) h1 = alpha / (alpha + 1) h2 = (1 - alpha) / (alpha + 1) h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] h = np.array(h) return h def fspecial(filter_type, *args, **kwargs): ''' python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py ''' if filter_type == 'gaussian': return fspecial_gaussian(*args, **kwargs) if filter_type == 'laplacian': return fspecial_laplacian(*args, **kwargs) """ # -------------------------------------------- # degradation models # -------------------------------------------- """ def bicubic_degradation(x, sf=3): ''' Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image ''' x = util.imresize_np(x, scale=1 / sf) return x def srmd_degradation(x, k, sf=3): ''' blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' x = bicubic_degradation(x, sf=sf) return x def dpsr_degradation(x, k, sf=3): ''' bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } ''' x = bicubic_degradation(x, sf=sf) x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') return x def classical_degradation(x, k, sf=3): ''' blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) st = 0 return x[st::sf, st::sf, ...] def add_sharpening(img, weight=0.5, radius=50, threshold=10): """USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) K = img + weight * residual K = np.clip(K, 0, 1) return soft_mask * K + (1 - soft_mask) * img def add_blur(img, sf=4): wd2 = 4.0 + sf wd = 2.0 + 0.2 * sf wd2 = wd2/4 wd = wd/4 if random.random() < 0.5: l1 = wd2 * random.random() l2 = wd2 * random.random() k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) else: k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') return img def add_resize(img, sf=4): rnum = np.random.rand() if rnum > 0.8: # up sf1 = random.uniform(1, 2) elif rnum < 0.7: # down sf1 = random.uniform(0.5 / sf, 1) else: sf1 = 1.0 img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) return img # def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): # noise_level = random.randint(noise_level1, noise_level2) # rnum = np.random.rand() # if rnum > 0.6: # add color Gaussian noise # img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) # elif rnum < 0.4: # add grayscale Gaussian noise # img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) # else: # add noise # L = noise_level2 / 255. # D = np.diag(np.random.rand(3)) # U = orth(np.random.rand(3, 3)) # conv = np.dot(np.dot(np.transpose(U), D), U) # img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) # img = np.clip(img, 0.0, 1.0) # return img def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) rnum = np.random.rand() if rnum > 0.6: # add color Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: # add grayscale Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: # add noise L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_speckle_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) img = np.clip(img, 0.0, 1.0) rnum = random.random() if rnum > 0.6: img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_Poisson_noise(img): img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = 10 ** (2 * random.random() + 2.0) # [2, 4] if random.random() < 0.5: img = np.random.poisson(img * vals).astype(np.float32) / vals else: img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray img += noise_gray[:, :, np.newaxis] img = np.clip(img, 0.0, 1.0) return img def add_JPEG_noise(img): quality_factor = random.randint(80, 95) img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) img = cv2.imdecode(encimg, 1) img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) return img def random_crop(lq, hq, sf=4, lq_patchsize=64): h, w = lq.shape[:2] rnd_h = random.randint(0, h - lq_patchsize) rnd_w = random.randint(0, w - lq_patchsize) lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] return lq, hq def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') hq = img.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: img = util.imresize_np(img, 1 / 2, True) img = np.clip(img, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_blur(img, sf=sf) elif i == 2: a, b = img.shape[1], img.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') img = img[0::sf, 0::sf, ...] # nearest downsampling img = np.clip(img, 0.0, 1.0) elif i == 3: # downsample3 img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) elif i == 4: # add Gaussian noise img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: img = add_JPEG_noise(img) elif i == 6: # add processed camera sensor noise if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf_ori, lq_patchsize) return img, hq # todo no isp_model? def degradation_bsrgan_variant(image, sf=4, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ image = util.uint2single(image) isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = image.shape[:2] image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = image.shape[:2] hq = image.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: image = util.imresize_np(image, 1 / 2, True) image = np.clip(image, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: image = add_blur(image, sf=sf) # elif i == 1: # image = add_blur(image, sf=sf) if i == 0: pass elif i == 2: a, b = image.shape[1], image.shape[0] # downsample2 if random.random() < 0.8: sf1 = random.uniform(1, 2 * sf) image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') image = image[0::sf, 0::sf, ...] # nearest downsampling image = np.clip(image, 0.0, 1.0) elif i == 3: # downsample3 image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) image = np.clip(image, 0.0, 1.0) elif i == 4: # add Gaussian noise image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: image = add_JPEG_noise(image) # # elif i == 6: # # add processed camera sensor noise # if random.random() < isp_prob and isp_model is not None: # with torch.no_grad(): # img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise image = add_JPEG_noise(image) image = util.single2uint(image) example = {"image": image} return example if __name__ == '__main__': print("hey") img = util.imread_uint('utils/test.png', 3) img = img[:448, :448] h = img.shape[0] // 4 print("resizing to", h) sf = 4 deg_fn = partial(degradation_bsrgan_variant, sf=sf) for i in range(20): print(i) img_hq = img img_lq = deg_fn(img)["image"] img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) print(img_lq) img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] print(img_lq.shape) print("bicubic", img_lq_bicubic.shape) print(img_hq.shape) lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) util.imsave(img_concat, str(i) + '.png') ================================================ FILE: ldm_exp/ldm/modules/image_degradation/utils_image.py ================================================ import os import math import random import numpy as np import torch import cv2 from torchvision.utils import make_grid from datetime import datetime #import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" ''' # -------------------------------------------- # Kai Zhang (github: https://github.com/cszn) # 03/Mar/2019 # -------------------------------------------- # https://github.com/twhui/SRGAN-pyTorch # https://github.com/xinntao/BasicSR # -------------------------------------------- ''' IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def get_timestamp(): return datetime.now().strftime('%y%m%d-%H%M%S') def imshow(x, title=None, cbar=False, figsize=None): plt.figure(figsize=figsize) plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') if title: plt.title(title) if cbar: plt.colorbar() plt.show() def surf(Z, cmap='rainbow', figsize=None): plt.figure(figsize=figsize) ax3 = plt.axes(projection='3d') w, h = Z.shape[:2] xx = np.arange(0,w,1) yy = np.arange(0,h,1) X, Y = np.meshgrid(xx, yy) ax3.plot_surface(X,Y,Z,cmap=cmap) #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) plt.show() ''' # -------------------------------------------- # get image pathes # -------------------------------------------- ''' def get_image_paths(dataroot): paths = None # return None if dataroot is None if dataroot is not None: paths = sorted(_get_paths_from_images(dataroot)) return paths def _get_paths_from_images(path): assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) images = [] for dirpath, _, fnames in sorted(os.walk(path)): for fname in sorted(fnames): if is_image_file(fname): img_path = os.path.join(dirpath, fname) images.append(img_path) assert images, '{:s} has no valid image file'.format(path) return images ''' # -------------------------------------------- # split large images into small images # -------------------------------------------- ''' def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): w, h = img.shape[:2] patches = [] if w > p_max and h > p_max: w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) w1.append(w-p_size) h1.append(h-p_size) # print(w1) # print(h1) for i in w1: for j in h1: patches.append(img[i:i+p_size, j:j+p_size,:]) else: patches.append(img) return patches def imssave(imgs, img_path): """ imgs: list, N images of size WxHxC """ img_name, ext = os.path.splitext(os.path.basename(img_path)) for i, img in enumerate(imgs): if img.ndim == 3: img = img[:, :, [2, 1, 0]] new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') cv2.imwrite(new_path, img) def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): """ split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) will be splitted. Args: original_dataroot: taget_dataroot: p_size: size of small images p_overlap: patch size in training is a good choice p_max: images with smaller size than (p_max)x(p_max) keep unchanged. """ paths = get_image_paths(original_dataroot) for img_path in paths: # img_name, ext = os.path.splitext(os.path.basename(img_path)) img = imread_uint(img_path, n_channels=n_channels) patches = patches_from_image(img, p_size, p_overlap, p_max) imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) #if original_dataroot == taget_dataroot: #del img_path ''' # -------------------------------------------- # makedir # -------------------------------------------- ''' def mkdir(path): if not os.path.exists(path): os.makedirs(path) def mkdirs(paths): if isinstance(paths, str): mkdir(paths) else: for path in paths: mkdir(path) def mkdir_and_rename(path): if os.path.exists(path): new_name = path + '_archived_' + get_timestamp() print('Path already exists. Rename it to [{:s}]'.format(new_name)) os.rename(path, new_name) os.makedirs(path) ''' # -------------------------------------------- # read image from path # opencv is fast, but read BGR numpy image # -------------------------------------------- ''' # -------------------------------------------- # get uint8 image of size HxWxn_channles (RGB) # -------------------------------------------- def imread_uint(path, n_channels=3): # input: path # output: HxWx3(RGB or GGG), or HxWx1 (G) if n_channels == 1: img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE img = np.expand_dims(img, axis=2) # HxWx1 elif n_channels == 3: img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G if img.ndim == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG else: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB return img # -------------------------------------------- # matlab's imwrite # -------------------------------------------- def imsave(img, img_path): img = np.squeeze(img) if img.ndim == 3: img = img[:, :, [2, 1, 0]] cv2.imwrite(img_path, img) def imwrite(img, img_path): img = np.squeeze(img) if img.ndim == 3: img = img[:, :, [2, 1, 0]] cv2.imwrite(img_path, img) # -------------------------------------------- # get single image of size HxWxn_channles (BGR) # -------------------------------------------- def read_img(path): # read image by cv2 # return: Numpy float32, HWC, BGR, [0,1] img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE img = img.astype(np.float32) / 255. if img.ndim == 2: img = np.expand_dims(img, axis=2) # some images have 4 channels if img.shape[2] > 3: img = img[:, :, :3] return img ''' # -------------------------------------------- # image format conversion # -------------------------------------------- # numpy(single) <---> numpy(unit) # numpy(single) <---> tensor # numpy(unit) <---> tensor # -------------------------------------------- ''' # -------------------------------------------- # numpy(single) [0, 1] <---> numpy(unit) # -------------------------------------------- def uint2single(img): return np.float32(img/255.) def single2uint(img): return np.uint8((img.clip(0, 1)*255.).round()) def uint162single(img): return np.float32(img/65535.) def single2uint16(img): return np.uint16((img.clip(0, 1)*65535.).round()) # -------------------------------------------- # numpy(unit) (HxWxC or HxW) <---> tensor # -------------------------------------------- # convert uint to 4-dimensional torch tensor def uint2tensor4(img): if img.ndim == 2: img = np.expand_dims(img, axis=2) return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) # convert uint to 3-dimensional torch tensor def uint2tensor3(img): if img.ndim == 2: img = np.expand_dims(img, axis=2) return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) # convert 2/3/4-dimensional torch tensor to uint def tensor2uint(img): img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) return np.uint8((img*255.0).round()) # -------------------------------------------- # numpy(single) (HxWxC) <---> tensor # -------------------------------------------- # convert single (HxWxC) to 3-dimensional torch tensor def single2tensor3(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() # convert single (HxWxC) to 4-dimensional torch tensor def single2tensor4(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) # convert torch tensor to single def tensor2single(img): img = img.data.squeeze().float().cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) return img # convert torch tensor to single def tensor2single3(img): img = img.data.squeeze().float().cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) elif img.ndim == 2: img = np.expand_dims(img, axis=2) return img def single2tensor5(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) def single32tensor5(img): return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) def single42tensor4(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() # from skimage.io import imread, imsave def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): ''' Converts a torch Tensor into an image Numpy array of BGR channel order Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) ''' tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] n_dim = tensor.dim() if n_dim == 4: n_img = len(tensor) img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 3: img_np = tensor.numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 2: img_np = tensor.numpy() else: raise TypeError( 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) if out_type == np.uint8: img_np = (img_np * 255.0).round() # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. return img_np.astype(out_type) ''' # -------------------------------------------- # Augmentation, flipe and/or rotate # -------------------------------------------- # The following two are enough. # (1) augmet_img: numpy image of WxHxC or WxH # (2) augment_img_tensor4: tensor image 1xCxWxH # -------------------------------------------- ''' def augment_img(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' if mode == 0: return img elif mode == 1: return np.flipud(np.rot90(img)) elif mode == 2: return np.flipud(img) elif mode == 3: return np.rot90(img, k=3) elif mode == 4: return np.flipud(np.rot90(img, k=2)) elif mode == 5: return np.rot90(img) elif mode == 6: return np.rot90(img, k=2) elif mode == 7: return np.flipud(np.rot90(img, k=3)) def augment_img_tensor4(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' if mode == 0: return img elif mode == 1: return img.rot90(1, [2, 3]).flip([2]) elif mode == 2: return img.flip([2]) elif mode == 3: return img.rot90(3, [2, 3]) elif mode == 4: return img.rot90(2, [2, 3]).flip([2]) elif mode == 5: return img.rot90(1, [2, 3]) elif mode == 6: return img.rot90(2, [2, 3]) elif mode == 7: return img.rot90(3, [2, 3]).flip([2]) def augment_img_tensor(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' img_size = img.size() img_np = img.data.cpu().numpy() if len(img_size) == 3: img_np = np.transpose(img_np, (1, 2, 0)) elif len(img_size) == 4: img_np = np.transpose(img_np, (2, 3, 1, 0)) img_np = augment_img(img_np, mode=mode) img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) if len(img_size) == 3: img_tensor = img_tensor.permute(2, 0, 1) elif len(img_size) == 4: img_tensor = img_tensor.permute(3, 2, 0, 1) return img_tensor.type_as(img) def augment_img_np3(img, mode=0): if mode == 0: return img elif mode == 1: return img.transpose(1, 0, 2) elif mode == 2: return img[::-1, :, :] elif mode == 3: img = img[::-1, :, :] img = img.transpose(1, 0, 2) return img elif mode == 4: return img[:, ::-1, :] elif mode == 5: img = img[:, ::-1, :] img = img.transpose(1, 0, 2) return img elif mode == 6: img = img[:, ::-1, :] img = img[::-1, :, :] return img elif mode == 7: img = img[:, ::-1, :] img = img[::-1, :, :] img = img.transpose(1, 0, 2) return img def augment_imgs(img_list, hflip=True, rot=True): # horizontal flip OR rotate hflip = hflip and random.random() < 0.5 vflip = rot and random.random() < 0.5 rot90 = rot and random.random() < 0.5 def _augment(img): if hflip: img = img[:, ::-1, :] if vflip: img = img[::-1, :, :] if rot90: img = img.transpose(1, 0, 2) return img return [_augment(img) for img in img_list] ''' # -------------------------------------------- # modcrop and shave # -------------------------------------------- ''' def modcrop(img_in, scale): # img_in: Numpy, HWC or HW img = np.copy(img_in) if img.ndim == 2: H, W = img.shape H_r, W_r = H % scale, W % scale img = img[:H - H_r, :W - W_r] elif img.ndim == 3: H, W, C = img.shape H_r, W_r = H % scale, W % scale img = img[:H - H_r, :W - W_r, :] else: raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) return img def shave(img_in, border=0): # img_in: Numpy, HWC or HW img = np.copy(img_in) h, w = img.shape[:2] img = img[border:h-border, border:w-border] return img ''' # -------------------------------------------- # image processing process on numpy image # channel_convert(in_c, tar_type, img_list): # rgb2ycbcr(img, only_y=True): # bgr2ycbcr(img, only_y=True): # ycbcr2rgb(img): # -------------------------------------------- ''' def rgb2ycbcr(img, only_y=True): '''same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert if only_y: rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 else: rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def ycbcr2rgb(img): '''same as matlab ycbcr2rgb Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def bgr2ycbcr(img, only_y=True): '''bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert if only_y: rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 else: rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def channel_convert(in_c, tar_type, img_list): # conversion among BGR, gray and y if in_c == 3 and tar_type == 'gray': # BGR to gray gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] return [np.expand_dims(img, axis=2) for img in gray_list] elif in_c == 3 and tar_type == 'y': # BGR to y y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] return [np.expand_dims(img, axis=2) for img in y_list] elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] else: return img_list ''' # -------------------------------------------- # metric, PSNR and SSIM # -------------------------------------------- ''' # -------------------------------------------- # PSNR # -------------------------------------------- def calculate_psnr(img1, img2, border=0): # img1 and img2 have range [0, 255] #img1 = img1.squeeze() #img2 = img2.squeeze() if not img1.shape == img2.shape: raise ValueError('Input images must have the same dimensions.') h, w = img1.shape[:2] img1 = img1[border:h-border, border:w-border] img2 = img2[border:h-border, border:w-border] img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) mse = np.mean((img1 - img2)**2) if mse == 0: return float('inf') return 20 * math.log10(255.0 / math.sqrt(mse)) # -------------------------------------------- # SSIM # -------------------------------------------- def calculate_ssim(img1, img2, border=0): '''calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255] ''' #img1 = img1.squeeze() #img2 = img2.squeeze() if not img1.shape == img2.shape: raise ValueError('Input images must have the same dimensions.') h, w = img1.shape[:2] img1 = img1[border:h-border, border:w-border] img2 = img2[border:h-border, border:w-border] if img1.ndim == 2: return ssim(img1, img2) elif img1.ndim == 3: if img1.shape[2] == 3: ssims = [] for i in range(3): ssims.append(ssim(img1[:,:,i], img2[:,:,i])) return np.array(ssims).mean() elif img1.shape[2] == 1: return ssim(np.squeeze(img1), np.squeeze(img2)) else: raise ValueError('Wrong input image dimensions.') def ssim(img1, img2): C1 = (0.01 * 255)**2 C2 = (0.03 * 255)**2 img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] mu1_sq = mu1**2 mu2_sq = mu2**2 mu1_mu2 = mu1 * mu2 sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) return ssim_map.mean() ''' # -------------------------------------------- # matlab's bicubic imresize (numpy and torch) [0, 1] # -------------------------------------------- ''' # matlab 'imresize' function, now only support 'bicubic' def cubic(x): absx = torch.abs(x) absx2 = absx**2 absx3 = absx**3 return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): if (scale < 1) and (antialiasing): # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width kernel_width = kernel_width / scale # Output-space coordinates x = torch.linspace(1, out_length, out_length) # Input-space coordinates. Calculate the inverse mapping such that 0.5 # in output space maps to 0.5 in input space, and 0.5+scale in output # space maps to 1.5 in input space. u = x / scale + 0.5 * (1 - 1 / scale) # What is the left-most pixel that can be involved in the computation? left = torch.floor(u - kernel_width / 2) # What is the maximum number of pixels that can be involved in the # computation? Note: it's OK to use an extra pixel here; if the # corresponding weights are all zero, it will be eliminated at the end # of this function. P = math.ceil(kernel_width) + 2 # The indices of the input pixels involved in computing the k-th output # pixel are in row k of the indices matrix. indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( 1, P).expand(out_length, P) # The weights used to compute the k-th output pixel are in row k of the # weights matrix. distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices # apply cubic kernel if (scale < 1) and (antialiasing): weights = scale * cubic(distance_to_center * scale) else: weights = cubic(distance_to_center) # Normalize the weights matrix so that each row sums to 1. weights_sum = torch.sum(weights, 1).view(out_length, 1) weights = weights / weights_sum.expand(out_length, P) # If a column in weights is all zero, get rid of it. only consider the first and last column. weights_zero_tmp = torch.sum((weights == 0), 0) if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): indices = indices.narrow(1, 1, P - 2) weights = weights.narrow(1, 1, P - 2) if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): indices = indices.narrow(1, 0, P - 2) weights = weights.narrow(1, 0, P - 2) weights = weights.contiguous() indices = indices.contiguous() sym_len_s = -indices.min() + 