Full Code of open-mmlab/mmagic for AI

gitextract_ofufjmnn/

├── .circleci/
│   ├── config.yml
│   ├── docker/
│   │   └── Dockerfile
│   └── test.yml
├── .dele.yml
├── .dev_scripts/
│   ├── README.md
│   ├── create_ceph_configs.py
│   ├── doc_link_checker.py
│   ├── download_models.py
│   ├── inference_benchmark.sh
│   ├── job_watcher.py
│   ├── metric_mapping.py
│   ├── task_mapping.py
│   ├── test_benchmark.py
│   ├── test_benchmark.yml
│   ├── train_benchmark.py
│   ├── train_benchmark.yml
│   ├── update_config_readme.py
│   ├── update_model_index.py
│   ├── update_ut.py
│   └── utils/
│       ├── __init__.py
│       ├── job_util.py
│       └── modelindex.py
├── .github/
│   ├── CODE_OF_CONDUCT.md
│   ├── CONTRIBUTING.md
│   ├── ISSUE_TEMPLATE/
│   │   ├── 1-bug-report.yml
│   │   ├── 2-feature-request.yml
│   │   ├── 3-new-model.yml
│   │   ├── 4-documentation.yml
│   │   └── config.yml
│   ├── pull_request_template.md
│   └── workflows/
│       ├── lint.yml
│       ├── merge_stage_test.yml
│       ├── pr_stage_test.yml
│       ├── publish-to-pypi.yml
│       └── test_mim.yml
├── .gitignore
├── .owners.yml
├── .pre-commit-config.yaml
├── .pylintrc
├── .readthedocs.yml
├── CITATION.cff
├── LICENSE
├── MANIFEST.in
├── README.md
├── README_zh-CN.md
├── configs/
│   ├── _base_/
│   │   ├── datasets/
│   │   │   ├── basicvsr_test_config.py
│   │   │   ├── celeba.py
│   │   │   ├── cifar10_noaug.py
│   │   │   ├── cifar10_nopad.py
│   │   │   ├── comp1k.py
│   │   │   ├── deblurring-defocus_test_config.py
│   │   │   ├── deblurring-motion_test_config.py
│   │   │   ├── decompression_test_config.py
│   │   │   ├── denoising-gaussian_color_test_config.py
│   │   │   ├── denoising-gaussian_gray_test_config.py
│   │   │   ├── denoising-real_test_config.py
│   │   │   ├── deraining_test_config.py
│   │   │   ├── ffhq_flip.py
│   │   │   ├── grow_scale_imgs_ffhq_styleganv1.py
│   │   │   ├── imagenet_128.py
│   │   │   ├── imagenet_256.py
│   │   │   ├── imagenet_512.py
│   │   │   ├── imagenet_64.py
│   │   │   ├── imagenet_noaug_128.py
│   │   │   ├── liif_test_config.py
│   │   │   ├── lsun_stylegan.py
│   │   │   ├── paired_imgs_256x256_crop.py
│   │   │   ├── places.py
│   │   │   ├── sisr_x2_test_config.py
│   │   │   ├── sisr_x3_test_config.py
│   │   │   ├── sisr_x4_test_config.py
│   │   │   ├── tdan_test_config.py
│   │   │   ├── unconditional_imgs_128x128.py
│   │   │   ├── unconditional_imgs_64x64.py
│   │   │   ├── unconditional_imgs_flip_512x512.py
│   │   │   ├── unconditional_imgs_flip_lanczos_resize_256x256.py
│   │   │   └── unpaired_imgs_256x256.py
│   │   ├── default_runtime.py
│   │   ├── gen_default_runtime.py
│   │   ├── inpaint_default_runtime.py
│   │   ├── matting_default_runtime.py
│   │   ├── models/
│   │   │   ├── base_cyclegan.py
│   │   │   ├── base_deepfillv1.py
│   │   │   ├── base_deepfillv2.py
│   │   │   ├── base_edvr.py
│   │   │   ├── base_gl.py
│   │   │   ├── base_glean.py
│   │   │   ├── base_liif.py
│   │   │   ├── base_pconv.py
│   │   │   ├── base_pix2pix.py
│   │   │   ├── base_styleganv1.py
│   │   │   ├── base_styleganv2.py
│   │   │   ├── base_styleganv3.py
│   │   │   ├── base_tof.py
│   │   │   ├── biggan/
│   │   │   │   └── base_biggan_128x128.py
│   │   │   ├── dcgan/
│   │   │   │   ├── base_dcgan_128x128.py
│   │   │   │   └── base_dcgan_64x64.py
│   │   │   ├── sagan/
│   │   │   │   ├── base_sagan_128x128.py
│   │   │   │   └── base_sagan_32x32.py
│   │   │   └── sngan_proj/
│   │   │       ├── base_sngan_proj_128x128.py
│   │   │       └── base_sngan_proj_32x32.py
│   │   └── schedules/
│   │       └── .gitkeep
│   ├── animatediff/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── animatediff_Lyriel.py
│   │   ├── animatediff_MajicMix.py
│   │   ├── animatediff_RcnzCartoon.py
│   │   ├── animatediff_RealisticVision.py
│   │   ├── animatediff_RealisticVision_v2.py
│   │   ├── animatediff_ToonYou.py
│   │   └── metafile.yml
│   ├── aot_gan/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── aot-gan_smpgan_4xb4_places-512x512.py
│   │   └── metafile.yml
│   ├── basicvsr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── basicvsr_2xb4_reds4.py
│   │   ├── basicvsr_2xb4_vimeo90k-bd.py
│   │   ├── basicvsr_2xb4_vimeo90k-bi.py
│   │   └── metafile.yml
│   ├── basicvsr_pp/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── basicvsr-pp_c128n25_600k_ntire-decompress-track1.py
│   │   ├── basicvsr-pp_c128n25_600k_ntire-decompress-track2.py
│   │   ├── basicvsr-pp_c128n25_600k_ntire-decompress-track3.py
│   │   ├── basicvsr-pp_c128n25_600k_ntire-vsr.py
│   │   ├── basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py
│   │   ├── basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py
│   │   ├── basicvsr-pp_c64n7_8xb1-600k_reds4.py
│   │   └── metafile.yml
│   ├── biggan/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py
│   │   ├── biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py
│   │   ├── biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py
│   │   ├── biggan_2xb25-500kiters_cifar10-32x32.py
│   │   ├── biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py
│   │   ├── biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py
│   │   └── metafile.yml
│   ├── cain/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── cain_g1b32_1xb5_vimeo90k-triplet.py
│   │   └── metafile.yml
│   ├── controlnet/
│   │   ├── README.md
│   │   ├── controlnet-1xb1-fill50k.py
│   │   ├── controlnet-canny.py
│   │   ├── controlnet-pose.py
│   │   ├── controlnet-seg.py
│   │   └── metafile.yml
│   ├── controlnet_animation/
│   │   ├── README.md
│   │   ├── anythingv3_config.py
│   │   └── metafile.yml
│   ├── cyclegan/
│   │   ├── README.md
│   │   ├── cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py
│   │   ├── cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py
│   │   ├── cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py
│   │   ├── cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py
│   │   ├── cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py
│   │   ├── cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py
│   │   └── metafile.yml
│   ├── dcgan/
│   │   ├── README.md
│   │   ├── dcgan_1xb128-300kiters_celeba-cropped-64.py
│   │   ├── dcgan_1xb128-5epoches_lsun-bedroom-64x64.py
│   │   ├── dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py
│   │   └── metafile.yml
│   ├── deblurganv2/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── deblurganv2_fpn-inception_1xb1_gopro.py
│   │   ├── deblurganv2_fpn-mobilenet_1xb1_gopro.py
│   │   └── metafile.yml
│   ├── deepfillv1/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── deepfillv1_4xb4_celeba-256x256.py
│   │   ├── deepfillv1_8xb2_places-256x256.py
│   │   └── metafile.yml
│   ├── deepfillv2/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── deepfillv2_8xb2_celeba-256x256.py
│   │   ├── deepfillv2_8xb2_places-256x256.py
│   │   └── metafile.yml
│   ├── dic/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── dic_gan-x8c48b6_4xb2-500k_celeba-hq.py
│   │   ├── dic_x8c48b6_4xb2-150k_celeba-hq.py
│   │   └── metafile.yml
│   ├── diffusers_pipeline/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   └── sd_xl_pipeline.py
│   ├── dim/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── dim_stage1-v16_1xb1-1000k_comp1k.py
│   │   ├── dim_stage1-v16_1xb1-1000k_comp1k_online-merge.py
│   │   ├── dim_stage2-v16-pln_1xb1-1000k_comp1k.py
│   │   ├── dim_stage3-v16-pln_1xb1-1000k_comp1k.py
│   │   └── metafile.yml
│   ├── disco_diffusion/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── disco-diffusion_adm-u-finetuned_imagenet-256x256.py
│   │   ├── disco-diffusion_adm-u-finetuned_imagenet-512x512.py
│   │   ├── disco-diffusion_portrait-generator-v001.py
│   │   ├── metafile.yml
│   │   └── tutorials.ipynb
│   ├── draggan/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── stylegan2_1024x1024.py
│   │   ├── stylegan2_256x256.py
│   │   └── stylegan2_512x512.py
│   ├── dreambooth/
│   │   ├── README.md
│   │   ├── dreambooth-finetune_text_encoder.py
│   │   ├── dreambooth-lora-prior_pre.py
│   │   ├── dreambooth-lora.py
│   │   ├── dreambooth-prior_pre.py
│   │   ├── dreambooth.py
│   │   └── metafile.yml
│   ├── edsr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── edsr_x2c64b16_1xb16-300k_div2k.py
│   │   ├── edsr_x3c64b16_1xb16-300k_div2k.py
│   │   ├── edsr_x4c64b16_1xb16-300k_div2k.py
│   │   └── metafile.yml
│   ├── edvr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py
│   │   ├── edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py
│   │   ├── edvrm_8xb4-600k_reds.py
│   │   ├── edvrm_wotsa_8xb4-600k_reds.py
│   │   └── metafile.yml
│   ├── eg3d/
│   │   ├── README.md
│   │   ├── eg3d_cvt-official-rgb_afhq-512x512.py
│   │   ├── eg3d_cvt-official-rgb_ffhq-512x512.py
│   │   ├── eg3d_cvt-official-rgb_shapenet-128x128.py
│   │   └── metafile.yml
│   ├── esrgan/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py
│   │   ├── esrgan_x4c64b23g32_1xb16-400k_div2k.py
│   │   └── metafile.yml
│   ├── fastcomposer/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── fastcomposer_8xb16_FFHQ.py
│   │   └── metafile.yml
│   ├── flavr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── flavr_in4out1_8xb4_vimeo90k-septuplet.py
│   │   └── metafile.yml
│   ├── gca/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── baseline_r34_4xb10-200k_comp1k.py
│   │   ├── baseline_r34_4xb10-dimaug-200k_comp1k.py
│   │   ├── gca_r34_4xb10-200k_comp1k.py
│   │   ├── gca_r34_4xb10-dimaug-200k_comp1k.py
│   │   └── metafile.yml
│   ├── ggan/
│   │   ├── README.md
│   │   ├── ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py
│   │   ├── ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py
│   │   ├── ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py
│   │   └── metafile.yml
│   ├── glean/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py
│   │   ├── glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py
│   │   ├── glean_x16-fp16_2xb8_ffhq.py
│   │   ├── glean_x16_2xb8_cat.py
│   │   ├── glean_x16_2xb8_ffhq.py
│   │   ├── glean_x8-fp16_2xb8_cat.py
│   │   ├── glean_x8_2xb8_cat.py
│   │   └── metafile.yml
│   ├── global_local/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── gl_8xb12_celeba-256x256.py
│   │   ├── gl_8xb12_places-256x256.py
│   │   └── metafile.yml
│   ├── guided_diffusion/
│   │   ├── README.md
│   │   ├── adm-g_ddim25_8xb32_imagenet-256x256.py
│   │   ├── adm-g_ddim25_8xb32_imagenet-512x512.py
│   │   ├── adm-g_ddim25_8xb32_imagenet-64x64.py
│   │   ├── adm_ddim250_8xb32_imagenet-256x256.py
│   │   ├── adm_ddim250_8xb32_imagenet-512x512.py
│   │   ├── adm_ddim250_8xb32_imagenet-64x64.py
│   │   └── metafile.yml
│   ├── iconvsr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── iconvsr_2xb4_reds4.py
│   │   ├── iconvsr_2xb4_vimeo90k-bd.py
│   │   ├── iconvsr_2xb4_vimeo90k-bi.py
│   │   └── metafile.yml
│   ├── indexnet/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── indexnet_mobv2-dimaug_1xb16-78k_comp1k.py
│   │   ├── indexnet_mobv2_1xb16-78k_comp1k.py
│   │   └── metafile.yml
│   ├── inst_colorization/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── inst-colorizatioon_full_official_cocostuff-256x256.py
│   │   └── metafile.yml
│   ├── liif/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── liif-edsr-norm_c64b16_1xb16-1000k_div2k.py
│   │   ├── liif-rdn-norm_c64b16_1xb16-1000k_div2k.py
│   │   └── metafile.yml
│   ├── lsgan/
│   │   ├── README.md
│   │   ├── lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py
│   │   ├── lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py
│   │   ├── lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py
│   │   ├── lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py
│   │   └── metafile.yml
│   ├── nafnet/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py
│   │   └── nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py
│   ├── partial_conv/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── pconv_stage1_8xb12_places-256x256.py
│   │   ├── pconv_stage1_8xb1_celeba-256x256.py
│   │   ├── pconv_stage2_4xb2_celeba-256x256.py
│   │   └── pconv_stage2_4xb2_places-256x256.py
│   ├── pggan/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── pggan_8xb4-12Mimg_celeba-hq-1024x1024.py
│   │   ├── pggan_8xb4-12Mimgs_celeba-cropped-128x128.py
│   │   └── pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py
│   ├── pix2pix/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py
│   │   ├── pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py
│   │   ├── pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py
│   │   └── pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py
│   ├── positional_encoding_in_gans/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py
│   │   ├── mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py
│   │   ├── mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py
│   │   ├── singan-csg_bohemian.py
│   │   ├── singan-csg_fish.py
│   │   ├── singan_interp-pad_balloons.py
│   │   ├── singan_interp-pad_disc-nobn_balloons.py
│   │   ├── singan_interp-pad_disc-nobn_fish.py
│   │   ├── singan_spe-dim4_bohemian.py
│   │   ├── singan_spe-dim4_fish.py
│   │   ├── singan_spe-dim8_bohemian.py
│   │   ├── stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py
│   │   └── stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py
│   ├── rdn/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── rdn_x2c64b16_1xb16-1000k_div2k.py
│   │   ├── rdn_x3c64b16_1xb16-1000k_div2k.py
│   │   └── rdn_x4c64b16_1xb16-1000k_div2k.py
│   ├── real_basicvsr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py
│   │   └── realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py
│   ├── real_esrgan/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py
│   │   └── realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py
│   ├── restormer/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── restormer_official_dfwb-color-sigma15.py
│   │   ├── restormer_official_dfwb-color-sigma25.py
│   │   ├── restormer_official_dfwb-color-sigma50.py
│   │   ├── restormer_official_dfwb-gray-sigma15.py
│   │   ├── restormer_official_dfwb-gray-sigma25.py
│   │   ├── restormer_official_dfwb-gray-sigma50.py
│   │   ├── restormer_official_dpdd-dual.py
│   │   ├── restormer_official_dpdd-single.py
│   │   ├── restormer_official_gopro.py
│   │   ├── restormer_official_rain13k.py
│   │   └── restormer_official_sidd.py
│   ├── sagan/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── sagan_128_cvt_studioGAN.py
│   │   ├── sagan_cvt-studioGAN_cifar10-32x32.py
│   │   ├── sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py
│   │   ├── sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py
│   │   ├── sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py
│   │   └── sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py
│   ├── singan/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── singan_balloons.py
│   │   ├── singan_bohemian.py
│   │   └── singan_fish.py
│   ├── sngan_proj/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── sngan-proj-cvt-studioGAN_cifar10-32x32.py
│   │   ├── sngan-proj-cvt-studioGAN_imagenet1k-128x128.py
│   │   ├── sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py
│   │   ├── sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py
│   │   ├── sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py
│   │   └── sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py
│   ├── srcnn/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   └── srcnn_x4k915_1xb16-1000k_div2k.py
│   ├── srgan_resnet/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── msrresnet_x4c64b16_1xb16-1000k_div2k.py
│   │   └── srgan_x4c64b16_1xb16-1000k_div2k.py
│   ├── stable_diffusion/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── stable-diffusion_ddim_denoisingunet-inpaint.py
│   │   ├── stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py
│   │   └── stable-diffusion_ddim_denoisingunet.py
│   ├── stable_diffusion_xl/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   └── stable-diffusion_xl_ddim_denoisingunet.py
│   ├── styleganv1/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py
│   │   └── styleganv1_ffhq-256x256_8xb4-25Mimgs.py
│   ├── styleganv2/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py
│   │   ├── stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py
│   │   ├── stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py
│   │   ├── stylegan2_c2_8xb4-800kiters_ffhq-256x256.py
│   │   ├── stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py
│   │   ├── stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py
│   │   ├── stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py
│   │   ├── stylegan2_c2_8xb4_ffhq-1024x1024.py
│   │   └── stylegan2_c2_8xb4_lsun-car-384x512.py
│   ├── styleganv3/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   ├── stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py
│   │   ├── stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py
│   │   ├── stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py
│   │   ├── stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py
│   │   ├── stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py
│   │   ├── stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py
│   │   ├── stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py
│   │   ├── stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256.py
│   │   ├── stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py
│   │   └── stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024.py
│   ├── swinir/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
│   │   ├── swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
│   │   ├── swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py
│   │   ├── swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
│   │   ├── swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
│   │   ├── swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py
│   │   ├── swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py
│   │   ├── swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py
│   │   ├── swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py
│   │   ├── swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py
│   │   ├── swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py
│   │   ├── swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py
│   │   ├── swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py
│   │   ├── swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
│   │   ├── swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
│   │   ├── swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
│   │   ├── swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
│   │   ├── swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
│   │   ├── swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
│   │   ├── swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
│   │   ├── swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
│   │   └── swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
│   ├── tdan/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py
│   │   ├── tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py
│   │   ├── tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py
│   │   └── tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py
│   ├── textual_inversion/
│   │   ├── README.md
│   │   ├── metafile.yml
│   │   └── textual_inversion.py
│   ├── tof/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py
│   │   ├── tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py
│   │   ├── tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py
│   │   ├── tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py
│   │   ├── tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py
│   │   └── tof_x4_official_vimeo90k.py
│   ├── ttsr/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   ├── ttsr-gan_x4c64b16_1xb9-500k_CUFED.py
│   │   └── ttsr-rec_x4c64b16_1xb9-200k_CUFED.py
│   ├── vico/
│   │   ├── README.md
│   │   ├── README_zh-CN.md
│   │   ├── metafile.yml
│   │   └── vico.py
│   └── wgan-gp/
│       ├── README.md
│       ├── metafile.yml
│       ├── wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py
│       └── wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py
├── demo/
│   ├── README.md
│   ├── download_inference_resources.py
│   ├── gradio_animatediff.py
│   ├── gradio_controlnet_animation.py
│   ├── gradio_draggan.py
│   ├── gradio_fastcomposer.py
│   ├── gradio_inpainting.py
│   ├── gradio_vico.py
│   ├── mmagic_inference_demo.py
│   ├── mmagic_inference_tutorial.ipynb
│   ├── singan_demo.py
│   └── utils/
│       ├── gradio_utils.py
│       └── renderer.py
├── docker/
│   ├── Dockerfile
│   └── README.md
├── docs/
│   ├── en/
│   │   ├── .dev_scripts/
│   │   │   ├── update_dataset_zoo.py
│   │   │   └── update_model_zoo.py
│   │   ├── .gitignore
│   │   ├── Makefile
│   │   ├── _static/
│   │   │   └── css/
│   │   │       └── readthedocs.css
│   │   ├── _templates/
│   │   │   ├── 404.html
│   │   │   └── python/
│   │   │       ├── attribute.rst
│   │   │       ├── class.rst
│   │   │       ├── data.rst
│   │   │       ├── exception.rst
│   │   │       ├── function.rst
│   │   │       ├── method.rst
│   │   │       ├── module.rst
│   │   │       ├── package.rst
│   │   │       └── property.rst
│   │   ├── advanced_guides/
│   │   │   ├── data_flow.md
│   │   │   ├── data_preprocessor.md
│   │   │   ├── evaluator.md
│   │   │   └── structures.md
│   │   ├── changelog.md
│   │   ├── community/
│   │   │   ├── contributing.md
│   │   │   └── projects.md
│   │   ├── conf.py
│   │   ├── device/
│   │   │   └── npu.md
│   │   ├── docutils.conf
│   │   ├── faq.md
│   │   ├── get_started/
│   │   │   ├── install.md
│   │   │   ├── overview.md
│   │   │   └── quick_run.md
│   │   ├── howto/
│   │   │   ├── dataset.md
│   │   │   ├── losses.md
│   │   │   ├── models.md
│   │   │   └── transforms.md
│   │   ├── index.rst
│   │   ├── make.bat
│   │   ├── migration/
│   │   │   ├── amp.md
│   │   │   ├── data.md
│   │   │   ├── distributed_train.md
│   │   │   ├── eval_test.md
│   │   │   ├── models.md
│   │   │   ├── optimizers.md
│   │   │   ├── overview.md
│   │   │   ├── runtime.md
│   │   │   ├── schedule.md
│   │   │   └── visualization.md
│   │   ├── switch_language.md
│   │   └── user_guides/
│   │       ├── config.md
│   │       ├── dataset_prepare.md
│   │       ├── deploy.md
│   │       ├── inference.md
│   │       ├── metrics.md
│   │       ├── train_test.md
│   │       ├── useful_tools.md
│   │       └── visualization.md
│   └── zh_cn/
│       ├── .dev_scripts/
│       │   ├── update_dataset_zoo.py
│       │   └── update_model_zoo.py
│       ├── .gitignore
│       ├── Makefile
│       ├── _static/
│       │   └── css/
│       │       └── readthedocs.css
│       ├── _templates/
│       │   └── 404.html
│       ├── advanced_guides/
│       │   ├── data_flow.md
│       │   ├── data_preprocessor.md
│       │   ├── evaluator.md
│       │   └── structures.md
│       ├── changelog.md
│       ├── community/
│       │   ├── contributing.md
│       │   └── projects.md
│       ├── conf.py
│       ├── device/
│       │   └── npu_zh.md
│       ├── faq.md
│       ├── get_started/
│       │   ├── install.md
│       │   ├── overview.md
│       │   └── quick_run.md
│       ├── howto/
│       │   ├── dataset.md
│       │   ├── losses.md
│       │   ├── models.md
│       │   └── transforms.md
│       ├── index.rst
│       ├── make.bat
│       ├── migration/
│       │   ├── amp.md
│       │   ├── data.md
│       │   ├── distributed_train.md
│       │   ├── eval_test.md
│       │   ├── models.md
│       │   ├── optimizers.md
│       │   ├── overview.md
│       │   ├── runtime.md
│       │   ├── schedule.md
│       │   └── visualization.md
│       ├── stat.py
│       ├── switch_language.md
│       └── user_guides/
│           ├── config.md
│           ├── dataset_prepare.md
│           ├── deploy.md
│           ├── index.rst
│           ├── inference.md
│           ├── metrics.md
│           ├── train_test.md
│           ├── useful_tools.md
│           └── visualization.md
├── mmagic/
│   ├── __init__.py
│   ├── apis/
│   │   ├── __init__.py
│   │   ├── inferencers/
│   │   │   ├── __init__.py
│   │   │   ├── base_mmagic_inferencer.py
│   │   │   ├── colorization_inferencer.py
│   │   │   ├── conditional_inferencer.py
│   │   │   ├── controlnet_animation_inferencer.py
│   │   │   ├── diffusers_pipeline_inferencer.py
│   │   │   ├── eg3d_inferencer.py
│   │   │   ├── image_super_resolution_inferencer.py
│   │   │   ├── inference_functions.py
│   │   │   ├── inpainting_inferencer.py
│   │   │   ├── matting_inferencer.py
│   │   │   ├── text2image_inferencer.py
│   │   │   ├── translation_inferencer.py
│   │   │   ├── unconditional_inferencer.py
│   │   │   ├── video_interpolation_inferencer.py
│   │   │   └── video_restoration_inferencer.py
│   │   └── mmagic_inferencer.py
│   ├── configs/
│   │   ├── _base_/
│   │   │   ├── datasets/
│   │   │   │   ├── basicvsr_test_config.py
│   │   │   │   ├── celeba.py
│   │   │   │   ├── cifar10_noaug.py
│   │   │   │   ├── cifar10_nopad.py
│   │   │   │   ├── comp1k.py
│   │   │   │   ├── deblurring-defocus_test_config.py
│   │   │   │   ├── deblurring-motion_test_config.py
│   │   │   │   ├── decompression_test_config.py
│   │   │   │   ├── denoising-gaussian_color_test_config.py
│   │   │   │   ├── denoising-gaussian_gray_test_config.py
│   │   │   │   ├── denoising-real_test_config.py
│   │   │   │   ├── deraining_test_config.py
│   │   │   │   ├── ffhq_flip.py
│   │   │   │   ├── grow_scale_imgs_ffhq_styleganv1.py
│   │   │   │   ├── imagenet_128.py
│   │   │   │   ├── imagenet_256.py
│   │   │   │   ├── imagenet_512.py
│   │   │   │   ├── imagenet_64.py
│   │   │   │   ├── imagenet_noaug_128.py
│   │   │   │   ├── liif_test_config.py
│   │   │   │   ├── lsun_stylegan.py
│   │   │   │   ├── paired_imgs_256x256_crop.py
│   │   │   │   ├── places.py
│   │   │   │   ├── sisr_x2_test_config.py
│   │   │   │   ├── sisr_x3_test_config.py
│   │   │   │   ├── sisr_x4_test_config.py
│   │   │   │   ├── tdan_test_config.py
│   │   │   │   ├── unconditional_imgs_128x128.py
│   │   │   │   ├── unconditional_imgs_64x64.py
│   │   │   │   ├── unconditional_imgs_flip_512x512.py
│   │   │   │   ├── unconditional_imgs_flip_lanczos_resize_256x256.py
│   │   │   │   └── unpaired_imgs_256x256.py
│   │   │   ├── default_runtime.py
│   │   │   ├── gen_default_runtime.py
│   │   │   ├── inpaint_default_runtime.py
│   │   │   ├── matting_default_runtime.py
│   │   │   ├── models/
│   │   │   │   ├── base_cyclegan.py
│   │   │   │   ├── base_deepfillv1.py
│   │   │   │   ├── base_deepfillv2.py
│   │   │   │   ├── base_edvr.py
│   │   │   │   ├── base_gl.py
│   │   │   │   ├── base_glean.py
│   │   │   │   ├── base_liif.py
│   │   │   │   ├── base_pconv.py
│   │   │   │   ├── base_pix2pix.py
│   │   │   │   ├── base_styleganv1.py
│   │   │   │   ├── base_styleganv2.py
│   │   │   │   ├── base_styleganv3.py
│   │   │   │   ├── base_tof.py
│   │   │   │   ├── biggan/
│   │   │   │   │   └── base_biggan_128x128.py
│   │   │   │   ├── dcgan/
│   │   │   │   │   ├── base_dcgan_128x128.py
│   │   │   │   │   └── base_dcgan_64x64.py
│   │   │   │   ├── sagan/
│   │   │   │   │   ├── base_sagan_128x128.py
│   │   │   │   │   └── base_sagan_32x32.py
│   │   │   │   └── sngan_proj/
│   │   │   │       ├── base_sngan_proj_128x128.py
│   │   │   │       └── base_sngan_proj_32x32.py
│   │   │   └── schedules/
│   │   │       └── .gitkeep
│   │   ├── biggan/
│   │   │   ├── biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py
│   │   │   ├── biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py
│   │   │   ├── biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py
│   │   │   ├── biggan_2xb25-500kiters_cifar10-32x32.py
│   │   │   ├── biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py
│   │   │   └── biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py
│   │   ├── dreambooth/
│   │   │   ├── dreambooth-finetune_text_encoder.py
│   │   │   ├── dreambooth-prior_pre.py
│   │   │   ├── dreambooth.py
│   │   │   ├── dreambooth_lora-prior_pre.py
│   │   │   └── dreambooth_lora.py
│   │   ├── eg3d/
│   │   │   ├── eg3d_cvt-official-rgb_afhq-512x512.py
│   │   │   ├── eg3d_cvt-official-rgb_ffhq-512x512.py
│   │   │   └── eg3d_cvt-official-rgb_shapenet-128x128.py
│   │   ├── guided_diffusion/
│   │   │   ├── adm-g_ddim25_8xb32_imagenet_256x256.py
│   │   │   ├── adm-g_ddim25_8xb32_imagenet_512x512.py
│   │   │   ├── adm-g_ddim25_8xb32_imagenet_64x64.py
│   │   │   ├── adm_ddim250_8xb32_imagenet_256x256.py
│   │   │   ├── adm_ddim250_8xb32_imagenet_512x512.py
│   │   │   └── adm_ddim250_8xb32_imagenet_64x64.py
│   │   ├── inst_colorization/
│   │   │   └── inst-colorizatioon_full_official_cocostuff-256x256.py
│   │   ├── real_basicvsr/
│   │   │   ├── realbasicvsr_c64b20_1x30x8_8xb1_lr5e_5_150k_reds.py
│   │   │   └── realbasicvsr_wogan_c64b20_2x30x8_8xb2_lr1e_4_300k_reds.py
│   │   ├── styleganv2/
│   │   │   ├── stylegan2_c2_8xb4_800kiters_ffhq_256x256.py
│   │   │   ├── stylegan2_c2_8xb4_800kiters_lsun_cat_256x256.py
│   │   │   ├── stylegan2_c2_8xb4_800kiters_lsun_church_256x256.py
│   │   │   ├── stylegan2_c2_8xb4_800kiters_lsun_horse_256x256.py
│   │   │   ├── stylegan2_c2_8xb4_ffhq_1024x1024.py
│   │   │   ├── stylegan2_c2_8xb4_lsun_car_384x512.py
│   │   │   ├── stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py
│   │   │   ├── stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256.py
│   │   │   └── stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256.py
│   │   └── styleganv3/
│   │       ├── stylegan3_r_ada_gamma33_8xb4_fp16_metfaces_1024x1024.py
│   │       ├── stylegan3_r_cvt_official_rgb_8xb4_ffhq_1024x1024.py
│   │       ├── stylegan3_r_cvt_official_rgb_8xb4_ffhqu_256x256.py
│   │       ├── stylegan3_r_cvt_official_rgb_8xb4x8_afhqv2_512x512.py
│   │       ├── stylegan3_t_ada_gamma66_8xb4_fp16_metfaces_1024x1024.py
│   │       ├── stylegan3_t_cvt_official_rgb_8xb4_afhqv2_512x512.py
│   │       ├── stylegan3_t_cvt_official_rgb_8xb4_ffhq_1024x1024.py
│   │       ├── stylegan3_t_cvt_official_rgb_8xb4_ffhqu_256x256.py
│   │       ├── stylegan3_t_gamma20_8xb4_fp16_noaug_ffhq_256x256.py
│   │       └── stylegan3_t_gamma328_8xb4_fp16_noaug_ffhq_1024x1024.py
│   ├── datasets/
│   │   ├── __init__.py
│   │   ├── basic_conditional_dataset.py
│   │   ├── basic_frames_dataset.py
│   │   ├── basic_image_dataset.py
│   │   ├── categories.py
│   │   ├── cifar10_dataset.py
│   │   ├── comp1k_dataset.py
│   │   ├── controlnet_dataset.py
│   │   ├── data_utils.py
│   │   ├── dreambooth_dataset.py
│   │   ├── grow_scale_image_dataset.py
│   │   ├── imagenet_dataset.py
│   │   ├── mscoco_dataset.py
│   │   ├── paired_image_dataset.py
│   │   ├── singan_dataset.py
│   │   ├── textual_inversion_dataset.py
│   │   ├── transforms/
│   │   │   ├── __init__.py
│   │   │   ├── albu_function.py
│   │   │   ├── albumentations.py
│   │   │   ├── alpha.py
│   │   │   ├── aug_frames.py
│   │   │   ├── aug_pixel.py
│   │   │   ├── aug_shape.py
│   │   │   ├── blur_kernels.py
│   │   │   ├── crop.py
│   │   │   ├── fgbg.py
│   │   │   ├── formatting.py
│   │   │   ├── generate_assistant.py
│   │   │   ├── generate_frame_indices.py
│   │   │   ├── get_masked_image.py
│   │   │   ├── loading.py
│   │   │   ├── matlab_like_resize.py
│   │   │   ├── normalization.py
│   │   │   ├── random_degradations.py
│   │   │   ├── random_down_sampling.py
│   │   │   ├── trimap.py
│   │   │   └── values.py
│   │   └── unpaired_image_dataset.py
│   ├── engine/
│   │   ├── __init__.py
│   │   ├── hooks/
│   │   │   ├── __init__.py
│   │   │   ├── ema.py
│   │   │   ├── iter_time_hook.py
│   │   │   ├── pggan_fetch_data_hook.py
│   │   │   ├── pickle_data_hook.py
│   │   │   ├── reduce_lr_scheduler_hook.py
│   │   │   └── visualization_hook.py
│   │   ├── optimizers/
│   │   │   ├── __init__.py
│   │   │   ├── multi_optimizer_constructor.py
│   │   │   ├── pggan_optimizer_constructor.py
│   │   │   └── singan_optimizer_constructor.py
│   │   ├── runner/
│   │   │   ├── __init__.py
│   │   │   ├── log_processor.py
│   │   │   ├── loop_utils.py
│   │   │   └── multi_loops.py
│   │   └── schedulers/
│   │       ├── __init__.py
│   │       ├── linear_lr_scheduler_with_interval.py
│   │       └── reduce_lr_scheduler.py
│   ├── evaluation/
│   │   ├── __init__.py
│   │   ├── evaluator.py
│   │   ├── functional/
│   │   │   ├── __init__.py
│   │   │   ├── fid_inception.py
│   │   │   ├── gaussian_funcs.py
│   │   │   └── inception_utils.py
│   │   └── metrics/
│   │       ├── __init__.py
│   │       ├── base_gen_metric.py
│   │       ├── base_sample_wise_metric.py
│   │       ├── connectivity_error.py
│   │       ├── equivariance.py
│   │       ├── fid.py
│   │       ├── gradient_error.py
│   │       ├── inception_score.py
│   │       ├── mae.py
│   │       ├── matting_mse.py
│   │       ├── metrics_utils.py
│   │       ├── ms_ssim.py
│   │       ├── mse.py
│   │       ├── niqe.py
│   │       ├── niqe_pris_params.npz
│   │       ├── ppl.py
│   │       ├── precision_and_recall.py
│   │       ├── psnr.py
│   │       ├── sad.py
│   │       ├── snr.py
│   │       ├── ssim.py
│   │       └── swd.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── archs/
│   │   │   ├── __init__.py
│   │   │   ├── all_gather_layer.py
│   │   │   ├── aspp.py
│   │   │   ├── attention_injection.py
│   │   │   ├── conv.py
│   │   │   ├── downsample.py
│   │   │   ├── ensemble.py
│   │   │   ├── gated_conv_module.py
│   │   │   ├── img_normalize.py
│   │   │   ├── linear_module.py
│   │   │   ├── lora.py
│   │   │   ├── multi_layer_disc.py
│   │   │   ├── patch_disc.py
│   │   │   ├── resnet.py
│   │   │   ├── separable_conv_module.py
│   │   │   ├── simple_encoder_decoder.py
│   │   │   ├── smpatch_disc.py
│   │   │   ├── sr_backbone.py
│   │   │   ├── tokenizer.py
│   │   │   ├── upsample.py
│   │   │   ├── vgg.py
│   │   │   └── wrapper.py
│   │   ├── base_models/
│   │   │   ├── __init__.py
│   │   │   ├── average_model.py
│   │   │   ├── base_conditional_gan.py
│   │   │   ├── base_edit_model.py
│   │   │   ├── base_gan.py
│   │   │   ├── base_mattor.py
│   │   │   ├── base_translation_model.py
│   │   │   ├── basic_interpolator.py
│   │   │   ├── one_stage.py
│   │   │   └── two_stage.py
│   │   ├── data_preprocessors/
│   │   │   ├── __init__.py
│   │   │   ├── data_preprocessor.py
│   │   │   └── mattor_preprocessor.py
│   │   ├── diffusion_schedulers/
│   │   │   ├── __init__.py
│   │   │   ├── ddim_scheduler.py
│   │   │   └── ddpm_scheduler.py
│   │   ├── editors/
│   │   │   ├── __init__.py
│   │   │   ├── animatediff/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── animatediff.py
│   │   │   │   ├── animatediff_utils.py
│   │   │   │   ├── attention_3d.py
│   │   │   │   ├── motion_module.py
│   │   │   │   ├── resnet_3d.py
│   │   │   │   ├── unet_3d.py
│   │   │   │   └── unet_block.py
│   │   │   ├── aotgan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── aot_decoder.py
│   │   │   │   ├── aot_encoder.py
│   │   │   │   ├── aot_encoder_decoder.py
│   │   │   │   ├── aot_inpaintor.py
│   │   │   │   └── aot_neck.py
│   │   │   ├── arcface/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── arcface_modules.py
│   │   │   │   ├── id_loss.py
│   │   │   │   └── model_irse.py
│   │   │   ├── basicvsr/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── basicvsr.py
│   │   │   │   └── basicvsr_net.py
│   │   │   ├── basicvsr_plusplus_net/
│   │   │   │   ├── __init__.py
│   │   │   │   └── basicvsr_plusplus_net.py
│   │   │   ├── biggan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── biggan.py
│   │   │   │   ├── biggan_deep_discriminator.py
│   │   │   │   ├── biggan_deep_generator.py
│   │   │   │   ├── biggan_discriminator.py
│   │   │   │   ├── biggan_generator.py
│   │   │   │   ├── biggan_modules.py
│   │   │   │   └── biggan_snmodule.py
│   │   │   ├── cain/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── cain.py
│   │   │   │   └── cain_net.py
│   │   │   ├── controlnet/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── controlnet.py
│   │   │   │   └── controlnet_utils.py
│   │   │   ├── cyclegan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── cyclegan.py
│   │   │   │   ├── cyclegan_generator.py
│   │   │   │   └── cyclegan_modules.py
│   │   │   ├── dcgan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── dcgan.py
│   │   │   │   ├── dcgan_discriminator.py
│   │   │   │   └── dcgan_generator.py
│   │   │   ├── ddpm/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── attention.py
│   │   │   │   ├── denoising_unet.py
│   │   │   │   ├── embeddings.py
│   │   │   │   ├── res_blocks.py
│   │   │   │   └── unet_blocks.py
│   │   │   ├── deblurganv2/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── deblurganv2.py
│   │   │   │   ├── deblurganv2_discriminator.py
│   │   │   │   ├── deblurganv2_generator.py
│   │   │   │   └── deblurganv2_util.py
│   │   │   ├── deepfillv1/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── contextual_attention.py
│   │   │   │   ├── contextual_attention_neck.py
│   │   │   │   ├── deepfill_decoder.py
│   │   │   │   ├── deepfill_disc.py
│   │   │   │   ├── deepfill_encoder.py
│   │   │   │   ├── deepfill_refiner.py
│   │   │   │   └── deepfillv1.py
│   │   │   ├── deepfillv2/
│   │   │   │   ├── __init__.py
│   │   │   │   └── two_stage_encoder_decoder.py
│   │   │   ├── dic/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── dic.py
│   │   │   │   ├── dic_net.py
│   │   │   │   ├── feedback_hour_glass.py
│   │   │   │   └── light_cnn.py
│   │   │   ├── dim/
│   │   │   │   ├── __init__.py
│   │   │   │   └── dim.py
│   │   │   ├── disco_diffusion/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── clip_wrapper.py
│   │   │   │   ├── disco.py
│   │   │   │   ├── guider.py
│   │   │   │   └── secondary_model.py
│   │   │   ├── dreambooth/
│   │   │   │   ├── __init__.py
│   │   │   │   └── dreambooth.py
│   │   │   ├── duf/
│   │   │   │   ├── __init__.py
│   │   │   │   └── duf.py
│   │   │   ├── edsr/
│   │   │   │   ├── __init__.py
│   │   │   │   └── edsr_net.py
│   │   │   ├── edvr/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── edvr.py
│   │   │   │   └── edvr_net.py
│   │   │   ├── eg3d/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── camera.py
│   │   │   │   ├── dual_discriminator.py
│   │   │   │   ├── eg3d.py
│   │   │   │   ├── eg3d_generator.py
│   │   │   │   ├── eg3d_modules.py
│   │   │   │   ├── eg3d_utils.py
│   │   │   │   ├── ray_sampler.py
│   │   │   │   └── renderer.py
│   │   │   ├── esrgan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── esrgan.py
│   │   │   │   └── rrdb_net.py
│   │   │   ├── fastcomposer/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── fastcomposer.py
│   │   │   │   └── fastcomposer_util.py
│   │   │   ├── fba/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── fba_decoder.py
│   │   │   │   └── fba_encoder.py
│   │   │   ├── flavr/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── flavr.py
│   │   │   │   └── flavr_net.py
│   │   │   ├── gca/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── gca.py
│   │   │   │   ├── gca_module.py
│   │   │   │   ├── resgca_dec.py
│   │   │   │   └── resgca_enc.py
│   │   │   ├── ggan/
│   │   │   │   ├── __init__.py
│   │   │   │   └── ggan.py
│   │   │   ├── glean/
│   │   │   │   ├── __init__.py
│   │   │   │   └── glean_styleganv2.py
│   │   │   ├── global_local/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── gl_decoder.py
│   │   │   │   ├── gl_dilation.py
│   │   │   │   ├── gl_disc.py
│   │   │   │   ├── gl_encoder.py
│   │   │   │   ├── gl_encoder_decoder.py
│   │   │   │   └── gl_inpaintor.py
│   │   │   ├── guided_diffusion/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── adm.py
│   │   │   │   └── classifier.py
│   │   │   ├── iconvsr/
│   │   │   │   ├── __init__.py
│   │   │   │   └── iconvsr_net.py
│   │   │   ├── indexnet/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── indexnet.py
│   │   │   │   ├── indexnet_decoder.py
│   │   │   │   └── indexnet_encoder.py
│   │   │   ├── inst_colorization/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── color_utils.py
│   │   │   │   ├── colorization_net.py
│   │   │   │   ├── fusion_net.py
│   │   │   │   ├── inst_colorization.py
│   │   │   │   └── weight_layer.py
│   │   │   ├── liif/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── liif.py
│   │   │   │   ├── liif_net.py
│   │   │   │   └── mlp_refiner.py
│   │   │   ├── lsgan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── lsgan.py
│   │   │   │   ├── lsgan_discriminator.py
│   │   │   │   └── lsgan_generator.py
│   │   │   ├── mspie/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── mspie_stylegan2.py
│   │   │   │   ├── mspie_stylegan2_discriminator.py
│   │   │   │   ├── mspie_stylegan2_generator.py
│   │   │   │   ├── mspie_stylegan2_modules.py
│   │   │   │   ├── pe_singan.py
│   │   │   │   ├── pe_singan_generator.py
│   │   │   │   └── positional_encoding.py
│   │   │   ├── nafnet/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── naf_avgpool2d.py
│   │   │   │   ├── naf_layerNorm2d.py
│   │   │   │   ├── nafbaseline_net.py
│   │   │   │   └── nafnet_net.py
│   │   │   ├── pconv/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── mask_conv_module.py
│   │   │   │   ├── partial_conv.py
│   │   │   │   ├── pconv_decoder.py
│   │   │   │   ├── pconv_encoder.py
│   │   │   │   ├── pconv_encoder_decoder.py
│   │   │   │   └── pconv_inpaintor.py
│   │   │   ├── pggan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── pggan.py
│   │   │   │   ├── pggan_discriminator.py
│   │   │   │   ├── pggan_generator.py
│   │   │   │   └── pggan_modules.py
│   │   │   ├── pix2pix/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── pix2pix.py
│   │   │   │   ├── pix2pix_generator.py
│   │   │   │   └── pix2pix_modules.py
│   │   │   ├── plain/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── plain_decoder.py
│   │   │   │   └── plain_refiner.py
│   │   │   ├── rdn/
│   │   │   │   ├── __init__.py
│   │   │   │   └── rdn_net.py
│   │   │   ├── real_basicvsr/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── real_basicvsr.py
│   │   │   │   └── real_basicvsr_net.py
│   │   │   ├── real_esrgan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── real_esrgan.py
│   │   │   │   └── unet_disc.py
│   │   │   ├── restormer/
│   │   │   │   ├── __init__.py
│   │   │   │   └── restormer_net.py
│   │   │   ├── sagan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── sagan.py
│   │   │   │   ├── sagan_discriminator.py
│   │   │   │   ├── sagan_generator.py
│   │   │   │   └── sagan_modules.py
│   │   │   ├── singan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── singan.py
│   │   │   │   ├── singan_discriminator.py
│   │   │   │   ├── singan_generator.py
│   │   │   │   └── singan_modules.py
│   │   │   ├── srcnn/
│   │   │   │   ├── __init__.py
│   │   │   │   └── srcnn_net.py
│   │   │   ├── srgan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── modified_vgg.py
│   │   │   │   ├── sr_resnet.py
│   │   │   │   └── srgan.py
│   │   │   ├── stable_diffusion/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── clip_wrapper.py
│   │   │   │   ├── stable_diffusion.py
│   │   │   │   ├── stable_diffusion_inpaint.py
│   │   │   │   └── vae.py
│   │   │   ├── stable_diffusion_xl/
│   │   │   │   ├── __init__.py
│   │   │   │   └── stable_diffusion_xl.py
│   │   │   ├── stylegan1/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── stylegan1.py
│   │   │   │   ├── stylegan1_discriminator.py
│   │   │   │   ├── stylegan1_generator.py
│   │   │   │   ├── stylegan1_modules.py
│   │   │   │   └── stylegan_utils.py
│   │   │   ├── stylegan2/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── ada/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── augment.py
│   │   │   │   │   ├── grid_sample_gradfix.py
│   │   │   │   │   ├── misc.py
│   │   │   │   │   └── upfirdn2d.py
│   │   │   │   ├── stylegan2.py
│   │   │   │   ├── stylegan2_discriminator.py
│   │   │   │   ├── stylegan2_generator.py
│   │   │   │   └── stylegan2_modules.py
│   │   │   ├── stylegan3/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── stylegan3.py
│   │   │   │   ├── stylegan3_generator.py
│   │   │   │   ├── stylegan3_modules.py
│   │   │   │   └── stylegan3_utils.py
│   │   │   ├── swinir/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── swinir_modules.py
│   │   │   │   ├── swinir_net.py
│   │   │   │   ├── swinir_rstb.py
│   │   │   │   └── swinir_utils.py
│   │   │   ├── tdan/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── tdan.py
│   │   │   │   └── tdan_net.py
│   │   │   ├── textual_inversion/
│   │   │   │   ├── __init__.py
│   │   │   │   └── textual_inversion.py
│   │   │   ├── tof/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── tof_vfi_net.py
│   │   │   │   └── tof_vsr_net.py
│   │   │   ├── ttsr/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── lte.py
│   │   │   │   ├── search_transformer.py
│   │   │   │   ├── ttsr.py
│   │   │   │   ├── ttsr_disc.py
│   │   │   │   └── ttsr_net.py
│   │   │   ├── vico/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── vico.py
│   │   │   │   └── vico_utils.py
│   │   │   └── wgan_gp/
│   │   │       ├── __init__.py
│   │   │       ├── wgan_discriminator.py
│   │   │       ├── wgan_generator.py
│   │   │       ├── wgan_gp.py
│   │   │       └── wgan_gp_module.py
│   │   ├── losses/
│   │   │   ├── __init__.py
│   │   │   ├── adv_loss.py
│   │   │   ├── clip_loss.py
│   │   │   ├── composition_loss.py
│   │   │   ├── face_id_loss.py
│   │   │   ├── feature_loss.py
│   │   │   ├── gan_loss.py
│   │   │   ├── gradient_loss.py
│   │   │   ├── loss_comps/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── clip_loss_comps.py
│   │   │   │   ├── disc_auxiliary_loss_comps.py
│   │   │   │   ├── face_id_loss_comps.py
│   │   │   │   ├── gan_loss_comps.py
│   │   │   │   └── gen_auxiliary_loss_comps.py
│   │   │   ├── loss_wrapper.py
│   │   │   ├── perceptual_loss.py
│   │   │   └── pixelwise_loss.py
│   │   └── utils/
│   │       ├── __init__.py
│   │       ├── bbox_utils.py
│   │       ├── diffusion_utils.py
│   │       ├── flow_warp.py
│   │       ├── model_utils.py
│   │       ├── sampling_utils.py
│   │       ├── tensor_utils.py
│   │       └── tome_utils.py
│   ├── registry.py
│   ├── structures/
│   │   ├── __init__.py
│   │   └── data_sample.py
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── cli.py
│   │   ├── collect_env.py
│   │   ├── img_utils.py
│   │   ├── io_utils.py
│   │   ├── logger.py
│   │   ├── sampler.py
│   │   ├── setup_env.py
│   │   ├── trans_utils.py
│   │   └── typing.py
│   ├── version.py
│   └── visualization/
│       ├── __init__.py
│       ├── concat_visualizer.py
│       ├── vis_backend.py
│       └── visualizer.py
├── model-index.yml
├── projects/
│   ├── README.md
│   ├── animated_drawings/
│   │   ├── README.md
│   │   ├── bvh/
│   │   │   └── zombie.bvh
│   │   ├── characters/
│   │   │   └── slamdunk/
│   │   │       └── char_cfg.yaml
│   │   ├── configs/
│   │   │   ├── motion/
│   │   │   │   └── zombie.yaml
│   │   │   ├── mvc/
│   │   │   │   └── slamdunk.yaml
│   │   │   └── retarget/
│   │   │       └── fair1_spf.yaml
│   │   └── tools/
│   │       ├── generate_character.py
│   │       └── generate_video.py
│   ├── example_project/
│   │   ├── README.md
│   │   ├── configs/
│   │   │   └── examplenet_8xb32_in1k.py
│   │   └── models/
│   │       ├── __init__.py
│   │       └── example_net.py
│   ├── flow_style_vton/
│   │   ├── .gitignore
│   │   ├── README.md
│   │   ├── configs/
│   │   │   └── flow_style_vton_PFAFN_epoch_101.py
│   │   ├── inference.py
│   │   ├── models/
│   │   │   ├── __init__.py
│   │   │   ├── afwm.py
│   │   │   ├── flow_style_vton_model.py
│   │   │   └── generator.py
│   │   ├── test_pairs.txt
│   │   └── vton_dataset.py
│   ├── glide/
│   │   ├── configs/
│   │   │   ├── README.md
│   │   │   ├── glide_ddim-classifier-free_laion-64-256.py
│   │   │   └── glide_ddim-classifier-free_laion-64x64.py
│   │   └── models/
│   │       ├── __init__.py
│   │       ├── glide.py
│   │       ├── glide_modules.py
│   │       ├── glide_tokenizer/
│   │       │   ├── __init__.py
│   │       │   ├── bpe.py
│   │       │   └── simple_tokenizer.py
│   │       └── text2im_unet.py
│   ├── magicmaker/
│   │   └── index.html
│   ├── powerpaint/
│   │   ├── README.md
│   │   ├── gradio_PowerPaint.py
│   │   ├── pipeline/
│   │   │   ├── pipeline_PowerPaint.py
│   │   │   └── pipeline_PowerPaint_ControlNet.py
│   │   ├── requirements.txt
│   │   └── utils/
│   │       └── utils.py
│   └── prompt_to_prompt/
│       ├── README.md
│       ├── inversions/
│       │   ├── __init__.py
│       │   ├── ddim_inversion.py
│       │   └── null_text_inversion.py
│       ├── models/
│       │   ├── ptp.py
│       │   ├── ptp_utils.py
│       │   └── seq_aligner.py
│       └── visualize.ipynb
├── requirements/
│   ├── docs.txt
│   ├── mminstall.txt
│   ├── optional.txt
│   ├── readthedocs.txt
│   ├── runtime.txt
│   └── tests.txt
├── requirements.txt
├── setup.cfg
├── setup.py
├── tests/
│   ├── configs/
│   │   ├── aot_test.py
│   │   ├── deepfillv1_test.py
│   │   ├── diffuser_wrapper_cfg/
│   │   │   └── config.json
│   │   ├── gl_test.py
│   │   ├── one_stage_gl.py
│   │   ├── pconv_test.py
│   │   └── two_stage_test.py
│   ├── data/
│   │   ├── coco/
│   │   │   └── annotations/
│   │   │       ├── captions_train2014.json
│   │   │       └── captions_val2014.json
│   │   ├── controlnet/
│   │   │   └── prompt.json
│   │   ├── dataset/
│   │   │   ├── anno.json
│   │   │   ├── anno.txt
│   │   │   ├── c/
│   │   │   │   └── c.not_support_type
│   │   │   ├── classes.txt
│   │   │   └── wrong.yml
│   │   ├── frames/
│   │   │   ├── ann1.txt
│   │   │   ├── ann2.txt
│   │   │   └── ann3.txt
│   │   ├── image/
│   │   │   └── train.txt
│   │   ├── inpainting/
│   │   │   ├── mask_list.txt
│   │   │   └── mask_list_single_ch.txt
│   │   ├── lq.lmdb/
│   │   │   ├── data.mdb
│   │   │   ├── lock.mdb
│   │   │   └── meta_info.txt
│   │   ├── matting_dataset/
│   │   │   ├── ann.json
│   │   │   └── ann_old.json
│   │   └── textual_inversion/
│   │       └── imagenet_templates_small.txt
│   ├── test_apis/
│   │   ├── test_inferencers/
│   │   │   ├── test_base_mmagic_inferencer.py
│   │   │   ├── test_colorization_inferencer.py
│   │   │   ├── test_conditional_inferencer.py
│   │   │   ├── test_diffusers_pipeline_inferencer.py
│   │   │   ├── test_eg3d_inferencer.py
│   │   │   ├── test_image_super_resolution_inferencer.py
│   │   │   ├── test_inference_functions.py
│   │   │   ├── test_inpainting_inferencer.py
│   │   │   ├── test_matting_inferencer.py
│   │   │   ├── test_mmedit_inferencer.py
│   │   │   ├── test_text2image_inferencers.py
│   │   │   ├── test_translation_inferencer.py
│   │   │   ├── test_unconditional_inferencer.py
│   │   │   ├── test_video_interpolation_inferencer.py
│   │   │   └── test_video_restoration_inferencer.py
│   │   └── test_mmagic_inferencer.py
│   ├── test_datasets/
│   │   ├── test_basic_conditional_dataset.py
│   │   ├── test_basic_frames_dataset.py
│   │   ├── test_basic_image_dataset.py
│   │   ├── test_categories.py
│   │   ├── test_cifar10_dataset.py
│   │   ├── test_comp1k_dataset.py
│   │   ├── test_controlnet_dataset.py
│   │   ├── test_data_utils.py
│   │   ├── test_dreambooth_dataset.py
│   │   ├── test_grow_scale_image_dataset.py
│   │   ├── test_imagenet_dataset.py
│   │   ├── test_mscoco_dataset.py
│   │   ├── test_paired_image_dataset.py
│   │   ├── test_singan_dataset.py
│   │   ├── test_textual_inversion_dataset.py
│   │   ├── test_transforms/
│   │   │   ├── test_albumentations.py
│   │   │   ├── test_alpha.py
│   │   │   ├── test_aug_frames.py
│   │   │   ├── test_aug_pixel.py
│   │   │   ├── test_aug_shape.py
│   │   │   ├── test_blur_kernels.py
│   │   │   ├── test_crop.py
│   │   │   ├── test_fgbg.py
│   │   │   ├── test_formatting.py
│   │   │   ├── test_generate_assistant.py
│   │   │   ├── test_generate_frame_indices.py
│   │   │   ├── test_get_masked_image.py
│   │   │   ├── test_loading.py
│   │   │   ├── test_matlab_like_resize.py
│   │   │   ├── test_normalization.py
│   │   │   ├── test_random_degradations.py
│   │   │   ├── test_random_down_sampling.py
│   │   │   ├── test_trimap.py
│   │   │   └── test_values.py
│   │   └── test_unpaired_image_dataset.py
│   ├── test_engine/
│   │   ├── test_hooks/
│   │   │   ├── test_ema.py
│   │   │   ├── test_iter_time_hook.py
│   │   │   ├── test_pggan_fetch_data_hook.py
│   │   │   ├── test_pickle_data_hook.py
│   │   │   ├── test_reduce_lr_scheduler_hook.py
│   │   │   └── test_visualization_hook.py
│   │   ├── test_optimizers/
│   │   │   ├── test_multi_optimizer_constructor.py
│   │   │   ├── test_pggan_optimizer_constructor.py
│   │   │   └── test_singan_optimizer_constructor.py
│   │   ├── test_runner/
│   │   │   ├── test_log_processor.py
│   │   │   ├── test_loop_utils.py
│   │   │   └── test_multi_loops.py
│   │   └── test_schedulers/
│   │       ├── test_linear_lr_scheduler_with_interval.py
│   │       └── test_reduce_lr_scheduler.py
│   ├── test_evaluation/
│   │   ├── test_evaluator.py
│   │   ├── test_functional/
│   │   │   ├── test_fid_inception.py
│   │   │   ├── test_gaussian_funcs.py
│   │   │   └── test_inception_utils.py
│   │   └── test_metrics/
│   │       ├── test_base_gen_metric.py
│   │       ├── test_base_sample_wise_metric.py
│   │       ├── test_connectivity_error.py
│   │       ├── test_equivariance.py
│   │       ├── test_fid.py
│   │       ├── test_gradient_error.py
│   │       ├── test_inception_score.py
│   │       ├── test_mae.py
│   │       ├── test_matting_mse.py
│   │       ├── test_metrics_utils.py
│   │       ├── test_ms_ssim.py
│   │       ├── test_mse.py
│   │       ├── test_niqe.py
│   │       ├── test_ppl.py
│   │       ├── test_precision_and_recall.py
│   │       ├── test_psnr.py
│   │       ├── test_sad.py
│   │       ├── test_snr.py
│   │       ├── test_ssim.py
│   │       └── test_swd.py
│   ├── test_models/
│   │   ├── test_archs/
│   │   │   ├── test_aspp.py
│   │   │   ├── test_conv.py
│   │   │   ├── test_downsample.py
│   │   │   ├── test_ensemble.py
│   │   │   ├── test_gated_conv_module.py
│   │   │   ├── test_img_normalize.py
│   │   │   ├── test_linear_module.py
│   │   │   ├── test_lora.py
│   │   │   ├── test_multi_layer_disc.py
│   │   │   ├── test_patch_disc.py
│   │   │   ├── test_resnet.py
│   │   │   ├── test_separable_conv_module.py
│   │   │   ├── test_simple_encoder_decoder.py
│   │   │   ├── test_smpatch_disc.py
│   │   │   ├── test_sr_backbone.py
│   │   │   ├── test_tokenizer.py
│   │   │   ├── test_upsample.py
│   │   │   ├── test_vgg.py
│   │   │   └── test_wrapper.py
│   │   ├── test_base_models/
│   │   │   ├── test_average_model.py
│   │   │   ├── test_base_conditional_gan.py
│   │   │   ├── test_base_edit_model.py
│   │   │   ├── test_base_gan.py
│   │   │   ├── test_base_mattor.py
│   │   │   ├── test_base_translation_model.py
│   │   │   ├── test_basic_interpolator.py
│   │   │   ├── test_one_stage.py
│   │   │   └── test_two_stage.py
│   │   ├── test_data_preprocessors/
│   │   │   ├── test_data_preprocessor.py
│   │   │   └── test_mattor_preprocessor.py
│   │   ├── test_diffusion_schedulers/
│   │   │   ├── test_ddim_scheduler.py
│   │   │   └── test_init.py
│   │   ├── test_editors/
│   │   │   ├── test_animatediff/
│   │   │   │   ├── test_animatediff.py
│   │   │   │   ├── test_attention3d.py
│   │   │   │   ├── test_motion_module.py
│   │   │   │   ├── test_res_blocks3d.py
│   │   │   │   ├── test_unet3d.py
│   │   │   │   └── test_unet_blocks3d.py
│   │   │   ├── test_aotgan/
│   │   │   │   ├── test_aot_decoder.py
│   │   │   │   ├── test_aot_encoder.py
│   │   │   │   ├── test_aot_encoder_decoder.py
│   │   │   │   ├── test_aot_inpaintor.py
│   │   │   │   └── test_aot_neck.py
│   │   │   ├── test_arcface/
│   │   │   │   ├── test_arcface_modules.py
│   │   │   │   ├── test_id_loss.py
│   │   │   │   └── test_model_irse.py
│   │   │   ├── test_basicvsr/
│   │   │   │   ├── test_basicvsr.py
│   │   │   │   └── test_basicvsr_net.py
│   │   │   ├── test_basicvsr_plusplus_net/
│   │   │   │   └── test_basicvsr_plusplus_net.py
│   │   │   ├── test_biggan/
│   │   │   │   ├── test_biggan.py
│   │   │   │   ├── test_biggan_deep_discriminator.py
│   │   │   │   ├── test_biggan_deep_generator.py
│   │   │   │   ├── test_biggan_discriminator.py
│   │   │   │   ├── test_biggan_generator.py
│   │   │   │   ├── test_biggan_modules.py
│   │   │   │   └── test_biggan_snmodule.py
│   │   │   ├── test_cain/
│   │   │   │   ├── test_cain.py
│   │   │   │   └── test_cain_net.py
│   │   │   ├── test_controlnet/
│   │   │   │   ├── test_controlnet.py
│   │   │   │   └── test_controlnet_utils.py
│   │   │   ├── test_cyclegan/
│   │   │   │   ├── test_cyclegan.py
│   │   │   │   ├── test_cyclegan_generator.py
│   │   │   │   └── test_cyclegan_modules.py
│   │   │   ├── test_dcgan/
│   │   │   │   ├── test_dcgan.py
│   │   │   │   ├── test_dcgan_discriminator.py
│   │   │   │   └── test_dcgan_generator.py
│   │   │   ├── test_ddpm/
│   │   │   │   ├── test_attention.py
│   │   │   │   ├── test_ddpm_scheduler.py
│   │   │   │   ├── test_denoising_unet.py
│   │   │   │   ├── test_embeddings.py
│   │   │   │   ├── test_res_blocks.py
│   │   │   │   └── test_unet_blocks.py
│   │   │   ├── test_deblurganv2/
│   │   │   │   ├── test_deblurganv2.py
│   │   │   │   ├── test_deblurganv2_discriminator.py
│   │   │   │   └── test_deblurganv2_generator.py
│   │   │   ├── test_deepfillv1/
│   │   │   │   ├── test_contextual_attention.py
│   │   │   │   ├── test_contextual_attention_neck.py
│   │   │   │   ├── test_deepfill_decoder.py
│   │   │   │   ├── test_deepfill_disc.py
│   │   │   │   ├── test_deepfill_encoder.py
│   │   │   │   ├── test_deepfill_refiner.py
│   │   │   │   └── test_deepfillv1.py
│   │   │   ├── test_deepfillv2/
│   │   │   │   └── test_two_stage_encoder_decoder.py
│   │   │   ├── test_dic/
│   │   │   │   ├── test_dic.py
│   │   │   │   ├── test_dic_net.py
│   │   │   │   ├── test_feedback_hour_glass.py
│   │   │   │   └── test_light_cnn.py
│   │   │   ├── test_dim/
│   │   │   │   └── test_dim.py
│   │   │   ├── test_disco_diffusion/
│   │   │   │   ├── test_disco_diffusion.py
│   │   │   │   └── test_disco_diffusion_clip_wrapper.py
│   │   │   ├── test_dreambooth/
│   │   │   │   └── test_dreambooth.py
│   │   │   ├── test_duf/
│   │   │   │   └── test_duf.py
│   │   │   ├── test_edsr/
│   │   │   │   └── test_edsr_net.py
│   │   │   ├── test_edvr/
│   │   │   │   ├── test_edvr.py
│   │   │   │   └── test_edvr_net.py
│   │   │   ├── test_eg3d/
│   │   │   │   ├── test_camera.py
│   │   │   │   ├── test_dual_discriminator.py
│   │   │   │   ├── test_eg3d.py
│   │   │   │   ├── test_eg3d_generator.py
│   │   │   │   ├── test_eg3d_modules.py
│   │   │   │   ├── test_eg3d_utils.py
│   │   │   │   ├── test_ray_sampler.py
│   │   │   │   └── test_renderer.py
│   │   │   ├── test_esrgan/
│   │   │   │   ├── test_esrgan.py
│   │   │   │   └── test_rrdb_net.py
│   │   │   ├── test_fastcomposer/
│   │   │   │   └── test_fastcomposer.py
│   │   │   ├── test_fba/
│   │   │   │   ├── test_fba_decoder.py
│   │   │   │   └── test_fba_encoder.py
│   │   │   ├── test_flavr/
│   │   │   │   ├── test_flavr.py
│   │   │   │   └── test_flavr_net.py
│   │   │   ├── test_gca/
│   │   │   │   ├── test_gca.py
│   │   │   │   ├── test_gca_module.py
│   │   │   │   ├── test_resgca_dec.py
│   │   │   │   └── test_resgca_enc.py
│   │   │   ├── test_ggan/
│   │   │   │   └── test_ggan.py
│   │   │   ├── test_glean/
│   │   │   │   └── test_glean_styleganv2.py
│   │   │   ├── test_glide/
│   │   │   │   └── test_glide.py
│   │   │   ├── test_global_local/
│   │   │   │   ├── test_gl_decoder.py
│   │   │   │   ├── test_gl_dilation.py
│   │   │   │   ├── test_gl_disc.py
│   │   │   │   ├── test_gl_encoder.py
│   │   │   │   ├── test_gl_encoder_decoder.py
│   │   │   │   └── test_gl_inpaintor.py
│   │   │   ├── test_guided_diffusion/
│   │   │   │   └── test_adm.py
│   │   │   ├── test_iconvsr/
│   │   │   │   └── test_iconvsr_net.py
│   │   │   ├── test_indexnet/
│   │   │   │   ├── test_indexnet.py
│   │   │   │   ├── test_indexnet_decoder.py
│   │   │   │   └── test_indexnet_encoder.py
│   │   │   ├── test_inst_colorization/
│   │   │   │   ├── test_color_utils.py
│   │   │   │   ├── test_colorization_net.py
│   │   │   │   ├── test_fusion_net.py
│   │   │   │   ├── test_inst_colorization.py
│   │   │   │   └── test_weight_layer.py
│   │   │   ├── test_liif/
│   │   │   │   ├── test_liif.py
│   │   │   │   ├── test_liif_net.py
│   │   │   │   └── test_mlp_refiner.py
│   │   │   ├── test_lsgan/
│   │   │   │   ├── test_lsgan.py
│   │   │   │   ├── test_lsgan_discriminator.py
│   │   │   │   └── test_lsgan_generator.py
│   │   │   ├── test_mspie/
│   │   │   │   ├── test_mspie_stylegan2.py
│   │   │   │   ├── test_mspie_stylegan2_discriminator.py
│   │   │   │   ├── test_mspie_stylegan2_generator.py
│   │   │   │   ├── test_mspie_stylegan2_modules.py
│   │   │   │   ├── test_pe_singan.py
│   │   │   │   ├── test_pe_singan_generator.py
│   │   │   │   └── test_positional_encoding.py
│   │   │   ├── test_nafnet/
│   │   │   │   ├── test_naf_avgpool2d.py
│   │   │   │   ├── test_naf_layernorm2d.py
│   │   │   │   ├── test_nafbaseline.py
│   │   │   │   └── test_nafnet.py
│   │   │   ├── test_pconv/
│   │   │   │   ├── test_mask_conv_module.py
│   │   │   │   ├── test_partial_conv.py
│   │   │   │   ├── test_pconv_decoder.py
│   │   │   │   ├── test_pconv_encoder.py
│   │   │   │   ├── test_pconv_encoder_decoder.py
│   │   │   │   └── test_pconv_inpaintor.py
│   │   │   ├── test_pggan/
│   │   │   │   ├── test_pggan.py
│   │   │   │   ├── test_pggan_discriminator.py
│   │   │   │   ├── test_pggan_generator.py
│   │   │   │   └── test_pggan_modules.py
│   │   │   ├── test_pix2pix/
│   │   │   │   ├── test_pix2pix.py
│   │   │   │   ├── test_pix2pix_generator.py
│   │   │   │   └── test_pix2pix_modules.py
│   │   │   ├── test_plain/
│   │   │   │   ├── test_plain_decoder.py
│   │   │   │   └── test_plain_refiner.py
│   │   │   ├── test_rdn/
│   │   │   │   └── test_rdn_net.py
│   │   │   ├── test_real_basicvsr/
│   │   │   │   ├── test_real_basicvsr.py
│   │   │   │   └── test_real_basicvsr_net.py
│   │   │   ├── test_real_esrgan/
│   │   │   │   ├── test_real_esrgan.py
│   │   │   │   └── test_unet_disc.py
│   │   │   ├── test_restormer/
│   │   │   │   └── test_restormer_net.py
│   │   │   ├── test_sagan/
│   │   │   │   ├── test_sagan.py
│   │   │   │   ├── test_sagan_discriminator.py
│   │   │   │   ├── test_sagan_generator.py
│   │   │   │   └── test_sagan_modules.py
│   │   │   ├── test_singan/
│   │   │   │   ├── test_singan.py
│   │   │   │   ├── test_singan_discriminator.py
│   │   │   │   ├── test_singan_generator.py
│   │   │   │   └── test_singan_modules.py
│   │   │   ├── test_srcnn/
│   │   │   │   └── test_srcnn_net.py
│   │   │   ├── test_srgan/
│   │   │   │   ├── test_modified_vgg.py
│   │   │   │   ├── test_sr_resnet.py
│   │   │   │   └── test_srgan.py
│   │   │   ├── test_stable_diffusion/
│   │   │   │   ├── test_clip_wrapper.py
│   │   │   │   ├── test_stable_diffusion.py
│   │   │   │   ├── test_stable_diffusion_inpaint.py
│   │   │   │   ├── test_stable_diffusion_tomesd.py
│   │   │   │   └── test_vae.py
│   │   │   ├── test_stable_diffusion_xl/
│   │   │   │   └── test_stable_diffusion_xl.py
│   │   │   ├── test_stylegan1/
│   │   │   │   ├── test_stylegan1.py
│   │   │   │   ├── test_stylegan1_discriminator.py
│   │   │   │   ├── test_stylegan1_generator.py
│   │   │   │   ├── test_stylegan1_modules.py
│   │   │   │   └── test_stylegan_utils.py
│   │   │   ├── test_stylegan2/
│   │   │   │   ├── test_ada/
│   │   │   │   │   └── test_augment.py
│   │   │   │   ├── test_stylegan2.py
│   │   │   │   ├── test_stylegan2_discriminator.py
│   │   │   │   ├── test_stylegan2_generator.py
│   │   │   │   └── test_stylegan2_modules.py
│   │   │   ├── test_stylegan3/
│   │   │   │   ├── test_stylegan3.py
│   │   │   │   ├── test_stylegan3_generator.py
│   │   │   │   ├── test_stylegan3_modules.py
│   │   │   │   └── test_stylegan3_utils.py
│   │   │   ├── test_swinir/
│   │   │   │   ├── test_swinir_modules.py
│   │   │   │   ├── test_swinir_net.py
│   │   │   │   ├── test_swinir_rstb.py
│   │   │   │   └── test_swinir_utils.py
│   │   │   ├── test_tdan/
│   │   │   │   ├── test_tdan.py
│   │   │   │   └── test_tdan_net.py
│   │   │   ├── test_tof/
│   │   │   │   ├── test_tof_vfi_net.py
│   │   │   │   └── test_tof_vsr_net.py
│   │   │   ├── test_ttsr/
│   │   │   │   ├── test_lte.py
│   │   │   │   ├── test_search_transformer.py
│   │   │   │   ├── test_ttsr.py
│   │   │   │   ├── test_ttsr_disc.py
│   │   │   │   └── test_ttsr_net.py
│   │   │   ├── test_vico/
│   │   │   │   ├── test_vico.py
│   │   │   │   └── test_vico_utils.py
│   │   │   └── test_wgan_gp/
│   │   │       ├── test_wgan_discriminator.py
│   │   │       ├── test_wgan_generator.py
│   │   │       ├── test_wgan_gp.py
│   │   │       └── test_wgan_gp_module.py
│   │   ├── test_losses/
│   │   │   ├── test_clip_loss.py
│   │   │   ├── test_composition_loss.py
│   │   │   ├── test_face_id_loss.py
│   │   │   ├── test_feature_loss.py
│   │   │   ├── test_gan_loss.py
│   │   │   ├── test_gradient_loss.py
│   │   │   ├── test_loss_comps/
│   │   │   │   ├── test_clip_loss_comps.py
│   │   │   │   ├── test_disc_auxiliary_loss_comps.py
│   │   │   │   ├── test_face_id_loss_comps.py
│   │   │   │   ├── test_gan_loss_comps.py
│   │   │   │   └── test_gen_auxiliary_loss_comps.py
│   │   │   ├── test_loss_wrapper.py
│   │   │   ├── test_perceptual_loss.py
│   │   │   └── test_pixelwise_loss.py
│   │   └── test_utils/
│   │       ├── test_bbox_utils.py
│   │       ├── test_flow_warp.py
│   │       ├── test_model_utils.py
│   │       ├── test_sampling_utils.py
│   │       └── test_tensor_utils.py
│   ├── test_structures/
│   │   └── test_data_sample.py
│   ├── test_utils/
│   │   ├── test_cli.py
│   │   ├── test_img_utils.py
│   │   ├── test_io_utils.py
│   │   ├── test_logger.py
│   │   ├── test_sampler.py
│   │   ├── test_setup_env.py
│   │   └── test_trans_utils.py
│   └── test_visualization/
│       ├── test_concat_visualizer.py
│       ├── test_vis_backend.py
│       └── test_visualizer.py
└── tools/
    ├── analysis_tools/
    │   ├── get_flops.py
    │   └── print_config.py
    ├── cpu_train.sh
    ├── dataset_converters/
    │   ├── bgm/
    │   │   └── preprocess_bgm_dataset.py
    │   ├── celeba-hq/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── classic5/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── comp1k/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   ├── check_extended_fg.py
    │   │   ├── evaluate_comp1k.py
    │   │   ├── extend_fg.py
    │   │   ├── filter_comp1k_anno.py
    │   │   └── preprocess_comp1k_dataset.py
    │   ├── denoising/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── deraining/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── df2k_ost/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   └── preprocess_df2k_ost_dataset.py
    │   ├── div2k/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   └── preprocess_div2k_dataset.py
    │   ├── dpdd/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── glean/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   ├── preprocess_cat_test_dataset.py
    │   │   ├── preprocess_cat_train_dataset.py
    │   │   └── preprocess_ffhq_celebahq_dataset.py
    │   ├── gopro/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── hide/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── live1/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── ntire21_decompression/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── paired-pix2pix/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── paris-street-view/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── places365/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── realblur/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── realsrset/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── reds/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   ├── crop_sub_images.py
    │   │   └── preprocess_reds_dataset.py
    │   ├── sidd/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   └── preprocess_sidd_test_dataset.py
    │   ├── spmcs/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── udm10/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── unconditional_gans/
    │   │   └── README.md
    │   ├── unpaired-cyclegan/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── vid4/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   └── preprocess_vid4_dataset.py
    │   ├── videolq/
    │   │   ├── README.md
    │   │   └── README_zh-CN.md
    │   ├── vimeo90k/
    │   │   ├── README.md
    │   │   ├── README_zh-CN.md
    │   │   └── preprocess_vimeo90k_dataset.py
    │   └── vimeo90k-triplet/
    │       ├── README.md
    │       └── README_zh-CN.md
    ├── dist_test.sh
    ├── dist_train.sh
    ├── gui/
    │   ├── README.md
    │   ├── component.py
    │   ├── gui.py
    │   ├── page_general.py
    │   ├── page_sr.py
    │   └── utils.py
    ├── model_converters/
    │   └── publish_model.py
    ├── slurm_test.sh
    ├── slurm_train.sh
    ├── test.py
    └── train.py