1 sym_len_e = indices.max() - in_length indices = indices + sym_len_s - 1 return weights, indices, int(sym_len_s), int(sym_len_e) # -------------------------------------------- # imresize for tensor image [0, 1] # -------------------------------------------- def imresize(img, scale, antialiasing=True): # Now the scale should be the same for H and W # input: img: pytorch tensor, CHW or HW [0,1] # output: CHW or HW [0,1] w/o round need_squeeze = True if img.dim() == 2 else False if need_squeeze: img.unsqueeze_(0) in_C, in_H, in_W = img.size() out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) kernel_width = 4 kernel = 'cubic' # Return the desired dimension order for performing the resize. The # strategy is to perform the resize first along the dimension with the # smallest scale factor. # Now we do not support this. # get weights and indices weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( in_H, out_H, scale, kernel, kernel_width, antialiasing) weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( in_W, out_W, scale, kernel, kernel_width, antialiasing) # process H dimension # symmetric copying img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) sym_patch = img[:, :sym_len_Hs, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) sym_patch = img[:, -sym_len_He:, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) out_1 = torch.FloatTensor(in_C, out_H, in_W) kernel_width = weights_H.size(1) for i in range(out_H): idx = int(indices_H[i][0]) for j in range(out_C): out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) # process W dimension # symmetric copying out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) sym_patch = out_1[:, :, :sym_len_Ws] inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(2, inv_idx) out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) sym_patch = out_1[:, :, -sym_len_We:] inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(2, inv_idx) out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) out_2 = torch.FloatTensor(in_C, out_H, out_W) kernel_width = weights_W.size(1) for i in range(out_W): idx = int(indices_W[i][0]) for j in range(out_C): out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) if need_squeeze: out_2.squeeze_() return out_2 # -------------------------------------------- # imresize for numpy image [0, 1] # -------------------------------------------- def imresize_np(img, scale, antialiasing=True): # Now the scale should be the same for H and W # input: img: Numpy, HWC or HW [0,1] # output: HWC or HW [0,1] w/o round img = torch.from_numpy(img) need_squeeze = True if img.dim() == 2 else False if need_squeeze: img.unsqueeze_(2) in_H, in_W, in_C = img.size() out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) kernel_width = 4 kernel = 'cubic' # Return the desired dimension order for performing the resize. The # strategy is to perform the resize first along the dimension with the # smallest scale factor. # Now we do not support this. # get weights and indices weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( in_H, out_H, scale, kernel, kernel_width, antialiasing) weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( in_W, out_W, scale, kernel, kernel_width, antialiasing) # process H dimension # symmetric copying img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) sym_patch = img[:sym_len_Hs, :, :] inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(0, inv_idx) img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) sym_patch = img[-sym_len_He:, :, :] inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(0, inv_idx) img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) out_1 = torch.FloatTensor(out_H, in_W, in_C) kernel_width = weights_H.size(1) for i in range(out_H): idx = int(indices_H[i][0]) for j in range(out_C): out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) # process W dimension # symmetric copying out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) sym_patch = out_1[:, :sym_len_Ws, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) sym_patch = out_1[:, -sym_len_We:, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) out_2 = torch.FloatTensor(out_H, out_W, in_C) kernel_width = weights_W.size(1) for i in range(out_W): idx = int(indices_W[i][0]) for j in range(out_C): out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) if need_squeeze: out_2.squeeze_() return out_2.numpy() if __name__ == '__main__': print('---') # img = imread_uint('test.bmp', 3) # img = uint2single(img) # img_bicubic = imresize_np(img, 1/4) ================================================ FILE: ldm_exp/ldm/modules/losses/__init__.py ================================================ from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator ================================================ FILE: ldm_exp/ldm/modules/losses/contperceptual.py ================================================ import torch import torch.nn as nn from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no? class LPIPSWithDiscriminator(nn.Module): def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_loss="hinge"): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.kl_weight = kl_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPIPS().eval() self.perceptual_weight = perceptual_weight # output log variance self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm ).apply(weights_init) self.discriminator_iter_start = disc_start self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, inputs, reconstructions, posteriors, optimizer_idx, global_step, last_layer=None, cond=None, split="train", weights=None): rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar weighted_nll_loss = nll_loss if weights is not None: weighted_nll_loss = weights*nll_loss weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] kl_loss = posteriors.kl() kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) if self.disc_factor > 0.0: try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = torch.tensor(0.0) else: d_weight = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), } return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss, log ================================================ FILE: ldm_exp/ldm/modules/losses/vqperceptual.py ================================================ import torch from torch import nn import torch.nn.functional as F from einops import repeat from taming.modules.discriminator.model import NLayerDiscriminator, weights_init from taming.modules.losses.lpips import LPIPS from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0] loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3]) loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3]) loss_real = (weights * loss_real).sum() / weights.sum() loss_fake = (weights * loss_fake).sum() / weights.sum() d_loss = 0.5 * (loss_real + loss_fake) return d_loss def adopt_weight(weight, global_step, threshold=0, value=0.): if global_step < threshold: weight = value return weight def measure_perplexity(predicted_indices, n_embed): # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed) avg_probs = encodings.mean(0) perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp() cluster_use = torch.sum(avg_probs > 0) return perplexity, cluster_use def l1(x, y): return torch.abs(x-y) def l2(x, y): return torch.pow((x-y), 2) class VQLPIPSWithDiscriminator(nn.Module): def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips", pixel_loss="l1"): super().__init__() assert disc_loss in ["hinge", "vanilla"] assert perceptual_loss in ["lpips", "clips", "dists"] assert pixel_loss in ["l1", "l2"] self.codebook_weight = codebook_weight self.pixel_weight = pixelloss_weight if perceptual_loss == "lpips": print(f"{self.__class__.__name__}: Running with LPIPS.") self.perceptual_loss = LPIPS().eval() else: raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<") self.perceptual_weight = perceptual_weight if pixel_loss == "l1": self.pixel_loss = l1 else: self.pixel_loss = l2 self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf ).apply(weights_init) self.discriminator_iter_start = disc_start if disc_loss == "hinge": self.disc_loss = hinge_d_loss elif disc_loss == "vanilla": self.disc_loss = vanilla_d_loss else: raise ValueError(f"Unknown GAN loss '{disc_loss}'.") print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional self.n_classes = n_classes def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, cond=None, split="train", predicted_indices=None): if not exists(codebook_loss): codebook_loss = torch.tensor([0.]).to(inputs.device) #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss else: p_loss = torch.tensor([0.0]) nll_loss = rec_loss #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] nll_loss = torch.mean(nll_loss) # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/quant_loss".format(split): codebook_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/p_loss".format(split): p_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), } if predicted_indices is not None: assert self.n_classes is not None with torch.no_grad(): perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) log[f"{split}/perplexity"] = perplexity log[f"{split}/cluster_usage"] = cluster_usage return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss, log ================================================ FILE: ldm_exp/ldm/modules/x_transformer.py ================================================ """shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" import torch from torch import nn, einsum import torch.nn.functional as F from functools import partial from inspect import isfunction from collections import namedtuple from einops import rearrange, repeat, reduce # constants DEFAULT_DIM_HEAD = 64 Intermediates = namedtuple('Intermediates', [ 'pre_softmax_attn', 'post_softmax_attn' ]) LayerIntermediates = namedtuple('Intermediates', [ 'hiddens', 'attn_intermediates' ]) class AbsolutePositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len): super().__init__() self.emb = nn.Embedding(max_seq_len, dim) self.init_() def init_(self): nn.init.normal_(self.emb.weight, std=0.02) def forward(self, x): n = torch.arange(x.shape[1], device=x.device) return self.emb(n)[None, :, :] class FixedPositionalEmbedding(nn.Module): def __init__(self, dim): super().__init__() inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', inv_freq) def forward(self, x, seq_dim=1, offset=0): t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) return emb[None, :, :] # helpers def exists(val): return val is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def always(val): def inner(*args, **kwargs): return val return inner def not_equals(val): def inner(x): return x != val return inner def equals(val): def inner(x): return x == val return inner def max_neg_value(tensor): return -torch.finfo(tensor.dtype).max # keyword argument helpers def pick_and_pop(keys, d): values = list(map(lambda key: d.pop(key), keys)) return dict(zip(keys, values)) def group_dict_by_key(cond, d): return_val = [dict(), dict()] for key in d.keys(): match = bool(cond(key)) ind = int(not match) return_val[ind][key] = d[key] return (*return_val,) def string_begins_with(prefix, str): return str.startswith(prefix) def group_by_key_prefix(prefix, d): return group_dict_by_key(partial(string_begins_with, prefix), d) def groupby_prefix_and_trim(prefix, d): kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) return kwargs_without_prefix, kwargs # classes class Scale(nn.Module): def __init__(self, value, fn): super().__init__() self.value = value self.fn = fn def forward(self, x, **kwargs): x, *rest = self.fn(x, **kwargs) return (x * self.value, *rest) class Rezero(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn self.g = nn.Parameter(torch.zeros(1)) def forward(self, x, **kwargs): x, *rest = self.fn(x, **kwargs) return (x * self.g, *rest) class ScaleNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.scale = dim ** -0.5 self.eps = eps self.g = nn.Parameter(torch.ones(1)) def forward(self, x): norm = torch.norm(x, dim=-1, keepdim=True) * self.scale return x / norm.clamp(min=self.eps) * self.g class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-8): super().__init__() self.scale = dim ** -0.5 self.eps = eps self.g = nn.Parameter(torch.ones(dim)) def forward(self, x): norm = torch.norm(x, dim=-1, keepdim=True) * self.scale return x / norm.clamp(min=self.eps) * self.g class Residual(nn.Module): def forward(self, x, residual): return x + residual class GRUGating(nn.Module): def __init__(self, dim): super().__init__() self.gru = nn.GRUCell(dim, dim) def forward(self, x, residual): gated_output = self.gru( rearrange(x, 'b n d -> (b n) d'), rearrange(residual, 'b n d -> (b n) d') ) return gated_output.reshape_as(x) # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) 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.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) # attention. class Attention(nn.Module): def __init__( self, dim, dim_head=DEFAULT_DIM_HEAD, heads=8, causal=False, mask=None, talking_heads=False, sparse_topk=None, use_entmax15=False, num_mem_kv=0, dropout=0., on_attn=False ): super().__init__() if use_entmax15: raise NotImplementedError("Check out entmax activation instead of softmax activation!") self.scale = dim_head ** -0.5 self.heads = heads self.causal = causal self.mask = mask inner_dim = dim_head * heads self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_k = nn.Linear(dim, inner_dim, bias=False) self.to_v = nn.Linear(dim, inner_dim, bias=False) self.dropout = nn.Dropout(dropout) # talking heads self.talking_heads = talking_heads if talking_heads: self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) # explicit topk sparse attention self.sparse_topk = sparse_topk # entmax #self.attn_fn = entmax15 if use_entmax15 else F.softmax self.attn_fn = F.softmax # add memory key / values self.num_mem_kv = num_mem_kv if num_mem_kv > 0: self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) # attention on attention self.attn_on_attn = on_attn self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) def forward( self, x, context=None, mask=None, context_mask=None, rel_pos=None, sinusoidal_emb=None, prev_attn=None, mem=None ): b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device kv_input = default(context, x) q_input = x k_input = kv_input v_input = kv_input if exists(mem): k_input = torch.cat((mem, k_input), dim=-2) v_input = torch.cat((mem, v_input), dim=-2) if exists(sinusoidal_emb): # in shortformer, the query would start at a position offset depending on the past cached memory offset = k_input.shape[-2] - q_input.shape[-2] q_input = q_input + sinusoidal_emb(q_input, offset=offset) k_input = k_input + sinusoidal_emb(k_input) q = self.to_q(q_input) k = self.to_k(k_input) v = self.to_v(v_input) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) input_mask = None if any(map(exists, (mask, context_mask))): q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) k_mask = q_mask if not exists(context) else context_mask k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) q_mask = rearrange(q_mask, 'b i -> b () i ()') k_mask = rearrange(k_mask, 'b j -> b () () j') input_mask = q_mask * k_mask if self.num_mem_kv > 0: mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) k = torch.cat((mem_k, k), dim=-2) v = torch.cat((mem_v, v), dim=-2) if exists(input_mask): input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale mask_value = max_neg_value(dots) if exists(prev_attn): dots = dots + prev_attn pre_softmax_attn = dots if talking_heads: dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() if exists(rel_pos): dots = rel_pos(dots) if exists(input_mask): dots.masked_fill_(~input_mask, mask_value) del input_mask if self.causal: i, j = dots.shape[-2:] r = torch.arange(i, device=device) mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') mask = F.pad(mask, (j - i, 0), value=False) dots.masked_fill_(mask, mask_value) del mask if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: top, _ = dots.topk(self.sparse_topk, dim=-1) vk = top[..., -1].unsqueeze(-1).expand_as(dots) mask = dots < vk dots.masked_fill_(mask, mask_value) del mask attn = self.attn_fn(dots, dim=-1) post_softmax_attn = attn attn = self.dropout(attn) if talking_heads: attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() out = einsum('b h i j, b h j d -> b h i d', attn, v) out = rearrange(out, 'b h n d -> b n (h d)') intermediates = Intermediates( pre_softmax_attn=pre_softmax_attn, post_softmax_attn=post_softmax_attn ) return self.to_out(out), intermediates class AttentionLayers(nn.Module): def __init__( self, dim, depth, heads=8, causal=False, cross_attend=False, only_cross=False, use_scalenorm=False, use_rmsnorm=False, use_rezero=False, rel_pos_num_buckets=32, rel_pos_max_distance=128, position_infused_attn=False, custom_layers=None, sandwich_coef=None, par_ratio=None, residual_attn=False, cross_residual_attn=False, macaron=False, pre_norm=True, gate_residual=False, **kwargs ): super().__init__() ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) self.dim = dim self.depth = depth self.layers = nn.ModuleList([]) self.has_pos_emb = position_infused_attn self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None self.rotary_pos_emb = always(None) assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' self.rel_pos = None self.pre_norm = pre_norm self.residual_attn = residual_attn self.cross_residual_attn = cross_residual_attn norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm norm_class = RMSNorm if use_rmsnorm else norm_class norm_fn = partial(norm_class, dim) norm_fn = nn.Identity if use_rezero else norm_fn branch_fn = Rezero if use_rezero else None if cross_attend and not only_cross: default_block = ('a', 'c', 'f') elif cross_attend and only_cross: default_block = ('c', 'f') else: default_block = ('a', 'f') if macaron: default_block = ('f',) + default_block if exists(custom_layers): layer_types = custom_layers elif exists(par_ratio): par_depth = depth * len(default_block) assert 1 < par_ratio <= par_depth, 'par ratio out of range' default_block = tuple(filter(not_equals('f'), default_block)) par_attn = par_depth // par_ratio depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper par_width = (depth_cut + depth_cut // par_attn) // par_attn assert len(default_block) <= par_width, 'default block is too large for par_ratio' par_block = default_block + ('f',) * (par_width - len(default_block)) par_head = par_block * par_attn layer_types = par_head + ('f',) * (par_depth - len(par_head)) elif exists(sandwich_coef): assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef else: layer_types = default_block * depth self.layer_types = layer_types self.num_attn_layers = len(list(filter(equals('a'), layer_types))) for layer_type in self.layer_types: if layer_type == 'a': layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) elif layer_type == 'c': layer = Attention(dim, heads=heads, **attn_kwargs) elif layer_type == 'f': layer = FeedForward(dim, **ff_kwargs) layer = layer if not macaron else Scale(0.5, layer) else: raise Exception(f'invalid layer type {layer_type}') if isinstance(layer, Attention) and exists(branch_fn): layer = branch_fn(layer) if gate_residual: residual_fn = GRUGating(dim) else: residual_fn = Residual() self.layers.append(nn.ModuleList([ norm_fn(), layer, residual_fn ])) def forward( self, x, context=None, mask=None, context_mask=None, mems=None, return_hiddens=False ): hiddens = [] intermediates = [] prev_attn = None prev_cross_attn = None mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): is_last = ind == (len(self.layers) - 1) if layer_type == 'a': hiddens.append(x) layer_mem = mems.pop(0) residual = x if self.pre_norm: x = norm(x) if layer_type == 'a': out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, prev_attn=prev_attn, mem=layer_mem) elif layer_type == 'c': out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) elif layer_type == 'f': out = block(x) x = residual_fn(out, residual) if layer_type in ('a', 'c'): intermediates.append(inter) if layer_type == 'a' and self.residual_attn: prev_attn = inter.pre_softmax_attn elif layer_type == 'c' and self.cross_residual_attn: prev_cross_attn = inter.pre_softmax_attn if not self.pre_norm and not is_last: x = norm(x) if return_hiddens: intermediates = LayerIntermediates( hiddens=hiddens, attn_intermediates=intermediates ) return x, intermediates return x class Encoder(AttentionLayers): def __init__(self, **kwargs): assert 'causal' not in kwargs, 'cannot set causality on encoder' super().__init__(causal=False, **kwargs) class TransformerWrapper(nn.Module): def __init__( self, *, num_tokens, max_seq_len, attn_layers, emb_dim=None, max_mem_len=0., emb_dropout=0., num_memory_tokens=None, tie_embedding=False, use_pos_emb=True ): super().