SYMBOL INDEX (5290 symbols across 781 files)

FILE: .dev_scripts/create_ceph_configs.py
  function update_intervals (line 11) | def update_intervals(config, args):
  function convert_data_config (line 32) | def convert_data_config(data_cfg):
  function update_ceph_config (line 138) | def update_ceph_config(filename, args, dry_run=False):

FILE: .dev_scripts/doc_link_checker.py
  function make_parser (line 13) | def make_parser():
  function analyze_doc (line 28) | def analyze_doc(home, path):
  function traverse (line 94) | def traverse(args):

FILE: .dev_scripts/download_models.py
  function additional_download (line 21) | def additional_download(args):
  function parse_args (line 51) | def parse_args():
  function download (line 71) | def download(args):

FILE: .dev_scripts/job_watcher.py
  function show_job_out (line 15) | def show_job_out(name, root, job_name_list):
  function show_job_status (line 40) | def show_job_status(root, job_name_list, csv_path=None):
  function save_for_resume (line 80) | def save_for_resume(root, job_name_list):
  function resume_from_file (line 99) | def resume_from_file(file_path):
  function start_from_proc (line 113) | def start_from_proc(root, proc):
  function show_tui (line 135) | def show_tui(root, job_name_list):

FILE: .dev_scripts/metric_mapping.py
  function filter_metric (line 174) | def filter_metric(metric_mapping, summary_data):

FILE: .dev_scripts/test_benchmark.py
  function parse_args (line 23) | def parse_args():
  function create_test_job_batch (line 84) | def create_test_job_batch(commands, model_info, args, port, script_name):
  function test (line 185) | def test(args):
  function show_summary (line 276) | def show_summary(summary_data,
  function summary (line 348) | def summary(args):
  function main (line 420) | def main():

FILE: .dev_scripts/train_benchmark.py
  function parse_args (line 87) | def parse_args():
  function create_train_job_batch (line 178) | def create_train_job_batch(commands, model_info, args, port, script_name):
  function train (line 282) | def train(args):
  function show_summary (line 357) | def show_summary(summary_data, models_map, work_dir, save=False):
  function summary (line 420) | def summary(args):
  function main (line 482) | def main():

FILE: .dev_scripts/update_config_readme.py
  function update_md (line 15) | def update_md(md_file):

FILE: .dev_scripts/update_model_index.py
  function parse_md (line 101) | def parse_md(md_file):
  function update_model_index (line 243) | def update_model_index():

FILE: .dev_scripts/update_ut.py
  function check_exclude (line 24) | def check_exclude(fn):
  function update_ut (line 31) | def update_ut():

FILE: .dev_scripts/utils/job_util.py
  function parse_job_list (line 9) | def parse_job_list(job_list) -> Tuple[list, list]:
  function parse_job_list_from_file (line 31) | def parse_job_list_from_file(job_list_file: str) -> Tuple[list, list]:
  function get_info_from_id (line 48) | def get_info_from_id(job_id: str) -> dict:
  function filter_jobs (line 76) | def filter_jobs(job_id_list: list,

FILE: .dev_scripts/utils/modelindex.py
  function dump_yaml_and_check_difference (line 8) | def dump_yaml_and_check_difference(obj, file):
  function collate_metrics (line 43) | def collate_metrics(keys):
  function found_table (line 60) | def found_table(lines, i):
  function modelindex_to_dict (line 74) | def modelindex_to_dict(model):

FILE: demo/download_inference_resources.py
  function parse_args (line 40) | def parse_args():
  function main (line 66) | def main():

FILE: demo/gradio_animatediff.py
  class AnimateController (line 37) | class AnimateController:
    method __init__ (line 39) | def __init__(self):
    method refresh_stable_diffusion (line 67) | def refresh_stable_diffusion(self):
    method refresh_motion_module (line 70) | def refresh_motion_module(self):
    method refresh_personalized_model (line 77) | def refresh_personalized_model(self):
    method update_stable_diffusion (line 84) | def update_stable_diffusion(self, stable_diffusion_dropdown):
    method update_motion_module (line 89) | def update_motion_module(self, motion_module_dropdown):
    method update_base_model (line 102) | def update_base_model(self, base_model_dropdown):
    method update_lora_model (line 116) | def update_lora_model(self, lora_model_dropdown):
    method animate (line 129) | def animate(
  function ui (line 196) | def ui():

FILE: demo/gradio_controlnet_animation.py
  function process (line 9) | def process(video, prompt, a_prompt, negative_prompt,

FILE: demo/gradio_draggan.py
  function get_default_gr_state (line 32) | def get_default_gr_state(device='cuda',
  function reverse_point_pairs (line 71) | def reverse_point_pairs(points):
  function clear_state (line 78) | def clear_state(global_state, target=None):
  function init_images (line 101) | def init_images(global_state, cache_dir='./checkpoints'):
  function update_image_draw (line 142) | def update_image_draw(image, points, mask, show_mask, global_state=None):
  function preprocess_mask_info (line 154) | def preprocess_mask_info(global_state, image):
  function on_change_pretrained_dropdown (line 360) | def on_change_pretrained_dropdown(pretrained_value, global_state):
  function on_click_reset_image (line 379) | def on_click_reset_image(global_state):
  function on_change_update_image_seed (line 397) | def on_change_update_image_seed(seed, global_state):
  function on_click_latent_space (line 416) | def on_click_latent_space(latent_space, global_state):
  function on_change_lr (line 443) | def on_change_lr(lr, global_state):
  function on_click_start (line 461) | def on_click_start(global_state, image):
  function on_click_stop (line 689) | def on_click_stop(global_state):
  function on_click_remove_point (line 715) | def on_click_remove_point(global_state):
  function on_click_reset_mask (line 730) | def on_click_reset_mask(global_state):
  function on_click_enable_draw (line 752) | def on_click_enable_draw(global_state, image):
  function on_click_remove_draw (line 768) | def on_click_remove_draw(global_state, image):
  function on_click_add_point (line 792) | def on_click_add_point(global_state, image: dict):
  function on_click_image (line 816) | def on_click_image(global_state, evt: gr.SelectData):
  function on_click_clear_points (line 855) | def on_click_clear_points(global_state):
  function on_click_show_mask (line 877) | def on_click_show_mask(global_state, show_mask):

FILE: demo/gradio_fastcomposer.py
  class ModelWrapper (line 18) | class ModelWrapper:
    method __init__ (line 20) | def __init__(self, model):
    method inference (line 27) | def inference(
  function create_demo (line 84) | def create_demo():

FILE: demo/gradio_inpainting.py
  class InpaintingGradio (line 20) | class InpaintingGradio:
    method __init__ (line 37) | def __init__(self,
    method model_reconfig (line 64) | def model_reconfig(self,
    method change_text2dict (line 109) | def change_text2dict(input_text: str) -> Union[Dict, None]:
    method get_package_path (line 120) | def get_package_path() -> str:
    method get_model_config (line 134) | def get_model_config(self, model_name: str) -> Dict:
    method init_inference_supported_models_cfg (line 151) | def init_inference_supported_models_cfg() -> None:
    method _get_inpainting_kwargs (line 167) | def _get_inpainting_kwargs(self, model_name: Optional[str],
    method send_notification (line 219) | def send_notification(msg: str, color: str, label: str) -> None:
    method get_inpainting_supported_models (line 223) | def get_inpainting_supported_models() -> List:
    method infer (line 227) | def infer(self, input_img_arg: Dict) -> np.ndarray:
    method run (line 233) | def run(self) -> None:

FILE: demo/gradio_vico.py
  function load_base_model (line 19) | def load_base_model():
  function train (line 25) | def train(train_data, init_token, placeholder):
  class DummyModel (line 47) | class DummyModel:
    method __init__ (line 49) | def __init__(self, model):
  function infer_fn (line 53) | def infer_fn(checkpoint, img_ref, prompt, negative_prompt, guidance_scale,

FILE: demo/mmagic_inference_demo.py
  function parse_args (line 8) | def parse_args():
  function main (line 97) | def main():

FILE: demo/singan_demo.py
  function parse_args (line 24) | def parse_args():
  function _tensor2img (line 52) | def _tensor2img(img):
  function main (line 60) | def main():

FILE: demo/utils/gradio_utils.py
  class EasyDict (line 8) | class EasyDict(dict):
    method __getattr__ (line 12) | def __getattr__(self, name: str):
    method __setattr__ (line 18) | def __setattr__(self, name: str, value) -> None:
    method __delattr__ (line 21) | def __delattr__(self, name: str) -> None:
  class ImageMask (line 25) | class ImageMask(gr.components.Image):
    method __init__ (line 32) | def __init__(self, **kwargs):
    method preprocess (line 36) | def preprocess(self, x):
  function get_valid_mask (line 50) | def get_valid_mask(mask: np.ndarray):
  function draw_points_on_image (line 60) | def draw_points_on_image(image,
  function draw_mask_on_image (line 126) | def draw_mask_on_image(image, mask):
  function on_change_single_global_state (line 142) | def on_change_single_global_state(keys,
  function get_latest_points_pair (line 163) | def get_latest_points_pair(points_dict):

FILE: demo/utils/renderer.py
  class CapturedException (line 25) | class CapturedException(Exception):
    method __init__ (line 27) | def __init__(self, msg=None):
  class CaptureSuccess (line 44) | class CaptureSuccess(Exception):
    method __init__ (line 46) | def __init__(self, out):
  function add_watermark_np (line 56) | def add_watermark_np(input_image_array, watermark_text='AI Generated'):
  class Renderer (line 83) | class Renderer:
    method __init__ (line 85) | def __init__(self, disable_timing=False):
    method render (line 101) | def render(self, **args):
    method get_network (line 150) | def get_network(self, pkl, key, **tweak_kwargs):
    method _get_pinned_buf (line 196) | def _get_pinned_buf(self, ref):
    method to_device (line 204) | def to_device(self, buf):
    method to_cpu (line 207) | def to_cpu(self, buf):
    method _ignore_timing (line 210) | def _ignore_timing(self):
    method _apply_cmap (line 213) | def _apply_cmap(self, x, name='viridis'):
    method init_network (line 225) | def init_network(self,
    method update_lr (line 299) | def update_lr(self, lr):
    method _render_drag_impl (line 306) | def _render_drag_impl(self,

FILE: docs/en/.dev_scripts/update_dataset_zoo.py
  function update_dataset_zoo (line 6) | def update_dataset_zoo():

FILE: docs/en/.dev_scripts/update_model_zoo.py
  function write_file (line 14) | def write_file(file, content):
  function update_model_zoo (line 20) | def update_model_zoo():

FILE: docs/en/conf.py
  function builder_inited_handler (line 150) | def builder_inited_handler(app):
  function skip_member (line 155) | def skip_member(app, what, name, obj, skip, options):
  function viewcode_follow_imported (line 161) | def viewcode_follow_imported(app, modname, attribute):
  function setup (line 180) | def setup(app):

FILE: docs/zh_cn/.dev_scripts/update_dataset_zoo.py
  function update_dataset_zoo (line 6) | def update_dataset_zoo():

FILE: docs/zh_cn/.dev_scripts/update_model_zoo.py
  function anchor (line 18) | def anchor(name):
  function summarize (line 24) | def summarize(stats, name):
  function update_model_zoo (line 69) | def update_model_zoo():

FILE: docs/zh_cn/conf.py
  function builder_inited_handler (line 146) | def builder_inited_handler(app):
  function skip_member (line 151) | def skip_member(app, what, name, obj, skip, options):
  function setup (line 157) | def setup(app):

FILE: docs/zh_cn/stat.py
  function anchor (line 12) | def anchor(name):

FILE: mmagic/__init__.py
  function digit_version (line 11) | def digit_version(version_str):

FILE: mmagic/apis/inferencers/__init__.py
  class Inferencers (line 31) | class Inferencers:
    method __init__ (line 43) | def __init__(self,
    method __call__ (line 101) | def __call__(self, **kwargs) -> Union[Dict, List[Dict]]:
    method get_extra_parameters (line 112) | def get_extra_parameters(self) -> List[str]:

FILE: mmagic/apis/inferencers/base_mmagic_inferencer.py
  class BaseMMagicInferencer (line 25) | class BaseMMagicInferencer(BaseInferencer):
    method __init__ (line 47) | def __init__(self,
    method _init_model (line 67) | def _init_model(self, cfg: Union[ConfigType, str], ckpt: Optional[str],
    method _init_pipeline (line 82) | def _init_pipeline(self, cfg: ConfigType) -> Compose:
    method _init_extra_parameters (line 91) | def _init_extra_parameters(self, extra_parameters: Dict) -> None:
    method _update_extra_parameters (line 98) | def _update_extra_parameters(self, **kwargs) -> None:
    method _dispatch_kwargs (line 108) | def _dispatch_kwargs(self, **kwargs) -> Tuple[Dict, Dict, Dict, Dict]:
    method __call__ (line 124) | def __call__(self, **kwargs) -> Union[Dict, List[Dict]]:
    method base_call (line 142) | def base_call(self, **kwargs) -> Union[Dict, List[Dict]]:
    method get_extra_parameters (line 170) | def get_extra_parameters(self) -> List[str]:
    method postprocess (line 179) | def postprocess(
    method _pred2dict (line 211) | def _pred2dict(self, pred_tensor: torch.Tensor) -> Dict:
    method visualize (line 226) | def visualize(self,

FILE: mmagic/apis/inferencers/colorization_inferencer.py
  class ColorizationInferencer (line 17) | class ColorizationInferencer(BaseMMagicInferencer):
    method preprocess (line 26) | def preprocess(self, img: InputsType) -> Dict:
    method forward (line 55) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 61) | def visualize(self,

FILE: mmagic/apis/inferencers/conditional_inferencer.py
  class ConditionalInferencer (line 14) | class ConditionalInferencer(BaseMMagicInferencer):
    method preprocess (line 25) | def preprocess(self, label: InputsType) -> Dict:
    method forward (line 42) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 46) | def visualize(self,
    method _pred2dict (line 73) | def _pred2dict(self, data_sample: DataSample) -> Dict:

FILE: mmagic/apis/inferencers/controlnet_animation_inferencer.py
  function load_image (line 23) | def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
  class ControlnetAnimationInferencer (line 52) | class ControlnetAnimationInferencer(BaseMMagicInferencer):
    method __init__ (line 72) | def __init__(self,
    method __call__ (line 93) | def __call__(self,

FILE: mmagic/apis/inferencers/diffusers_pipeline_inferencer.py
  class DiffusersPipelineInferencer (line 13) | class DiffusersPipelineInferencer(BaseMMagicInferencer):
    method preprocess (line 24) | def preprocess(self,
    method forward (line 53) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 59) | def visualize(self,

FILE: mmagic/apis/inferencers/eg3d_inferencer.py
  class EG3DInferencer (line 25) | class EG3DInferencer(BaseMMagicInferencer):
    method preprocess (line 38) | def preprocess(self, inputs: InputsType = None) -> ForwardInputs:
    method forward (line 86) | def forward(self,
    method visualize (line 134) | def visualize(self,
    method preprocess_img (line 271) | def preprocess_img(self, preds: List[dict]) -> torch.Tensor:
    method preprocess_depth (line 286) | def preprocess_depth(self, preds: List[dict]) -> torch.Tensor:
    method postprocess (line 312) | def postprocess(self,

FILE: mmagic/apis/inferencers/image_super_resolution_inferencer.py
  class ImageSuperResolutionInferencer (line 15) | class ImageSuperResolutionInferencer(BaseMMagicInferencer):
    method preprocess (line 24) | def preprocess(self, img: InputsType, ref: InputsType = None) -> Dict:
    method forward (line 75) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 82) | def visualize(self,

FILE: mmagic/apis/inferencers/inference_functions.py
  function set_random_seed (line 20) | def set_random_seed(seed, deterministic=False, use_rank_shift=True):
  function delete_cfg (line 38) | def delete_cfg(cfg, key='init_cfg'):
  function init_model (line 53) | def init_model(config, checkpoint=None, device='cuda:0'):
  function pad_sequence (line 89) | def pad_sequence(data, window_size):
  function read_image (line 111) | def read_image(filepath):
  function read_frames (line 126) | def read_frames(source, start_index, num_frames, from_video, end_index):
  function calculate_grid_size (line 153) | def calculate_grid_size(num_batches: int = 1, aspect_ratio: int = 1) -> ...

FILE: mmagic/apis/inferencers/inpainting_inferencer.py
  class InpaintingInferencer (line 15) | class InpaintingInferencer(BaseMMagicInferencer):
    method _init_pipeline (line 24) | def _init_pipeline(self, cfg) -> Compose:
    method preprocess (line 28) | def preprocess(self, img: InputsType, mask: InputsType) -> Dict:
    method forward (line 57) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 64) | def visualize(self,

FILE: mmagic/apis/inferencers/matting_inferencer.py
  class MattingInferencer (line 15) | class MattingInferencer(BaseMMagicInferencer):
    method preprocess (line 24) | def preprocess(self, img: InputsType, trimap: InputsType) -> Dict:
    method forward (line 58) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 64) | def visualize(self,
    method _pred2dict (line 89) | def _pred2dict(self, data_sample: DataSample) -> Dict:

FILE: mmagic/apis/inferencers/text2image_inferencer.py
  class Text2ImageInferencer (line 14) | class Text2ImageInferencer(BaseMMagicInferencer):
    method preprocess (line 25) | def preprocess(self,
    method forward (line 56) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 62) | def visualize(self,

FILE: mmagic/apis/inferencers/translation_inferencer.py
  class TranslationInferencer (line 16) | class TranslationInferencer(BaseMMagicInferencer):
    method preprocess (line 25) | def preprocess(self, img: InputsType) -> Dict:
    method forward (line 57) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 65) | def visualize(self,

FILE: mmagic/apis/inferencers/unconditional_inferencer.py
  class UnconditionalInferencer (line 14) | class UnconditionalInferencer(BaseMMagicInferencer):
    method preprocess (line 26) | def preprocess(self) -> Dict:
    method forward (line 45) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 49) | def visualize(self,
    method _pred2dict (line 79) | def _pred2dict(self, data_sample: DataSample) -> Dict:

FILE: mmagic/apis/inferencers/video_interpolation_inferencer.py
  class VideoInterpolationInferencer (line 22) | class VideoInterpolationInferencer(BaseMMagicInferencer):
    method preprocess (line 39) | def preprocess(self, video: InputsType) -> Dict:
    method forward (line 70) | def forward(self,
    method visualize (line 192) | def visualize(self,
    method postprocess (line 200) | def postprocess(

FILE: mmagic/apis/inferencers/video_restoration_inferencer.py
  class VideoRestorationInferencer (line 22) | class VideoRestorationInferencer(BaseMMagicInferencer):
    method preprocess (line 37) | def preprocess(self, video: InputsType) -> Dict:
    method forward (line 101) | def forward(self, inputs: InputsType) -> PredType:
    method visualize (line 142) | def visualize(self,
    method postprocess (line 186) | def postprocess(

FILE: mmagic/apis/mmagic_inferencer.py
  class MMagicInferencer (line 14) | class MMagicInferencer:
    method __init__ (line 130) | def __init__(self,
    method _get_inferencer_kwargs (line 150) | def _get_inferencer_kwargs(self, model_name: Optional[str],
    method print_extra_parameters (line 203) | def print_extra_parameters(self):
    method infer (line 208) | def infer(self,
    method get_model_config (line 240) | def get_model_config(self, model_name: str) -> Dict:
    method init_inference_supported_models_cfg (line 255) | def init_inference_supported_models_cfg() -> None:
    method get_inference_supported_models (line 280) | def get_inference_supported_models() -> List:
    method get_inference_supported_tasks (line 285) | def get_inference_supported_tasks() -> List:
    method get_task_supported_models (line 299) | def get_task_supported_models(task: str) -> List:

FILE: mmagic/datasets/basic_conditional_dataset.py
  class BasicConditionalDataset (line 15) | class BasicConditionalDataset(BaseDataset):
    method __init__ (line 112) | def __init__(self,
    method _find_samples (line 146) | def _find_samples(self, file_backend):
    method load_data_list (line 180) | def load_data_list(self):
    method is_valid_file (line 212) | def is_valid_file(self, filename: str) -> bool:
    method img_prefix (line 217) | def img_prefix(self):
    method CLASSES (line 222) | def CLASSES(self):
    method class_to_idx (line 227) | def class_to_idx(self):
    method get_gt_labels (line 236) | def get_gt_labels(self):
    method get_cat_ids (line 247) | def get_cat_ids(self, idx: int) -> List[int]:
    method _compat_classes (line 259) | def _compat_classes(self, metainfo, classes):
    method full_init (line 277) | def full_init(self):
    method __repr__ (line 290) | def __repr__(self):
    method extra_repr (line 318) | def extra_repr(self) -> List[str]:

FILE: mmagic/datasets/basic_frames_dataset.py
  class BasicFramesDataset (line 13) | class BasicFramesDataset(BaseDataset):
    method __init__ (line 129) | def __init__(self,
    method load_data_list (line 177) | def load_data_list(self) -> List[dict]:
    method _get_path_list (line 217) | def _get_path_list(self):
    method _get_path_list_from_ann (line 231) | def _get_path_list_from_ann(self):
    method _get_path_list_from_folder (line 271) | def _get_path_list_from_folder(self,
    method _set_seq_lens (line 312) | def _set_seq_lens(self):
    method _get_frames_list (line 325) | def _get_frames_list(self, key, folder):

FILE: mmagic/datasets/basic_image_dataset.py
  class BasicImageDataset (line 17) | class BasicImageDataset(BaseDataset):
    method __init__ (line 133) | def __init__(self,
    method load_data_list (line 175) | def load_data_list(self) -> List[dict]:
    method _get_path_list (line 200) | def _get_path_list(self):
    method _get_path_list_from_ann (line 215) | def _get_path_list_from_ann(self):
    method _get_path_list_from_folder (line 235) | def _get_path_list_from_folder(self):

FILE: mmagic/datasets/cifar10_dataset.py
  class CIFAR10 (line 16) | class CIFAR10(BasicConditionalDataset):
    method __init__ (line 57) | def __init__(self,
    method load_data_list (line 74) | def load_data_list(self):
    method _load_meta (line 140) | def _load_meta(self):
    method _check_integrity (line 157) | def _check_integrity(self):
    method extra_repr (line 172) | def extra_repr(self) -> List[str]:

FILE: mmagic/datasets/comp1k_dataset.py
  class AdobeComp1kDataset (line 13) | class AdobeComp1kDataset(BaseDataset):
    method load_data_list (line 75) | def load_data_list(self) -> List[dict]:
    method parse_data_info (line 134) | def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dic...

FILE: mmagic/datasets/controlnet_dataset.py
  class ControlNetDataset (line 12) | class ControlNetDataset(BaseDataset):
    method __init__ (line 27) | def __init__(self,
    method load_data_list (line 38) | def load_data_list(self) -> List[dict]:

FILE: mmagic/datasets/data_utils.py
  function infer_io_backend (line 20) | def infer_io_backend(data_root: str) -> str:
  function calculate_md5 (line 45) | def calculate_md5(fpath: str,
  function check_md5 (line 70) | def check_md5(fpath, md5, **kwargs) -> bool:
  function check_integrity (line 83) | def check_integrity(fpath, md5=None) -> bool:
  function download_url_to_file (line 101) | def download_url_to_file(url, dst, hash_prefix=None, progress=True):
  function download_url (line 168) | def download_url(url, root, filename=None, md5=None):
  function _is_tarxz (line 205) | def _is_tarxz(filename):
  function _is_tar (line 210) | def _is_tar(filename):
  function _is_targz (line 215) | def _is_targz(filename):
  function _is_tgz (line 220) | def _is_tgz(filename):
  function _is_gzip (line 225) | def _is_gzip(filename):
  function _is_zip (line 230) | def _is_zip(filename):
  function extract_archive (line 235) | def extract_archive(from_path, to_path=None, remove_finished=False):
  function download_and_extract_archive (line 265) | def download_and_extract_archive(url,
  function open_maybe_compressed_file (line 285) | def open_maybe_compressed_file(path: str):
  function expanduser (line 302) | def expanduser(path):
  function find_folders (line 313) | def find_folders(root: str, file_backend: BaseStorageBackend
  function get_samples (line 338) | def get_samples(root: str, folder_to_idx: Dict[str, int],

FILE: mmagic/datasets/dreambooth_dataset.py
  class DreamBoothDataset (line 12) | class DreamBoothDataset(BaseDataset):
    method __init__ (line 22) | def __init__(self,
    method load_data_list (line 35) | def load_data_list(self) -> list:

FILE: mmagic/datasets/grow_scale_image_dataset.py
  class GrowScaleImgDataset (line 12) | class GrowScaleImgDataset(BaseDataset):
    method __init__ (line 62) | def __init__(self,
    method load_data_list (line 107) | def load_data_list(self):
    method update_annotations (line 125) | def update_annotations(self, curr_scale):
    method concat_imgs_list_to (line 151) | def concat_imgs_list_to(self, num):
    method prepare_train_data (line 166) | def prepare_train_data(self, idx):
    method prepare_test_data (line 178) | def prepare_test_data(self, idx):
    method __getitem__ (line 190) | def __getitem__(self, idx):
    method __repr__ (line 211) | def __repr__(self):

FILE: mmagic/datasets/imagenet_dataset.py
  class ImageNet (line 10) | class ImageNet(BasicConditionalDataset):
    method __init__ (line 30) | def __init__(self,

FILE: mmagic/datasets/mscoco_dataset.py
  class MSCoCoDataset (line 15) | class MSCoCoDataset(BasicConditionalDataset):
    method __init__ (line 45) | def __init__(self,
    method load_data_list (line 81) | def load_data_list(self):

FILE: mmagic/datasets/paired_image_dataset.py
  class PairedImageDataset (line 15) | class PairedImageDataset(BaseDataset):
    method __init__ (line 32) | def __init__(self,
    method load_data_list (line 51) | def load_data_list(self):
    method scan_folder (line 64) | def scan_folder(self, path):

FILE: mmagic/datasets/singan_dataset.py
  function create_real_pyramid (line 11) | def create_real_pyramid(real, min_size, max_size, scale_factor_init):
  class SinGANDataset (line 63) | class SinGANDataset(BaseDataset):
    method __init__ (line 80) | def __init__(self,
    method full_init (line 93) | def full_init(self):
    method load_data_list (line 99) | def load_data_list(self, min_size, max_size, scale_factor_init):
    method __getitem__ (line 119) | def __getitem__(self, index):
    method __len__ (line 132) | def __len__(self):

FILE: mmagic/datasets/textual_inversion_dataset.py
  class TextualInversionDataset (line 14) | class TextualInversionDataset(BaseDataset):
    method __init__ (line 26) | def __init__(
    method load_data_list (line 49) | def load_data_list(self) -> list:
    method prepare_data (line 63) | def prepare_data(self, idx):

FILE: mmagic/datasets/transforms/albu_function.py
  class PairedAlbuTransForms (line 11) | class PairedAlbuTransForms(BaseTransform):
    method __init__ (line 17) | def __init__(self,
    method transform (line 55) | def transform(self, results):
    method __repr__ (line 71) | def __repr__(self):
  class AlbuTransForms (line 84) | class AlbuTransForms(BaseTransform):
    method __init__ (line 90) | def __init__(self,
    method transform (line 125) | def transform(self, results):
    method __repr__ (line 140) | def __repr__(self):
  class PairedAlbuNormalize (line 152) | class PairedAlbuNormalize(BaseTransform):
    method __init__ (line 158) | def __init__(self,
    method transform (line 182) | def transform(self, results):
    method __repr__ (line 202) | def __repr__(self):
  class AlbuNormalize (line 216) | class AlbuNormalize(BaseTransform):
    method __init__ (line 222) | def __init__(self,
    method transform (line 243) | def transform(self, results):
    method __repr__ (line 258) | def __repr__(self):
  function _resolve_aug_fn (line 270) | def _resolve_aug_fn(name):
  class AlbuCorruptFunction (line 293) | class AlbuCorruptFunction(BaseTransform):
    method __init__ (line 299) | def __init__(self, keys: List[str], config: List[dict], p: float = 1.0):
    method transform (line 312) | def transform(self, results):
    method __repr__ (line 326) | def __repr__(self):

FILE: mmagic/datasets/transforms/albumentations.py
  class Albumentations (line 20) | class Albumentations(BaseTransform):
    method __init__ (line 70) | def __init__(self, keys: List[str], transforms: List[dict]) -> None:
    method albu_builder (line 82) | def albu_builder(self, cfg: dict) -> albumentations:
    method _apply_albu (line 115) | def _apply_albu(self, imgs):
    method transform (line 130) | def transform(self, results):
    method __repr__ (line 138) | def __repr__(self):

FILE: mmagic/datasets/transforms/alpha.py
  class GenerateSeg (line 17) | class GenerateSeg(BaseTransform):
    method __init__ (line 38) | def __init__(self,
    method _crop_hole (line 53) | def _crop_hole(img, start_point, hole_size):
    method transform (line 74) | def transform(self, results: dict) -> dict:
    method __repr__ (line 113) | def __repr__(self):
  class GenerateSoftSeg (line 125) | class GenerateSoftSeg(BaseTransform):
    method __init__ (line 148) | def __init__(self,
    method transform (line 187) | def transform(self, results: dict) -> dict:
    method __repr__ (line 231) | def __repr__(self):

FILE: mmagic/datasets/transforms/aug_frames.py
  class MirrorSequence (line 9) | class MirrorSequence(BaseTransform):
    method __init__ (line 27) | def __init__(self, keys):
    method transform (line 31) | def transform(self, results):
    method __repr__ (line 51) | def __repr__(self):
  class TemporalReverse (line 60) | class TemporalReverse(BaseTransform):
    method __init__ (line 72) | def __init__(self, keys, reverse_ratio=0.5):
    method transform (line 77) | def transform(self, results):
    method __repr__ (line 97) | def __repr__(self):

FILE: mmagic/datasets/transforms/aug_pixel.py
  class BinarizeImage (line 17) | class BinarizeImage(BaseTransform):
    method __init__ (line 28) | def __init__(self, keys, binary_thr, a_min=0, a_max=1, dtype=np.uint8):
    method _binarize (line 36) | def _binarize(self, img):
    method transform (line 55) | def transform(self, results):
    method __repr__ (line 71) | def __repr__(self):
  class Clip (line 81) | class Clip(BaseTransform):
    method __init__ (line 92) | def __init__(self, keys, a_min=0, a_max=255):
    method _clip (line 98) | def _clip(self, input_):
    method transform (line 120) | def transform(self, results):
    method __repr__ (line 137) | def __repr__(self):
  class ColorJitter (line 146) | class ColorJitter(BaseTransform):
    method __init__ (line 189) | def __init__(self, keys, channel_order='rgb', **kwargs):
    method _color_jitter (line 200) | def _color_jitter(self, image, this_seed):
    method transform (line 224) | def transform(self, results: Dict) -> Dict:
    method __repr__ (line 246) | def __repr__(self):
  class RandomAffine (line 259) | class RandomAffine(BaseTransform):
    method __init__ (line 298) | def __init__(self,
    method _get_params (line 352) | def _get_params(degrees, translate, scale_ranges, shears, flip_ratio,
    method _get_inverse_affine_matrix (line 389) | def _get_inverse_affine_matrix(center, angle, translate, scale, shear,
    method transform (line 439) | def transform(self, results):
    method __repr__ (line 475) | def __repr__(self):
  class RandomMaskDilation (line 486) | class RandomMaskDilation(BaseTransform):
    method __init__ (line 496) | def __init__(self, keys, binary_thr=0., kernel_min=9, kernel_max=49):
    method _random_dilate (line 503) | def _random_dilate(self, img):
    method transform (line 514) | def transform(self, results):
    method __repr__ (line 533) | def __repr__(self):
  class UnsharpMasking (line 543) | class UnsharpMasking(BaseTransform):
    method __init__ (line 558) | def __init__(self, kernel_size, sigma, weight, threshold, keys):
    method _unsharp_masking (line 573) | def _unsharp_masking(self, imgs):
    method transform (line 596) | def transform(self, results):
    method __repr__ (line 611) | def __repr__(self):

FILE: mmagic/datasets/transforms/aug_shape.py
  class Flip (line 15) | class Flip(BaseTransform):
    method __init__ (line 40) | def __init__(self, keys, flip_ratio=0.5, direction='horizontal'):
    method transform (line 50) | def transform(self, results):
    method __repr__ (line 83) | def __repr__(self):
  class RandomRotation (line 93) | class RandomRotation(BaseTransform):
    method __init__ (line 103) | def __init__(self, keys, degrees):
    method transform (line 118) | def transform(self, results):
    method __repr__ (line 139) | def __repr__(self):
  class RandomTransposeHW (line 148) | class RandomTransposeHW(BaseTransform):
    method __init__ (line 165) | def __init__(self, keys, transpose_ratio=0.5):
    method transform (line 170) | def transform(self, results):
    method __repr__ (line 194) | def __repr__(self):
  class Resize (line 204) | class Resize(BaseTransform):
    method __init__ (line 267) | def __init__(self,
    method _resize (line 315) | def _resize(self, img):
    method transform (line 350) | def transform(self, results: Dict) -> Dict:
    method __repr__ (line 393) | def __repr__(self):
  class NumpyPad (line 406) | class NumpyPad(BaseTransform):
    method __init__ (line 429) | def __init__(self, keys, padding, **kwargs):
    method transform (line 436) | def transform(self, results):
    method __repr__ (line 451) | def __repr__(self) -> str:

FILE: mmagic/datasets/transforms/blur_kernels.py
  function get_rotated_sigma_matrix (line 9) | def get_rotated_sigma_matrix(sig_x, sig_y, theta):
  function _mesh_grid (line 28) | def _mesh_grid(kernel_size):
  function calculate_gaussian_pdf (line 53) | def calculate_gaussian_pdf(sigma_matrix, grid):
  function bivariate_gaussian (line 71) | def bivariate_gaussian(kernel_size,
  function bivariate_generalized_gaussian (line 118) | def bivariate_generalized_gaussian(kernel_size,
  function bivariate_plateau (line 168) | def bivariate_plateau(kernel_size,
  function random_bivariate_gaussian_kernel (line 211) | def random_bivariate_gaussian_kernel(kernel_size,
  function random_bivariate_generalized_gaussian_kernel (line 266) | def random_bivariate_generalized_gaussian_kernel(kernel_size,
  function random_bivariate_plateau_kernel (line 334) | def random_bivariate_plateau_kernel(kernel_size,
  function random_circular_lowpass_kernel (line 402) | def random_circular_lowpass_kernel(omega_range, kernel_size, pad_to=0):
  function random_mixed_kernels (line 441) | def random_mixed_kernels(kernel_list,