__init__() assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' dim = attn_layers.dim emb_dim = default(emb_dim, dim) self.max_seq_len = max_seq_len self.max_mem_len = max_mem_len self.num_tokens = num_tokens self.token_emb = nn.Embedding(num_tokens, emb_dim) self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( use_pos_emb and not attn_layers.has_pos_emb) else always(0) self.emb_dropout = nn.Dropout(emb_dropout) self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() self.attn_layers = attn_layers self.norm = nn.LayerNorm(dim) self.init_() self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() # memory tokens (like [cls]) from Memory Transformers paper num_memory_tokens = default(num_memory_tokens, 0) self.num_memory_tokens = num_memory_tokens if num_memory_tokens > 0: self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) # let funnel encoder know number of memory tokens, if specified if hasattr(attn_layers, 'num_memory_tokens'): attn_layers.num_memory_tokens = num_memory_tokens def init_(self): nn.init.normal_(self.token_emb.weight, std=0.02) def forward( self, x, return_embeddings=False, mask=None, return_mems=False, return_attn=False, mems=None, **kwargs ): b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens x = self.token_emb(x) x += self.pos_emb(x) x = self.emb_dropout(x) x = self.project_emb(x) if num_mem > 0: mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) x = torch.cat((mem, x), dim=1) # auto-handle masking after appending memory tokens if exists(mask): mask = F.pad(mask, (num_mem, 0), value=True) x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) x = self.norm(x) mem, x = x[:, :num_mem], x[:, num_mem:] out = self.to_logits(x) if not return_embeddings else x if return_mems: hiddens = intermediates.hiddens new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) return out, new_mems if return_attn: attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) return out, attn_maps return out ================================================ FILE: ldm_exp/ldm/util.py ================================================ import importlib import torch import numpy as np from collections import abc from einops import rearrange from functools import partial import multiprocessing as mp from threading import Thread from queue import Queue 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: print("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: print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") return total_params def instantiate_from_config(config): if not "target" 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) def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): # create dummy dataset instance # run prefetching if idx_to_fn: res = func(data, worker_id=idx) else: res = func(data) Q.put([idx, res]) Q.put("Done") def parallel_data_prefetch( func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False ): # if target_data_type not in ["ndarray", "list"]: # raise ValueError( # "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray." # ) if isinstance(data, np.ndarray) and target_data_type == "list": raise ValueError("list expected but function got ndarray.") elif isinstance(data, abc.Iterable): if isinstance(data, dict): print( f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' ) data = list(data.values()) if target_data_type == "ndarray": data = np.asarray(data) else: data = list(data) else: raise TypeError( f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." ) if cpu_intensive: Q = mp.Queue(1000) proc = mp.Process else: Q = Queue(1000) proc = Thread # spawn processes if target_data_type == "ndarray": arguments = [ [func, Q, part, i, use_worker_id] for i, part in enumerate(np.array_split(data, n_proc)) ] else: step = ( int(len(data) / n_proc + 1) if len(data) % n_proc != 0 else int(len(data) / n_proc) ) arguments = [ [func, Q, part, i, use_worker_id] for i, part in enumerate( [data[i: i + step] for i in range(0, len(data), step)] ) ] processes = [] for i in range(n_proc): p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) processes += [p] # start processes print(f"Start prefetching...") import time start = time.time() gather_res = [[] for _ in range(n_proc)] try: for p in processes: p.start() k = 0 while k < n_proc: # get result res = Q.get() if res == "Done": k += 1 else: gather_res[res[0]] = res[1] except Exception as e: print("Exception: ", e) for p in processes: p.terminate() raise e finally: for p in processes: p.join() print(f"Prefetching complete. [{time.time() - start} sec.]") if target_data_type == 'ndarray': if not isinstance(gather_res[0], np.ndarray): return np.concatenate([np.asarray(r) for r in gather_res], axis=0) # order outputs return np.concatenate(gather_res, axis=0) elif target_data_type == 'list': out = [] for r in gather_res: out.extend(r) return out else: return gather_res ================================================ FILE: ldm_exp/main.py ================================================ import argparse, os, sys, datetime, glob, importlib, csv import numpy as np import time import torch import torchvision import pytorch_lightning as pl from packaging import version from omegaconf import OmegaConf from torch.utils.data import random_split, DataLoader, Dataset, Subset from functools import partial from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True from PIL import Image from pytorch_lightning import seed_everything from pytorch_lightning.trainer import Trainer from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor from pytorch_lightning.utilities.distributed import rank_zero_only from pytorch_lightning.utilities import rank_zero_info from ldm.data.base import Txt2ImgIterableBaseDataset from ldm.util import instantiate_from_config def get_parser(**parser_kwargs): def str2bool(v): if isinstance(v, bool): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise argparse.ArgumentTypeError("Boolean value expected.") parser = argparse.ArgumentParser(**parser_kwargs) parser.add_argument( "-n", "--name", type=str, const=True, default="", nargs="?", help="postfix for logdir", ) parser.add_argument( "-r", "--resume", type=str, const=True, default="", nargs="?", help="resume from logdir or checkpoint in logdir", ) parser.add_argument( "--load_pruned_model", type=str, default=None, ) parser.add_argument( "-b", "--base", nargs="*", metavar="base_config.yaml", help="paths to base configs. Loaded from left-to-right. " "Parameters can be overwritten or added with command-line options of the form `--key value`.", default=list(), ) parser.add_argument( "-t", "--train", type=str2bool, const=True, default=False, nargs="?", help="train", ) parser.add_argument( "--no-test", type=str2bool, const=True, default=False, nargs="?", help="disable test", ) parser.add_argument( "-p", "--project", help="name of new or path to existing project" ) parser.add_argument( "-d", "--debug", type=str2bool, nargs="?", const=True, default=False, help="enable post-mortem debugging", ) parser.add_argument( "-s", "--seed", type=int, default=23, help="seed for seed_everything", ) parser.add_argument( "-f", "--postfix", type=str, default="", help="post-postfix for default name", ) parser.add_argument( "-l", "--logdir", type=str, default="logs", help="directory for logging dat shit", ) parser.add_argument( "--scale_lr", type=str2bool, nargs="?", const=True, default=True, help="scale base-lr by ngpu * batch_size * n_accumulate", ) return parser def nondefault_trainer_args(opt): parser = argparse.ArgumentParser() parser = Trainer.add_argparse_args(parser) args = parser.parse_args([]) return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k)) class WrappedDataset(Dataset): """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" def __init__(self, dataset): self.data = dataset def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] def worker_init_fn(_): worker_info = torch.utils.data.get_worker_info() dataset = worker_info.dataset worker_id = worker_info.id if isinstance(dataset, Txt2ImgIterableBaseDataset): split_size = dataset.num_records // worker_info.num_workers # reset num_records to the true number to retain reliable length information dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size] current_id = np.random.choice(len(np.random.get_state()[1]), 1) return np.random.seed(np.random.get_state()[1][current_id] + worker_id) else: return np.random.seed(np.random.get_state()[1][0] + worker_id) class DataModuleFromConfig(pl.LightningDataModule): def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, shuffle_val_dataloader=False): super().__init__() self.batch_size = batch_size self.dataset_configs = dict() self.num_workers = num_workers if num_workers is not None else batch_size * 2 self.use_worker_init_fn = use_worker_init_fn if train is not None: self.dataset_configs["train"] = train self.train_dataloader = self._train_dataloader if validation is not None: self.dataset_configs["validation"] = validation self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader) if test is not None: self.dataset_configs["test"] = test self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader) if predict is not None: self.dataset_configs["predict"] = predict self.predict_dataloader = self._predict_dataloader self.wrap = wrap def prepare_data(self): for data_cfg in self.dataset_configs.values(): instantiate_from_config(data_cfg) def setup(self, stage=None): self.datasets = dict( (k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) if self.wrap: for k in self.datasets: self.datasets[k] = WrappedDataset(self.datasets[k]) def _train_dataloader(self): is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None return DataLoader(self.datasets["train"], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True, worker_init_fn=init_fn) def _val_dataloader(self, shuffle=False): if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None return DataLoader(self.datasets["validation"], batch_size=self.batch_size, num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle) def _test_dataloader(self, shuffle=False): is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None # do not shuffle dataloader for iterable dataset shuffle = shuffle and (not is_iterable_dataset) return DataLoader(self.datasets["test"], batch_size=self.batch_size, num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle) def _predict_dataloader(self, shuffle=False): if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None return DataLoader(self.datasets["predict"], batch_size=self.batch_size, num_workers=self.num_workers, worker_init_fn=init_fn) class SetupCallback(Callback): def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): super().__init__() self.resume = resume self.now = now self.logdir = logdir self.ckptdir = ckptdir self.cfgdir = cfgdir self.config = config self.lightning_config = lightning_config def on_keyboard_interrupt(self, trainer, pl_module): if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(self.ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) def on_pretrain_routine_start(self, trainer, pl_module): if trainer.global_rank == 0: # Create logdirs and save configs os.makedirs(self.logdir, exist_ok=True) os.makedirs(self.ckptdir, exist_ok=True) os.makedirs(self.cfgdir, exist_ok=True) with open(os.path.join(self.ckptdir, 'train.sh'), 'w') as f: f.write('python ' + ' '.join(sys.argv)) if "callbacks" in self.lightning_config: if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']: os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True) print("Project config") print(OmegaConf.to_yaml(self.config)) OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) print("Lightning config") print(OmegaConf.to_yaml(self.lightning_config)) OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}), os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now))) else: # ModelCheckpoint callback created log directory --- remove it if not self.resume and os.path.exists(self.logdir): dst, name = os.path.split(self.logdir) dst = os.path.join(dst, "child_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) try: os.rename(self.logdir, dst) except FileNotFoundError: pass class ImageLogger(Callback): def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=False, rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False, log_images_kwargs=None): super().__init__() self.rescale = rescale self.batch_freq = batch_frequency self.max_images = max_images self.logger_log_images = { pl.loggers.TestTubeLogger: self._testtube, } self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)] if not increase_log_steps: self.log_steps = [self.batch_freq] self.clamp = clamp self.disabled = disabled self.log_on_batch_idx = log_on_batch_idx self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} self.log_first_step = log_first_step @rank_zero_only def _testtube(self, pl_module, images, batch_idx, split): for k in images: grid = torchvision.utils.make_grid(images[k]) grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w tag = f"{split}/{k}" pl_module.logger.experiment.add_image( tag, grid, global_step=pl_module.global_step) @rank_zero_only def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx): root = os.path.join(save_dir, "images", split) for k in images: grid = torchvision.utils.make_grid(images[k], nrow=4) if self.rescale: grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1) grid = grid.numpy() grid = (grid * 255).astype(np.uint8) filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format( k, global_step, current_epoch, batch_idx) path = os.path.join(root, filename) os.makedirs(os.path.split(path)[0], exist_ok=True) Image.fromarray(grid).save(path) def log_img(self, pl_module, batch, batch_idx, split="train"): check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0 hasattr(pl_module, "log_images") and callable(pl_module.log_images) and self.max_images > 0): logger = type(pl_module.logger) is_train = pl_module.training if is_train: pl_module.eval() with torch.no_grad(): images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) for k in images: N = min(images[k].shape[0], self.max_images) images[k] = images[k][:N] if isinstance(images[k], torch.Tensor): images[k] = images[k].detach().cpu() if self.clamp: images[k] = torch.clamp(images[k], -1., 1.) self.log_local(pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch, batch_idx) logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None) logger_log_images(pl_module, images, pl_module.global_step, split) if is_train: pl_module.train() def check_frequency(self, check_idx): if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and ( check_idx > 0 or self.log_first_step): try: self.log_steps.pop(0) except IndexError as e: print(e) pass return True return False def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): if not self.disabled and (pl_module.global_step > 0 or self.log_first_step): self.log_img(pl_module, batch, batch_idx, split="train") def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): if not self.disabled and pl_module.global_step > 0: self.log_img(pl_module, batch, batch_idx, split="val") if hasattr(pl_module, 'calibrate_grad_norm'): if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0: self.log_gradients(trainer, pl_module, batch_idx=batch_idx) class CUDACallback(Callback): # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py def on_train_epoch_start(self, trainer, pl_module): # Reset the memory use counter torch.cuda.reset_peak_memory_stats(trainer.root_gpu) torch.cuda.synchronize(trainer.root_gpu) self.start_time = time.time() def on_train_epoch_end(self, trainer, pl_module, outputs): torch.cuda.synchronize(trainer.root_gpu) max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20 epoch_time = time.time() - self.start_time try: max_memory = trainer.training_type_plugin.reduce(max_memory) epoch_time = trainer.training_type_plugin.reduce(epoch_time) rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds") rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB") except AttributeError: pass if __name__ == "__main__": # custom parser to specify config files, train, test and debug mode, # postfix, resume. # `--key value` arguments are interpreted as arguments to the trainer. # `nested.key=value` arguments are interpreted as config parameters. # configs are merged from left-to-right followed by command line parameters. # model: # base_learning_rate: float # target: path to lightning module # params: # key: value # data: # target: main.DataModuleFromConfig # params: # batch_size: int # wrap: bool # train: # target: path to train dataset # params: # key: value # validation: # target: path to validation dataset # params: # key: value # test: # target: path to test dataset # params: # key: value # lightning: (optional, has sane defaults and can be specified on cmdline) # trainer: # additional arguments to trainer # logger: # logger to instantiate # modelcheckpoint: # modelcheckpoint to instantiate # callbacks: # callback1: # target: importpath # params: # key: value now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") # add cwd for convenience and to make classes in this file available when # running as `python main.py` # (in particular `main.DataModuleFromConfig`) sys.path.append(os.getcwd()) parser = get_parser() parser = Trainer.add_argparse_args(parser) opt, unknown = parser.parse_known_args() if opt.name and opt.resume: raise ValueError( "-n/--name and -r/--resume cannot be specified both." "If you want to resume training in a new log folder, " "use -n/--name in combination with --resume_from_checkpoint" ) if opt.resume: if not os.path.exists(opt.resume): raise ValueError("Cannot find {}".format(opt.resume)) if os.path.isfile(opt.resume): paths = opt.resume.split("/") # idx = len(paths)-paths[::-1].index("logs")+1 # logdir = "/".join(paths[:idx]) logdir = "/".join(paths[:-2]) ckpt = opt.resume else: assert os.path.isdir(opt.resume), opt.resume logdir = opt.resume.rstrip("/") ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") opt.resume_from_checkpoint = ckpt base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml"))) opt.base = base_configs + opt.base _tmp = logdir.split("/") nowname = _tmp[-1] else: if opt.name: name = "_" + opt.name elif opt.base: cfg_fname = os.path.split(opt.base[0])[-1] cfg_name = os.path.splitext(cfg_fname)[0] name = "_" + cfg_name else: name = "" nowname = now + name + opt.postfix logdir = os.path.join(opt.logdir, nowname) ckptdir = os.path.join(logdir, "checkpoints") cfgdir = os.path.join(logdir, "configs") seed_everything(opt.seed) try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] cli = OmegaConf.from_dotlist(unknown) config = OmegaConf.merge(*configs, cli) lightning_config = config.pop("lightning", OmegaConf.create()) # merge trainer cli with config trainer_config = lightning_config.get("trainer", OmegaConf.create()) # default to ddp trainer_config["accelerator"] = "ddp" for k in nondefault_trainer_args(opt): trainer_config[k] = getattr(opt, k) trainer_config.pop('load_pruned_model', None) if not "gpus" in trainer_config: del trainer_config["accelerator"] cpu = True else: gpuinfo = trainer_config["gpus"] print(f"Running on GPUs {gpuinfo}") cpu = False trainer_opt = argparse.Namespace(**trainer_config) lightning_config.trainer = trainer_config # model def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt)#, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model if opt.load_pruned_model is not None: print("Loading pruned model from {}".format(opt.load_pruned_model)) model = torch.load(opt.load_pruned_model) else: model = get_model() # trainer and callbacks trainer_kwargs = dict() # default logger configs default_logger_cfgs = { "wandb": { "target": "pytorch_lightning.loggers.WandbLogger", "params": { "name": nowname, "save_dir": logdir, "offline": opt.debug, "id": nowname, } }, "testtube": { "target": "pytorch_lightning.loggers.TestTubeLogger", "params": { "name": "testtube", "save_dir": logdir, } }, } default_logger_cfg = default_logger_cfgs["testtube"] if "logger" in lightning_config: logger_cfg = lightning_config.logger else: logger_cfg = OmegaConf.create() logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { "target": "pytorch_lightning.callbacks.ModelCheckpoint", "params": { "dirpath": ckptdir, "filename": "{epoch:06}", "verbose": True, "save_last": True, } } if hasattr(model, "monitor"): print(f"Monitoring {model.monitor} as checkpoint metric.") default_modelckpt_cfg["params"]["monitor"] = model.monitor default_modelckpt_cfg["params"]["save_top_k"] = 3 if "modelcheckpoint" in lightning_config: modelckpt_cfg = lightning_config.modelcheckpoint else: modelckpt_cfg = OmegaConf.create() modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}") if version.parse(pl.__version__) < version.parse('1.4.0'): trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) # add callback which sets up log directory default_callbacks_cfg = { "setup_callback": { "target": "main.SetupCallback", "params": { "resume": opt.resume, "now": now, "logdir": logdir, "ckptdir": ckptdir, "cfgdir": cfgdir, "config": config, "lightning_config": lightning_config, } }, "image_logger": { "target": "main.ImageLogger", "params": { "batch_frequency": 750, "max_images": 4, "clamp": True } }, "learning_rate_logger": { "target": "main.LearningRateMonitor", "params": { "logging_interval": "step", # "log_momentum": True } }, "cuda_callback": { "target": "main.CUDACallback" }, } if version.parse(pl.__version__) >= version.parse('1.4.0'): default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg}) if "callbacks" in lightning_config: callbacks_cfg = lightning_config.callbacks else: callbacks_cfg = OmegaConf.create() if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg: print( 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') default_metrics_over_trainsteps_ckpt_dict = { 'metrics_over_trainsteps_checkpoint': {"target": 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': { "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), "filename": "{epoch:06}-{step:09}", "verbose": True, 'save_top_k': -1, 'every_n_train_steps': 10000, 'save_weights_only': True } } } default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict) callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'): callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint elif 'ignore_keys_callback' in callbacks_cfg: del callbacks_cfg['ignore_keys_callback'] trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) trainer.logdir = logdir ### # data data = instantiate_from_config(config.data) # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. # lightning still takes care of proper multiprocessing though data.prepare_data() data.setup() print("#### Data #####") for k in data.datasets: print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}") # configure learning rate bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate if not cpu: ngpu = len(lightning_config.trainer.gpus.strip(",").split(',')) else: ngpu = 1 if 'accumulate_grad_batches' in lightning_config.trainer: accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches else: accumulate_grad_batches = 1 print(f"accumulate_grad_batches = {accumulate_grad_batches}") lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches if opt.scale_lr: model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr print( "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format( model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr)) else: model.learning_rate = base_lr print("++++ NOT USING LR SCALING ++++") print(f"Setting learning rate to {model.learning_rate:.2e}") # allow checkpointing via USR1 def melk(*args, **kwargs): # run all checkpoint hooks if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) def divein(*args, **kwargs): if trainer.global_rank == 0: import pudb; pudb.set_trace() import signal signal.signal(signal.SIGUSR1, melk) signal.signal(signal.SIGUSR2, divein) # run if opt.train: try: trainer.fit(model, data) except Exception: melk() raise if not opt.no_test and not trainer.interrupted: trainer.test(model, data) except Exception: if opt.debug and trainer.global_rank == 0: try: import pudb as debugger except ImportError: import pdb as debugger debugger.post_mortem() raise finally: # move newly created debug project to debug_runs if opt.debug and not opt.resume and trainer.global_rank == 0: dst, name = os.path.split(logdir) dst = os.path.join(dst, "debug_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) os.rename(logdir, dst) if trainer.global_rank == 0: print(trainer.profiler.summary()) ================================================ FILE: ldm_exp/models/first_stage_models/kl-f16/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.AutoencoderKL params: monitor: val/rec_loss embed_dim: 16 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 1.0e-06 disc_weight: 0.5 ddconfig: double_z: true z_channels: 16 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 16 dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 6 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/kl-f32/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.AutoencoderKL params: monitor: val/rec_loss embed_dim: 64 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 1.0e-06 disc_weight: 0.5 ddconfig: double_z: true z_channels: 64 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 1 - 2 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: - 16 - 8 dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 6 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/kl-f4/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.AutoencoderKL params: monitor: val/rec_loss embed_dim: 3 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 1.0e-06 disc_weight: 0.5 ddconfig: double_z: true z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 data: target: main.DataModuleFromConfig params: batch_size: 10 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/kl-f8/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.AutoencoderKL params: monitor: val/rec_loss embed_dim: 4 lossconfig: target: ldm.modules.losses.LPIPSWithDiscriminator params: disc_start: 50001 kl_weight: 1.0e-06 disc_weight: 0.5 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 data: target: main.DataModuleFromConfig params: batch_size: 4 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/vq-f16/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.VQModel params: embed_dim: 8 n_embed: 16384 ddconfig: double_z: false z_channels: 8 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 16 dropout: 0.0 lossconfig: target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator params: disc_conditional: false disc_in_channels: 3 disc_start: 250001 disc_weight: 0.75 disc_num_layers: 2 codebook_weight: 1.0 data: target: main.DataModuleFromConfig params: batch_size: 14 num_workers: 20 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/vq-f4/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.VQModel params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator params: disc_conditional: false disc_in_channels: 3 disc_start: 0 disc_weight: 0.75 codebook_weight: 1.0 data: target: main.DataModuleFromConfig params: batch_size: 8 num_workers: 16 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/vq-f4-noattn/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.VQModel params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: attn_type: none double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator params: disc_conditional: false disc_in_channels: 3 disc_start: 11 disc_weight: 0.75 codebook_weight: 1.0 data: target: main.DataModuleFromConfig params: batch_size: 8 num_workers: 12 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/vq-f8/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.VQModel params: embed_dim: 4 n_embed: 16384 monitor: val/rec_loss ddconfig: double_z: false z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 32 dropout: 0.0 lossconfig: target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator params: disc_conditional: false disc_in_channels: 3 disc_num_layers: 2 disc_start: 1 disc_weight: 0.6 codebook_weight: 1.0 data: target: main.DataModuleFromConfig params: batch_size: 10 num_workers: 20 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/first_stage_models/vq-f8-n256/config.yaml ================================================ model: base_learning_rate: 4.5e-06 target: ldm.models.autoencoder.VQModel params: embed_dim: 4 n_embed: 256 monitor: val/rec_loss ddconfig: double_z: false z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 32 dropout: 0.0 lossconfig: target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator params: disc_conditional: false disc_in_channels: 3 disc_start: 250001 disc_weight: 0.75 codebook_weight: 1.0 data: target: main.DataModuleFromConfig params: batch_size: 10 num_workers: 20 wrap: true train: target: ldm.data.openimages.FullOpenImagesTrain params: size: 384 crop_size: 256 validation: target: ldm.data.openimages.FullOpenImagesValidation params: size: 384 crop_size: 256 ================================================ FILE: ldm_exp/models/ldm/bsr_sr/config.yaml ================================================ model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0155 log_every_t: 100 timesteps: 1000 loss_type: l2 first_stage_key: image cond_stage_key: LR_image image_size: 64 channels: 3 concat_mode: true cond_stage_trainable: false unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 6 out_channels: 3 model_channels: 160 attention_resolutions: - 16 - 8 num_res_blocks: 2 channel_mult: - 1 - 2 - 2 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: torch.nn.Identity data: target: main.DataModuleFromConfig params: batch_size: 64 wrap: false num_workers: 12 train: target: ldm.data.openimages.SuperresOpenImagesAdvancedTrain params: size: 256 degradation: bsrgan_light downscale_f: 4 min_crop_f: 0.5 max_crop_f: 1.0 random_crop: true validation: target: ldm.data.openimages.SuperresOpenImagesAdvancedValidation params: size: 256 degradation: bsrgan_light downscale_f: 4 min_crop_f: 0.5 max_crop_f: 1.0 random_crop: true ================================================ FILE: ldm_exp/models/ldm/celeba256/config.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: class_label image_size: 64 channels: 3 cond_stage_trainable: false concat_mode: false monitor: val/loss unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 48 num_workers: 5 wrap: false train: target: ldm.data.faceshq.CelebAHQTrain params: size: 256 validation: target: ldm.data.faceshq.CelebAHQValidation params: size: 256 ================================================ FILE: ldm_exp/models/ldm/cin256/config.yaml ================================================ model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: class_label image_size: 32 channels: 4 cond_stage_trainable: true conditioning_key: crossattn monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 256 attention_resolutions: - 4 - 2 - 1 num_res_blocks: 2 channel_mult: - 1 - 2 - 4 num_head_channels: 32 use_spatial_transformer: true transformer_depth: 1 context_dim: 512 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 4 n_embed: 16384 ddconfig: double_z: false z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 2 - 4 num_res_blocks: 2 attn_resolutions: - 32 dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.ClassEmbedder params: embed_dim: 512 key: class_label data: target: main.DataModuleFromConfig params: batch_size: 64 num_workers: 12 wrap: false train: target: ldm.data.imagenet.ImageNetTrain params: config: size: 256 validation: target: ldm.data.imagenet.ImageNetValidation params: config: size: 256 ================================================ FILE: ldm_exp/models/ldm/ffhq256/config.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: class_label image_size: 64 channels: 3 cond_stage_trainable: false concat_mode: false monitor: val/loss unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 42 num_workers: 5 wrap: false train: target: ldm.data.faceshq.FFHQTrain params: size: 256 validation: target: ldm.data.faceshq.FFHQValidation params: size: 256 ================================================ FILE: ldm_exp/models/ldm/inpainting_big/config.yaml ================================================ model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0205 log_every_t: 100 timesteps: 1000 loss_type: l1 first_stage_key: image cond_stage_key: masked_image image_size: 64 channels: 3 concat_mode: true monitor: val/loss scheduler_config: target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler params: verbosity_interval: 0 warm_up_steps: 1000 max_decay_steps: 50000 lr_start: 0.001 lr_max: 0.1 lr_min: 0.0001 unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 7 out_channels: 3 model_channels: 256 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_heads: 8 resblock_updown: true first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: attn_type: none double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: ldm.modules.losses.contperceptual.DummyLoss cond_stage_config: __is_first_stage__ ================================================ FILE: ldm_exp/models/ldm/layout2img-openimages256/config.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0205 log_every_t: 100 timesteps: 1000 loss_type: l1 first_stage_key: image cond_stage_key: coordinates_bbox image_size: 64 channels: 3 conditioning_key: crossattn cond_stage_trainable: true unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 128 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 use_spatial_transformer: true transformer_depth: 3 context_dim: 512 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.BERTEmbedder params: n_embed: 512 n_layer: 16 vocab_size: 8192 max_seq_len: 92 use_tokenizer: false monitor: val/loss_simple_ema data: target: main.DataModuleFromConfig params: batch_size: 24 wrap: false num_workers: 10 train: target: ldm.data.openimages.OpenImagesBBoxTrain params: size: 256 validation: target: ldm.data.openimages.OpenImagesBBoxValidation params: size: 256 ================================================ FILE: ldm_exp/models/ldm/lsun_beds256/config.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: class_label image_size: 64 channels: 3 cond_stage_trainable: false concat_mode: false monitor: val/loss unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 224 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 4 num_head_channels: 32 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: __is_unconditional__ data: target: main.DataModuleFromConfig params: batch_size: 48 num_workers: 5 wrap: false train: target: ldm.data.lsun.LSUNBedroomsTrain params: size: 256 validation: target: ldm.data.lsun.LSUNBedroomsValidation params: size: 256 ================================================ FILE: ldm_exp/models/ldm/lsun_churches256/config.yaml ================================================ model: base_learning_rate: 5.0e-05 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0155 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 loss_type: l1 first_stage_key: image cond_stage_key: image image_size: 32 channels: 4 cond_stage_trainable: false concat_mode: false scale_by_std: true monitor: val/loss_simple_ema scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: - 10000 cycle_lengths: - 10000000000000 f_start: - 1.0e-06 f_max: - 1.0 f_min: - 1.0 unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 192 attention_resolutions: - 1 - 2 - 4 - 8 num_res_blocks: 2 channel_mult: - 1 - 2 - 2 - 4 - 4 num_heads: 8 use_scale_shift_norm: true resblock_updown: true first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss 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 lossconfig: target: torch.nn.Identity cond_stage_config: '__is_unconditional__' data: target: main.DataModuleFromConfig params: batch_size: 96 num_workers: 5 wrap: false train: target: ldm.data.lsun.LSUNChurchesTrain params: size: 256 validation: target: ldm.data.lsun.LSUNChurchesValidation params: size: 256 ================================================ FILE: ldm_exp/models/ldm/semantic_synthesis256/config.yaml ================================================ model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0205 log_every_t: 100 timesteps: 1000 loss_type: l1 first_stage_key: image cond_stage_key: segmentation image_size: 64 channels: 3 concat_mode: true cond_stage_trainable: true unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 6 out_channels: 3 model_channels: 128 attention_resolutions: - 32 - 16 - 8 num_res_blocks: 2 channel_mult: - 1 - 4 - 8 num_heads: 8 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.SpatialRescaler params: n_stages: 2 in_channels: 182 out_channels: 3 ================================================ FILE: ldm_exp/models/ldm/semantic_synthesis512/config.yaml ================================================ model: base_learning_rate: 1.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0205 log_every_t: 100 timesteps: 1000 loss_type: l1 first_stage_key: image cond_stage_key: segmentation image_size: 128 channels: 3 concat_mode: true cond_stage_trainable: true unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 128 in_channels: 6 out_channels: 3 model_channels: 128 attention_resolutions: - 32 - 16 - 8 num_res_blocks: 2 channel_mult: - 1 - 4 - 8 num_heads: 8 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 monitor: val/rec_loss ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.SpatialRescaler params: n_stages: 2 in_channels: 182 out_channels: 3 data: target: main.DataModuleFromConfig params: batch_size: 8 wrap: false num_workers: 10 train: target: ldm.data.landscapes.RFWTrain params: size: 768 crop_size: 512 segmentation_to_float32: true validation: target: ldm.data.landscapes.RFWValidation params: size: 768 crop_size: 512 segmentation_to_float32: true ================================================ FILE: ldm_exp/models/ldm/text2img256/config.yaml ================================================ model: base_learning_rate: 2.0e-06 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.0015 linear_end: 0.0195 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: image cond_stage_key: caption image_size: 64 channels: 3 cond_stage_trainable: true conditioning_key: crossattn monitor: val/loss_simple_ema unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 64 in_channels: 3 out_channels: 3 model_channels: 192 attention_resolutions: - 8 - 4 - 2 num_res_blocks: 2 channel_mult: - 1 - 2 - 3 - 5 num_head_channels: 32 use_spatial_transformer: true transformer_depth: 1 context_dim: 640 first_stage_config: target: ldm.models.autoencoder.VQModelInterface params: embed_dim: 3 n_embed: 8192 ddconfig: double_z: false z_channels: 3 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.BERTEmbedder params: n_embed: 640 n_layer: 32 data: target: main.DataModuleFromConfig params: batch_size: 28 num_workers: 10 wrap: false train: target: ldm.data.previews.pytorch_dataset.PreviewsTrain params: size: 256 validation: target: ldm.data.previews.pytorch_dataset.PreviewsValidation params: size: 256 ================================================ FILE: ldm_exp/notebook_helpers.py ================================================ from torchvision.datasets.utils import download_url from ldm.util import instantiate_from_config import torch import os # todo ? from google.colab import files from IPython.display import Image as ipyimg import ipywidgets as widgets from PIL import Image from numpy import asarray from einops import rearrange, repeat import torch, torchvision from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import ismap import time from omegaconf import OmegaConf def download_models(mode): if mode == "superresolution": # this is the small bsr light model url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1' url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1' path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml' path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt' download_url(url_conf, path_conf) download_url(url_ckpt, path_ckpt) path_conf = path_conf + '/?dl=1' # fix it path_ckpt = path_ckpt + '/?dl=1' # fix it return path_conf, path_ckpt else: raise NotImplementedError def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") global_step = pl_sd["global_step"] sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return {"model": model}, global_step def get_model(mode): path_conf, path_ckpt = download_models(mode) config = OmegaConf.load(path_conf) model, step = load_model_from_config(config, path_ckpt) return model def get_custom_cond(mode): dest = "data/example_conditioning" if mode == "superresolution": uploaded_img = files.upload() filename = next(iter(uploaded_img)) name, filetype = filename.split(".") # todo assumes just one dot in name ! os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}") elif mode == "text_conditional": w = widgets.Text(value='A cake with cream!', disabled=True) display(w) with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f: f.write(w.value) elif mode == "class_conditional": w = widgets.IntSlider(min=0, max=1000) display(w) with open(f"{dest}/{mode}/custom.txt", 'w') as f: f.write(w.value) else: raise NotImplementedError(f"cond not implemented for mode{mode}") def get_cond_options(mode): path = "data/example_conditioning" path = os.path.join(path, mode) onlyfiles = [f for f in sorted(os.listdir(path))] return path, onlyfiles def select_cond_path(mode): path = "data/example_conditioning" # todo path = os.path.join(path, mode) onlyfiles = [f for f in sorted(os.listdir(path))] selected = widgets.RadioButtons( options=onlyfiles, description='Select conditioning:', disabled=False ) display(selected) selected_path = os.path.join(path, selected.value) return selected_path def get_cond(mode, selected_path): example = dict() if mode == "superresolution": up_f = 4 visualize_cond_img(selected_path) c = Image.open(selected_path) c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True) c_up = rearrange(c_up, '1 c h w -> 1 h w c') c = rearrange(c, '1 c h w -> 1 h w c') c = 2. * c - 1. c = c.to(torch.device("cuda")) example["LR_image"] = c example["image"] = c_up return example def visualize_cond_img(path): display(ipyimg(filename=path)) def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None): example = get_cond(task, selected_path) save_intermediate_vid = False n_runs = 1 masked = False guider = None ckwargs = None mode = 'ddim' ddim_use_x0_pred = False temperature = 1. eta = 1. make_progrow = True custom_shape = None height, width = example["image"].shape[1:3] split_input = height >= 128 and width >= 128 if split_input: ks = 128 stride = 64 vqf = 4 # model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride), "vqf": vqf, "patch_distributed_vq": True, "tie_braker": False, "clip_max_weight": 0.5, "clip_min_weight": 0.01, "clip_max_tie_weight": 0.5, "clip_min_tie_weight": 0.01} else: if hasattr(model, "split_input_params"): delattr(model, "split_input_params") invert_mask = False x_T = None for n in range(n_runs): if custom_shape is not None: x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0]) logs = make_convolutional_sample(example, model, mode=mode, custom_steps=custom_steps, eta=eta, swap_mode=False , masked=masked, invert_mask=invert_mask, quantize_x0=False, custom_schedule=None, decode_interval=10, resize_enabled=resize_enabled, custom_shape=custom_shape, temperature=temperature, noise_dropout=0., corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid, make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred ) return logs @torch.no_grad() def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None, mask=None, x0=None, quantize_x0=False, img_callback=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, x_T=None, log_every_t=None ): ddim = DDIMSampler(model) bs = shape[0] # dont know where this comes from but wayne shape = shape[1:] # cut batch dim print(f"Sampling with eta = {eta}; steps: {steps}") samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback, normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta, mask=mask, x0=x0, temperature=temperature, verbose=False, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T) return samples, intermediates @torch.no_grad() def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False, invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000, resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None, corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False): log = dict() z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key, return_first_stage_outputs=True, force_c_encode=not (hasattr(model, 'split_input_params') and model.cond_stage_key == 'coordinates_bbox'), return_original_cond=True) log_every_t = 1 if save_intermediate_vid else None if custom_shape is not None: z = torch.randn(custom_shape) print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}") z0 = None log["input"] = x log["reconstruction"] = xrec if ismap(xc): log["original_conditioning"] = model.to_rgb(xc) if hasattr(model, 'cond_stage_key'): log[model.cond_stage_key] = model.to_rgb(xc) else: log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x) if model.cond_stage_model: log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x) if model.cond_stage_key =='class_label': log[model.cond_stage_key] = xc[model.cond_stage_key] with model.ema_scope("Plotting"): t0 = time.time() img_cb = None sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape, eta=eta, quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0, temperature=temperature, noise_dropout=noise_dropout, score_corrector=corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t) t1 = time.time() if ddim_use_x0_pred: sample = intermediates['pred_x0'][-1] x_sample = model.decode_first_stage(sample) try: x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True) log["sample_noquant"] = x_sample_noquant log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) except: pass log["sample"] = x_sample log["time"] = t1 - t0 return log ================================================ FILE: ldm_exp/profile_ldm.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse parser = argparse.ArgumentParser() parser.add_argument("--pruned_model", type=str) parser.add_argument("--finetuned_ckpt", type=str) args = parser.parse_args() #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp from ldm.models.diffusion.ddim import DDIMSampler print("Loading model from ", args.pruned_model) model = torch.load(args.pruned_model, map_location="cpu") print("Loading finetuned parameters from ", args.finetuned_ckpt) pl_sd = torch.load(args.finetuned_ckpt, map_location="cpu") sd = pl_sd["state_dict"] m, u = model.load_state_dict(sd, strict=False) model.cuda() print(model) sampler = DDIMSampler(model) n_samples_per_class=1 xc = torch.tensor(n_samples_per_class*[0]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} num_macs, num_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) print("Number of parameters: {}", num_params/1000000, "M") print("Number of MACs: {}", num_macs/1e9, "G") used = torch.cuda.max_memory_allocated() print("gpu used {} MB".format(used/1024**2)) ================================================ FILE: ldm_exp/profile_ldm_pretrained.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import torch import torchvision.models as models from torch.profiler import profile, record_function, ProfilerActivity #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model from ldm.models.diffusion.ddim import DDIMSampler model = get_model() sampler = DDIMSampler(model) n_samples_per_class=1 xc = torch.tensor(n_samples_per_class*[0]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} num_macs, num_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], profile_memory=True, record_shapes=True) as prof: model.model.diffusion_model(**example_inputs) print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10)) ================================================ FILE: ldm_exp/profile_model.