FILE: mmagic/datasets/transforms/crop.py
  class Crop (line 22) | class Crop(BaseTransform):
    method __init__ (line 34) | def __init__(self, keys, crop_size, random_crop=True, is_pad_zeros=Fal...
    method _crop (line 46) | def _crop(self, data):
    method transform (line 109) | def transform(self, results):
    method __repr__ (line 127) | def __repr__(self):
  class CropLike (line 137) | class CropLike(BaseTransform):
    method __init__ (line 147) | def __init__(self, target_key, reference_key=None):
    method transform (line 153) | def transform(self, results):
    method __repr__ (line 179) | def __repr__(self):
  class FixedCrop (line 186) | class FixedCrop(BaseTransform):
    method __init__ (line 197) | def __init__(self, keys, crop_size, crop_pos=None):
    method _crop (line 213) | def _crop(self, data, x_offset, y_offset, crop_w, crop_h):
    method transform (line 232) | def transform(self, results):
    method __repr__ (line 286) | def __repr__(self):
  class ModCrop (line 296) | class ModCrop(BaseTransform):
    method __init__ (line 306) | def __init__(self, key='gt') -> None:
    method transform (line 311) | def transform(self, results):
    method __repr__ (line 334) | def __repr__(self):
  class PairedRandomCrop (line 343) | class PairedRandomCrop(BaseTransform):
    method __init__ (line 357) | def __init__(self, gt_patch_size, lq_key='img', gt_key='gt'):
    method transform (line 363) | def transform(self, results):
    method __repr__ (line 421) | def __repr__(self):
  class RandomResizedCrop (line 432) | class RandomResizedCrop(BaseTransform):
    method __init__ (line 458) | def __init__(self,
    method get_params (line 485) | def get_params(self, data):
    method transform (line 528) | def transform(self, results):
    method __repr__ (line 552) | def __repr__(self):
  class CropAroundCenter (line 563) | class CropAroundCenter(BaseTransform):
    method __init__ (line 579) | def __init__(self, crop_size):
    method transform (line 587) | def transform(self, results):
    method __repr__ (line 657) | def __repr__(self):
  class CropAroundFg (line 663) | class CropAroundFg(BaseTransform):
    method __init__ (line 682) | def __init__(self, keys, bd_ratio_range=(0.1, 0.4), test_mode=False):
    method transform (line 694) | def transform(self, results):
  class CropAroundUnknown (line 737) | class CropAroundUnknown(BaseTransform):
    method __init__ (line 760) | def __init__(self,
    method transform (line 796) | def transform(self, results):
    method __repr__ (line 842) | def __repr__(self):
  class RandomCropLongEdge (line 853) | class RandomCropLongEdge(BaseTransform):
    method __init__ (line 860) | def __init__(self, keys='img'):
    method transform (line 866) | def transform(self, results):
    method __repr__ (line 892) | def __repr__(self):
  class CenterCropLongEdge (line 899) | class CenterCropLongEdge(BaseTransform):
    method __init__ (line 906) | def __init__(self, keys='img'):
    method transform (line 912) | def transform(self, results):
    method __repr__ (line 939) | def __repr__(self):
  class InstanceCrop (line 946) | class InstanceCrop(BaseTransform):
    method __init__ (line 959) | def __init__(self,
    method transform (line 983) | def transform(self, results: dict) -> dict:
    method predict_bbox (line 1043) | def predict_bbox(self, image):

FILE: mmagic/datasets/transforms/fgbg.py
  class CompositeFg (line 17) | class CompositeFg(BaseTransform):
    method __init__ (line 45) | def __init__(self, fg_dirs, alpha_dirs, interpolation='nearest'):
    method transform (line 57) | def transform(self, results: dict) -> dict:
    method _get_file_list (line 96) | def _get_file_list(self, fg_dirs, alpha_dirs):
    method __repr__ (line 115) | def __repr__(self):
  class MergeFgAndBg (line 124) | class MergeFgAndBg(BaseTransform):
    method transform (line 130) | def transform(self, results: dict) -> dict:
    method __repr__ (line 147) | def __repr__(self) -> str:
  class PerturbBg (line 153) | class PerturbBg(BaseTransform):
    method __init__ (line 163) | def __init__(self, gamma_ratio=0.6):
    method transform (line 169) | def transform(self, results: dict) -> dict:
    method __repr__ (line 190) | def __repr__(self):
  class RandomJitter (line 195) | class RandomJitter(BaseTransform):
    method __init__ (line 209) | def __init__(self, hue_range=40):
    method transform (line 220) | def transform(self, results):
    method __repr__ (line 264) | def __repr__(self):
  class RandomLoadResizeBg (line 270) | class RandomLoadResizeBg(BaseTransform):
    method __init__ (line 283) | def __init__(self, bg_dir, flag='color', channel_order='bgr'):
    method transform (line 293) | def transform(self, results: dict) -> dict:
    method __repr__ (line 313) | def __repr__(self):

FILE: mmagic/datasets/transforms/formatting.py
  class PackInputs (line 12) | class PackInputs(BaseTransform):
    method __init__ (line 31) | def __init__(
    method transform (line 51) | def transform(self, results: dict) -> dict:
    method __repr__ (line 104) | def __repr__(self) -> str:

FILE: mmagic/datasets/transforms/generate_assistant.py
  class GenerateCoordinateAndCell (line 17) | class GenerateCoordinateAndCell(BaseTransform):
    method __init__ (line 54) | def __init__(self,
    method transform (line 65) | def transform(self, results):
    method __repr__ (line 117) | def __repr__(self):
  class GenerateFacialHeatmap (line 128) | class GenerateFacialHeatmap(BaseTransform):
    method __init__ (line 139) | def __init__(self,
    method transform (line 168) | def transform(self, results):
    method generate_heatmap_from_img (line 196) | def generate_heatmap_from_img(self, image):
    method _face_alignment_detector (line 217) | def _face_alignment_detector(self, image):
    method _generate_one_heatmap (line 239) | def _generate_one_heatmap(self, keypoint):
    method __repr__ (line 260) | def __repr__(self):

FILE: mmagic/datasets/transforms/generate_frame_indices.py
  class GenerateFrameIndices (line 12) | class GenerateFrameIndices(BaseTransform):
    method __init__ (line 41) | def __init__(self, interval_list, frames_per_clip=99):
    method transform (line 46) | def transform(self, results):
    method __repr__ (line 93) | def __repr__(self):
  class GenerateFrameIndiceswithPadding (line 103) | class GenerateFrameIndiceswithPadding(BaseTransform):
    method __init__ (line 135) | def __init__(self, padding, filename_tmpl='{:08d}'):
    method transform (line 145) | def transform(self, results):
    method __repr__ (line 200) | def __repr__(self):
  class GenerateSegmentIndices (line 208) | class GenerateSegmentIndices(BaseTransform):
    method __init__ (line 239) | def __init__(self, interval_list, start_idx=0, filename_tmpl='{:08d}.p...
    method transform (line 245) | def transform(self, results):
    method __repr__ (line 295) | def __repr__(self):

FILE: mmagic/datasets/transforms/get_masked_image.py
  class GetMaskedImage (line 11) | class GetMaskedImage(BaseTransform):
    method __init__ (line 23) | def __init__(self,
    method transform (line 33) | def transform(self, results):
    method __repr__ (line 62) | def __repr__(self):

FILE: mmagic/datasets/transforms/loading.py
  class LoadImageFromFile (line 16) | class LoadImageFromFile(BaseTransform):
    method __init__ (line 48) | def __init__(
    method transform (line 83) | def transform(self, results: dict) -> dict:
    method _load_image (line 129) | def _load_image(self, filename):
    method _convert (line 160) | def _convert(self, img: np.ndarray):
    method __repr__ (line 187) | def __repr__(self):
  class LoadMask (line 204) | class LoadMask(BaseTransform):
    method __init__ (line 271) | def __init__(self, mask_mode='bbox', mask_config=None):
    method _init_info (line 279) | def _init_info(self):
    method _get_random_mask_from_set (line 301) | def _get_random_mask_from_set(self):
    method _get_mask_from_file (line 317) | def _get_mask_from_file(self, path):
    method transform (line 332) | def transform(self, results):
    method __repr__ (line 361) | def __repr__(self):
  class GetSpatialDiscountMask (line 366) | class GetSpatialDiscountMask(BaseTransform):
    method __init__ (line 379) | def __init__(self, gamma=0.99, beta=1.5):
    method spatial_discount_mask (line 383) | def spatial_discount_mask(self, mask_width, mask_height):
    method transform (line 402) | def transform(self, results):
    method __repr__ (line 426) | def __repr__(self):
  class LoadPairedImageFromFile (line 432) | class LoadPairedImageFromFile(LoadImageFromFile):
    method __init__ (line 474) | def __init__(self,
    method transform (line 494) | def transform(self, results: dict) -> dict:

FILE: mmagic/datasets/transforms/matlab_like_resize.py
  function get_size_from_scale (line 11) | def get_size_from_scale(input_size, scale_factor):
  function get_scale_from_size (line 30) | def get_scale_from_size(input_size, output_size):
  function _cubic (line 49) | def _cubic(x):
  function get_weights_indices (line 73) | def get_weights_indices(input_length, output_length, scale, kernel,
  function resize_along_dim (line 127) | def resize_along_dim(img_in, weights, indices, dim):
  class MATLABLikeResize (line 169) | class MATLABLikeResize(BaseTransform):
    method __init__ (line 193) | def __init__(self,
    method _resize (line 216) | def _resize(self, img):
    method transform (line 259) | def transform(self, results):
    method __repr__ (line 285) | def __repr__(self):

FILE: mmagic/datasets/transforms/normalization.py
  class Normalize (line 10) | class Normalize(BaseTransform):
    method __init__ (line 26) | def __init__(self, keys, mean, std, to_rgb=False, save_original=False):
    method transform (line 33) | def transform(self, results):
    method __repr__ (line 63) | def __repr__(self):
  class RescaleToZeroOne (line 72) | class RescaleToZeroOne(BaseTransform):
    method __init__ (line 83) | def __init__(self, keys):
    method transform (line 86) | def transform(self, results):
    method __repr__ (line 105) | def __repr__(self):

FILE: mmagic/datasets/transforms/random_degradations.py
  class RandomBlur (line 20) | class RandomBlur:
    method __init__ (line 31) | def __init__(self, params, keys):
    method get_kernel (line 35) | def get_kernel(self, num_kernels: int):
    method _apply_random_blur (line 114) | def _apply_random_blur(self, imgs):
    method __call__ (line 140) | def __call__(self, results):
    method __repr__ (line 150) | def __repr__(self):
  class RandomJPEGCompression (line 158) | class RandomJPEGCompression:
    method __init__ (line 170) | def __init__(self, params, keys, color_type='color', bgr2rgb=False):
    method _apply_random_compression (line 176) | def _apply_random_compression(self, imgs):
    method __call__ (line 212) | def __call__(self, results):
    method __repr__ (line 222) | def __repr__(self):
  class RandomNoise (line 230) | class RandomNoise:
    method __init__ (line 243) | def __init__(self, params, keys):
    method _apply_gaussian_noise (line 247) | def _apply_gaussian_noise(self, imgs):
    method _apply_poisson_noise (line 277) | def _apply_poisson_noise(self, imgs):
    method _apply_random_noise (line 305) | def _apply_random_noise(self, imgs):
    method __call__ (line 335) | def __call__(self, results):
    method __repr__ (line 345) | def __repr__(self):
  class RandomResize (line 353) | class RandomResize:
    method __init__ (line 364) | def __init__(self, params, keys):
    method _random_resize (line 374) | def _random_resize(self, imgs):
    method __call__ (line 451) | def __call__(self, results):
    method __repr__ (line 461) | def __repr__(self):
  class RandomVideoCompression (line 469) | class RandomVideoCompression:
    method __init__ (line 480) | def __init__(self, params, keys):
    method _apply_random_compression (line 487) | def _apply_random_compression(self, imgs):
    method __call__ (line 529) | def __call__(self, results):
    method __repr__ (line 539) | def __repr__(self):
  class DegradationsWithShuffle (line 556) | class DegradationsWithShuffle:
    method __init__ (line 583) | def __init__(self, degradations, keys, shuffle_idx=None):
    method _build_degradations (line 594) | def _build_degradations(self, degradations):
    method __call__ (line 605) | def __call__(self, results):
    method __repr__ (line 624) | def __repr__(self):

FILE: mmagic/datasets/transforms/random_down_sampling.py
  class RandomDownSampling (line 13) | class RandomDownSampling(BaseTransform):
    method __init__ (line 36) | def __init__(self,
    method transform (line 50) | def transform(self, results):
    method __repr__ (line 91) | def __repr__(self):
  function resize_fn (line 102) | def resize_fn(img, size, interpolation='bicubic', backend='pillow'):

FILE: mmagic/datasets/transforms/trimap.py
  class FormatTrimap (line 13) | class FormatTrimap(BaseTransform):
    method __init__ (line 26) | def __init__(self, to_onehot=False):
    method transform (line 29) | def transform(self, results):
    method __repr__ (line 56) | def __repr__(self):
  class GenerateTrimap (line 61) | class GenerateTrimap(BaseTransform):
    method __init__ (line 82) | def __init__(self, kernel_size, iterations=1, random=True):
    method transform (line 113) | def transform(self, results: dict) -> dict:
    method __repr__ (line 148) | def __repr__(self):
  class GenerateTrimapWithDistTransform (line 156) | class GenerateTrimapWithDistTransform(BaseTransform):
    method __init__ (line 169) | def __init__(self, dist_thr=20, random=True):
    method transform (line 176) | def transform(self, results: dict) -> dict:
    method __repr__ (line 201) | def __repr__(self):
  class TransformTrimap (line 208) | class TransformTrimap(BaseTransform):
    method transform (line 223) | def transform(self, results: dict) -> dict:
    method __repr__ (line 255) | def __repr__(self):

FILE: mmagic/datasets/transforms/values.py
  class CopyValues (line 11) | class CopyValues(BaseTransform):
    method __init__ (line 34) | def __init__(self, src_keys, dst_keys):
    method transform (line 46) | def transform(self, results):
    method __repr__ (line 62) | def __repr__(self):
  class SetValues (line 72) | class SetValues(BaseTransform):
    method __init__ (line 91) | def __init__(self, dictionary):
    method transform (line 95) | def transform(self, results: Dict):
    method __repr__ (line 111) | def __repr__(self):

FILE: mmagic/datasets/unpaired_image_dataset.py
  class UnpairedImageDataset (line 16) | class UnpairedImageDataset(BaseDataset):
    method __init__ (line 40) | def __init__(self,
    method load_data_list (line 70) | def load_data_list(self):
    method _load_domain_data_list (line 80) | def _load_domain_data_list(self, dataroot):
    method get_data_info (line 97) | def get_data_info(self, idx) -> dict:
    method __len__ (line 118) | def __len__(self):
    method scan_folder (line 122) | def scan_folder(self, path):

FILE: mmagic/engine/hooks/ema.py
  class ExponentialMovingAverageHook (line 18) | class ExponentialMovingAverageHook(Hook):
    method __init__ (line 45) | def __init__(self,
    method lerp (line 72) | def lerp(a, b, momentum=0.001, momentum_nontrainable=1., trainable=True):
    method every_n_iters (line 103) | def every_n_iters(self, runner: Runner, n: int):
    method after_train_iter (line 118) | def after_train_iter(self,
    method before_run (line 154) | def before_run(self, runner: Runner):

FILE: mmagic/engine/hooks/iter_time_hook.py
  class IterTimerHook (line 14) | class IterTimerHook(BaseIterTimerHook):
    method _after_iter (line 23) | def _after_iter(self,

FILE: mmagic/engine/hooks/pggan_fetch_data_hook.py
  class PGGANFetchDataHook (line 18) | class PGGANFetchDataHook(Hook):
    method __init__ (line 26) | def __init__(self):
    method before_train_iter (line 29) | def before_train_iter(self,
    method update_dataloader (line 49) | def update_dataloader(self, dataloader: DataLoader,

FILE: mmagic/engine/hooks/pickle_data_hook.py
  class PickleDataHook (line 22) | class PickleDataHook(Hook):
    method __init__ (line 43) | def __init__(self,
    method after_run (line 59) | def after_run(self, runner):
    method before_run (line 69) | def before_run(self, runner):
    method after_train_iter (line 79) | def after_train_iter(self,
    method _pickle_data (line 98) | def _pickle_data(self, runner: Runner):
    method _get_numpy_data (line 130) | def _get_numpy_data(

FILE: mmagic/engine/hooks/reduce_lr_scheduler_hook.py
  class ReduceLRSchedulerHook (line 14) | class ReduceLRSchedulerHook(ParamSchedulerHook):
    method __init__ (line 24) | def __init__(self,
    method _calculate_average_value (line 38) | def _calculate_average_value(self):
    method after_train_epoch (line 44) | def after_train_epoch(self, runner: Runner):
    method after_train_iter (line 61) | def after_train_iter(self,
    method after_val_epoch (line 98) | def after_val_epoch(self,

FILE: mmagic/engine/hooks/visualization_hook.py
  class BasicVisualizationHook (line 23) | class BasicVisualizationHook(Hook):
    method __init__ (line 34) | def __init__(self,
    method _after_iter (line 46) | def _after_iter(
  class VisualizationHook (line 82) | class VisualizationHook(Hook):
    method __init__ (line 173) | def __init__(self,
    method after_val_iter (line 216) | def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: d...
    method after_test_iter (line 231) | def after_test_iter(self, runner: Runner, batch_idx: int, data_batch: ...
    method after_train_iter (line 276) | def after_train_iter(self,
    method vis_sample (line 294) | def vis_sample(self,
    method vis_from_message_hub (line 393) | def vis_from_message_hub(self, batch_idx: int):

FILE: mmagic/engine/optimizers/multi_optimizer_constructor.py
  class MultiOptimWrapperConstructor (line 15) | class MultiOptimWrapperConstructor:
    method __init__ (line 116) | def __init__(self, optim_wrapper_cfg: dict, paramwise_cfg=None):
    method __call__ (line 143) | def __call__(self,
  function get_params_by_names (line 215) | def get_params_by_names(module: nn.Module,

FILE: mmagic/engine/optimizers/pggan_optimizer_constructor.py
  class PGGANOptimWrapperConstructor (line 13) | class PGGANOptimWrapperConstructor:
    method __init__ (line 76) | def __init__(self,
    method __call__ (line 98) | def __call__(self, module: nn.Module) -> OptimWrapperDict:

FILE: mmagic/engine/optimizers/singan_optimizer_constructor.py
  class SinGANOptimWrapperConstructor (line 11) | class SinGANOptimWrapperConstructor:
    method __init__ (line 56) | def __init__(self,
    method __call__ (line 72) | def __call__(self, module: nn.Module) -> OptimWrapperDict:

FILE: mmagic/engine/runner/log_processor.py
  class LogProcessor (line 8) | class LogProcessor(BaseLogProcessor):
    method _get_dataloader_size (line 17) | def _get_dataloader_size(self, runner, mode) -> int:

FILE: mmagic/engine/runner/loop_utils.py
  function update_and_check_evaluator (line 11) | def update_and_check_evaluator(evaluator: EVALUATOR_TYPE
  function is_evaluator (line 51) | def is_evaluator(evaluator: Any) -> bool:

FILE: mmagic/engine/runner/multi_loops.py
  class MultiValLoop (line 19) | class MultiValLoop(BaseLoop):
    method __init__ (line 79) | def __init__(self,
    method total_length (line 99) | def total_length(self) -> int:
    method _build_dataloaders (line 109) | def _build_dataloaders(self,
    method _build_evaluators (line 136) | def _build_evaluators(self, evaluator: EVALUATOR_TYPE) -> List[Evaluat...
    method run (line 167) | def run(self):
    method run_iter (line 251) | def run_iter(self, idx, data_batch: dict, metrics: Sequence[BaseMetric]):
  class MultiTestLoop (line 274) | class MultiTestLoop(BaseLoop):
    method __init__ (line 334) | def __init__(self, runner, dataloader, evaluator, fp16=False):
    method total_length (line 350) | def total_length(self) -> int:
    method _build_dataloaders (line 360) | def _build_dataloaders(self,
    method _build_evaluators (line 387) | def _build_evaluators(self, evaluator: EVALUATOR_TYPE) -> List[Evaluat...
    method run (line 418) | def run(self):
    method run_iter (line 502) | def run_iter(self, idx, data_batch: dict, metrics: Sequence[BaseMetric]):

FILE: mmagic/engine/schedulers/linear_lr_scheduler_with_interval.py
  class LinearLrInterval (line 9) | class LinearLrInterval(LinearLR):
    method __init__ (line 23) | def __init__(self, *args, interval=1, **kwargs):
    method _get_value (line 27) | def _get_value(self):

FILE: mmagic/engine/schedulers/reduce_lr_scheduler.py
  class ReduceLR (line 9) | class ReduceLR(_ParamScheduler):
    method __init__ (line 59) | def __init__(self,
    method _get_value (line 103) | def _get_value(self):
    method _init_is_better (line 138) | def _init_is_better(self, mode):
    method _reset (line 144) | def _reset(self):
    method is_better (line 149) | def is_better(self, a, best):
    method in_cooldown (line 162) | def in_cooldown(self):

FILE: mmagic/evaluation/evaluator.py
  class Evaluator (line 16) | class Evaluator(Evaluator):
    method __init__ (line 46) | def __init__(self, metrics: Union[dict, BaseMetric, Sequence]):
    method prepare_metrics (line 53) | def prepare_metrics(self, module: BaseModel, dataloader: DataLoader):
    method _cal_metric_hash (line 83) | def _cal_metric_hash(metric: GenMetric):
    method prepare_samplers (line 97) | def prepare_samplers(self, module: BaseModel, dataloader: DataLoader
    method process (line 137) | def process(self, data_samples: Sequence[DataSample],
    method evaluate (line 164) | def evaluate(self) -> dict:

FILE: mmagic/evaluation/functional/fid_inception.py
  class InceptionV3 (line 19) | class InceptionV3(nn.Module):
    method __init__ (line 34) | def __init__(self,
    method forward (line 126) | def forward(self, inp):
  function fid_inception_v3 (line 158) | def fid_inception_v3(load_ckpt=True):
  class FIDInceptionA (line 186) | class FIDInceptionA(models.inception.InceptionA):
    method __init__ (line 189) | def __init__(self, in_channels, pool_features):
    method forward (line 192) | def forward(self, x):
  class FIDInceptionC (line 220) | class FIDInceptionC(models.inception.InceptionC):
    method __init__ (line 223) | def __init__(self, in_channels, channels_7x7):
    method forward (line 226) | def forward(self, x):
  class FIDInceptionE_1 (line 257) | class FIDInceptionE_1(models.inception.InceptionE):
    method __init__ (line 260) | def __init__(self, in_channels):
    method forward (line 263) | def forward(self, x):
  class FIDInceptionE_2 (line 299) | class FIDInceptionE_2(models.inception.InceptionE):
    method __init__ (line 302) | def __init__(self, in_channels):
    method forward (line 305) | def forward(self, x):

FILE: mmagic/evaluation/functional/gaussian_funcs.py
  function gaussian (line 6) | def gaussian(x, sigma):
  function dgaussian (line 19) | def dgaussian(x, sigma):
  function gauss_filter (line 32) | def gauss_filter(sigma, epsilon=1e-2):
  function gauss_gradient (line 63) | def gauss_gradient(img, sigma):

FILE: mmagic/evaluation/functional/inception_utils.py
  function disable_gpu_fuser_on_pt19 (line 32) | def disable_gpu_fuser_on_pt19():
  function load_inception (line 48) | def load_inception(inception_args, metric):
  function _load_inception_from_path (line 107) | def _load_inception_from_path(inception_path):
  function _load_inception_from_url (line 129) | def _load_inception_from_url(inception_url: str) -> nn.Module:
  function _load_inception_torch (line 143) | def _load_inception_torch(inception_args, metric) -> nn.Module:
  function get_inception_feat_cache_name_and_args (line 162) | def get_inception_feat_cache_name_and_args(dataloader: DataLoader,
  function get_vgg_feat_cache_name_and_args (line 229) | def get_vgg_feat_cache_name_and_args(dataloader: DataLoader,
  function prepare_inception_feat (line 276) | def prepare_inception_feat(dataloader: DataLoader,
  function prepare_vgg_feat (line 450) | def prepare_vgg_feat(dataloader: DataLoader,

FILE: mmagic/evaluation/metrics/base_gen_metric.py
  class GenMetric (line 21) | class GenMetric(BaseMetric):
    method __init__ (line 45) | def __init__(self,
    method real_nums_per_device (line 64) | def real_nums_per_device(self):
    method fake_nums_per_device (line 69) | def fake_nums_per_device(self):
    method _collect_target_results (line 73) | def _collect_target_results(self, target: str) -> Optional[list]:
    method evaluate (line 110) | def evaluate(self) -> dict:
    method get_metric_sampler (line 145) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,
    method compute_metrics (line 177) | def compute_metrics(self, results_fake, results_real) -> dict:
    method prepare (line 188) | def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:
  class GenerativeMetric (line 201) | class GenerativeMetric(GenMetric):
    method __init__ (line 235) | def __init__(self,
    method get_metric_sampler (line 253) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,
    method evaluate (line 330) | def evaluate(self) -> dict():
    method compute_metrics (line 361) | def compute_metrics(self, results) -> dict:

FILE: mmagic/evaluation/metrics/base_sample_wise_metric.py
  class BaseSampleWiseMetric (line 16) | class BaseSampleWiseMetric(BaseMetric):
    method __init__ (line 45) | def __init__(self,
    method compute_metrics (line 65) | def compute_metrics(self, results: List):
    method process (line 80) | def process(self, data_batch: Sequence[dict],
    method process_image (line 112) | def process_image(self, gt, pred, mask):
    method evaluate (line 115) | def evaluate(self) -> dict:
    method prepare (line 121) | def prepare(self, module: nn.Module, dataloader: DataLoader):
    method get_metric_sampler (line 127) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,

FILE: mmagic/evaluation/metrics/connectivity_error.py
  class ConnectivityError (line 18) | class ConnectivityError(BaseSampleWiseMetric):
    method __init__ (line 45) | def __init__(
    method prepare (line 55) | def prepare(self, module: nn.Module, dataloader: DataLoader):
    method process (line 61) | def process(self, data_batch: Sequence[dict],
    method compute_metrics (line 115) | def compute_metrics(self, results: List):

FILE: mmagic/evaluation/metrics/equivariance.py
  class Equivariance (line 18) | class Equivariance(GenerativeMetric):
    method __init__ (line 22) | def __init__(self,
    method process (line 57) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method get_metric_sampler (line 78) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,
    method compute_metrics (line 109) | def compute_metrics(self, results) -> dict:
    method _collect_target_results (line 130) | def _collect_target_results(self, target: str) -> Optional[list]:
  class eq_iterator (line 154) | class eq_iterator:
    method __init__ (line 156) | def __init__(self, batch_size, max_length, sample_mode, eq_cfg,
    method __iter__ (line 164) | def __iter__(self) -> Iterator:
    method __len__ (line 168) | def __len__(self) -> int:
    method __next__ (line 171) | def __next__(self) -> dict:

FILE: mmagic/evaluation/metrics/fid.py
  class FrechetInceptionDistance (line 21) | class FrechetInceptionDistance(GenerativeMetric):
    method __init__ (line 65) | def __init__(self,
    method prepare (line 88) | def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:
    method _load_inception (line 104) | def _load_inception(self, inception_style: str,
    method forward_inception (line 126) | def forward_inception(self, image: Tensor) -> Tensor:
    method process (line 148) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method _calc_fid (line 189) | def _calc_fid(sample_mean: np.ndarray,
    method compute_metrics (line 224) | def compute_metrics(self, fake_results: list) -> dict:
  class TransFID (line 246) | class TransFID(FrechetInceptionDistance):
    method __init__ (line 248) | def __init__(self,
    method get_metric_sampler (line 267) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,

FILE: mmagic/evaluation/metrics/gradient_error.py
  class GradientError (line 17) | class GradientError(BaseSampleWiseMetric):
    method __init__ (line 44) | def __init__(
    method prepare (line 54) | def prepare(self, module: nn.Module, dataloader: DataLoader):
    method process (line 60) | def process(self, data_batch: Sequence[dict],
    method compute_metrics (line 93) | def compute_metrics(self, results: List):