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse from ldm.modules.attention import CrossAttention parser = argparse.ArgumentParser() parser.add_argument("--sparsity", type=float, default=0.0) parser.add_argument("--pruner", type=str, choices=["magnitude", "random", "taylor", "diff-pruning", "reinit", "diff0"], default="magnitude") args = parser.parse_args() #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model from ldm.models.diffusion.ddim import DDIMSampler model = get_model() sampler = DDIMSampler(model) import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid classes = [25, 187, 448, 992] # define classes to be sampled here n_samples_per_class = 6 ddim_steps = 20 ddim_eta = 0.0 scale = 3.0 # for unconditional guidance print(model) print("Pruning ...") model.eval() if args.pruner == "magnitude": imp = tp.importance.MagnitudeImportance() elif args.pruner == "random": imp = tp.importance.RandomImportance() elif args.pruner == 'taylor': imp = tp.importance.TaylorImportance(multivariable=True) # standard first-order taylor expansion elif args.pruner == 'diff-pruning' or args.pruner == 'diff0': imp = tp.importance.TaylorImportance(multivariable=False) # a modified version, estimating the accumulated error of weight removal else: raise ValueError(f"Unknown pruner '{args.pruner}'") ignored_layers = [model.model.diffusion_model.out] channel_groups = {} iterative_steps = 1 uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for m in model.model.diffusion_model.modules(): if isinstance(m, CrossAttention): channel_groups[m.to_q] = m.heads channel_groups[m.to_k] = m.heads channel_groups[m.to_v] = m.heads xc = torch.tensor(n_samples_per_class*[classes[0]]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} base_macs, base_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) pruner = tp.pruner.MagnitudePruner( model.model.diffusion_model, example_inputs, importance=imp, iterative_steps=1, channel_groups =channel_groups, ch_sparsity=args.sparsity, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear], round_to=2 ) model.zero_grad() import random max_loss = -1 for t in range(1000): if args.pruner not in ['diff-pruning', 'taylor', 'diff0']: break xc = torch.tensor(random.sample(range(1000), n_samples_per_class)) #xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) encoded = model.encode_first_stage(samples_ddim) example_inputs = {"x": encoded.to(model.device), "timesteps": torch.full((n_samples_per_class,), t, device=model.device, dtype=torch.long), "context": c} loss = model.get_loss_at_t(example_inputs['x'], {model.cond_stage_key: xc.to(model.device)}, example_inputs['timesteps']) loss = loss[0] if loss > max_loss: max_loss = loss thres = 0.1 if args.pruner == 'diff-pruning' else 0.0 if args.pruner == 'diff-pruning' or args.pruner == 'diff0': if loss / max_loss {pruend_macs / 1e9:.2f}G") print(f"Params: {pruned_params / base_params * 100:.2f}%, {base_params / 1e6:.2f}M => {pruned_params / 1e6:.2f}M") device = torch.device("cuda") model.to(device) #base_macs, base_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) #print("Number of parameters: {}", base_params/1000000, "M") #print("Number of MACs: {}", base_macs/1e9, "G") # INIT LOGGERS starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) repetitions = 30 timings=np.zeros((repetitions,1)) #GPU-WARM-UP for _ in range(10): _ = model.model.diffusion_model(**example_inputs) # MEASURE PERFORMANCE with torch.no_grad(): for rep in range(repetitions): starter.record() _ = model.model.diffusion_model(**example_inputs) ender.record() # WAIT FOR GPU SYNC torch.cuda.synchronize() if rep == 0: print(torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024, "GB") curr_time = starter.elapsed_time(ender) timings[rep] = curr_time mean_syn = np.sum(timings) / repetitions std_syn = np.std(timings) print("Inference:") print("Latency:", mean_syn) print("FPS:", 1000/mean_syn) print("-----") for _ in range(10): _ = model.model.diffusion_model(**example_inputs) starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) repetitions = 30 timings=np.zeros((repetitions,1)) for rep in range(repetitions): c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} starter.record() model.model.diffusion_model.zero_grad() output = model.model.diffusion_model(**example_inputs) loss = torch.nn.functional.mse_loss(output, torch.randn_like(output)) loss.backward() if rep == 0: print(torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024, "GB") ender.record() # WAIT FOR GPU SYNC torch.cuda.synchronize() curr_time = starter.elapsed_time(ender) timings[rep] = curr_time mean_syn = np.sum(timings) / repetitions std_syn = np.std(timings) print("Training:") print("Latency:", mean_syn) print("FPS:", 1000/mean_syn) print("-----") ================================================ FILE: ldm_exp/prune_ldm.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse from ldm.modules.attention import CrossAttention parser = argparse.ArgumentParser() parser.add_argument("--sparsity", type=float, default=0.0) parser.add_argument("--pruner", type=str, choices=["magnitude", "random", "taylor", "diff-pruning", "reinit", "diff0"], default="magnitude") args = parser.parse_args() #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model from ldm.models.diffusion.ddim import DDIMSampler model = get_model() sampler = DDIMSampler(model) import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid classes = [25, 187, 448, 992] # define classes to be sampled here n_samples_per_class = 6 ddim_steps = 20 ddim_eta = 0.0 scale = 3.0 # for unconditional guidance print(model) print("Pruning ...") model.eval() if args.pruner == "magnitude": imp = tp.importance.MagnitudeImportance() elif args.pruner == "random": imp = tp.importance.RandomImportance() elif args.pruner == 'taylor': imp = tp.importance.TaylorImportance(multivariable=True) # standard first-order taylor expansion elif args.pruner == 'diff-pruning' or args.pruner == 'diff0': imp = tp.importance.TaylorImportance(multivariable=False) # a modified version, estimating the accumulated error of weight removal else: raise ValueError(f"Unknown pruner '{args.pruner}'") ignored_layers = [model.model.diffusion_model.out] channel_groups = {} iterative_steps = 1 uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for m in model.model.diffusion_model.modules(): if isinstance(m, CrossAttention): channel_groups[m.to_q] = m.heads channel_groups[m.to_k] = m.heads channel_groups[m.to_v] = m.heads xc = torch.tensor(n_samples_per_class*[classes[0]]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} base_macs, base_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) pruner = tp.pruner.MagnitudePruner( model.model.diffusion_model, example_inputs, importance=imp, iterative_steps=1, channel_groups =channel_groups, ch_sparsity=args.sparsity, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, target_layer_types=[torch.nn.Conv2d, torch.nn.Linear], round_to=2 ) model.zero_grad() import random max_loss = -1 for t in range(1000): if args.pruner not in ['diff-pruning', 'taylor', 'diff0']: break xc = torch.tensor(random.sample(range(1000), n_samples_per_class)) #xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) #encoded = model.encode_first_stage(samples_ddim) example_inputs = {"x": samples_ddim.to(model.device), "timesteps": torch.full((n_samples_per_class,), t, device=model.device, dtype=torch.long), "context": c} loss = model.get_loss_at_t(example_inputs['x'], {model.cond_stage_key: xc.to(model.device)}, example_inputs['timesteps']) loss = loss[0] if loss > max_loss: max_loss = loss thres = 0.1 if args.pruner == 'diff-pruning' else 0.0 if args.pruner == 'diff-pruning' or args.pruner == 'diff0': if loss / max_loss {pruend_macs / 1e9:.2f}G") print(f"Params: {pruned_params / base_params * 100:.2f}%, {base_params / 1e6:.2f}M => {pruned_params / 1e6:.2f}M") all_samples = list() with torch.no_grad(): with model.ema_scope(): uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for class_label in classes: print(f"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.") xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) all_samples.append(x_samples_ddim) # display as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') grid = make_grid(grid, nrow=n_samples_per_class) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() img = Image.fromarray(grid.astype(np.uint8)) img.save("samples.png") print("Saving pruned model ...") torch.save(model, "logs/pruned_model_{}_{}.pt".format(args.sparsity, args.pruner)) ================================================ FILE: ldm_exp/prune_ldm_no_grad.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse parser = argparse.ArgumentParser() parser.add_argument("--sparsity", type=float, default=0.0) parser.add_argument("--pruner", type=str, choices=["magnitude", "random"], default="magnitude") args = parser.parse_args() #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt)#, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model from ldm.models.diffusion.ddim import DDIMSampler model = get_model() sampler = DDIMSampler(model) import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid classes = [25, 187, 448, 992] # define classes to be sampled here n_samples_per_class = 6 ddim_steps = 20 ddim_eta = 0.0 scale = 3.0 # for unconditional guidance print(model) print("Pruning ...") model.eval() if args.pruner == "magnitude": imp = tp.importance.MagnitudeImportance() elif args.pruner == "random": imp = tp.importance.RandomImportance() if args.pruner == 'taylor': imp = tp.importance.TaylorImportance(multivariable=True) # standard first-order taylor expansion elif args.pruner == 'diff-pruning': imp = tp.importance.TaylorImportance(multivariable=False) # a modified version, estimating the accumulated error of weight removal else: raise ValueError(f"Unknown pruner '{args.pruner}'") ignored_layers = [model.model.diffusion_model.out] channel_groups = {} iterative_steps = 1 uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) class_label = classes[0] print(f"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.") xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} base_macs, base_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) pruner = tp.pruner.MagnitudePruner( model.model.diffusion_model, example_inputs, importance=imp, iterative_steps=1, channel_groups =channel_groups, ch_sparsity=args.sparsity, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear] ) pruner.step() print("After pruning") print(model) pruend_macs, pruned_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) print(f"MACs: {pruend_macs / base_macs * 100:.2f}%, {base_macs / 1e9:.2f}G => {pruend_macs / 1e9:.2f}G") print(f"Params: {pruned_params / base_params * 100:.2f}%, {base_params / 1e6:.2f}M => {pruned_params / 1e6:.2f}M") all_samples = list() with torch.no_grad(): with model.ema_scope(): uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for class_label in classes: print(f"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.") xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) all_samples.append(x_samples_ddim) # display as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') grid = make_grid(grid, nrow=n_samples_per_class) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() img = Image.fromarray(grid.astype(np.uint8)) img.save("samples.png") print("Saving pruned model ...") torch.save(model, "logs/pruned_model_{}_{}.pt".format(args.sparsity, args.pruner)) ================================================ FILE: ldm_exp/run.sh ================================================ CUDA_VISIBLE_DEVICES=0 python prune_ldm.py --sparsity 0.3 --pruner diff-pruning python main.py --base configs/latent-diffusion/cin256-v2.yaml -t --gpus 0,1,2,3 --load_pruned_model logs/pruned_model_0.3_diff-pruning.pt ================================================ FILE: ldm_exp/sample_for_FID.py ================================================ import sys, os sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse parser = argparse.ArgumentParser() parser.add_argument("--pruned_model", type=str, default=None) parser.add_argument("--finetuned_ckpt", type=str, default=None) parser.add_argument("--ipc", type=int, default=50) parser.add_argument("--output", type=str, default='run') parser.add_argument("--batch_size", type=int, default=50) args = parser.parse_args() #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp from ldm.models.diffusion.ddim import DDIMSampler def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model if args.pruned_model is None: model = get_model() else: print("Loading model from ", args.pruned_model) model = torch.load(args.pruned_model, map_location="cpu") print("Loading finetuned parameters from ", args.finetuned_ckpt) pl_sd = torch.load(args.finetuned_ckpt, map_location="cpu") sd = pl_sd["state_dict"] m, u = model.load_state_dict(sd, strict=False) model.cuda() print(model) sampler = DDIMSampler(model) num_params = sum(p.numel() for p in model.parameters()) print("Number of parameters: {}", num_params/1000000, "M") import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid classes = range(1000) # define classes to be sampled here n_samples_per_class = args.batch_size n_batch_per_class = args.ipc // args.batch_size ddim_steps = 250 ddim_eta = 0.0 scale = 3.0 # for unconditional guidance all_samples = list() from torchvision import utils as tvu os.makedirs(args.output, exist_ok=True) img_id = 0 with torch.no_grad(): with model.ema_scope(): uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for _ in range(n_batch_per_class): for class_label in classes: print(f"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.") xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) #all_samples.append(x_samples_ddim) for i in range(len(x_samples_ddim)): tvu.save_image( x_samples_ddim[i], os.path.join(args.output, f"{class_label}_{img_id}.png") ) img_id += 1 ================================================ FILE: ldm_exp/sample_imagenet.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp def load_model_from_config(config, ckpt): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model from ldm.models.diffusion.ddim import DDIMSampler model = get_model() sampler = DDIMSampler(model) print(model) num_params = sum(p.numel() for p in model.parameters()) print("Number of parameters: {}", num_params/1000000, "M") import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid classes = [0, 1, 2, 3]#[25, 187, 448, 992] # define classes to be sampled here n_samples_per_class = 6 ddim_steps = 20 ddim_eta = 0.0 scale = 1.5 # for unconditional guidance all_samples = list() with torch.no_grad(): with model.ema_scope(): uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for class_label in classes: print(f"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.") xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) all_samples.append(x_samples_ddim) # display as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') grid = make_grid(grid, nrow=n_samples_per_class) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() img = Image.fromarray(grid.astype(np.uint8)) img.save("samples_pretrained.png") ================================================ FILE: ldm_exp/sample_pruned.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse parser = argparse.ArgumentParser() parser.add_argument("--pruned_model", type=str) parser.add_argument("--finetuned_ckpt", type=str) args = parser.parse_args() #@title loading utils import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp from ldm.models.diffusion.ddim import DDIMSampler print("Loading model from ", args.pruned_model) model = torch.load(args.pruned_model, map_location="cpu") print("Loading finetuned parameters from ", args.finetuned_ckpt) pl_sd = torch.load(args.finetuned_ckpt, map_location="cpu") sd = pl_sd["state_dict"] m, u = model.load_state_dict(sd, strict=False) model.cuda() print(model) sampler = DDIMSampler(model) n_samples_per_class=1 xc = torch.tensor(n_samples_per_class*[0]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} num_macs, num_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) print("Number of parameters: {}", num_params/1000000, "M") print("Number of MACs: {}", num_macs/1e9, "G") import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid classes = [0, 1, 2, 3] #[25, 187, 448, 992] # define classes to be sampled here n_samples_per_class = 6 ddim_steps = 250 ddim_eta = 0.0 scale = 3 # for unconditional guidance all_samples = list() with torch.no_grad(): with model.ema_scope(): uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for class_label in classes: print(f"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.") xc = torch.tensor(n_samples_per_class*[class_label]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) all_samples.append(x_samples_ddim) # display as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') grid = make_grid(grid, nrow=n_samples_per_class) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() img = Image.fromarray(grid.astype(np.uint8)) img.save("samples.png") ================================================ FILE: ldm_exp/scripts/download_first_stages.sh ================================================ #!/bin/bash wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip cd models/first_stage_models/kl-f4 unzip -o model.zip cd ../kl-f8 unzip -o model.zip cd ../kl-f16 unzip -o model.zip cd ../kl-f32 unzip -o model.zip cd ../vq-f4 unzip -o model.zip cd ../vq-f4-noattn unzip -o model.zip cd ../vq-f8 unzip -o model.zip cd ../vq-f8-n256 unzip -o model.zip cd ../vq-f16 unzip -o model.zip cd ../.. ================================================ FILE: ldm_exp/scripts/download_models.sh ================================================ #!/bin/bash wget -O models/ldm/celeba256/celeba-256.zip https://ommer-lab.com/files/latent-diffusion/celeba.zip wget -O models/ldm/ffhq256/ffhq-256.zip https://ommer-lab.com/files/latent-diffusion/ffhq.zip wget -O models/ldm/lsun_churches256/lsun_churches-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip cd models/ldm/celeba256 unzip -o celeba-256.zip cd ../ffhq256 unzip -o ffhq-256.zip cd ../lsun_churches256 unzip -o lsun_churches-256.zip cd ../lsun_beds256 unzip -o lsun_beds-256.zip cd ../text2img256 unzip -o model.zip cd ../cin256 unzip -o model.zip cd ../semantic_synthesis512 unzip -o model.zip cd ../semantic_synthesis256 unzip -o model.zip cd ../bsr_sr unzip -o model.zip cd ../layout2img-openimages256 unzip -o model.zip cd ../inpainting_big unzip -o model.zip cd ../.. ================================================ FILE: ldm_exp/scripts/inpaint.py ================================================ import argparse, os, sys, glob from omegaconf import OmegaConf from PIL import Image from tqdm import tqdm import numpy as np import torch from main import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler def make_batch(image, mask, device): image = np.array(Image.open(image).convert("RGB")) image = image.astype(np.float32)/255.0 image = image[None].transpose(0,3,1,2) image = torch.from_numpy(image) mask = np.array(Image.open(mask).convert("L")) mask = mask.astype(np.float32)/255.0 mask = mask[None,None] mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 mask = torch.from_numpy(mask) masked_image = (1-mask)*image batch = {"image": image, "mask": mask, "masked_image": masked_image} for k in batch: batch[k] = batch[k].to(device=device) batch[k] = batch[k]*2.0-1.0 return batch if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--indir", type=str, nargs="?", help="dir containing image-mask pairs (`example.png` and `example_mask.png`)", ) parser.add_argument( "--outdir", type=str, nargs="?", help="dir to write results to", ) parser.add_argument( "--steps", type=int, default=50, help="number of ddim sampling steps", ) opt = parser.parse_args() masks = sorted(glob.glob(os.path.join(opt.indir, "*_mask.png"))) images = [x.replace("_mask.png", ".png") for x in masks] print(f"Found {len(masks)} inputs.") config = OmegaConf.load("models/ldm/inpainting_big/config.yaml") model = instantiate_from_config(config.model) model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"], strict=False) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) sampler = DDIMSampler(model) os.makedirs(opt.outdir, exist_ok=True) with torch.no_grad(): with model.ema_scope(): for image, mask in tqdm(zip(images, masks)): outpath = os.path.join(opt.outdir, os.path.split(image)[1]) batch = make_batch(image, mask, device=device) # encode masked image and concat downsampled mask c = model.cond_stage_model.encode(batch["masked_image"]) cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:]) c = torch.cat((c, cc), dim=1) shape = (c.shape[1]-1,)+c.shape[2:] samples_ddim, _ = sampler.sample(S=opt.steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False) x_samples_ddim = model.decode_first_stage(samples_ddim) image = torch.clamp((batch["image"]+1.0)/2.0, min=0.0, max=1.0) mask = torch.clamp((batch["mask"]+1.0)/2.0, min=0.0, max=1.0) predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) inpainted = (1-mask)*image+mask*predicted_image inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255 Image.fromarray(inpainted.astype(np.uint8)).save(outpath) ================================================ FILE: ldm_exp/scripts/knn2img.py ================================================ import argparse, os, sys, glob import clip import torch import torch.nn as nn import numpy as np from omegaconf import OmegaConf from PIL import Image from tqdm import tqdm, trange from itertools import islice from einops import rearrange, repeat from torchvision.utils import make_grid import scann import time from multiprocessing import cpu_count from ldm.util import instantiate_from_config, parallel_data_prefetch from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder DATABASES = [ "openimages", "artbench-art_nouveau", "artbench-baroque", "artbench-expressionism", "artbench-impressionism", "artbench-post_impressionism", "artbench-realism", "artbench-romanticism", "artbench-renaissance", "artbench-surrealism", "artbench-ukiyo_e", ] def chunk(it, size): it = iter(it) return iter(lambda: tuple(islice(it, size)), ()) def load_model_from_config(config, ckpt, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) model.cuda() model.eval() return model class Searcher(object): def __init__(self, database, retriever_version='ViT-L/14'): assert database in DATABASES # self.database = self.load_database(database) self.database_name = database self.searcher_savedir = f'data/rdm/searchers/{self.database_name}' self.database_path = f'data/rdm/retrieval_databases/{self.database_name}' self.retriever = self.load_retriever(version=retriever_version) self.database = {'embedding': [], 'img_id': [], 'patch_coords': []} self.load_database() self.load_searcher() def train_searcher(self, k, metric='dot_product', searcher_savedir=None): print('Start training searcher') searcher = scann.scann_ops_pybind.builder(self.database['embedding'] / np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis], k, metric) self.searcher = searcher.score_brute_force().build() print('Finish training searcher') if searcher_savedir is not None: print(f'Save trained searcher under "{searcher_savedir}"') os.makedirs(searcher_savedir, exist_ok=True) self.searcher.serialize(searcher_savedir) def load_single_file(self, saved_embeddings): compressed = np.load(saved_embeddings) self.database = {key: compressed[key] for key in compressed.files} print('Finished loading of clip embeddings.') def load_multi_files(self, data_archive): out_data = {key: [] for key in self.database} for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'): for key in d.files: out_data[key].append(d[key]) return out_data def load_database(self): print(f'Load saved patch embedding from "{self.database_path}"') file_content = glob.glob(os.path.join(self.database_path, '*.npz')) if len(file_content) == 1: self.load_single_file(file_content[0]) elif len(file_content) > 1: data = [np.load(f) for f in file_content] prefetched_data = parallel_data_prefetch(self.load_multi_files, data, n_proc=min(len(data), cpu_count()), target_data_type='dict') self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in self.database} else: raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?') print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.') def load_retriever(self, version='ViT-L/14', ): model = FrozenClipImageEmbedder(model=version) if torch.cuda.is_available(): model.cuda() model.eval() return model def load_searcher(self): print(f'load searcher for database {self.database_name} from {self.searcher_savedir}') self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir) print('Finished loading searcher.') def search(self, x, k): if self.searcher is None and self.database['embedding'].shape[0] < 2e4: self.train_searcher(k) # quickly fit searcher on the fly for small databases assert self.searcher is not None, 'Cannot search with uninitialized searcher' if isinstance(x, torch.Tensor): x = x.detach().cpu().numpy() if len(x.shape) == 3: x = x[:, 0] query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis] start = time.time() nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k) end = time.time() out_embeddings = self.database['embedding'][nns] out_img_ids = self.database['img_id'][nns] out_pc = self.database['patch_coords'][nns] out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis], 'img_ids': out_img_ids, 'patch_coords': out_pc, 'queries': x, 'exec_time': end - start, 'nns': nns, 'q_embeddings': query_embeddings} return out def __call__(self, x, n): return self.search(x, n) if __name__ == "__main__": parser = argparse.ArgumentParser() # TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc) # TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt? parser.add_argument( "--prompt", type=str, nargs="?", default="a painting of a virus monster playing guitar", help="the prompt to render" ) parser.add_argument( "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples" ) parser.add_argument( "--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples", ) parser.add_argument( "--ddim_steps", type=int, default=50, help="number of ddim sampling steps", ) parser.add_argument( "--n_repeat", type=int, default=1, help="number of repeats in CLIP latent space", ) parser.add_argument( "--plms", action='store_true', help="use plms sampling", ) parser.add_argument( "--ddim_eta", type=float, default=0.0, help="ddim eta (eta=0.0 corresponds to deterministic sampling", ) parser.add_argument( "--n_iter", type=int, default=1, help="sample this often", ) parser.add_argument( "--H", type=int, default=768, help="image height, in pixel space", ) parser.add_argument( "--W", type=int, default=768, help="image width, in pixel space", ) parser.add_argument( "--n_samples", type=int, default=3, help="how many samples to produce for each given prompt. A.k.a batch size", ) parser.add_argument( "--n_rows", type=int, default=0, help="rows in the grid (default: n_samples)", ) parser.add_argument( "--scale", type=float, default=5.0, help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", ) parser.add_argument( "--from-file", type=str, help="if specified, load prompts from this file", ) parser.add_argument( "--config", type=str, default="configs/retrieval-augmented-diffusion/768x768.yaml", help="path to config which constructs model", ) parser.add_argument( "--ckpt", type=str, default="models/rdm/rdm768x768/model.ckpt", help="path to checkpoint of model", ) parser.add_argument( "--clip_type", type=str, default="ViT-L/14", help="which CLIP model to use for retrieval and NN encoding", ) parser.add_argument( "--database", type=str, default='artbench-surrealism', choices=DATABASES, help="The database used for the search, only applied when --use_neighbors=True", ) parser.add_argument( "--use_neighbors", default=False, action='store_true', help="Include neighbors in addition to text prompt for conditioning", ) parser.add_argument( "--knn", default=10, type=int, help="The number of included neighbors, only applied when --use_neighbors=True", ) opt = parser.parse_args() config = OmegaConf.load(f"{opt.config}") model = load_model_from_config(config, f"{opt.ckpt}") device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device) if opt.plms: sampler = PLMSSampler(model) else: sampler = DDIMSampler(model) os.makedirs(opt.outdir, exist_ok=True) outpath = opt.outdir batch_size = opt.n_samples n_rows = opt.n_rows if opt.n_rows > 0 else batch_size if not opt.from_file: prompt = opt.prompt assert prompt is not None data = [batch_size * [prompt]] else: print(f"reading prompts from {opt.from_file}") with open(opt.from_file, "r") as f: data = f.read().splitlines() data = list(chunk(data, batch_size)) sample_path = os.path.join(outpath, "samples") os.makedirs(sample_path, exist_ok=True) base_count = len(os.listdir(sample_path)) grid_count = len(os.listdir(outpath)) - 1 print(f"sampling scale for cfg is {opt.scale:.2f}") searcher = None if opt.use_neighbors: searcher = Searcher(opt.database) with torch.no_grad(): with model.ema_scope(): for n in trange(opt.n_iter, desc="Sampling"): all_samples = list() for prompts in tqdm(data, desc="data"): print("sampling prompts:", prompts) if isinstance(prompts, tuple): prompts = list(prompts) c = clip_text_encoder.encode(prompts) uc = None if searcher is not None: nn_dict = searcher(c, opt.knn) c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1) if opt.scale != 1.0: uc = torch.zeros_like(c) if isinstance(prompts, tuple): prompts = list(prompts) shape = [16, opt.H // 16, opt.W // 16] # note: currently hardcoded for f16 model samples_ddim, _ = sampler.sample(S=opt.ddim_steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False, unconditional_guidance_scale=opt.scale, unconditional_conditioning=uc, eta=opt.ddim_eta, ) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) for x_sample in x_samples_ddim: x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') Image.fromarray(x_sample.astype(np.uint8)).save( os.path.join(sample_path, f"{base_count:05}.png")) base_count += 1 all_samples.append(x_samples_ddim) if not opt.skip_grid: # additionally, save as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') grid = make_grid(grid, nrow=n_rows) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png')) grid_count += 1 print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.") ================================================ FILE: ldm_exp/scripts/latent_imagenet_diffusion.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "latent-imagenet-diffusion.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "# Class-Conditional Synthesis with Latent Diffusion Models" ], "metadata": { "id": "NUmmV5ZvrPbP" } }, { "cell_type": "markdown", "source": [ "Install all the requirements" ], "metadata": { "id": "zh7u8gOx0ivw" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NHgUAp48qwoG", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "411d4df6-d91a-42d4-819e-9cf641c12248", "cellView": "form" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'latent-diffusion'...\n", "remote: Enumerating objects: 992, done.\u001B[K\n", "remote: Counting objects: 100% (695/695), done.\u001B[K\n", "remote: Compressing objects: 100% (397/397), done.\u001B[K\n", "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n", "Receiving objects: 100% (992/992), 30.78 MiB | 29.43 MiB/s, done.\n", "Resolving deltas: 100% (510/510), done.\n", "Cloning into 'taming-transformers'...\n", "remote: Enumerating objects: 1335, done.\u001B[K\n", "remote: Counting objects: 100% (525/525), done.\u001B[K\n", "remote: Compressing objects: 100% (493/493), done.\u001B[K\n", "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001B[K\n", "Receiving objects: 100% (1335/1335), 412.35 MiB | 30.53 MiB/s, done.\n", "Resolving deltas: 100% (267/267), done.\n", "Obtaining file:///content/taming-transformers\n", "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from taming-transformers==0.0.1) (1.10.0+cu111)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from taming-transformers==0.0.1) (1.21.5)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from taming-transformers==0.0.1) (4.63.0)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->taming-transformers==0.0.1) (3.10.0.2)\n", "Installing collected packages: taming-transformers\n", " Running setup.py develop for taming-transformers\n", "Successfully installed taming-transformers-0.0.1\n", "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n", "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001B[0m\n" ] } ], "source": [ "#@title Installation\n", "!git clone https://github.com/CompVis/latent-diffusion.git\n", "!git clone https://github.com/CompVis/taming-transformers\n", "!pip install -e ./taming-transformers\n", "!pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops\n", "\n", "import sys\n", "sys.path.append(\".\")\n", "sys.path.append('./taming-transformers')\n", "from taming.models import vqgan " ] }, { "cell_type": "markdown", "source": [ "Now, download the checkpoint (~1.7 GB). This will usually take 1-2 minutes." ], "metadata": { "id": "fNqCqQDoyZmq" } }, { "cell_type": "code", "source": [ "#@title Download\n", "%cd latent-diffusion/ \n", "\n", "!mkdir -p models/ldm/cin256-v2/\n", "!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt " ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cNHvQBhzyXCI", "outputId": "0a79e979-8484-4c62-96d9-7c79b1835162", "cellView": "form" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/latent-diffusion\n", "--2022-04-03 13:04:51-- https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt\n", "Resolving ommer-lab.com (ommer-lab.com)... 141.84.41.65\n", "Connecting to ommer-lab.com (ommer-lab.com)|141.84.41.65|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1827378153 (1.7G)\n", "Saving to: ‘models/ldm/cin256-v2/model.ckpt’\n", "\n", "models/ldm/cin256-v 100%[===================>] 1.70G 24.9MB/s in 70s \n", "\n", "2022-04-03 13:06:02 (24.9 MB/s) - ‘models/ldm/cin256-v2/model.ckpt’ saved [1827378153/1827378153]\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "Let's also check what type of GPU we've got." ], "metadata": { "id": "ThxmCePqt1mt" } }, { "cell_type": "code", "source": [ "!nvidia-smi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jbL2zJ7Pt7Jl", "outputId": "c8242be9-dba2-4a9f-da44-a294a70bb449" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Sun Apr 3 13:06:21 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 66C P8 33W / 149W | 0MiB / 11441MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "markdown", "source": [ "Load it." ], "metadata": { "id": "1tWAqdwk0Nrn" } }, { "cell_type": "code", "source": [ "#@title loading utils\n", "import torch\n", "from omegaconf import OmegaConf\n", "\n", "from ldm.util import instantiate_from_config\n", "\n", "\n", "def load_model_from_config(config, ckpt):\n", " print(f\"Loading model from {ckpt}\")\n", " pl_sd = torch.load(ckpt)#, map_location=\"cpu\")\n", " sd = pl_sd[\"state_dict\"]\n", " model = instantiate_from_config(config.model)\n", " m, u = model.load_state_dict(sd, strict=False)\n", " model.cuda()\n", " model.eval()\n", " return model\n", "\n", "\n", "def get_model():\n", " config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\") \n", " model = load_model_from_config(config, \"models/ldm/cin256-v2/model.ckpt\")\n", " return model" ], "metadata": { "id": "fnGwQRhtyBhb", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from ldm.models.diffusion.ddim import DDIMSampler\n", "\n", "model = get_model()\n", "sampler = DDIMSampler(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BPnyd-XUKbfE", "outputId": "0fcd10e4-0df2-4ab9-cbf5-f08f4902c954" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading model from models/ldm/cin256-v2/model.ckpt\n", "LatentDiffusion: Running in eps-prediction mode\n", "DiffusionWrapper has 400.92 M params.\n", "making attention of type 'vanilla' with 512 in_channels\n", "Working with z of shape (1, 3, 64, 64) = 12288 dimensions.\n", "making attention of type 'vanilla' with 512 in_channels\n" ] } ] }, { "cell_type": "markdown", "source": [ "And go. Quality, sampling speed and diversity are best controlled via the `scale`, `ddim_steps` and `ddim_eta` variables. As a rule of thumb, higher values of `scale` produce better samples at the cost of a reduced output diversity. Furthermore, increasing `ddim_steps` generally also gives higher quality samples, but returns are diminishing for values > 250. Fast sampling (i e. low values of `ddim_steps`) while retaining good quality can be achieved by using `ddim_eta = 0.0`." ], "metadata": { "id": "iIEAhY8AhUrh" } }, { "cell_type": "code", "source": [ "import numpy as np \n", "from PIL import Image\n", "from einops import rearrange\n", "from torchvision.utils import make_grid\n", "\n", "\n", "classes = [25, 187, 448, 992] # define classes to be sampled here\n", "n_samples_per_class = 6\n", "\n", "ddim_steps = 20\n", "ddim_eta = 0.0\n", "scale = 3.0 # for unconditional guidance\n", "\n", "\n", "all_samples = list()\n", "\n", "with torch.no_grad():\n", " with model.ema_scope():\n", " uc = model.get_learned_conditioning(\n", " {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)}\n", " )\n", " \n", " for class_label in classes:\n", " print(f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\")\n", " xc = torch.tensor(n_samples_per_class*[class_label])\n", " c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)})\n", " \n", " samples_ddim, _ = sampler.sample(S=ddim_steps,\n", " conditioning=c,\n", " batch_size=n_samples_per_class,\n", " shape=[3, 64, 64],\n", " verbose=False,\n", " unconditional_guidance_scale=scale,\n", " unconditional_conditioning=uc, \n", " eta=ddim_eta)\n", "\n", " x_samples_ddim = model.decode_first_stage(samples_ddim)\n", " x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, \n", " min=0.0, max=1.0)\n", " all_samples.append(x_samples_ddim)\n", "\n", "\n", "# display as grid\n", "grid = torch.stack(all_samples, 0)\n", "grid = rearrange(grid, 'n b c h w -> (n b) c h w')\n", "grid = make_grid(grid, nrow=n_samples_per_class)\n", "\n", "# to image\n", "grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()\n", "Image.fromarray(grid.astype(np.uint8))" ], "metadata": { "id": "jcbqWX2Ytu9t", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "3b7adde0-d80e-4c01-82d2-bf988aee7455" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "rendering 6 examples of class '25' in 20 steps and using s=3.00.\n", "Data shape for DDIM sampling is (6, 3, 64, 64), eta 0.0\n", "Running DDIM Sampling with 20 timesteps\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "DDIM Sampler: 100%|██████████| 20/20 [00:37<00:00, 1.89s/it]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "rendering 6 examples of class '187' in 20 steps and using s=3.00.\n", "Data shape for DDIM sampling is (6, 3, 64, 64), eta 0.0\n", "Running DDIM Sampling with 20 timesteps\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "DDIM Sampler: 100%|██████████| 20/20 [00:37<00:00, 1.87s/it]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "rendering 6 examples of class '448' in 20 steps and using s=3.00.\n", "Data shape for DDIM sampling is (6, 3, 64, 64), eta 0.0\n", "Running DDIM Sampling with 20 timesteps\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "DDIM Sampler: 100%|██████████| 20/20 [00:37<00:00, 1.86s/it]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "rendering 6 examples of class '992' in 20 steps and using s=3.00.\n", "Data shape for DDIM sampling is (6, 3, 64, 64), eta 0.0\n", "Running DDIM Sampling with 20 timesteps\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "DDIM Sampler: 100%|██████████| 20/20 [00:37<00:00, 1.86s/it]\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/png": 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\n" }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "92QkRfm0e6K0" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: ldm_exp/scripts/sample_diffusion.py ================================================ import argparse, os, sys, glob, datetime, yaml import torch import time import numpy as np from tqdm import trange from omegaconf import OmegaConf from PIL import Image from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config rescale = lambda x: (x + 1.) / 2. def custom_to_pil(x): x = x.detach().cpu() x = torch.clamp(x, -1., 1.) x = (x + 1.) / 2. x = x.permute(1, 2, 0).numpy() x = (255 * x).astype(np.uint8) x = Image.fromarray(x) if not x.mode == "RGB": x = x.convert("RGB") return x def custom_to_np(x): # saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py sample = x.detach().cpu() sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8) sample = sample.permute(0, 2, 3, 1) sample = sample.contiguous() return sample def logs2pil(logs, keys=["sample"]): imgs = dict() for k in logs: try: if len(logs[k].shape) == 4: img = custom_to_pil(logs[k][0, ...]) elif len(logs[k].shape) == 3: img = custom_to_pil(logs[k]) else: print(f"Unknown format for key {k}. ") img = None except: img = None imgs[k] = img return imgs @torch.no_grad() def convsample(model, shape, return_intermediates=True, verbose=True, make_prog_row=False): if not make_prog_row: return model.p_sample_loop(None, shape, return_intermediates=return_intermediates, verbose=verbose) else: return model.progressive_denoising( None, shape, verbose=True ) @torch.no_grad() def convsample_ddim(model, steps, shape, eta=1.0 ): ddim = DDIMSampler(model) bs = shape[0] shape = shape[1:] samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,) return samples, intermediates @torch.no_grad() def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,): log = dict() shape = [batch_size, model.model.diffusion_model.in_channels, model.model.diffusion_model.image_size, model.model.diffusion_model.image_size] with model.ema_scope("Plotting"): t0 = time.time() if vanilla: sample, progrow = convsample(model, shape, make_prog_row=True) else: sample, intermediates = convsample_ddim(model, steps=custom_steps, shape=shape, eta=eta) t1 = time.time() x_sample = model.decode_first_stage(sample) log["sample"] = x_sample log["time"] = t1 - t0 log['throughput'] = sample.shape[0] / (t1 - t0) print(f'Throughput for this batch: {log["throughput"]}') return log def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None): if vanilla: print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.') else: print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}') tstart = time.time() n_saved = len(glob.glob(os.path.join(logdir,'*.png')))-1 # path = logdir if model.cond_stage_model is None: all_images = [] print(f"Running unconditional sampling for {n_samples} samples") for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"): logs = make_convolutional_sample(model, batch_size=batch_size, vanilla=vanilla, custom_steps=custom_steps, eta=eta) n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample") all_images.extend([custom_to_np(logs["sample"])]) if n_saved >= n_samples: print(f'Finish after generating {n_saved} samples') break all_img = np.concatenate(all_images, axis=0) all_img = all_img[:n_samples] shape_str = "x".join([str(x) for x in all_img.shape]) nppath = os.path.join(nplog, f"{shape_str}-samples.npz") np.savez(nppath, all_img) else: raise NotImplementedError('Currently only sampling for unconditional models supported.') print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.") def save_logs(logs, path, n_saved=0, key="sample", np_path=None): for k in logs: if k == key: batch = logs[key] if np_path is None: for x in batch: img = custom_to_pil(x) imgpath = os.path.join(path, f"{key}_{n_saved:06}.png") img.save(imgpath) n_saved += 1 else: npbatch = custom_to_np(batch) shape_str = "x".join([str(x) for x in npbatch.shape]) nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz") np.savez(nppath, npbatch) n_saved += npbatch.shape[0] return n_saved def get_parser(): parser = argparse.ArgumentParser() parser.add_argument( "-r", "--resume", type=str, nargs="?", help="load from logdir or checkpoint in logdir", ) parser.add_argument( "-n", "--n_samples", type=int, nargs="?", help="number of samples to draw", default=50000 ) parser.add_argument( "-e", "--eta", type=float, nargs="?", help="eta for ddim sampling (0.0 yields deterministic sampling)", default=1.0 ) parser.add_argument( "-v", "--vanilla_sample", default=False, action='store_true', help="vanilla sampling (default option is DDIM sampling)?", ) parser.add_argument( "-l", "--logdir", type=str, nargs="?", help="extra logdir", default="none" ) parser.add_argument( "-c", "--custom_steps", type=int, nargs="?", help="number of steps for ddim and fastdpm sampling", default=50 ) parser.add_argument( "--batch_size", type=int, nargs="?", help="the bs", default=10 ) return parser def load_model_from_config(config, sd): model = instantiate_from_config(config) model.load_state_dict(sd,strict=False) model.cuda() model.eval() return model def load_model(config, ckpt, gpu, eval_mode): if ckpt: print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") global_step = pl_sd["global_step"] else: pl_sd = {"state_dict": None} global_step = None model = load_model_from_config(config.model, pl_sd["state_dict"]) return model, global_step if __name__ == "__main__": now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") sys.path.append(os.getcwd()) command = " ".join(sys.argv) parser = get_parser() opt, unknown = parser.parse_known_args() ckpt = None if not os.path.exists(opt.resume): raise ValueError("Cannot find {}".format(opt.resume)) if os.path.isfile(opt.resume): # paths = opt.resume.split("/") try: logdir = '/'.join(opt.resume.split('/')[:-1]) # idx = len(paths)-paths[::-1].index("logs")+1 print(f'Logdir is {logdir}') except ValueError: paths = opt.resume.split("/") idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt logdir = "/".join(paths[:idx]) ckpt = opt.resume else: assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory" logdir = opt.resume.rstrip("/") ckpt = os.path.join(logdir, "model.ckpt") base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml"))) opt.base = base_configs configs = [OmegaConf.load(cfg) for cfg in opt.base] cli = OmegaConf.from_dotlist(unknown) config = OmegaConf.merge(*configs, cli) gpu = True eval_mode = True if opt.logdir != "none": locallog = logdir.split(os.sep)[-1] if locallog == "": locallog = logdir.split(os.sep)[-2] print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'") logdir = os.path.join(opt.logdir, locallog) print(config) model, global_step = load_model(config, ckpt, gpu, eval_mode) print(f"global step: {global_step}") print(75 * "=") print("logging to:") logdir = os.path.join(logdir, "samples", f"{global_step:08}", now) imglogdir = os.path.join(logdir, "img") numpylogdir = os.path.join(logdir, "numpy") os.makedirs(imglogdir) os.makedirs(numpylogdir) print(logdir) print(75 * "=") # write config out sampling_file = os.path.join(logdir, "sampling_config.yaml") sampling_conf = vars(opt) with open(sampling_file, 'w') as f: yaml.dump(sampling_conf, f, default_flow_style=False) print(sampling_conf) run(model, imglogdir, eta=opt.eta, vanilla=opt.vanilla_sample, n_samples=opt.n_samples, custom_steps=opt.custom_steps, batch_size=opt.batch_size, nplog=numpylogdir) print("done.") ================================================ FILE: ldm_exp/scripts/train_searcher.py ================================================ import os, sys import numpy as np import scann import argparse import glob from multiprocessing import cpu_count from tqdm import tqdm from ldm.util import parallel_data_prefetch def search_bruteforce(searcher): return searcher.score_brute_force().build() def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k, partioning_trainsize, num_leaves, num_leaves_to_search): return searcher.tree(num_leaves=num_leaves, num_leaves_to_search=num_leaves_to_search, training_sample_size=partioning_trainsize). \ score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build() def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k): return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder( reorder_k).build() def load_datapool(dpath): def load_single_file(saved_embeddings): compressed = np.load(saved_embeddings) database = {key: compressed[key] for key in compressed.files} return database def load_multi_files(data_archive): database = {key: [] for key in data_archive[0].files} for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'): for key in d.files: database[key].append(d[key]) return database print(f'Load saved patch embedding from "{dpath}"') file_content = glob.glob(os.path.join(dpath, '*.npz')) if len(file_content) == 1: data_pool = load_single_file(file_content[0]) elif len(file_content) > 1: data = [np.load(f) for f in file_content] prefetched_data = parallel_data_prefetch(load_multi_files, data, n_proc=min(len(data), cpu_count()), target_data_type='dict') data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()} else: raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?') print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.') return data_pool def train_searcher(opt, metric='dot_product', partioning_trainsize=None, reorder_k=None, # todo tune aiq_thld=0.2, dims_per_block=2, num_leaves=None, num_leaves_to_search=None,): data_pool = load_datapool(opt.database) k = opt.knn if not reorder_k: reorder_k = 2 * k # normalize # embeddings = searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric) pool_size = data_pool['embedding'].shape[0] print(*(['#'] * 100)) print('Initializing scaNN searcher with the following values:') print(f'k: {k}') print(f'metric: {metric}') print(f'reorder_k: {reorder_k}') print(f'anisotropic_quantization_threshold: {aiq_thld}') print(f'dims_per_block: {dims_per_block}') print(*(['#'] * 100)) print('Start training searcher....') print(f'N samples in pool is {pool_size}') # this reflects the recommended design choices proposed at # https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md if pool_size < 2e4: print('Using brute force search.') searcher = search_bruteforce(searcher) elif 2e4 <= pool_size and pool_size < 1e5: print('Using asymmetric hashing search and reordering.') searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k) else: print('Using using partioning, asymmetric hashing search and reordering.') if not partioning_trainsize: partioning_trainsize = data_pool['embedding'].shape[0] // 10 if not num_leaves: num_leaves = int(np.sqrt(pool_size)) if not num_leaves_to_search: num_leaves_to_search = max(num_leaves // 20, 1) print('Partitioning params:') print(f'num_leaves: {num_leaves}') print(f'num_leaves_to_search: {num_leaves_to_search}') # self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k) searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k, partioning_trainsize, num_leaves, num_leaves_to_search) print('Finish training searcher') searcher_savedir = opt.target_path os.makedirs(searcher_savedir, exist_ok=True) searcher.serialize(searcher_savedir) print(f'Saved trained searcher under "{searcher_savedir}"') if __name__ == '__main__': sys.path.append(os.getcwd()) parser = argparse.ArgumentParser() parser.add_argument('--database', '-d', default='data/rdm/retrieval_databases/openimages', type=str, help='path to folder containing the clip feature of the database') parser.add_argument('--target_path', '-t', default='data/rdm/searchers/openimages', type=str, help='path to the target folder where the searcher shall be stored.') parser.add_argument('--knn', '-k', default=20, type=int, help='number of nearest neighbors, for which the searcher shall be optimized') opt, _ = parser.parse_known_args() train_searcher(opt,) ================================================ FILE: ldm_exp/scripts/txt2img.py ================================================ import argparse, os, sys, glob import torch import numpy as np from omegaconf import OmegaConf from PIL import Image from tqdm import tqdm, trange from einops import rearrange from torchvision.utils import make_grid from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler def load_model_from_config(config, ckpt, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) model.cuda() model.eval() return model if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--prompt", type=str, nargs="?", default="a painting of a virus monster playing guitar", help="the prompt to render" ) parser.add_argument( "--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples" ) parser.add_argument( "--ddim_steps", type=int, default=200, help="number of ddim sampling steps", ) parser.add_argument( "--plms", action='store_true', help="use plms sampling", ) parser.add_argument( "--ddim_eta", type=float, default=0.0, help="ddim eta (eta=0.0 corresponds to deterministic sampling", ) parser.add_argument( "--n_iter", type=int, default=1, help="sample this often", ) parser.add_argument( "--H", type=int, default=256, help="image height, in pixel space", ) parser.add_argument( "--W", type=int, default=256, help="image width, in pixel space", ) parser.add_argument( "--n_samples", type=int, default=4, help="how many samples to produce for the given prompt", ) parser.add_argument( "--scale", type=float, default=5.0, help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", ) opt = parser.parse_args() config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-eval.yaml") # TODO: Optionally download from same location as ckpt and chnage this logic model = load_model_from_config(config, "models/ldm/text2img-large/model.ckpt") # TODO: check path device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) if opt.plms: sampler = PLMSSampler(model) else: sampler = DDIMSampler(model) os.makedirs(opt.outdir, exist_ok=True) outpath = opt.outdir prompt = opt.prompt sample_path = os.path.join(outpath, "samples") os.makedirs(sample_path, exist_ok=True) base_count = len(os.listdir(sample_path)) all_samples=list() with torch.no_grad(): with model.ema_scope(): uc = None if opt.scale != 1.0: uc = model.get_learned_conditioning(opt.n_samples * [""]) for n in trange(opt.n_iter, desc="Sampling"): c = model.get_learned_conditioning(opt.n_samples * [prompt]) shape = [4, opt.H//8, opt.W//8] samples_ddim, _ = sampler.sample(S=opt.ddim_steps, conditioning=c, batch_size=opt.n_samples, shape=shape, verbose=False, unconditional_guidance_scale=opt.scale, unconditional_conditioning=uc, eta=opt.ddim_eta) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) for x_sample in x_samples_ddim: x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:04}.png")) base_count += 1 all_samples.append(x_samples_ddim) # additionally, save as grid grid = torch.stack(all_samples, 0) grid = rearrange(grid, 'n b c h w -> (n b) c h w') grid = make_grid(grid, nrow=opt.n_samples) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}.png')) print(f"Your samples are ready and waiting four you here: \n{outpath} \nEnjoy.") ================================================ FILE: ldm_exp/setup.py ================================================ from setuptools import setup, find_packages setup( name='latent-diffusion', version='0.0.1', description='', packages=find_packages(), install_requires=[ 'torch', 'numpy', 'tqdm', ], ) ================================================ FILE: ldm_exp/test_criterion.py ================================================ import sys sys.path.append(".") sys.path.append('./taming-transformers') from taming.models import vqgan import argparse from ldm.modules.attention import CrossAttention import numpy as np parser = argparse.ArgumentParser() parser.add_argument("--pruner", type=str, choices=["magnitude", "random", "taylor", "diff-pruning", "reinit", "diff0"], default="magnitude") args = parser.parse_args() import torch torch.manual_seed(0) torch.cuda.manual_seed(0) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False #@title loading utils from omegaconf import OmegaConf from ldm.util import instantiate_from_config import torch_pruning as tp def load_model_from_config(config, ckpt): #print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) model.cuda() model.eval() return model def get_model(): config = OmegaConf.load("configs/latent-diffusion/cin256-v2.yaml") model = load_model_from_config(config, "models/ldm/cin256-v2/model.ckpt") return model from ldm.models.diffusion.ddim import DDIMSampler classes = [25, 187, 448, 992] # define classes to be sampled here n_samples_per_class = 6 ddim_steps = 20 ddim_eta = 0.0 scale = 3.0 # for unconditional guidance x_T = torch.randn(n_samples_per_class, 3, 64, 64).cuda() for _sparsity in range(0,10,1): sparsity = _sparsity / 100.0 print("sparsity: ", sparsity) model = get_model() sampler = DDIMSampler(model) import numpy as np from PIL import Image from einops import rearrange from torchvision.utils import make_grid model.eval() if args.pruner == "magnitude": imp = tp.importance.MagnitudeImportance() elif args.pruner == "random": imp = tp.importance.RandomImportance() elif args.pruner == 'taylor': imp = tp.importance.TaylorImportance(multivariable=True) # standard first-order taylor expansion elif args.pruner == 'diff-pruning' or args.pruner == 'diff0': imp = tp.importance.TaylorImportance(multivariable=False) # a modified version, estimating the accumulated error of weight removal else: raise ValueError(f"Unknown pruner '{args.pruner}'") ignored_layers = [model.model.diffusion_model.out] channel_groups = {} iterative_steps = 1 uc = model.get_learned_conditioning( {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)} ) for m in model.model.diffusion_model.modules(): if isinstance(m, CrossAttention): channel_groups[m.to_q] = m.heads channel_groups[m.to_k] = m.heads channel_groups[m.to_v] = m.heads xc = torch.tensor(n_samples_per_class*[classes[0]]) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) example_inputs = {"x": torch.randn(n_samples_per_class, 3, 64, 64).to(model.device), "timesteps": torch.full((n_samples_per_class,), 1, device=model.device, dtype=torch.long), "context": c} base_macs, base_params = tp.utils.count_ops_and_params(model.model.diffusion_model, example_inputs) pruner = tp.pruner.MagnitudePruner( model.model.diffusion_model, example_inputs, importance=imp, iterative_steps=1, channel_groups =channel_groups, ch_sparsity=sparsity, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256} ignored_layers=ignored_layers, root_module_types=[torch.nn.Conv2d, torch.nn.Linear], round_to=2 ) model.zero_grad() import random max_loss = -1 for t in range(1000): if args.pruner not in ['diff-pruning', 'taylor', 'diff0']: break xc = torch.tensor(random.sample(range(1000), n_samples_per_class)) c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)}) samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples_per_class, shape=[3, 64, 64], verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, eta=ddim_eta) encoded = model.encode_first_stage(samples_ddim) example_inputs = {"x": encoded.to(model.device), "timesteps": torch.full((n_samples_per_class,), t, device=model.device, dtype=torch.long), "context": c} loss = model.get_loss_at_t(example_inputs['x'], {model.cond_stage_key: xc.to(model.device)}, example_inputs['timesteps']) loss = loss[0] if loss > max_loss: max_loss = loss thres = 0.1 if args.pruner == 'diff-pruning' else 0.0 if args.pruner == 'diff-pruning' or args.pruner == 'diff0': if loss / max_loss (n b) c h w') grid = make_grid(grid, nrow=n_samples_per_class) # to image grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() img = Image.fromarray(grid.astype(np.uint8)) import os os.makedirs("run/criteria/{}".format(args.pruner), exist_ok=True) img.save("run/criteria/{}/sparsity-{}.png".format(args.pruner, sparsity)) ================================================ FILE: ldm_exp/test_diffusion.py ================================================ import torch import torch_fidelity import argparse parser = argparse.ArgumentParser() parser.add_argument('--input1', type=str) parser.add_argument('--input2', type=str) args = parser.parse_args() metrics_dict = torch_fidelity.calculate_metrics( input1=args.input1, input2=args.input2, cuda=True, isc=True, fid=True, kid=True, prc=True, verbose=False, samples_find_deep=True ) print(metrics_dict) ================================================ FILE: ldm_prune.py ================================================ from diffusers import LDMPipeline, DDPMPipeline, DDIMPipeline, DDIMScheduler, DDPMScheduler, VQModel from diffusers.models import UNet2DModel import torch_pruning as tp import torch import torchvision from torchvision import transforms import torchvision from tqdm import tqdm import os from glob import glob from PIL import Image import accelerate import utils import argparse parser = argparse.ArgumentParser() #parser.add_argument("--dataset", type=str, required=True) parser.add_argument("--model_path", type=str, required=True) parser.add_argument("--save_path", type=str, required=True) parser.add_argument("--pruning_ratio", type=float, default=0.3) parser.add_argument("--batch_size", type=int, default=128) parser.add_argument("--device", type=str, default='cpu') #parser.add_argument("--pruner", type=str, default='taylor', choices=['taylor', 'random', 'magnitude', 'reinit', 'diff-pruning']) parser.add_argument("--pruner", type=str, default='random', choices=['random', 'magnitude', 'reinit']) #parser.add_argument("--thr", type=float, default=0.05, help="threshold for diff-pruning") args = parser.parse_args() batch_size = args.batch_size if __name__=='__main__': #dataset = utils.get_dataset(args.dataset) #print(f"Dataset size: {len(dataset)}") #train_dataloader = torch.utils.data.DataLoader( # dataset, batch_size=args.batch_size, shuffle=True, num_workers=4, drop_last=True #) #import torch_pruning as tp # loading images for gradient-based pruning #clean_images = iter(train_dataloader).next() #if isinstance(clean_images, (list, tuple)): # clean_images = clean_images[0] #clean_images = clean_images.to(args.device) #noise = torch.randn(clean_images.shape).to(clean_images.device) # Loading pretrained model print("Loading pretrained model from {}".format(args.model_path)) # load all models unet = UNet2DModel.from_pretrained("CompVis/ldm-celebahq-256", subfolder="unet") vqvae = VQModel.from_pretrained("CompVis/ldm-celebahq-256", subfolder="vqvae") scheduler = DDIMScheduler.from_config("CompVis/ldm-celebahq-256", subfolder="scheduler") # set to cuda torch_device = torch.device(args.device) if torch.cuda.is_available() else "cpu" unet.to(torch_device) vqvae.to(torch_device) example_inputs = {'sample': torch.randn(1, unet.in_channels, unet.sample_size, unet.sample_size).to(args.device), 'timestep': torch.ones((1,)).long().to(args.device)} if args.pruning_ratio>0: if args.pruner == 'taylor': imp = tp.importance.TaylorImportance() elif args.pruner == 'random' or args.pruner=='reinit': imp = tp.importance.RandomImportance() elif args.pruner == 'magnitude': imp = tp.importance.MagnitudeImportance() elif args.pruner == 'diff-pruning': imp = tp.importance.TaylorImportance(multivariable=False) else: raise NotImplementedError ignored_layers = [unet.conv_out] ignored_layers = [unet.conv_out] from diffusers.models.attention import Attention channel_groups = {} for m in unet.modules(): if isinstance(m, Attention): channel_groups[m.to_q] = m.heads channel_groups[m.to_k] = m.heads channel_groups[m.to_v] = m.heads pruner = tp.pruner.MagnitudePruner( unet, example_inputs, importance=imp, iterative_steps=1, channel_groups=channel_groups, ch_sparsity=args.pruning_ratio, ignored_layers=ignored_layers, ) base_macs, base_params = tp.utils.count_ops_and_params(unet, example_inputs) unet.zero_grad() unet.eval() import random for g in pruner.step(interactive=True): g.prune() # Update static attributes from diffusers.models.resnet import Upsample2D, Downsample2D for m in unet.modules(): if isinstance(m, (Upsample2D, Downsample2D)): m.channels = m.conv.in_channels m.out_channels == m.conv.out_channels macs, params = tp.utils.count_ops_and_params(unet, example_inputs) print(unet) print("#Params: {:.4f} M => {:.4f} M".format(base_params/1e6, params/1e6)) print("#MACS: {:.4f} G => {:.4f} G".format(base_macs/1e9, macs/1e9)) unet.zero_grad() del pruner if args.pruner=='reinit': def reset_parameters(model): for m in model.modules(): if hasattr(m, 'reset_parameters'): m.reset_parameters() reset_parameters(unet) pipeline = LDMPipeline( unet=unet, vqvae=vqvae, scheduler=scheduler, ).to(torch_device) pipeline.save_pretrained(args.save_path) if args.pruning_ratio>0: os.makedirs(os.path.join(args.save_path, "pruned"), exist_ok=True) torch.save(unet, os.path.join(args.save_path, "pruned", "unet_pruned.pth")) with torch.no_grad(): generator = torch.Generator(device=torch_device).manual_seed(0) images = pipeline(num_inference_steps=100, batch_size=args.batch_size, output_type="numpy").images os.makedirs(os.path.join(args.save_path, 'vis'), exist_ok=True) torchvision.utils.save_image(torch.from_numpy(images).permute([0, 3, 1, 2]), "{}/vis/after_pruning.png".format(args.save_path)) ================================================ FILE: requirements.txt ================================================ accelerate torchvision>=0.13.1 scipy torch>=1.12.1 tqdm numpy torch_pruning huggingface_hub ================================================ FILE: scripts/finetune_ddpm_cifar10.sh ================================================ python ddpm_train.py \ --dataset="cifar10" \ --model_path="run/pruned/ddpm_cifar10_pruned" \ --pruned_model_ckpt="run/pruned/ddpm_cifar10_pruned/pruned/unet_pruned.pth" \ --resolution=32 \ --output_dir="run/finetuned/ddpm_cifar10_pruned_post_training" \ --train_batch_size=128 \ --num_iters=100000 \ --gradient_accumulation_steps=1 \ --learning_rate=2e-4 \ --lr_warmup_steps=0 \ --save_model_steps 1000 \ --dataloader_num_workers 8 \ --adam_weight_decay 0.00 \ --ema_max_decay 0.9999 \ --dropout 0.1 \ --use_ema \ ================================================ FILE: scripts/prune_ddpm_cifar10.sh ================================================ python ddpm_prune.py \ --dataset cifar10 \ --model_path pretrained/ddpm_ema_cifar10 \ --save_path run/pruned/ddpm_cifar10_pruned \ --pruning_ratio $1 \ --batch_size 128 \ --pruner diff-pruning \ --thr 0.05 \ --device cuda:0 \ ================================================ FILE: scripts/prune_ddpm_ema_bedroom_random.sh ================================================ python ddpm_prune.py \ --dataset data/lsun/bedroom \ --model_path google/ddpm-ema-bedroom-256 \ --save_path run/pruned/ddpm_ema_bedroom_256_pruned \ --pruning_ratio $1 \ --batch_size 4 \ --pruner random \ --thr 0.05 \ --device cuda:0 \ ================================================ FILE: scripts/prune_ddpm_ema_church_random.sh ================================================ python ddpm_prune.py \ --dataset data/lsun/church \ --model_path google/ddpm-ema-church-256 \ --save_path run/pruned/ddpm_ema_church_256_pruned \ --pruning_ratio $1 \ --batch_size 4 \ --pruner random \ --thr 0.05 \ --device cuda:0 \ ================================================ FILE: scripts/prune_ldm.sh ================================================ python ldm_prune.py \ --model_path CompVis/ldm-celebahq-256 \ --save_path run/pruned/ldm_celeba_pruned \ --pruning_ratio 0.5 \ --pruner magnitude \ --device cuda:0 \ --batch_size 4 \ ================================================ FILE: scripts/sample_ddpm_cifar10_pretrained.sh ================================================ python ddpm_sample.py \ --output_dir run/sample/ddpm_cifar10_pretrained \ --batch_size 128 \ --model_path pretrained/ddpm_ema_cifar10 \ ================================================ FILE: scripts/sample_ddpm_cifar10_pretrained_distributed.sh ================================================ python -m torch.distributed.launch --nproc_per_node=8 --master_port 22222 --use_env ddpm_sample.py \ --output_dir run/sample/ddpm_cifar10_pretrained \ --model_path pretrained/ddpm_ema_cifar10 \ --batch_size 128 \ ================================================ FILE: scripts/sample_ddpm_cifar10_pruned.sh ================================================ python ddpm_sample.py \ --output_dir run/sample/ddpm_cifar10_pruned \ --batch_size 128 \ --pruned_model_ckpt run/finetuned/ddpm_cifar10_pruned_post_training/pruned/unet_ema_pruned.pth \ --model_path run/finetuned/ddpm_cifar10_pruned_post_training \ --skip_type uniform \ ================================================ FILE: tools/convert_cifar10_ddpm_ema.sh ================================================ mkdir -p pretrained wget https://heibox.uni-heidelberg.de/f/2e4f01e2d9ee49bab1d5/?dl=1 -O pretrained/cifar10-ema-model-790000.ckpt python tools/convert_ddpm_original_checkpoint_to_diffusers_cifar10.py --checkpoint_path pretrained/cifar10-ema-model-790000.ckpt --config_file tools/ddpm_cifar10_config.json --dump_path pretrained/ddpm_ema_cifar10 ================================================ FILE: tools/convert_ddpm_original_checkpoint_to_diffusers_cifar10.py ================================================ import argparse import json import torch from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel def shave_segments(path, n_shave_prefix_segments=1): """ Removes segments. Positive values shave the first segments, negative shave the last segments. """ if n_shave_prefix_segments >= 0: return ".".join(path.split(".")[n_shave_prefix_segments:]) else: return ".".join(path.split(".")[:n_shave_prefix_segments]) def renew_resnet_paths(old_list, n_shave_prefix_segments=0): mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("block.", "resnets.") new_item = new_item.replace("conv_shorcut", "conv1") new_item = new_item.replace("in_shortcut", "conv_shortcut") new_item = new_item.replace("temb_proj", "time_emb_proj") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False): mapping = [] for old_item in old_list: new_item = old_item # In `model.mid`, the layer is called `attn`. if not in_mid: new_item = new_item.replace("attn", "attentions") new_item = new_item.replace(".k.", ".to_k.") new_item = new_item.replace(".v.", ".to_v.") new_item = new_item.replace(".q.", ".to_q.") new_item = new_item.replace("proj_out", "to_out.0") new_item = new_item.replace("norm", "group_norm") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def assign_to_checkpoint( paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None ): assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." if attention_paths_to_split is not None: if config is None: raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'.") for path, path_map in attention_paths_to_split.items(): old_tensor = old_checkpoint[path] channels = old_tensor.shape[0] // 3 target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) num_heads = old_tensor.shape[0] // config.get("num_head_channels", 1) // 3 old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) query, key, value = old_tensor.split(channels // num_heads, dim=1) checkpoint[path_map["query"]] = query.reshape(target_shape).squeeze() checkpoint[path_map["key"]] = key.reshape(target_shape).squeeze() checkpoint[path_map["value"]] = value.reshape(target_shape).squeeze() for path in paths: new_path = path["new"] if attention_paths_to_split is not None and new_path in attention_paths_to_split: continue new_path = new_path.replace("down.", "down_blocks.") new_path = new_path.replace("up.", "up_blocks.") if additional_replacements is not None: for replacement in additional_replacements: new_path = new_path.replace(replacement["old"], replacement["new"]) if "attentions" in new_path: checkpoint[new_path] = old_checkpoint[path["old"]].squeeze() else: checkpoint[new_path] = old_checkpoint[path["old"]] def convert_ddpm_checkpoint(checkpoint, config): """ Takes a state dict and a config, and returns a converted checkpoint. """ new_checkpoint = {} new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["temb.dense.0.weight"] new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["temb.dense.0.bias"] new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["temb.dense.1.weight"] new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["temb.dense.1.bias"] new_checkpoint["conv_norm_out.weight"] = checkpoint["norm_out.weight"] new_checkpoint["conv_norm_out.bias"] = checkpoint["norm_out.bias"] new_checkpoint["conv_in.weight"] = checkpoint["conv_in.weight"] new_checkpoint["conv_in.bias"] = checkpoint["conv_in.bias"] new_checkpoint["conv_out.weight"] = checkpoint["conv_out.weight"] new_checkpoint["conv_out.bias"] = checkpoint["conv_out.bias"] num_down_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "down" in layer}) down_blocks = { layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) } num_up_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "up" in layer}) up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} for i in range(num_down_blocks): block_id = (i - 1) // (config["layers_per_block"] + 1) if any("downsample" in layer for layer in down_blocks[i]): new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[ f"down.