FILE: mmagic/evaluation/metrics/inception_score.py
  class InceptionScore (line 22) | class InceptionScore(GenerativeMetric):
    method __init__ (line 92) | def __init__(self,
    method prepare (line 132) | def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:
    method _load_inception (line 143) | def _load_inception(self, inception_style: str,
    method _preprocess (line 161) | def _preprocess(self, image: Tensor) -> Tensor:
    method process (line 193) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method compute_metrics (line 232) | def compute_metrics(self, fake_results: list) -> dict:
  class TransIS (line 262) | class TransIS(InceptionScore):
    method __init__ (line 315) | def __init__(self,
    method get_metric_sampler (line 334) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,

FILE: mmagic/evaluation/metrics/mae.py
  class MAE (line 11) | class MAE(BaseSampleWiseMetric):
    method process_image (line 36) | def process_image(self, gt, pred, mask):

FILE: mmagic/evaluation/metrics/matting_mse.py
  class MattingMSE (line 14) | class MattingMSE(BaseSampleWiseMetric):
    method __init__ (line 44) | def __init__(
    method prepare (line 52) | def prepare(self, module: nn.Module, dataloader: DataLoader):
    method process (line 58) | def process(self, data_batch: Sequence[dict],
    method compute_metrics (line 79) | def compute_metrics(self, results: List):

FILE: mmagic/evaluation/metrics/metrics_utils.py
  function _assert_ndim (line 9) | def _assert_ndim(input, name, ndim, shape_hint):
  function _assert_masked (line 15) | def _assert_masked(pred_alpha, trimap):
  function _fetch_data_and_check (line 22) | def _fetch_data_and_check(data_samples):
  function average (line 53) | def average(results, key):
  function img_transform (line 74) | def img_transform(img,
  function obtain_data (line 129) | def obtain_data(data_sample, key, device='cpu'):

FILE: mmagic/evaluation/metrics/ms_ssim.py
  function _f_special_gauss (line 14) | def _f_special_gauss(size, sigma):
  function _hox_downsample (line 38) | def _hox_downsample(img):
  function _ssim_for_multi_scale (line 53) | def _ssim_for_multi_scale(img1,
  function ms_ssim (line 143) | def ms_ssim(img1,
  class MultiScaleStructureSimilarity (line 229) | class MultiScaleStructureSimilarity(GenerativeMetric):
    method __init__ (line 262) | def __init__(self,
    method process (line 275) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method _collect_target_results (line 309) | def _collect_target_results(self, target: str) -> Optional[list]:
    method compute_metrics (line 341) | def compute_metrics(self, results_fake: List):

FILE: mmagic/evaluation/metrics/mse.py
  class MSE (line 9) | class MSE(BaseSampleWiseMetric):
    method process_image (line 34) | def process_image(self, gt, pred, mask):

FILE: mmagic/evaluation/metrics/niqe.py
  class NIQE (line 19) | class NIQE(BaseSampleWiseMetric):
    method __init__ (line 57) | def __init__(self,
    method process_image (line 79) | def process_image(self, gt, pred, mask) -> None:
  function estimate_aggd_param (line 98) | def estimate_aggd_param(block):
  function compute_feature (line 128) | def compute_feature(block):
  function niqe_core (line 154) | def niqe_core(img,
  function niqe (line 242) | def niqe(img, crop_border, input_order='HWC', convert_to='y'):

FILE: mmagic/evaluation/metrics/ppl.py
  function slerp (line 16) | def slerp(a, b, percent):
  class PerceptualPathLength (line 40) | class PerceptualPathLength(GenerativeMetric):
    method __init__ (line 74) | def __init__(self,
    method process (line 98) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method _compute_distance (line 127) | def _compute_distance(self, images):
    method compute_metrics (line 155) | def compute_metrics(self, fake_results: list) -> dict:
    method get_metric_sampler (line 169) | def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,

FILE: mmagic/evaluation/metrics/precision_and_recall.py
  function compute_pr_distances (line 19) | def compute_pr_distances(row_features,
  class PrecisionAndRecall (line 49) | class PrecisionAndRecall(GenerativeMetric):
    method __init__ (line 104) | def __init__(self,
    method _load_vgg (line 131) | def _load_vgg(self, vgg16_script: Optional[str]) -> Tuple[nn.Module, b...
    method extract_features (line 154) | def extract_features(self, images: torch.Tensor) -> torch.Tensor:
    method compute_metrics (line 177) | def compute_metrics(self, results_fake) -> dict:
    method process (line 218) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method prepare (line 246) | def prepare(self, module: nn.Module, dataloader: DataLoader) -> None:

FILE: mmagic/evaluation/metrics/psnr.py
  class PSNR (line 12) | class PSNR(BaseSampleWiseMetric):
    method __init__ (line 43) | def __init__(self,
    method process_image (line 62) | def process_image(self, gt, pred, mask):
  function psnr (line 82) | def psnr(img1,

FILE: mmagic/evaluation/metrics/sad.py
  class SAD (line 15) | class SAD(BaseSampleWiseMetric):
    method __init__ (line 45) | def __init__(
    method prepare (line 53) | def prepare(self, module: nn.Module, dataloader: DataLoader):
    method process (line 59) | def process(self, data_batch: Sequence[dict],
    method compute_metrics (line 79) | def compute_metrics(self, results: List):

FILE: mmagic/evaluation/metrics/snr.py
  class SNR (line 12) | class SNR(BaseSampleWiseMetric):
    method __init__ (line 43) | def __init__(self,
    method process_image (line 62) | def process_image(self, gt, pred, mask):
  function snr (line 82) | def snr(gt,

FILE: mmagic/evaluation/metrics/ssim.py
  class SSIM (line 14) | class SSIM(BaseSampleWiseMetric):
    method __init__ (line 52) | def __init__(self,
    method process_image (line 71) | def process_image(self, gt, pred, mask):
  function _ssim (line 91) | def _ssim(img1, img2):
  function ssim (line 125) | def ssim(img1,

FILE: mmagic/evaluation/metrics/swd.py
  function sliced_wasserstein (line 13) | def sliced_wasserstein(distribution_a,
  function get_gaussian_kernel (line 54) | def get_gaussian_kernel():
  function get_pyramid_layer (line 67) | def get_pyramid_layer(image, gaussian_k, direction='down'):
  function gaussian_pyramid (line 93) | def gaussian_pyramid(original, n_pyramids, gaussian_k):
  function laplacian_pyramid (line 113) | def laplacian_pyramid(original, n_pyramids, gaussian_k):
  function get_descriptors_for_minibatch (line 140) | def get_descriptors_for_minibatch(minibatch, nhood_size, nhoods_per_image):
  function finalize_descriptors (line 165) | def finalize_descriptors(desc):
  class SlicedWassersteinDistance (line 187) | class SlicedWassersteinDistance(GenMetric):
    method __init__ (line 219) | def __init__(self,
    method process (line 248) | def process(self, data_batch: dict, data_samples: Sequence[dict]) -> N...
    method _collect_target_results (line 315) | def _collect_target_results(self, target: str) -> Optional[list]:
    method compute_metrics (line 345) | def compute_metrics(self, results_fake, results_real) -> dict:

FILE: mmagic/models/archs/__init__.py
  function register_diffusers_models (line 31) | def register_diffusers_models() -> List[str]:

FILE: mmagic/models/archs/all_gather_layer.py
  class AllGatherLayer (line 7) | class AllGatherLayer(autograd.Function):
    method forward (line 16) | def forward(ctx, x):
    method backward (line 24) | def backward(ctx, *grad_outputs):

FILE: mmagic/models/archs/aspp.py
  class ASPPPooling (line 12) | class ASPPPooling(nn.Sequential):
    method __init__ (line 28) | def __init__(self, in_channels: int, out_channels: int,
    method forward (line 41) | def forward(self, x: Tensor) -> Tensor:
  class ASPP (line 57) | class ASPP(nn.Module):
    method __init__ (line 85) | def __init__(self,
    method forward (line 138) | def forward(self, x: Tensor) -> Tensor:

FILE: mmagic/models/archs/attention_injection.py
  function torch_dfs (line 12) | def torch_dfs(model: torch.nn.Module):
  class AttentionInjection (line 19) | class AttentionInjection(nn.Module):
    method __init__ (line 26) | def __init__(self, module: nn.Module, injection_weight=5):
    method forward (line 122) | def forward(self,

FILE: mmagic/models/archs/downsample.py
  function pixel_unshuffle (line 5) | def pixel_unshuffle(x: Tensor, scale: int) -> Tensor:

FILE: mmagic/models/archs/ensemble.py
  class SpatialTemporalEnsemble (line 8) | class SpatialTemporalEnsemble(nn.Module):
    method __init__ (line 18) | def __init__(self, is_temporal_ensemble: Optional[bool] = False):
    method _transform (line 24) | def _transform(self, imgs: torch.Tensor, mode: str) -> torch.Tensor:
    method spatial_ensemble (line 57) | def spatial_ensemble(self, imgs: torch.Tensor,
    method forward (line 88) | def forward(self, imgs: torch.Tensor, model: nn.Module) -> torch.Tensor:

FILE: mmagic/models/archs/gated_conv_module.py
  class SimpleGatedConvModule (line 10) | class SimpleGatedConvModule(nn.Module):
    method __init__ (line 35) | def __init__(self,
    method forward (line 58) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: mmagic/models/archs/img_normalize.py
  class ImgNormalize (line 8) | class ImgNormalize(nn.Conv2d):
    method __init__ (line 20) | def __init__(self,

FILE: mmagic/models/archs/linear_module.py
  class LinearModule (line 10) | class LinearModule(nn.Module):
    method __init__ (line 29) | def __init__(self,
    method init_weights (line 68) | def init_weights(self) -> None:
    method forward (line 79) | def forward(self, x: Tensor, activate: Optional[bool] = True) -> Tensor:

FILE: mmagic/models/archs/lora.py
  class LoRALinear (line 10) | class LoRALinear(nn.Module):
    method __init__ (line 19) | def __init__(self, in_feat: int, out_feat: int, rank: int = 4):
    method forward (line 30) | def forward(self, x: Tensor) -> Tensor:
  class LoRAWrapper (line 39) | class LoRAWrapper(nn.Module):
    method __init__ (line 53) | def __init__(self,
    method add_lora (line 104) | def add_lora(self,
    method _set_value (line 145) | def _set_value(self,
    method set_scale (line 174) | def set_scale(self, scale: float, name: Optional[str] = None):
    method set_enable (line 183) | def set_enable(self, name: Optional[str] = None):
    method set_disable (line 191) | def set_disable(self, name: Optional[str] = None):
    method forward_lora_mapping (line 199) | def forward_lora_mapping(self, x: Tensor) -> Tensor:
    method forward (line 222) | def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
    method wrap_lora (line 235) | def wrap_lora(cls, module, rank=4, scale=1, names=None, state_dict=None):
  function replace_module (line 259) | def replace_module(parent_module: nn.Module, child_name: str,
  function get_submodule (line 265) | def get_submodule(module: nn.Module, key: str):
  function set_lora (line 273) | def set_lora(module: nn.Module,
  function set_only_lora_trainable (line 349) | def set_only_lora_trainable(module: nn.Module) -> nn.Module:
  function set_lora_enable (line 361) | def set_lora_enable(module: nn.Module) -> nn.Module:
  function set_lora_disable (line 371) | def set_lora_disable(module: nn.Module) -> nn.Module:

FILE: mmagic/models/archs/multi_layer_disc.py
  class MultiLayerDiscriminator (line 15) | class MultiLayerDiscriminator(nn.Module):
    method __init__ (line 49) | def __init__(self,
    method forward (line 134) | def forward(self, x: Tensor) -> Tensor:
    method init_weights (line 155) | def init_weights(self, pretrained: Optional[str] = None) -> None:

FILE: mmagic/models/archs/patch_disc.py
  class PatchDiscriminator (line 14) | class PatchDiscriminator(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method forward (line 110) | def forward(self, x: Tensor) -> Tensor:
    method init_weights (line 121) | def init_weights(self) -> None:

FILE: mmagic/models/archs/resnet.py
  class BasicBlock (line 14) | class BasicBlock(nn.Module):
    method __init__ (line 35) | def __init__(self,
    method norm1 (line 71) | def norm1(self) -> nn.Module:
    method norm2 (line 76) | def norm2(self) -> nn.Module:
    method forward (line 80) | def forward(self, x: Tensor) -> Tensor:
  class Bottleneck (line 110) | class Bottleneck(nn.Module):
    method __init__ (line 131) | def __init__(self,
    method norm1 (line 191) | def norm1(self) -> nn.Module:
    method norm2 (line 196) | def norm2(self) -> nn.Module:
    method norm3 (line 201) | def norm3(self) -> nn.Module:
    method forward (line 205) | def forward(self, x: Tensor) -> Tensor:
  class ResNet (line 228) | class ResNet(nn.Module):
    method __init__ (line 274) | def __init__(self,
    method _make_stem_layer (line 340) | def _make_stem_layer(self, in_channels: int, stem_channels: int) -> None:
    method norm1 (line 390) | def norm1(self) -> nn.Module:
    method _make_layer (line 394) | def _make_layer(self,
    method _nostride_dilate (line 435) | def _nostride_dilate(self, m: nn.Module, dilate: int) -> None:
    method init_weights (line 451) | def init_weights(self, pretrained: Optional[str] = None) -> None:
    method _freeze_stages (line 477) | def _freeze_stages(self) -> None:
    method forward (line 496) | def forward(self, x: Tensor) -> List[Tensor]:

FILE: mmagic/models/archs/separable_conv_module.py
  class DepthwiseSeparableConvModule (line 9) | class DepthwiseSeparableConvModule(nn.Module):
    method __init__ (line 44) | def __init__(self,
    method forward (line 89) | def forward(self, x: Tensor) -> Tensor:

FILE: mmagic/models/archs/simple_encoder_decoder.py
  class SimpleEncoderDecoder (line 11) | class SimpleEncoderDecoder(BaseModule):
    method __init__ (line 20) | def __init__(self,
    method forward (line 31) | def forward(self, *args, **kwargs) -> Tensor:

FILE: mmagic/models/archs/smpatch_disc.py
  class SoftMaskPatchDiscriminator (line 14) | class SoftMaskPatchDiscriminator(BaseModule):
    method __init__ (line 33) | def __init__(self,
    method forward (line 109) | def forward(self, x: Tensor) -> Tensor:
    method init_weights (line 120) | def init_weights(self) -> None:

FILE: mmagic/models/archs/sr_backbone.py
  class ResidualBlockNoBN (line 41) | class ResidualBlockNoBN(nn.Module):
    method __init__ (line 58) | def __init__(self, mid_channels: int = 64, res_scale: float = 1.0):
    method init_weights (line 71) | def init_weights(self) -> None:
    method forward (line 83) | def forward(self, x: Tensor) -> Tensor:

FILE: mmagic/models/archs/tokenizer.py
  class TokenizerWrapper (line 14) | class TokenizerWrapper:
    method __init__ (line 34) | def __init__(self,
    method __getattr__ (line 62) | def __getattr__(self, name: str) -> Any:
    method try_adding_tokens (line 77) | def try_adding_tokens(self, tokens: Union[str, List[str]], *args,
    method get_token_info (line 90) | def get_token_info(self, token: str) -> dict:
    method add_placeholder_token (line 105) | def add_placeholder_token(self,
    method replace_placeholder_tokens_in_text (line 137) | def replace_placeholder_tokens_in_text(self,
    method replace_text_with_placeholder_tokens (line 173) | def replace_text_with_placeholder_tokens(self, text: Union[str, List[s...
    method __call__ (line 197) | def __call__(self,
    method encode (line 220) | def encode(self, text: Union[str, List[str]], *args, **kwargs):
    method decode (line 230) | def decode(self,
    method __repr__ (line 252) | def __repr__(self):

FILE: mmagic/models/archs/upsample.py
  class PixelShufflePack (line 9) | class PixelShufflePack(nn.Module):
    method __init__ (line 22) | def __init__(self, in_channels: int, out_channels: int, scale_factor: ...
    method init_weights (line 36) | def init_weights(self) -> None:
    method forward (line 40) | def forward(self, x: Tensor) -> Tensor:

FILE: mmagic/models/archs/vgg.py
  class VGG16 (line 14) | class VGG16(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method _make_layer (line 59) | def _make_layer(self, inplanes: int, planes: int,
    method init_weights (line 73) | def init_weights(self) -> None:
    method forward (line 85) | def forward(self, x: Tensor) -> Dict[str, Tensor]:

FILE: mmagic/models/archs/wrapper.py
  class DiffusersWrapper (line 20) | class DiffusersWrapper(BaseModule):
    method __init__ (line 72) | def __init__(self,
    method init_weights (line 112) | def init_weights(self):
    method __getattr__ (line 126) | def __getattr__(self, name: str) -> Any:
    method __repr__ (line 158) | def __repr__(self):
    method forward (line 170) | def forward(self, *args, **kwargs) -> Any:
    method to (line 181) | def to(

FILE: mmagic/models/base_models/average_model.py
  class ExponentialMovingAverage (line 19) | class ExponentialMovingAverage(BaseAveragedModel):
    method __init__ (line 43) | def __init__(self,
    method avg_func (line 60) | def avg_func(self, averaged_param: Tensor, source_param: Tensor,
    method _load_from_state_dict (line 74) | def _load_from_state_dict(self, state_dict: dict, prefix: str,
    method sync_buffers (line 131) | def sync_buffers(self, model: nn.Module) -> None:
    method sync_parameters (line 148) | def sync_parameters(self, model: nn.Module) -> None:
  class RampUpEMA (line 163) | class RampUpEMA(BaseAveragedModel):
    method __init__ (line 183) | def __init__(self,
    method rampup (line 202) | def rampup(steps, ema_kimg=10, ema_rampup=0.05, batch_size=4, eps=1e-8):
    method avg_func (line 228) | def avg_func(self, averaged_param: Tensor, source_param: Tensor,
    method _load_from_state_dict (line 246) | def _load_from_state_dict(self, state_dict: dict, prefix: str,
    method sync_buffers (line 303) | def sync_buffers(self, model: nn.Module) -> None:
    method sync_parameters (line 320) | def sync_parameters(self, model: nn.Module) -> None:

FILE: mmagic/models/base_models/base_conditional_gan.py
  class BaseConditionalGAN (line 19) | class BaseConditionalGAN(BaseGAN):
    method __init__ (line 40) | def __init__(self,
    method label_fn (line 57) | def label_fn(self, label: LabelVar = None, num_batches: int = 1) -> Te...
    method data_sample_to_label (line 88) | def data_sample_to_label(self, data_sample: DataSample
    method _get_valid_num_classes (line 107) | def _get_valid_num_classes(num_classes: Optional[int],
    method forward (line 153) | def forward(self,
    method train_generator (line 234) | def train_generator(self, inputs: dict, data_samples: List[DataSample],
    method train_discriminator (line 271) | def train_discriminator(self, inputs: dict, data_samples: List[DataSam...

FILE: mmagic/models/base_models/base_edit_model.py
  class BaseEditModel (line 12) | class BaseEditModel(BaseModel):
    method __init__ (line 35) | def __init__(self,
    method forward (line 54) | def forward(self,
    method convert_to_datasample (line 121) | def convert_to_datasample(self, predictions: DataSample,
    method forward_tensor (line 151) | def forward_tensor(self,
    method forward_inference (line 171) | def forward_inference(self,
    method forward_train (line 196) | def forward_train(self,

FILE: mmagic/models/base_models/base_gan.py
  class BaseGAN (line 22) | class BaseGAN(BaseModel, metaclass=ABCMeta):
    method __init__ (line 39) | def __init__(self,
    method gather_log_vars (line 94) | def gather_log_vars(log_vars_list: List[Dict[str, Tensor]]
    method _init_loss (line 118) | def _init_loss(self, loss_config: Optional[Dict] = None) -> None:
    method noise_fn (line 202) | def noise_fn(self, noise: NoiseVar = None, num_batches: int = 1):
    method generator_steps (line 232) | def generator_steps(self) -> int:
    method discriminator_steps (line 238) | def discriminator_steps(self) -> int:
    method device (line 244) | def device(self) -> torch.device:
    method with_ema_gen (line 253) | def with_ema_gen(self) -> bool:
    method _init_ema_model (line 262) | def _init_ema_model(self, ema_config: dict):
    method _get_valid_model (line 277) | def _get_valid_model(self, batch_inputs: ForwardInputs) -> str:
    method forward (line 315) | def forward(self,
    method val_step (line 412) | def val_step(self, data: dict) -> SampleList:
    method test_step (line 430) | def test_step(self, data: dict) -> SampleList:
    method train_step (line 444) | def train_step(self, data: dict,
    method _get_gen_loss (line 520) | def _get_gen_loss(self, out_dict):
    method _get_disc_loss (line 543) | def _get_disc_loss(self, out_dict):
    method train_generator (line 572) | def train_generator(self, inputs: dict, data_samples: List[DataSample],
    method train_discriminator (line 605) | def train_discriminator(self, inputs: dict, data_samples: List[DataSam...

FILE: mmagic/models/base_models/base_mattor.py
  function _pad (line 18) | def _pad(batch_image: torch.Tensor,
  function _interpolate (line 38) | def _interpolate(batch_image: torch.Tensor,
  class BaseMattor (line 56) | class BaseMattor(BaseModel, metaclass=ABCMeta):
    method __init__ (line 87) | def __init__(self,
    method resize_inputs (line 105) | def resize_inputs(self, batch_inputs: torch.Tensor) -> torch.Tensor:
    method restore_size (line 129) | def restore_size(self, pred_alpha: torch.Tensor,
    method postprocess (line 159) | def postprocess(
    method forward (line 212) | def forward(self,
    method convert_to_datasample (line 256) | def convert_to_datasample(self, predictions: List[DataSample],

FILE: mmagic/models/base_models/base_translation_model.py
  class BaseTranslationModel (line 14) | class BaseTranslationModel(BaseModel, metaclass=ABCMeta):
    method __init__ (line 47) | def __init__(self,
    method init_weights (line 87) | def init_weights(self):
    method get_module (line 125) | def get_module(self, module):
    method forward (line 141) | def forward(self, img, test_mode=False, **kwargs):
    method forward_train (line 154) | def forward_train(self, img, target_domain, **kwargs):
    method forward_test (line 169) | def forward_test(self, img, target_domain, **kwargs):
    method is_domain_reachable (line 184) | def is_domain_reachable(self, domain):
    method get_other_domains (line 188) | def get_other_domains(self, domain):
    method _get_target_generator (line 192) | def _get_target_generator(self, domain):
    method _get_target_discriminator (line 201) | def _get_target_discriminator(self, domain):
    method translation (line 210) | def translation(self, image, target_domain=None, **kwargs):

FILE: mmagic/models/base_models/basic_interpolator.py
  class BasicInterpolator (line 14) | class BasicInterpolator(BaseEditModel):
    method __init__ (line 39) | def __init__(self,
    method split_frames (line 62) | def split_frames(self, input_tensors: torch.Tensor) -> torch.Tensor:
    method merge_frames (line 85) | def merge_frames(input_tensors: torch.Tensor,

FILE: mmagic/models/base_models/one_stage.py
  class OneStageInpaintor (line 19) | class OneStageInpaintor(BaseModel):
    method __init__ (line 51) | def __init__(self,
    method forward (line 107) | def forward(self,
    method train_step (line 156) | def train_step(self, data: List[dict],
    method forward_train (line 256) | def forward_train(self, *args, **kwargs) -> None:
    method forward_train_d (line 265) | def forward_train_d(self, data_batch: torch.Tensor, is_real: bool,
    method generator_loss (line 298) | def generator_loss(self, fake_res: torch.Tensor, fake_img: torch.Tensor,
    method forward_tensor (line 364) | def forward_tensor(self, inputs: torch.Tensor, data_samples: SampleList
    method forward_test (line 385) | def forward_test(self, inputs: torch.Tensor,
    method convert_to_datasample (line 409) | def convert_to_datasample(self, predictions: DataSample,
    method forward_dummy (line 437) | def forward_dummy(self, x: torch.Tensor) -> torch.Tensor:

FILE: mmagic/models/base_models/two_stage.py
  class TwoStageInpaintor (line 15) | class TwoStageInpaintor(OneStageInpaintor):
    method __init__ (line 55) | def __init__(
    method forward_tensor (line 100) | def forward_tensor(self, inputs: torch.Tensor, data_samples: SampleList
    method two_stage_loss (line 123) | def two_stage_loss(self, stage1_data: dict, stage2_data: dict,
    method calculate_loss_with_type (line 172) | def calculate_loss_with_type(self,
    method train_step (line 222) | def train_step(self, data: List[dict],

FILE: mmagic/models/data_preprocessors/data_preprocessor.py
  class DataPreprocessor (line 21) | class DataPreprocessor(ImgDataPreprocessor):
    method __init__ (line 74) | def __init__(self,
    method cast_data (line 128) | def cast_data(self, data: CastData) -> CastData:
    method _parse_channel_index (line 142) | def _parse_channel_index(inputs) -> int:
    method _parse_channel_order (line 161) | def _parse_channel_order(self,
    method _parse_batch_channel_order (line 213) | def _parse_batch_channel_order(self, key: str, inputs: Sequence,
    method _update_metainfo (line 233) | def _update_metainfo(self,
    method _do_conversion (line 284) | def _do_conversion(self,
    method _do_norm (line 329) | def _do_norm(self,
    method _preprocess_image_tensor (line 358) | def _preprocess_image_tensor(self,
    method _preprocess_image_list (line 407) | def _preprocess_image_list(self,
    method _preprocess_dict_inputs (line 479) | def _preprocess_dict_inputs(self,
    method _preprocess_data_sample (line 549) | def _preprocess_data_sample(self, data_samples: SampleList,
    method forward (line 606) | def forward(self, data: dict, training: bool = False) -> dict:
    method destruct (line 656) | def destruct(self,
    method _destruct_norm_and_conversion (line 701) | def _destruct_norm_and_conversion(self, batch_tensor: Tensor,
    method _destruct_padding (line 746) | def _destruct_padding(self,

FILE: mmagic/models/data_preprocessors/mattor_preprocessor.py
  class MattorPreprocessor (line 21) | class MattorPreprocessor(DataPreprocessor):
    method __init__ (line 50) | def __init__(self,
    method _proc_batch_trimap (line 68) | def _proc_batch_trimap(self, batch_trimaps: torch.Tensor):
    method _preprocess_data_sample (line 80) | def _preprocess_data_sample(self, data_samples: SampleList,
    method forward (line 137) | def forward(self,

FILE: mmagic/models/diffusion_schedulers/__init__.py
  class SchedulerWrapper (line 10) | class SchedulerWrapper:
    method __init__ (line 43) | def __init__(self,
    method __getattr__ (line 62) | def __getattr__(self, name: str) -> Any:
    method __repr__ (line 80) | def __repr__(self):
  function register_diffusers_schedulers (line 93) | def register_diffusers_schedulers() -> List[str]:

FILE: mmagic/models/diffusion_schedulers/ddim_scheduler.py
  class EditDDIMScheduler (line 12) | class EditDDIMScheduler:
    method __init__ (line 30) | def __init__(
    method set_timesteps (line 87) | def set_timesteps(self, num_inference_steps, offset=0):
    method scale_model_input (line 96) | def scale_model_input(self,
    method _get_variance (line 112) | def _get_variance(self, timestep, prev_timestep):
    method step (line 124) | def step(
    method add_noise (line 248) | def add_noise(self, original_samples, noise, timesteps):
    method __len__ (line 258) | def __len__(self):

FILE: mmagic/models/diffusion_schedulers/ddpm_scheduler.py
  class EditDDPMScheduler (line 12) | class EditDDPMScheduler:
    method __init__ (line 14) | def __init__(self,
    method set_timesteps (line 80) | def set_timesteps(self, num_inference_steps):
    method _get_variance (line 90) | def _get_variance(self, t, predicted_variance=None, variance_type=None):
    method step (line 132) | def step(self,
    method add_noise (line 214) | def add_noise(self, original_samples, noise, timesteps):
    method training_loss (line 224) | def training_loss(self, model, x_0, t):
    method sample_timestep (line 229) | def sample_timestep(self):
    method __len__ (line 232) | def __len__(self):