{i}.downsample.conv.weight" ] new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[f"down.{i}.downsample.conv.bias"] # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight'] # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias'] if any("block" in layer for layer in down_blocks[i]): num_blocks = len( {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "block" in layer} ) blocks = { layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) } if num_blocks > 0: for j in range(config["layers_per_block"]): paths = renew_resnet_paths(blocks[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint) if any("attn" in layer for layer in down_blocks[i]): num_attn = len( {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "attn" in layer} ) attns = { layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) } if num_attn > 0: for j in range(config["layers_per_block"]): paths = renew_attention_paths(attns[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config) mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key] mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key] mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key] # Mid new 2 paths = renew_resnet_paths(mid_block_1_layers) assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}], ) paths = renew_resnet_paths(mid_block_2_layers) assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}], ) paths = renew_attention_paths(mid_attn_1_layers, in_mid=True) assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}], ) for i in range(num_up_blocks): block_id = num_up_blocks - 1 - i if any("upsample" in layer for layer in up_blocks[i]): new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ f"up.{i}.upsample.conv.weight" ] new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[f"up.{i}.upsample.conv.bias"] if any("block" in layer for layer in up_blocks[i]): num_blocks = len( {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "block" in layer} ) blocks = { layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) } if num_blocks > 0: for j in range(config["layers_per_block"] + 1): replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} paths = renew_resnet_paths(blocks[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) if any("attn" in layer for layer in up_blocks[i]): num_attn = len( {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "attn" in layer} ) attns = { layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) } if num_attn > 0: for j in range(config["layers_per_block"] + 1): replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} paths = renew_attention_paths(attns[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()} new_checkpoint = {k.replace("nconv_shortcut", "conv_shortcut"): v for k, v in new_checkpoint.items()} return new_checkpoint def convert_vq_autoenc_checkpoint(checkpoint, config): """ Takes a state dict and a config, and returns a converted checkpoint. """ new_checkpoint = {} new_checkpoint["encoder.conv_norm_out.weight"] = checkpoint["encoder.norm_out.weight"] new_checkpoint["encoder.conv_norm_out.bias"] = checkpoint["encoder.norm_out.bias"] new_checkpoint["encoder.conv_in.weight"] = checkpoint["encoder.conv_in.weight"] new_checkpoint["encoder.conv_in.bias"] = checkpoint["encoder.conv_in.bias"] new_checkpoint["encoder.conv_out.weight"] = checkpoint["encoder.conv_out.weight"] new_checkpoint["encoder.conv_out.bias"] = checkpoint["encoder.conv_out.bias"] new_checkpoint["decoder.conv_norm_out.weight"] = checkpoint["decoder.norm_out.weight"] new_checkpoint["decoder.conv_norm_out.bias"] = checkpoint["decoder.norm_out.bias"] new_checkpoint["decoder.conv_in.weight"] = checkpoint["decoder.conv_in.weight"] new_checkpoint["decoder.conv_in.bias"] = checkpoint["decoder.conv_in.bias"] new_checkpoint["decoder.conv_out.weight"] = checkpoint["decoder.conv_out.weight"] new_checkpoint["decoder.conv_out.bias"] = checkpoint["decoder.conv_out.bias"] num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "down" in layer}) down_blocks = { layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) } num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "up" in layer}) up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} for i in range(num_down_blocks): block_id = (i - 1) // (config["layers_per_block"] + 1) if any("downsample" in layer for layer in down_blocks[i]): new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[ f"encoder.down.{i}.downsample.conv.weight" ] new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[ f"encoder.down.{i}.downsample.conv.bias" ] if any("block" in layer for layer in down_blocks[i]): num_blocks = len( {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "block" in layer} ) blocks = { layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) } if num_blocks > 0: for j in range(config["layers_per_block"]): paths = renew_resnet_paths(blocks[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint) if any("attn" in layer for layer in down_blocks[i]): num_attn = len( {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "attn" in layer} ) attns = { layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) } if num_attn > 0: for j in range(config["layers_per_block"]): paths = renew_attention_paths(attns[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config) mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key] mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key] mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key] # Mid new 2 paths = renew_resnet_paths(mid_block_1_layers) assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}], ) paths = renew_resnet_paths(mid_block_2_layers) assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}], ) paths = renew_attention_paths(mid_attn_1_layers, in_mid=True) assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}], ) for i in range(num_up_blocks): block_id = num_up_blocks - 1 - i if any("upsample" in layer for layer in up_blocks[i]): new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ f"decoder.up.{i}.upsample.conv.weight" ] new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[ f"decoder.up.{i}.upsample.conv.bias" ] if any("block" in layer for layer in up_blocks[i]): num_blocks = len( {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "block" in layer} ) blocks = { layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) } if num_blocks > 0: for j in range(config["layers_per_block"] + 1): replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} paths = renew_resnet_paths(blocks[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) if any("attn" in layer for layer in up_blocks[i]): num_attn = len( {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "attn" in layer} ) attns = { layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) } if num_attn > 0: for j in range(config["layers_per_block"] + 1): replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} paths = renew_attention_paths(attns[j]) assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()} new_checkpoint["quant_conv.weight"] = checkpoint["quant_conv.weight"] new_checkpoint["quant_conv.bias"] = checkpoint["quant_conv.bias"] if "quantize.embedding.weight" in checkpoint: new_checkpoint["quantize.embedding.weight"] = checkpoint["quantize.embedding.weight"] new_checkpoint["post_quant_conv.weight"] = checkpoint["post_quant_conv.weight"] new_checkpoint["post_quant_conv.bias"] = checkpoint["post_quant_conv.bias"] return new_checkpoint if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help="The config json file corresponding to the architecture.", ) parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") args = parser.parse_args() checkpoint = torch.load(args.checkpoint_path) print(checkpoint.keys()) with open(args.config_file) as f: config = json.loads(f.read()) # unet case key_prefix_set = {key.split(".")[0] for key in checkpoint.keys()} if "encoder" in key_prefix_set and "decoder" in key_prefix_set: converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config) else: converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config) if "ddpm" in config: del config["ddpm"] if config["_class_name"] == "VQModel": model = VQModel(**config) model.load_state_dict(converted_checkpoint) model.save_pretrained(args.dump_path) elif config["_class_name"] == "AutoencoderKL": model = AutoencoderKL(**config) model.load_state_dict(converted_checkpoint) model.save_pretrained(args.dump_path) else: model = UNet2DModel(**config) model.load_state_dict(converted_checkpoint) scheduler = DDPMScheduler.from_config('google/ddpm-cifar10-32') pipe = DDPMPipeline(unet=model, scheduler=scheduler) pipe.save_pretrained(args.dump_path) ================================================ FILE: tools/convert_ldm_original_checkpoint_to_diffusers.py ================================================ # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ Conversion script for the LDM checkpoints. """ import argparse import json import torch from diffusers import DDPMScheduler, LDMPipeline, UNet2DModel, VQModel def shave_segments(path, n_shave_prefix_segments=1): """ Removes segments. Positive values shave the first segments, negative shave the last segments. """ if n_shave_prefix_segments >= 0: return ".".join(path.split(".")[n_shave_prefix_segments:]) else: return ".".join(path.split(".")[:n_shave_prefix_segments]) def renew_resnet_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside resnets to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item.replace("in_layers.0", "norm1") new_item = new_item.replace("in_layers.2", "conv1") new_item = new_item.replace("out_layers.0", "norm2") new_item = new_item.replace("out_layers.3", "conv2") new_item = new_item.replace("emb_layers.1", "time_emb_proj") new_item = new_item.replace("skip_connection", "conv_shortcut") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def renew_attention_paths(old_list, n_shave_prefix_segments=0): """ Updates paths inside attentions to the new naming scheme (local renaming) """ mapping = [] for old_item in old_list: new_item = old_item new_item = new_item.replace("norm.weight", "group_norm.weight") new_item = new_item.replace("norm.bias", "group_norm.bias") new_item = new_item.replace("proj_out.weight", "proj_attn.weight") new_item = new_item.replace("proj_out.bias", "proj_attn.bias") new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) return mapping def assign_to_checkpoint( paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None ): """ This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits attention layers, and takes into account additional replacements that may arise. Assigns the weights to the new checkpoint. """ assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." # Splits the attention layers into three variables. if attention_paths_to_split is not None: for path, path_map in attention_paths_to_split.items(): old_tensor = old_checkpoint[path] channels = old_tensor.shape[0] // 3 target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) query, key, value = old_tensor.split(channels // num_heads, dim=1) checkpoint[path_map["query"]] = query.reshape(target_shape) checkpoint[path_map["key"]] = key.reshape(target_shape) checkpoint[path_map["value"]] = value.reshape(target_shape) for path in paths: new_path = path["new"] # These have already been assigned if attention_paths_to_split is not None and new_path in attention_paths_to_split: continue # Global renaming happens here new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") if additional_replacements is not None: for replacement in additional_replacements: new_path = new_path.replace(replacement["old"], replacement["new"]) # proj_attn.weight has to be converted from conv 1D to linear if "proj_attn.weight" in new_path: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] else: checkpoint[new_path] = old_checkpoint[path["old"]] def convert_ldm_checkpoint(checkpoint, config): """ Takes a state dict and a config, and returns a converted checkpoint. """ new_checkpoint = {} filtered_checkpoint = {} for k, v in checkpoint.items(): if 'model.diffusion_model.' in k: filtered_checkpoint[k.replace('model.diffusion_model.', '')] = v checkpoint = filtered_checkpoint print(checkpoint.keys()) new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"] new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"] new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"] new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"] new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"] new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"] new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"] new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"] new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"] new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"] # Retrieves the keys for the input blocks only num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "input_blocks" in layer}) input_blocks = { layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key] for layer_id in range(num_input_blocks) } # Retrieves the keys for the middle blocks only num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "middle_block" in layer}) middle_blocks = { layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key] for layer_id in range(num_middle_blocks) } # Retrieves the keys for the output blocks only num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "output_blocks" in layer}) output_blocks = { layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key] for layer_id in range(num_output_blocks) } for i in range(1, num_input_blocks): block_id = (i - 1) // (config["num_res_blocks"] + 1) layer_in_block_id = (i - 1) % (config["num_res_blocks"] + 1) resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key] attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] if f"input_blocks.{i}.0.op.weight" in checkpoint: new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = checkpoint[ f"input_blocks.{i}.0.op.weight" ] new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = checkpoint[ f"input_blocks.{i}.0.op.bias" ] continue paths = renew_resnet_paths(resnets) meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} resnet_op = {"old": "resnets.2.op", "new": "downsamplers.0.op"} assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config ) if len(attentions): paths = renew_attention_paths(attentions) meta_path = { "old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}", } to_split = { f"input_blocks.{i}.1.qkv.bias": { "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias", "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias", "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias", }, f"input_blocks.{i}.1.qkv.weight": { "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight", "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight", "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight", }, } assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], attention_paths_to_split=to_split, config=config, ) resnet_0 = middle_blocks[0] attentions = middle_blocks[1] resnet_1 = middle_blocks[2] resnet_0_paths = renew_resnet_paths(resnet_0) assign_to_checkpoint(resnet_0_paths, new_checkpoint, checkpoint, config=config) resnet_1_paths = renew_resnet_paths(resnet_1) assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config) attentions_paths = renew_attention_paths(attentions) to_split = { "middle_block.1.qkv.bias": { "key": "mid_block.attentions.0.key.bias", "query": "mid_block.attentions.0.query.bias", "value": "mid_block.attentions.0.value.bias", }, "middle_block.1.qkv.weight": { "key": "mid_block.attentions.0.key.weight", "query": "mid_block.attentions.0.query.weight", "value": "mid_block.attentions.0.value.weight", }, } assign_to_checkpoint( attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config ) for i in range(num_output_blocks): block_id = i // (config["num_res_blocks"] + 1) layer_in_block_id = i % (config["num_res_blocks"] + 1) output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] output_block_list = {} for layer in output_block_layers: layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) if layer_id in output_block_list: output_block_list[layer_id].append(layer_name) else: output_block_list[layer_id] = [layer_name] if len(output_block_list) > 1: resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] resnet_0_paths = renew_resnet_paths(resnets) paths = renew_resnet_paths(resnets) meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], config=config) if ["conv.weight", "conv.bias"] in output_block_list.values(): index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ f"output_blocks.{i}.{index}.conv.weight" ] new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[ f"output_blocks.{i}.{index}.conv.bias" ] # Clear attentions as they have been attributed above. if len(attentions) == 2: attentions = [] if len(attentions): paths = renew_attention_paths(attentions) meta_path = { "old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", } to_split = { f"output_blocks.{i}.1.qkv.bias": { "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias", "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias", "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias", }, f"output_blocks.{i}.1.qkv.weight": { "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight", "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight", "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight", }, } assign_to_checkpoint( paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], attention_paths_to_split=to_split if any("qkv" in key for key in attentions) else None, config=config, ) else: resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) for path in resnet_0_paths: old_path = ".".join(["output_blocks", str(i), path["old"]]) new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) new_checkpoint[new_path] = checkpoint[old_path] return new_checkpoint if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help="The config json file corresponding to the architecture.", ) parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") args = parser.parse_args() checkpoint = torch.load(args.checkpoint_path)['state_dict'] with open(args.config_file) as f: config = json.loads(f.read()) config["num_res_blocks"] = 2 config["num_head_channels"] = 32 converted_checkpoint = convert_ldm_checkpoint(checkpoint, config) if "ldm" in config: del config["ldm"] if "num_res_blocks" in config: del config["num_res_blocks"] if "num_head_channels" in config: del config["num_head_channels"] model = UNet2DModel(**config) model.load_state_dict(converted_checkpoint) try: scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1])) pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae) pipe.save_pretrained(args.dump_path) except: # noqa: E722 model.save_pretrained(args.dump_path) ================================================ FILE: tools/ddpm_cifar10_config.json ================================================ { "_class_name": "UNet2DModel", "_diffusers_version": "0.0.4", "act_fn": "silu", "attention_head_dim": null, "block_out_channels": [ 128, 256, 256, 256 ], "center_input_sample": false, "down_block_types": [ "DownBlock2D", "AttnDownBlock2D", "DownBlock2D", "DownBlock2D" ], "downsample_padding": 0, "flip_sin_to_cos": false, "freq_shift": 1, "in_channels": 3, "layers_per_block": 2, "mid_block_scale_factor": 1, "norm_eps": 1e-06, "norm_num_groups": 32, "out_channels": 3, "sample_size": 32, "time_embedding_type": "positional", "up_block_types": [ "UpBlock2D", "UpBlock2D", "AttnUpBlock2D", "UpBlock2D" ] } ================================================ FILE: tools/extract_cifar10.py ================================================ import os import torchvision from torchvision.datasets import CIFAR10 from tqdm import tqdm import argparse parser = argparse.ArgumentParser() parser.add_argument("--output", type=str, required=True) args = parser.parse_args() os.makedirs(args.output, exist_ok=True) # Define the path to the folder where the images will be saved save_path = os.path.join(args.output, 'cifar10_images') # Create the folder if it doesn't exist if not os.path.exists(save_path): os.makedirs(save_path) # Load the CIFAR10 dataset dataset = CIFAR10(root=args.output, train=True, download=True) # Loop through the dataset and save each image to the folder for i in tqdm(range(len(dataset))): image, label = dataset[i] image_name = f'{i}.png' image_path = os.path.join(save_path, image_name) image.save(image_path) ================================================ FILE: tools/ldm_unet_config.json ================================================ { "_class_name": "UNet2DModel", "_diffusers_version": "0.0.4", "act_fn": "silu", "attention_head_dim": 32, "block_out_channels": [ 224, 448, 672, 896 ], "center_input_sample": false, "down_block_types": [ "DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D" ], "downsample_padding": 1, "flip_sin_to_cos": true, "freq_shift": 0, "in_channels": 3, "layers_per_block": 2, "mid_block_scale_factor": 1, "norm_eps": 1e-05, "norm_num_groups": 32, "out_channels": 3, "sample_size": 64, "time_embedding_type": "positional", "up_block_types": [ "AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D" ] } ================================================ FILE: utils.py ================================================ import torch from glob import glob from PIL import Image import os from torchvision import transforms as T from torchvision.datasets import CIFAR10, CIFAR100 class UnlabeledImageFolder(torch.utils.data.Dataset): def __init__(self, root, transform=None, exts=["*.jpg", "*.png", "*.jpeg", "*.webp"]): self.root = root self.files = [] self.transform = transform for ext in exts: self.files.extend(glob(os.path.join(root, '**/*.{}'.format(ext)), recursive=True)) def __len__(self): return len(self.files) def __getitem__(self, idx): path = self.files[idx] img = Image.open(path).convert("RGB") if self.transform is not None: img = self.transform(img) return img def set_dropout(model, p): for m in model.modules(): if isinstance(m, torch.nn.Dropout): m.p = p def get_dataset(name_or_path, transform=None): if name_or_path.lower()=='cifar10': if transform is None: transform = T.Compose([ T.RandomHorizontalFlip(), T.ToTensor(), T.Normalize(mean=0.5, std=0.5), ]) dataset = CIFAR10(root='./data', train=True, download=True, transform=transform) elif name_or_path.lower()=='cifar100': if transform is None: transform = T.Compose([ T.RandomHorizontalFlip(), T.ToTensor(), T.Normalize(mean=0.5, std=0.5), ]) dataset = CIFAR100(root='./data', train=True, download=True, transform=transform) elif os.path.isdir(name_or_path): if transform is None: transform = T.Compose([ T.Resize(256), T.RandomCrop(256), T.RandomHorizontalFlip(), T.ToTensor(), T.Normalize(mean=0.5, std=0.5), ]) dataset = UnlabeledImageFolder(name_or_path, transform=transform) return dataset