FILE: mmagic/models/editors/animatediff/animatediff.py
  class AnimateDiff (line 31) | class AnimateDiff(BaseModel):
    method __init__ (line 76) | def __init__(
    method set_xformers (line 143) | def set_xformers(self, module: Optional[nn.Module] = None) -> nn.Module:
    method set_tomesd (line 155) | def set_tomesd(self) -> nn.Module:
    method device (line 165) | def device(self):
    method init_motion_module (line 169) | def init_motion_module(self, motion_module_cfg):
    method init_dreambooth_lora (line 181) | def init_dreambooth_lora(self, dream_booth_lora_cfg):
    method _encode_prompt (line 204) | def _encode_prompt(self, prompt, device, num_videos_per_prompt,
    method decode_latents (line 308) | def decode_latents(self, latents):
    method prepare_extra_step_kwargs (line 326) | def prepare_extra_step_kwargs(self, generator, eta):
    method check_inputs (line 350) | def check_inputs(self, prompt, height, width):
    method convert_lora (line 373) | def convert_lora(self,
    method prepare_latents (line 464) | def prepare_latents(self,
    method prepare_model (line 515) | def prepare_model(self):
    method set_lora (line 535) | def set_lora(self):
    method val_step (line 541) | def val_step(self, data: dict) -> SampleList:
    method test_step (line 571) | def test_step(self, data: dict) -> SampleList:
    method infer (line 600) | def infer(self,
    method forward (line 768) | def forward(self,

FILE: mmagic/models/editors/animatediff/animatediff_utils.py
  function shave_segments (line 18) | def shave_segments(path, n_shave_prefix_segments=1):
  function renew_resnet_paths (line 29) | def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
  function renew_vae_resnet_paths (line 51) | def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
  function renew_attention_paths (line 67) | def renew_attention_paths(old_list, n_shave_prefix_segments=0):
  function renew_vae_attention_paths (line 79) | def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
  function assign_to_checkpoint (line 109) | def assign_to_checkpoint(paths,
  function conv_attn_to_linear (line 168) | def conv_attn_to_linear(checkpoint):
  function create_unet_diffusers_config (line 180) | def create_unet_diffusers_config(original_config,
  function create_vae_diffusers_config (line 269) | def create_vae_diffusers_config(original_config, image_size: int):
  function convert_ldm_unet_checkpoint (line 293) | def convert_ldm_unet_checkpoint(checkpoint,
  function convert_ldm_vae_checkpoint (line 611) | def convert_ldm_vae_checkpoint(checkpoint, config):
  function convert_ldm_clip_checkpoint (line 815) | def convert_ldm_clip_checkpoint(checkpoint):
  function convert_paint_by_example_checkpoint (line 867) | def convert_paint_by_example_checkpoint(checkpoint):
  function convert_open_clip_checkpoint (line 934) | def convert_open_clip_checkpoint(checkpoint):
  function stable_unclip_image_encoder (line 994) | def stable_unclip_image_encoder(original_config):
  function stable_unclip_image_noising_components (line 1031) | def stable_unclip_image_noising_components(
  function save_videos_grid (line 1083) | def save_videos_grid(videos: torch.Tensor,

FILE: mmagic/models/editors/animatediff/attention_3d.py
  class Transformer3DModelOutput (line 22) | class Transformer3DModelOutput(BaseOutput):
  class Transformer3DModel (line 38) | class Transformer3DModel(ModelMixin, ConfigMixin):
    method __init__ (line 102) | def __init__(
    method forward (line 165) | def forward(self,
  class BasicTransformerBlock (line 252) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 278) | def __init__(
    method forward (line 371) | def forward(self,
  class CrossAttention (line 422) | class CrossAttention(nn.Module):
    method __init__ (line 442) | def __init__(
    method reshape_heads_to_batch_dim (line 497) | def reshape_heads_to_batch_dim(self, tensor):
    method reshape_batch_dim_to_heads (line 506) | def reshape_batch_dim_to_heads(self, tensor):
    method set_attention_slice (line 515) | def set_attention_slice(self, slice_size):
    method forward (line 522) | def forward(self,
    method _attention (line 606) | def _attention(self, query, key, value, attention_mask=None):
    method _sliced_attention (line 642) | def _sliced_attention(self, query, key, value, sequence_length, dim,
    method _memory_efficient_attention_xformers (line 693) | def _memory_efficient_attention_xformers(self, query, key, value,

FILE: mmagic/models/editors/animatediff/motion_module.py
  function zero_module (line 19) | def zero_module(module):
  class TemporalTransformer3DModelOutput (line 27) | class TemporalTransformer3DModelOutput(BaseOutput):
  function get_motion_module (line 39) | def get_motion_module(in_channels, motion_module_type: str,
  class VanillaTemporalModule (line 51) | class VanillaTemporalModule(nn.Module):
    method __init__ (line 54) | def __init__(
    method forward (line 84) | def forward(self,
  class TemporalTransformer3DModel (line 100) | class TemporalTransformer3DModel(nn.Module):
    method __init__ (line 103) | def __init__(
    method forward (line 153) | def forward(self,
  class TemporalTransformerBlock (line 194) | class TemporalTransformerBlock(nn.Module):
    method __init__ (line 197) | def __init__(
    method forward (line 246) | def forward(self,
  class PositionalEncoding (line 268) | class PositionalEncoding(nn.Module):
    method __init__ (line 271) | def __init__(self, d_model, dropout=0., max_len=24):
    method forward (line 282) | def forward(self, x):
  class VersatileAttention (line 288) | class VersatileAttention(CrossAttention):
    method __init__ (line 291) | def __init__(self,
    method extra_repr (line 313) | def extra_repr(self):
    method reshape_heads_to_batch_dim (line 318) | def reshape_heads_to_batch_dim(self, tensor):
    method reshape_batch_dim_to_heads (line 328) | def reshape_batch_dim_to_heads(self, tensor):
    method _memory_efficient_attention_xformers (line 338) | def _memory_efficient_attention_xformers(self, query, key, value,
    method forward (line 350) | def forward(self,
  class FeedForward (line 432) | class FeedForward(nn.Module):
    method __init__ (line 448) | def __init__(
    method forward (line 475) | def forward(self, hidden_states):
  class GELU (line 481) | class GELU(nn.Module):
    method __init__ (line 486) | def __init__(self, dim_in: int, dim_out: int):
    method gelu (line 490) | def gelu(self, gate):
    method forward (line 496) | def forward(self, hidden_states):

FILE: mmagic/models/editors/animatediff/resnet_3d.py
  class InflatedConv3d (line 11) | class InflatedConv3d(nn.Conv2d):
    method forward (line 14) | def forward(self, x):
  class InflatedGroupNorm (line 25) | class InflatedGroupNorm(nn.GroupNorm):
    method forward (line 27) | def forward(self, x):
  class Upsample3D (line 37) | class Upsample3D(nn.Module):
    method __init__ (line 47) | def __init__(self,
    method forward (line 66) | def forward(self, hidden_states, output_size=None):
  class Downsample3D (line 107) | class Downsample3D(nn.Module):
    method __init__ (line 117) | def __init__(self,
    method forward (line 141) | def forward(self, hidden_states):
  class ResnetBlock3D (line 153) | class ResnetBlock3D(nn.Module):
    method __init__ (line 172) | def __init__(
    method forward (line 265) | def forward(self, input_tensor, temb):
  class Mish (line 301) | class Mish(torch.nn.Module):
    method forward (line 304) | def forward(self, hidden_states):

FILE: mmagic/models/editors/animatediff/unet_3d.py
  class UNet3DConditionOutput (line 32) | class UNet3DConditionOutput(BaseOutput):
  class UNet3DConditionMotionModel (line 38) | class UNet3DConditionMotionModel(ModelMixin, ConfigMixin):
    method __init__ (line 43) | def __init__(
    method init_weights (line 269) | def init_weights(self, subfolder=None, from_pretrained=None):
    method set_attention_slice (line 325) | def set_attention_slice(self, slice_size):
    method _set_gradient_checkpointing (line 403) | def _set_gradient_checkpointing(self, module, value=False):
    method forward (line 409) | def forward(
    method from_pretrained_2d (line 578) | def from_pretrained_2d(cls,

FILE: mmagic/models/editors/animatediff/unet_block.py
  function get_down_block (line 13) | def get_down_block(
  function get_up_block (line 90) | def get_up_block(
  class UNetMidBlock3DCrossAttn (line 167) | class UNetMidBlock3DCrossAttn(nn.Module):
    method __init__ (line 170) | def __init__(
    method forward (line 262) | def forward(self,
  class CrossAttnDownBlock3D (line 286) | class CrossAttnDownBlock3D(nn.Module):
    method __init__ (line 289) | def __init__(
    method forward (line 383) | def forward(self,
  class DownBlock3D (line 443) | class DownBlock3D(nn.Module):
    method __init__ (line 446) | def __init__(
    method forward (line 510) | def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
  class CrossAttnUpBlock3D (line 553) | class CrossAttnUpBlock3D(nn.Module):
    method __init__ (line 556) | def __init__(
    method forward (line 650) | def forward(
  class UpBlock3D (line 714) | class UpBlock3D(nn.Module):
    method __init__ (line 717) | def __init__(
    method forward (line 781) | def forward(

FILE: mmagic/models/editors/aotgan/aot_decoder.py
  class AOTDecoder (line 11) | class AOTDecoder(BaseModule):
    method __init__ (line 28) | def __init__(self,
    method forward (line 60) | def forward(self, x):

FILE: mmagic/models/editors/aotgan/aot_encoder.py
  class AOTEncoder (line 10) | class AOTEncoder(BaseModule):
    method __init__ (line 27) | def __init__(self,
    method forward (line 56) | def forward(self, x):

FILE: mmagic/models/editors/aotgan/aot_encoder_decoder.py
  class AOTEncoderDecoder (line 8) | class AOTEncoderDecoder(GLEncoderDecoder):
    method __init__ (line 22) | def __init__(self,

FILE: mmagic/models/editors/aotgan/aot_inpaintor.py
  class AOTInpaintor (line 12) | class AOTInpaintor(OneStageInpaintor):
    method forward_train_d (line 19) | def forward_train_d(self, data_batch, is_real, is_disc, mask):
    method generator_loss (line 53) | def generator_loss(self, fake_res, fake_img, gt, mask, masked_img):
    method forward_tensor (line 101) | def forward_tensor(self, inputs, data_samples):
    method train_step (line 123) | def train_step(self, data: List[dict], optim_wrapper):

FILE: mmagic/models/editors/aotgan/aot_neck.py
  class AOTBlockNeck (line 11) | class AOTBlockNeck(BaseModule):
    method __init__ (line 28) | def __init__(self,
    method forward (line 44) | def forward(self, x):
  class AOTBlock (line 49) | class AOTBlock(BaseModule):
    method __init__ (line 72) | def __init__(self,
    method normalize (line 98) | def normalize(self, x):
    method forward (line 105) | def forward(self, x):

FILE: mmagic/models/editors/arcface/arcface_modules.py
  class Flatten (line 15) | class Flatten(Module):
    method forward (line 18) | def forward(self, input):
  function l2_norm (line 22) | def l2_norm(input, axis=1):
  class Bottleneck (line 38) | class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
  function get_block (line 42) | def get_block(in_channel, depth, num_units, stride=2):
  function get_blocks (line 58) | def get_blocks(num_layers):
  class SEModule (line 98) | class SEModule(Module):
    method __init__ (line 106) | def __init__(self, channels, reduction):
    method forward (line 124) | def forward(self, x):
  class bottleneck_IR (line 135) | class bottleneck_IR(Module):
    method __init__ (line 144) | def __init__(self, in_channel, depth, stride):
    method forward (line 165) | def forward(self, x):
  class bottleneck_IR_SE (line 172) | class bottleneck_IR_SE(Module):
    method __init__ (line 181) | def __init__(self, in_channel, depth, stride):
    method forward (line 195) | def forward(self, x):

FILE: mmagic/models/editors/arcface/id_loss.py
  class IDLossModel (line 11) | class IDLossModel(nn.Module):
    method __init__ (line 21) | def __init__(self, ir_se50_weights=None):
    method extract_feats (line 35) | def extract_feats(self, x):
    method forward (line 51) | def forward(self, pred=None, gt=None):

FILE: mmagic/models/editors/arcface/model_irse.py
  class Backbone (line 15) | class Backbone(Module):
    method __init__ (line 31) | def __init__(self,
    method forward (line 67) | def forward(self, x):
  function IR_50 (line 75) | def IR_50(input_size):
  function IR_101 (line 82) | def IR_101(input_size):
  function IR_152 (line 89) | def IR_152(input_size):
  function IR_SE_50 (line 96) | def IR_SE_50(input_size):
  function IR_SE_101 (line 103) | def IR_SE_101(input_size):
  function IR_SE_152 (line 110) | def IR_SE_152(input_size):

FILE: mmagic/models/editors/basicvsr/basicvsr.py
  class BasicVSR (line 10) | class BasicVSR(BaseEditModel):
    method __init__ (line 31) | def __init__(self,
    method check_if_mirror_extended (line 67) | def check_if_mirror_extended(self, lrs):
    method forward_train (line 85) | def forward_train(self, inputs, data_samples=None, **kwargs):
    method forward_inference (line 117) | def forward_inference(self, inputs, data_samples=None, **kwargs):

FILE: mmagic/models/editors/basicvsr/basicvsr_net.py
  class BasicVSRNet (line 18) | class BasicVSRNet(BaseModule):
    method __init__ (line 36) | def __init__(self, mid_channels=64, num_blocks=30, spynet_pretrained=N...
    method check_if_mirror_extended (line 68) | def check_if_mirror_extended(self, lrs):
    method compute_flow (line 84) | def compute_flow(self, lrs):
    method forward (line 113) | def forward(self, lrs):
  class ResidualBlocksWithInputConv (line 179) | class ResidualBlocksWithInputConv(BaseModule):
    method __init__ (line 189) | def __init__(self, in_channels, out_channels=64, num_blocks=30):
    method forward (line 205) | def forward(self, feat):
  class SPyNet (line 217) | class SPyNet(BaseModule):
    method __init__ (line 231) | def __init__(self, pretrained):
    method compute_flow (line 251) | def compute_flow(self, ref, supp):
    method forward (line 311) | def forward(self, ref, supp):
  class SPyNetBasicModule (line 350) | class SPyNetBasicModule(BaseModule):
    method __init__ (line 357) | def __init__(self):
    method forward (line 402) | def forward(self, tensor_input):

FILE: mmagic/models/editors/basicvsr_plusplus_net/basicvsr_plusplus_net.py
  class BasicVSRPlusPlusNet (line 16) | class BasicVSRPlusPlusNet(BaseModule):
    method __init__ (line 44) | def __init__(self,
    method check_if_mirror_extended (line 101) | def check_if_mirror_extended(self, lqs):
    method compute_flow (line 119) | def compute_flow(self, lqs):
    method propagate (line 153) | def propagate(self, feats, flows, module_name):
    method upsample (line 242) | def upsample(self, lqs, feats):
    method forward (line 285) | def forward(self, lqs):
  class SecondOrderDeformableAlignment (line 356) | class SecondOrderDeformableAlignment(ModulatedDeformConv2d):
    method __init__ (line 374) | def __init__(self, *args, **kwargs):
    method init_offset (line 391) | def init_offset(self):
    method forward (line 395) | def forward(self, x, extra_feat, flow_1, flow_2):

FILE: mmagic/models/editors/biggan/biggan.py
  class BigGAN (line 20) | class BigGAN(BaseConditionalGAN):
    method __init__ (line 47) | def __init__(self,
    method disc_loss (line 60) | def disc_loss(self, disc_pred_fake: Tensor,
    method gen_loss (line 87) | def gen_loss(self, disc_pred_fake):
    method train_discriminator (line 107) | def train_discriminator(self, inputs: dict, data_samples: DataSample,
    method train_generator (line 141) | def train_generator(self, inputs: dict, data_samples: DataSample,

FILE: mmagic/models/editors/biggan/biggan_deep_discriminator.py
  class BigGANDeepDiscriminator (line 19) | class BigGANDeepDiscriminator(nn.Module):
    method __init__ (line 87) | def __init__(self,
    method _get_default_arch_cfg (line 179) | def _get_default_arch_cfg(self, input_scale, base_channels):
    method forward (line 228) | def forward(self, x, label=None):
    method init_weights (line 251) | def init_weights(self, pretrained=None, init_type='ortho'):

FILE: mmagic/models/editors/biggan/biggan_deep_generator.py
  class BigGANDeepGenerator (line 20) | class BigGANDeepGenerator(nn.Module):
    method __init__ (line 108) | def __init__(self,
    method _get_default_arch_cfg (line 248) | def _get_default_arch_cfg(self, output_scale, base_channels):
    method forward (line 298) | def forward(self,
    method init_weights (line 423) | def init_weights(self, pretrained=None, init_type='ortho'):

FILE: mmagic/models/editors/biggan/biggan_discriminator.py
  class BigGANDiscriminator (line 16) | class BigGANDiscriminator(BaseModule):
    method __init__ (line 76) | def __init__(self,
    method _get_default_arch_cfg (line 150) | def _get_default_arch_cfg(self, input_scale, in_channels, base_channels):
    method forward (line 202) | def forward(self, x, label=None):
    method init_weights (line 225) | def init_weights(self):

FILE: mmagic/models/editors/biggan/biggan_generator.py
  class BigGANGenerator (line 17) | class BigGANGenerator(BaseModule):
    method __init__ (line 96) | def __init__(self,
    method _get_default_arch_cfg (line 226) | def _get_default_arch_cfg(self, output_scale, base_channels):
    method forward (line 276) | def forward(self,
    method init_weights (line 403) | def init_weights(self):

FILE: mmagic/models/editors/biggan/biggan_modules.py
  class SNConvModule (line 17) | class SNConvModule(ConvModule):
    method __init__ (line 31) | def __init__(self,
  class BigGANGenResBlock (line 64) | class BigGANGenResBlock(nn.Module):
    method __init__ (line 92) | def __init__(self,
    method forward (line 161) | def forward(self, x, y):
  class BigGANConditionBN (line 187) | class BigGANConditionBN(nn.Module):
    method __init__ (line 214) | def __init__(self,
    method forward (line 268) | def forward(self, x, y):
  class SelfAttentionBlock (line 291) | class SelfAttentionBlock(nn.Module):
    method __init__ (line 308) | def __init__(self,
    method forward (line 355) | def forward(self, x):
  class BigGANDiscResBlock (line 382) | class BigGANDiscResBlock(nn.Module):
    method __init__ (line 406) | def __init__(self,
    method forward_sc (line 453) | def forward_sc(self, x):
    method forward (line 474) | def forward(self, x):
  class BigGANDeepGenResBlock (line 497) | class BigGANDeepGenResBlock(nn.Module):
    method __init__ (line 529) | def __init__(self,
    method forward (line 634) | def forward(self, x, y):
  class BigGANDeepDiscResBlock (line 671) | class BigGANDeepDiscResBlock(nn.Module):
    method __init__ (line 695) | def __init__(self,
    method forward_sc (line 769) | def forward_sc(self, x):
    method forward (line 785) | def forward(self, x):

FILE: mmagic/models/editors/biggan/biggan_snmodule.py
  function proj (line 13) | def proj(x, y):
  function gram_schmidt (line 26) | def gram_schmidt(x, ys):
  function power_iteration (line 43) | def power_iteration(weight, u_list, update=True, eps=1e-12):
  class SpectralNorm (line 76) | class SpectralNorm(object):
    method __init__ (line 90) | def __init__(self,
    method u (line 106) | def u(self):
    method sv (line 111) | def sv(self):
    method sn_weight (line 115) | def sn_weight(self):
  class SNConv2d (line 132) | class SNConv2d(nn.Conv2d, SpectralNorm):
    method __init__ (line 154) | def __init__(self,
    method forward (line 170) | def forward(self, x):
  class SNLinear (line 176) | class SNLinear(nn.Linear, SpectralNorm):
    method __init__ (line 190) | def __init__(self,
    method forward (line 200) | def forward(self, x):
  class SNEmbedding (line 207) | class SNEmbedding(nn.Embedding, SpectralNorm):
    method __init__ (line 238) | def __init__(self,
    method forward (line 256) | def forward(self, x):

FILE: mmagic/models/editors/cain/cain.py
  class CAIN (line 7) | class CAIN(BasicInterpolator):
    method forward_inference (line 32) | def forward_inference(self, inputs, data_samples=None):

FILE: mmagic/models/editors/cain/cain_net.py
  class CAINNet (line 13) | class CAINNet(BaseModule):
    method __init__ (line 33) | def __init__(self,
    method forward (line 65) | def forward(self, imgs, padding_flag=False):
  function get_padding_functions (line 107) | def get_padding_functions(x, padding=7):
  class ConvNormWithReflectionPad (line 150) | class ConvNormWithReflectionPad(BaseModule):
    method __init__ (line 162) | def __init__(self, in_channels, out_channels, kernel_size, norm=None):
    method forward (line 179) | def forward(self, x):
  class ChannelAttentionLayer (line 196) | class ChannelAttentionLayer(BaseModule):
    method __init__ (line 204) | def __init__(self, mid_channels, reduction=16):
    method forward (line 224) | def forward(self, x):
  class ResidualChannelAttention (line 239) | class ResidualChannelAttention(BaseModule):
    method __init__ (line 251) | def __init__(self,
    method forward (line 266) | def forward(self, x):
  class ResidualGroup (line 280) | class ResidualGroup(BaseModule):
    method __init__ (line 295) | def __init__(self,
    method forward (line 316) | def forward(self, x):

FILE: mmagic/models/editors/controlnet/controlnet.py
  class ControlStableDiffusion (line 28) | class ControlStableDiffusion(StableDiffusion):
    method __init__ (line 60) | def __init__(self,
    method init_weights (line 99) | def init_weights(self):
    method train_step (line 187) | def train_step(self, data: dict,
    method val_step (line 264) | def val_step(self, data: dict) -> SampleList:
    method test_step (line 295) | def test_step(self, data: dict) -> SampleList:
    method prepare_control (line 328) | def prepare_control(image: Tuple[Image.Image, List[Image.Image], Tensor,
    method train (line 381) | def train(self, mode: bool = True):
    method infer (line 403) | def infer(self,
    method forward (line 593) | def forward(self, *args, **kwargs):
  class ControlStableDiffusionImg2Img (line 600) | class ControlStableDiffusionImg2Img(ControlStableDiffusion):
    method _default_height_width (line 602) | def _default_height_width(self, height, width, image):
    method get_timesteps (line 624) | def get_timesteps(self, num_inference_steps, strength, device):
    method prepare_latents (line 634) | def prepare_latents(self,
    method prepare_latent_image (line 690) | def prepare_latent_image(self, image, dtype):
    method infer (line 714) | def infer(

FILE: mmagic/models/editors/controlnet/controlnet_utils.py
  function change_base_model (line 10) | def change_base_model(controlnet: nn.Module,

FILE: mmagic/models/editors/cyclegan/cyclegan.py
  class CycleGAN (line 16) | class CycleGAN(BaseTranslationModel):
    method __init__ (line 24) | def __init__(self,
    method forward_test (line 38) | def forward_test(self, img, target_domain, **kwargs):
    method _get_disc_loss (line 56) | def _get_disc_loss(self, outputs):
    method _get_gen_loss (line 89) | def _get_gen_loss(self, outputs):
    method _get_opposite_domain (line 136) | def _get_opposite_domain(self, domain):
    method train_step (line 150) | def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):
    method test_step (line 215) | def test_step(self, data: dict) -> SampleList:
    method val_step (line 255) | def val_step(self, data: dict) -> SampleList:

FILE: mmagic/models/editors/cyclegan/cyclegan_generator.py
  class ResnetGenerator (line 12) | class ResnetGenerator(BaseModule):
    method __init__ (line 33) | def __init__(self,
    method forward (line 123) | def forward(self, x):
    method init_weights (line 134) | def init_weights(self):

FILE: mmagic/models/editors/cyclegan/cyclegan_modules.py
  class ResidualBlockWithDropout (line 8) | class ResidualBlockWithDropout(nn.Module):
    method __init__ (line 26) | def __init__(self,
    method forward (line 67) | def forward(self, x):
  class GANImageBuffer (line 80) | class GANImageBuffer:
    method __init__ (line 95) | def __init__(self, buffer_size, buffer_ratio=0.5):
    method query (line 103) | def query(self, images):

FILE: mmagic/models/editors/dcgan/dcgan.py
  class DCGAN (line 15) | class DCGAN(BaseGAN):
    method disc_loss (line 28) | def disc_loss(self, disc_pred_fake: Tensor,
    method gen_loss (line 51) | def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:
    method train_discriminator (line 67) | def train_discriminator(self, inputs: dict, data_samples: DataSample,
    method train_generator (line 95) | def train_generator(self, inputs: dict, data_samples: DataSample,

FILE: mmagic/models/editors/dcgan/dcgan_discriminator.py
  class DCGANDiscriminator (line 12) | class DCGANDiscriminator(BaseModule):
    method __init__ (line 43) | def __init__(self,
    method forward (line 95) | def forward(self, x):
    method init_weights (line 112) | def init_weights(self):

FILE: mmagic/models/editors/dcgan/dcgan_generator.py
  class DCGANGenerator (line 14) | class DCGANGenerator(BaseModule):
    method __init__ (line 55) | def __init__(self,
    method forward (line 117) | def forward(self, noise, num_batches=0, return_noise=False):
    method init_weights (line 167) | def init_weights(self):

FILE: mmagic/models/editors/ddpm/attention.py
  class Transformer2DModel (line 10) | class Transformer2DModel(nn.Module):
    method __init__ (line 65) | def __init__(
    method _set_attention_slice (line 147) | def _set_attention_slice(self, slice_size):
    method forward (line 153) | def forward(self,
  class BasicTransformerBlock (line 232) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 252) | def __init__(
    method _set_attention_slice (line 290) | def _set_attention_slice(self, slice_size):
    method forward (line 295) | def forward(self, hidden_states, context=None, timestep=None):
  class CrossAttention (line 317) | class CrossAttention(nn.Module):
    method __init__ (line 337) | def __init__(
    method reshape_heads_to_batch_dim (line 365) | def reshape_heads_to_batch_dim(self, tensor):
    method reshape_batch_dim_to_heads (line 375) | def reshape_batch_dim_to_heads(self, tensor):
    method forward (line 385) | def forward(self, hidden_states, context=None, mask=None):
    method _attention (line 416) | def _attention(self, query, key, value):
    method _sliced_attention (line 439) | def _sliced_attention(self, query, key, value, sequence_length, dim):
  class FeedForward (line 473) | class FeedForward(nn.Module):
    method __init__ (line 490) | def __init__(
    method forward (line 515) | def forward(self, hidden_states):
  class GEGLU (line 523) | class GEGLU(nn.Module):
    method __init__ (line 533) | def __init__(self, dim_in: int, dim_out: int):
    method gelu (line 537) | def gelu(self, gate):
    method forward (line 541) | def forward(self, hidden_states):
  class ApproximateGELU (line 547) | class ApproximateGELU(nn.Module):
    method __init__ (line 553) | def __init__(self, dim_in: int, dim_out: int):
    method forward (line 557) | def forward(self, x):

FILE: mmagic/models/editors/ddpm/denoising_unet.py
  class EmbedSequential (line 27) | class EmbedSequential(nn.Sequential):
    method forward (line 35) | def forward(self, x, y, encoder_out=None):
  class GroupNorm32 (line 49) | class GroupNorm32(nn.GroupNorm):
    method __init__ (line 51) | def __init__(self, num_channels, num_groups=32, **kwargs):
    method forward (line 54) | def forward(self, x):
  function convert_module_to_f16 (line 58) | def convert_module_to_f16(layer):
  function convert_module_to_f32 (line 66) | def convert_module_to_f32(layer):
  class SiLU (line 76) | class SiLU(BaseModule):
    method __init__ (line 84) | def __init__(self, inplace=False):
    method forward (line 92) | def forward(self, x):
  class MultiHeadAttention (line 108) | class MultiHeadAttention(BaseModule):
    method __init__ (line 121) | def __init__(self,
    method QKVAttention (line 133) | def QKVAttention(qkv):
    method forward (line 142) | def forward(self, x):
    method init_weights (line 159) | def init_weights(self):
  class MultiHeadAttentionBlock (line 164) | class MultiHeadAttentionBlock(BaseModule):
    method __init__ (line 172) | def __init__(self,
    method forward (line 202) | def forward(self, x, encoder_out=None):
  class QKVAttentionLegacy (line 216) | class QKVAttentionLegacy(BaseModule):
    method __init__ (line 222) | def __init__(self, n_heads):
    method forward (line 226) | def forward(self, qkv, encoder_kv=None):
  class QKVAttention (line 253) | class QKVAttention(BaseModule):
    method __init__ (line 257) | def __init__(self, n_heads):
    method forward (line 261) | def forward(self, qkv):
  class TimeEmbedding (line 284) | class TimeEmbedding(BaseModule):
    method __init__ (line 299) | def __init__(self,
    method sinusodial_embedding (line 322) | def sinusodial_embedding(timesteps, dim, max_period=10000):
    method forward (line 348) | def forward(self, t):
  class DenoisingResBlock (line 361) | class DenoisingResBlock(BaseModule):
    method __init__ (line 382) | def __init__(self,
    method forward_shortcut (line 450) | def forward_shortcut(self, x):
    method forward (line 455) | def forward(self, x, y):
    method init_weights (line 479) | def init_weights(self):
  class NormWithEmbedding (line 485) | class NormWithEmbedding(BaseModule):
    method __init__ (line 505) | def __init__(self,
    method forward (line 520) | def forward(self, x, y):
  class DenoisingDownsample (line 542) | class DenoisingDownsample(BaseModule):
    method __init__ (line 553) | def __init__(self, in_channels, with_conv=True):
    method forward (line 560) | def forward(self, x):
  class DenoisingUpsample (line 572) | class DenoisingUpsample(BaseModule):
    method __init__ (line 584) | def __init__(self, in_channels, with_conv=True):
    method forward (line 590) | def forward(self, x):
  function build_down_block_resattn (line 604) | def build_down_block_resattn(resblocks_per_downsample, resblock_cfg,
  function build_mid_blocks_resattn (line 651) | def build_mid_blocks_resattn(resblock_cfg, attention_cfg, in_channels_):
  function build_up_blocks_resattn (line 662) | def build_up_blocks_resattn(
  class DenoisingUnet (line 719) | class DenoisingUnet(BaseModule):
    method __init__ (line 840) | def __init__(self,
    method forward (line 1145) | def forward(self,
    method init_weights (line 1288) | def init_weights(self, pretrained=None):
    method convert_to_fp16 (line 1317) | def convert_to_fp16(self):
    method convert_to_fp32 (line 1323) | def convert_to_fp32(self):

FILE: mmagic/models/editors/ddpm/embeddings.py
  class TimestepEmbedding (line 11) | class TimestepEmbedding(nn.Module):
    method __init__ (line 14) | def __init__(self,
    method forward (line 38) | def forward(self, sample):
  class Timesteps (line 50) | class Timesteps(nn.Module):
    method __init__ (line 53) | def __init__(self,
    method forward (line 64) | def forward(self, timesteps):

FILE: mmagic/models/editors/ddpm/res_blocks.py
  class ResnetBlock2D (line 10) | class ResnetBlock2D(nn.Module):
    method __init__ (line 31) | def __init__(
    method forward (line 107) | def forward(self, input_tensor, temb):
  class Upsample2D (line 148) | class Upsample2D(nn.Module):
    method __init__ (line 158) | def __init__(self,
    method forward (line 179) | def forward(self, hidden_states, output_size=None):
  class Downsample2D (line 200) | class Downsample2D(nn.Module):
    method __init__ (line 210) | def __init__(self,
    method forward (line 237) | def forward(self, hidden_states):

FILE: mmagic/models/editors/ddpm/unet_blocks.py
  function get_down_block (line 9) | def get_down_block(
  function get_up_block (line 62) | def get_up_block(
  class UNetMidBlock2DCrossAttn (line 115) | class UNetMidBlock2DCrossAttn(nn.Module):
    method __init__ (line 118) | def __init__(
    method set_attention_slice (line 189) | def set_attention_slice(self, slice_size):
    method forward (line 203) | def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
  class CrossAttnDownBlock2D (line 214) | class CrossAttnDownBlock2D(nn.Module):
    method __init__ (line 217) | def __init__(
    method set_attention_slice (line 290) | def set_attention_slice(self, slice_size):
    method forward (line 304) | def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
  class DownBlock2D (line 326) | class DownBlock2D(nn.Module):
    method __init__ (line 329) | def __init__(
    method forward (line 380) | def forward(self, hidden_states, temb=None):
  class CrossAttnUpBlock2D (line 399) | class CrossAttnUpBlock2D(nn.Module):
    method __init__ (line 402) | def __init__(
    method set_attention_slice (line 475) | def set_attention_slice(self, slice_size):
    method forward (line 491) | def forward(
  class UpBlock2D (line 520) | class UpBlock2D(nn.Module):
    method __init__ (line 523) | def __init__(
    method forward (line 574) | def forward(self,

FILE: mmagic/models/editors/deblurganv2/deblurganv2.py
  class DeblurGanV2 (line 18) | class DeblurGanV2(BaseModel):
    method __init__ (line 20) | def __init__(self,
    method forward (line 70) | def forward(self,
    method convert_to_datasample (line 154) | def convert_to_datasample(self, predictions: DataSample,
    method forward_tensor (line 183) | def forward_tensor(self,
    method forward_inference (line 204) | def forward_inference(self,
    method forward_train (line 227) | def forward_train(self, inputs, data_samples=None, **kwargs):
    method val_step (line 243) | def val_step(self, data: Union[tuple, dict, list]) -> list:
    method test_step (line 260) | def test_step(self, data: Union[dict, tuple, list]) -> list:
    method _run_forward (line 272) | def _run_forward(self, data: Union[dict, tuple, list],
    method train_step (line 292) | def train_step(self, data: List[dict],
    method g_step (line 341) | def g_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Ten...
    method d_step (line 382) | def d_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Ten...
    method g_step_with_optim (line 402) | def g_step_with_optim(self, batch_outputs: torch.Tensor,
    method d_step_with_optim (line 429) | def d_step_with_optim(self, batch_outputs: torch.Tensor,
    method extract_gt_data (line 461) | def extract_gt_data(self, data_samples):

FILE: mmagic/models/editors/deblurganv2/deblurganv2_discriminator.py
  class NLayerDiscriminator (line 13) | class NLayerDiscriminator(nn.Module):
    method __init__ (line 16) | def __init__(self,
    method forward (line 77) | def forward(self, input):
  class DicsriminatorTail (line 89) | class DicsriminatorTail(nn.Module):
    method __init__ (line 91) | def __init__(self,
    method forward (line 128) | def forward(self, input):
  class MultiScaleDiscriminator (line 140) | class MultiScaleDiscriminator(nn.Module):
    method __init__ (line 144) | def __init__(self,
    method forward (line 206) | def forward(self, input):
  function get_fullD (line 224) | def get_fullD(norm_layer):
  class DoubleGan (line 237) | class DoubleGan(nn.Module):
    method __init__ (line 240) | def __init__(self, norm_layer='instance', d_layers=3):
    method forward (line 248) | def forward(self, x):
  class PatchGan (line 264) | class PatchGan(nn.Module):
    method __init__ (line 267) | def __init__(self, norm_layer='instance', d_layers=3):
    method forward (line 274) | def forward(self, x):
  class MultiScale (line 287) | class MultiScale(nn.Module):
    method __init__ (line 290) | def __init__(self, norm_layer='instance', d_layers=3):
    method forward (line 295) | def forward(self, x):
  class DeblurGanV2Discriminator (line 309) | class DeblurGanV2Discriminator:
    method __new__ (line 316) | def __new__(cls, backbone, *args, **kwargs):

FILE: mmagic/models/editors/deblurganv2/deblurganv2_generator.py
  class FPNHead (line 12) | class FPNHead(nn.Module):
    method __init__ (line 15) | def __init__(self, num_in, num_mid, num_out):
    method forward (line 23) | def forward(self, x):
  class FPN_inception (line 37) | class FPN_inception(nn.Module):
    method __init__ (line 39) | def __init__(self, norm_layer, num_filter=256, pretrained='imagenet'):
    method unfreeze (line 91) | def unfreeze(self):
    method forward (line 96) | def forward(self, x):
  class FPNInception (line 140) | class FPNInception(nn.Module):
    method __init__ (line 144) | def __init__(self,
    method unfreeze (line 177) | def unfreeze(self):
    method forward (line 181) | def forward(self, x):
  class FPN_inceptionsimple (line 214) | class FPN_inceptionsimple(nn.Module):
    method __init__ (line 216) | def __init__(self, norm_layer, num_filters=256):
    method unfreeze (line 260) | def unfreeze(self):
    method forward (line 265) | def forward(self, x):
  class FPNInceptionSimple (line 306) | class FPNInceptionSimple(nn.Module):
    method __init__ (line 310) | def __init__(self,
    method unfreeze (line 343) | def unfreeze(self):
    method forward (line 347) | def forward(self, x):
  class FPN_mobilenet (line 380) | class FPN_mobilenet(nn.Module):
    method __init__ (line 382) | def __init__(self, norm_layer, num_filters=128, pretrained=None):
    method unfreeze (line 429) | def unfreeze(self):
    method forward (line 434) | def forward(self, x):
  class FPNMobileNet (line 476) | class FPNMobileNet(nn.Module):
    method __init__ (line 478) | def __init__(self,
    method unfreeze (line 516) | def unfreeze(self):
    method forward (line 520) | def forward(self, x):
  class DeblurGanV2Generator (line 554) | class DeblurGanV2Generator:
    method __new__ (line 561) | def __new__(cls, backbone, *args, **kwargs):

FILE: mmagic/models/editors/deblurganv2/deblurganv2_util.py
  class BasicConv2d (line 39) | class BasicConv2d(nn.Module):
    method __init__ (line 41) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 57) | def forward(self, x):
  class Mixed_5b (line 72) | class Mixed_5b(nn.Module):
    method __init__ (line 74) | def __init__(self):
    method forward (line 92) | def forward(self, x):
  class Block35 (line 109) | class Block35(nn.Module):
    method __init__ (line 111) | def __init__(self, scale=1.0):
    method forward (line 130) | def forward(self, x):
  class Mixed_6a (line 149) | class Mixed_6a(nn.Module):
    method __init__ (line 151) | def __init__(self):
    method forward (line 163) | def forward(self, x):
  class Block17 (line 179) | class Block17(nn.Module):
    method __init__ (line 181) | def __init__(self, scale=1.0):
    method forward (line 198) | def forward(self, x):
  class Mixed_7a (line 216) | class Mixed_7a(nn.Module):
    method __init__ (line 218) | def __init__(self):
    method forward (line 236) | def forward(self, x):
  class Block8 (line 253) | class Block8(nn.Module):
    method __init__ (line 255) | def __init__(self, scale=1.0, noReLU=False):
    method forward (line 274) | def forward(self, x):
  class InceptionResNetV2 (line 293) | class InceptionResNetV2(nn.Module):
    method __init__ (line 296) | def __init__(self, num_classes=1001):
    method features (line 337) | def features(self, input):
    method logits (line 360) | def logits(self, features):
    method forward (line 372) | def forward(self, input):
  function inceptionresnetv2 (line 386) | def inceptionresnetv2(num_classes=1000, pretrained='imagenet'):
  function conv_bn (line 410) | def conv_bn(inp, oup, stride):
  function conv_1x1_bn (line 416) | def conv_1x1_bn(inp, oup):
  class InvertedResidual (line 422) | class InvertedResidual(nn.Module):
    method __init__ (line 424) | def __init__(self, inp, oup, stride, expand_ratio):
    method forward (line 471) | def forward(self, x):
  class MobileNetV2 (line 486) | class MobileNetV2(nn.Module):
    method __init__ (line 488) | def __init__(self, n_class=1000, input_size=224, width_mult=1.):
    method forward (line 536) | def forward(self, x):
    method _initialize_weights (line 550) | def _initialize_weights(self):
  function get_norm_layer (line 566) | def get_norm_layer(norm_type='instance'):

FILE: mmagic/models/editors/deepfillv1/contextual_attention.py
  class ContextualAttentionModule (line 10) | class ContextualAttentionModule(BaseModule):
    method __init__ (line 41) | def __init__(self,
    method forward (line 76) | def forward(self, x, context, mask=None):
    method patch_correlation (line 147) | def patch_correlation(self, x, kernel):
    method patch_copy_deconv (line 169) | def patch_copy_deconv(self, attention_score, context_filter):
    method fuse_correlation_map (line 190) | def fuse_correlation_map(self, correlation_map, h_unfold, w_unfold):
    method calculate_unfold_hw (line 239) | def calculate_unfold_hw(self,
    method calculate_overlap_factor (line 261) | def calculate_overlap_factor(self, attention_score):
    method mask_correlation_map (line 287) | def mask_correlation_map(self, correlation_map, mask):
    method im2col (line 321) | def im2col(self,

FILE: mmagic/models/editors/deepfillv1/contextual_attention_neck.py
  class ContextualAttentionNeck (line 12) | class ContextualAttentionNeck(BaseModule):
    method __init__ (line 30) | def __init__(self,
    method forward (line 61) | def forward(self, x, mask):

FILE: mmagic/models/editors/deepfillv1/deepfill_decoder.py
  class DeepFillDecoder (line 16) | class DeepFillDecoder(BaseModule):
    method __init__ (line 37) | def __init__(self,
    method forward (line 80) | def forward(self, input_dict):

FILE: mmagic/models/editors/deepfillv1/deepfill_disc.py
  class DeepFillv1Discriminators (line 10) | class DeepFillv1Discriminators(BaseModule):
    method __init__ (line 26) | def __init__(self, global_disc_cfg, local_disc_cfg):
    method forward (line 31) | def forward(self, x):
    method init_weights (line 49) | def init_weights(self):

FILE: mmagic/models/editors/deepfillv1/deepfill_encoder.py
  class DeepFillEncoder (line 10) | class DeepFillEncoder(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method forward (line 65) | def forward(self, x):

FILE: mmagic/models/editors/deepfillv1/deepfill_refiner.py
  class DeepFillRefiner (line 10) | class DeepFillRefiner(BaseModule):
    method __init__ (line 29) | def __init__(self,
    method forward (line 48) | def forward(self, x, mask):

FILE: mmagic/models/editors/deepfillv1/deepfillv1.py
  class DeepFillv1Inpaintor (line 13) | class DeepFillv1Inpaintor(TwoStageInpaintor):
    method __init__ (line 68) | def __init__(self,
    method forward_train_d (line 106) | def forward_train_d(self, data_batch, is_real, is_disc):
    method two_stage_loss (line 146) | def two_stage_loss(self, stage1_data, stage2_data, gt, mask, masked_img):
    method calculate_loss_with_type (line 199) | def calculate_loss_with_type(self,
    method train_step (line 254) | def train_step(self, data: List[dict], optim_wrapper):

FILE: mmagic/models/editors/deepfillv2/two_stage_encoder_decoder.py
  class DeepFillEncoderDecoder (line 12) | class DeepFillEncoderDecoder(BaseModule):
    method __init__ (line 27) | def __init__(self,
    method forward (line 47) | def forward(self, x):
    method init_weights (line 75) | def init_weights(self):

FILE: mmagic/models/editors/dic/dic.py
  class DIC (line 13) | class DIC(SRGAN):
    method __init__ (line 34) | def __init__(self,
    method forward_tensor (line 62) | def forward_tensor(self, inputs, data_samples=None, training=False):
    method if_run_g (line 84) | def if_run_g(self):
    method if_run_d (line 89) | def if_run_d(self):
    method g_step (line 94) | def g_step(self, batch_outputs, batch_gt_data):
    method train_step (line 132) | def train_step(self, data: List[dict],
    method extract_gt_data (line 190) | def extract_gt_data(data_samples):

FILE: mmagic/models/editors/dic/dic_net.py
  class DICNet (line 12) | class DICNet(BaseModule):
    method __init__ (line 38) | def __init__(self,
    method forward (line 84) | def forward(self, x):
  class FeedbackBlock (line 125) | class FeedbackBlock(nn.Module):
    method __init__ (line 144) | def __init__(self,
    method forward (line 196) | def forward(self, x):
  class FeedbackBlockCustom (line 240) | class FeedbackBlockCustom(FeedbackBlock):
    method __init__ (line 250) | def __init__(self, in_channels, mid_channels, num_blocks, upscale_fact...
    method forward (line 258) | def forward(self, x):
  class GroupResBlock (line 294) | class GroupResBlock(nn.Module):
    method __init__ (line 306) | def __init__(self,
    method forward (line 320) | def forward(self, x):
  class FeatureHeatmapFusingBlock (line 334) | class FeatureHeatmapFusingBlock(nn.Module):
    method __init__ (line 345) | def __init__(self,
    method forward (line 369) | def forward(self, feature, heatmap):
  class FeedbackBlockHeatmapAttention (line 396) | class FeedbackBlockHeatmapAttention(FeedbackBlock):
    method __init__ (line 408) | def __init__(self,
    method forward (line 427) | def forward(self, x, heatmap):

FILE: mmagic/models/editors/dic/feedback_hour_glass.py
  class FeedbackHourglass (line 10) | class FeedbackHourglass(BaseModule):
    method __init__ (line 26) | def __init__(self, mid_channels, num_keypoints):
    method forward (line 49) | def forward(self, x, last_hidden=None):
  class ResBlock (line 75) | class ResBlock(nn.Module):
    method __init__ (line 95) | def __init__(self, in_channels, out_channels):
    method forward (line 108) | def forward(self, x):
  class Hourglass (line 124) | class Hourglass(nn.Module):
    method __init__ (line 134) | def __init__(self, depth, mid_channels):
    method forward (line 145) | def forward(self, x):
  function reduce_to_five_heatmaps (line 164) | def reduce_to_five_heatmaps(ori_heatmap, detach):

FILE: mmagic/models/editors/dic/light_cnn.py
  class LightCNN (line 12) | class LightCNN(BaseModule):
    method __init__ (line 21) | def __init__(self, in_channels):
    method forward (line 43) | def forward(self, x):
    method init_weights (line 58) | def init_weights(self, pretrained=None, strict=True):
  class MaxFeature (line 75) | class MaxFeature(nn.Module):
    method __init__ (line 92) | def __init__(self,
    method forward (line 115) | def forward(self, x):

FILE: mmagic/models/editors/dim/dim.py
  class DIM (line 13) | class DIM(BaseMattor):
    method __init__ (line 44) | def __init__(self,
    method init_weights (line 93) | def init_weights(self):
    method with_refiner (line 100) | def with_refiner(self):
    method train (line 104) | def train(self, mode=True):
    method freeze_backbone (line 115) | def freeze_backbone(self):
    method _forward (line 121) | def _forward(self,
    method _forward_test (line 149) | def _forward_test(self, inputs):
    method _forward_train (line 157) | def _forward_train(self, inputs, data_samples):

FILE: mmagic/models/editors/disco_diffusion/clip_wrapper.py
  class ClipWrapper (line 12) | class ClipWrapper(nn.Module):
    method __init__ (line 78) | def __init__(self, clip_type, *args, **kwargs):
    method get_embedding_layer (line 117) | def get_embedding_layer(self):
    method add_embedding (line 136) | def add_embedding(self, embeddings: Union[dict, List[dict]]):
    method set_only_embedding_trainable (line 148) | def set_only_embedding_trainable(self):
    method set_embedding_layer (line 164) | def set_embedding_layer(self):
    method unset_embedding_layer (line 175) | def unset_embedding_layer(self):
    method forward (line 182) | def forward(self, *args, **kwargs):
  class EmbeddingLayerWithFixes (line 187) | class EmbeddingLayerWithFixes(nn.Module):
    method __init__ (line 199) | def __init__(self,
    method weight (line 214) | def weight(self):
    method check_duplicate_names (line 218) | def check_duplicate_names(self, embeddings: List[dict]):
    method check_ids_overlap (line 230) | def check_ids_overlap(self, embeddings):
    method add_embeddings (line 246) | def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]):
    method replace_input_ids (line 316) | def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
    method replace_embeddings (line 329) | def replace_embeddings(self, input_ids: torch.Tensor,
    method forward (line 386) | def forward(self,

FILE: mmagic/models/editors/disco_diffusion/disco.py
  class DiscoDiffusion (line 22) | class DiscoDiffusion(nn.Module):
    method __init__ (line 48) | def __init__(self,
    method load_pretrained_models (line 83) | def load_pretrained_models(self, pretrained_cfgs):
    method device (line 106) | def device(self):
    method infer (line 115) | def infer(self,

FILE: mmagic/models/editors/disco_diffusion/guider.py
  function sinc (line 27) | def sinc(x):
  function lanczos (line 44) | def lanczos(x, a):
  function ramp (line 51) | def ramp(ratio, width):
  function resample (line 70) | def resample(input, size, align_corners=True):
  function range_loss (line 104) | def range_loss(input):
  function spherical_dist_loss (line 109) | def spherical_dist_loss(x, y):
  class MakeCutouts (line 116) | class MakeCutouts(nn.Module):
    method __init__ (line 129) | def __init__(self, cut_size, cutn):
    method forward (line 144) | def forward(self, input, skip_augs=False):
  class MakeCutoutsDango (line 170) | class MakeCutoutsDango(nn.Module):
    method __init__ (line 200) | def __init__(self,
    method forward (line 231) | def forward(self, input, skip_augs=False):
  function parse_prompt (line 278) | def parse_prompt(prompt):
  function split_prompts (line 289) | def split_prompts(prompts, max_frames=1):
  class ImageTextGuider (line 299) | class ImageTextGuider(nn.Module):
    method __init__ (line 312) | def __init__(self, clip_models):
    method frame_prompt_from_text (line 322) | def frame_prompt_from_text(self, text_prompts, frame_num=0):
    method compute_prompt_stats (line 333) | def compute_prompt_stats(self,
    method cond_fn (line 385) | def cond_fn(self,
    method device (line 503) | def device(self):
    method forward (line 511) | def forward(self, x):

FILE: mmagic/models/editors/disco_diffusion/secondary_model.py
  function append_dims (line 14) | def append_dims(x, n):
  function expand_to_planes (line 19) | def expand_to_planes(x, shape):
  function alpha_sigma_to_t (line 24) | def alpha_sigma_to_t(alpha, sigma):
  function t_to_alpha_sigma (line 29) | def t_to_alpha_sigma(t):
  class ConvBlock (line 34) | class ConvBlock(nn.Sequential):
    method __init__ (line 42) | def __init__(self, c_in, c_out):
  class SkipBlock (line 49) | class SkipBlock(nn.Module):
    method __init__ (line 59) | def __init__(self, main, skip=None):
    method forward (line 64) | def forward(self, input):
  class FourierFeatures (line 69) | class FourierFeatures(nn.Module):
    method __init__ (line 78) | def __init__(self, in_features, out_features, std=1.):
    method forward (line 84) | def forward(self, input):
  class SecondaryDiffusionImageNet2 (line 91) | class SecondaryDiffusionImageNet2(nn.Module):
    method __init__ (line 98) | def __init__(self):
    method forward (line 156) | def forward(self, input, t):

FILE: mmagic/models/editors/dreambooth/dreambooth.py
  class DreamBooth (line 21) | class DreamBooth(StableDiffusion):
    method __init__ (line 66) | def __init__(self,
    method generate_class_prior_images (line 114) | def generate_class_prior_images(self, num_batches=None):
    method prepare_model (line 151) | def prepare_model(self):
    method set_lora (line 169) | def set_lora(self):
    method val_step (line 175) | def val_step(self, data: dict) -> SampleList:
    method test_step (line 205) | def test_step(self, data: dict) -> SampleList:
    method train_step (line 233) | def train_step(self, data, optim_wrapper):
    method forward (line 311) | def forward(self,

FILE: mmagic/models/editors/duf/duf.py
  class DynamicUpsamplingFilter (line 8) | class DynamicUpsamplingFilter(BaseModule):
    method __init__ (line 20) | def __init__(self, filter_size=(5, 5)):
    method forward (line 36) | def forward(self, x, filters):

FILE: mmagic/models/editors/edsr/edsr_net.py
  class EDSRNet (line 14) | class EDSRNet(BaseModule):
    method __init__ (line 36) | def __init__(self,
    method forward (line 66) | def forward(self, x):
  class UpsampleModule (line 90) | class UpsampleModule(nn.Sequential):
    method __init__ (line 98) | def __init__(self, scale, mid_channels):

FILE: mmagic/models/editors/edvr/edvr.py
  class EDVR (line 9) | class EDVR(BaseEditModel):
    method __init__ (line 25) | def __init__(self,
    method forward_train (line 46) | def forward_train(self, inputs, data_samples=None):

FILE: mmagic/models/editors/edvr/edvr_net.py
  class EDVRNet (line 16) | class EDVRNet(BaseModule):
    method __init__ (line 40) | def __init__(self,
    method forward (line 103) | def forward(self, x):
    method init_weights (line 163) | def init_weights(self):
  class ModulatedDCNPack (line 187) | class ModulatedDCNPack(ModulatedDeformConv2d):
    method __init__ (line 207) | def __init__(self, *args, **kwargs):
    method init_offset (line 219) | def init_offset(self):
    method forward (line 223) | def forward(self, x, extra_feat):
  class PCDAlignment (line 235) | class PCDAlignment(BaseModule):
    method __init__ (line 247) | def __init__(self,
    method forward (line 310) | def forward(self, neighbor_feats, ref_feats):
  class TSAFusion (line 364) | class TSAFusion(BaseModule):
    method __init__ (line 376) | def __init__(self,
    method forward (line 418) | def forward(self, aligned_feat):

FILE: mmagic/models/editors/eg3d/camera.py
  class BaseCamera (line 16) | class BaseCamera(object):
    method __init__ (line 42) | def __init__(self,
    method _sample_in_range (line 68) | def _sample_in_range(self, mean: float, std: float,
    method sample_intrinsic (line 91) | def sample_intrinsic(self,
    method fov_to_intrinsic (line 130) | def fov_to_intrinsic(self,
    method focal_to_instrinsic (line 156) | def focal_to_instrinsic(self,
    method sample_theta (line 180) | def sample_theta(self, mean: float, std: float,
    method sample_phi (line 195) | def sample_phi(self, mean: float, std: float,
    method sample_camera2world (line 219) | def sample_camera2world(self,
    method interpolation_cam2world (line 286) | def interpolation_cam2world(self,
    method __repr__ (line 353) | def __repr__(self):
  class GaussianCamera (line 367) | class GaussianCamera(BaseCamera):
    method __init__ (line 387) | def __init__(self,
  class UniformCamera (line 403) | class UniformCamera(BaseCamera):
    method __init__ (line 423) | def __init__(self,
  function create_cam2world_matrix (line 438) | def create_cam2world_matrix(forward_vector: torch.Tensor, origin: torch....

FILE: mmagic/models/editors/eg3d/dual_discriminator.py
  class DualDiscriminator (line 18) | class DualDiscriminator(StyleGAN2Discriminator):
    method __init__ (line 35) | def __init__(self,
    method forward (line 47) | def forward(self,

FILE: mmagic/models/editors/eg3d/eg3d.py
  class EG3D (line 21) | class EG3D(BaseConditionalGAN):
    method __init__ (line 55) | def __init__(self,
    method label_fn (line 76) | def label_fn(self,
    method data_sample_to_label (line 102) | def data_sample_to_label(self, data_sample: SampleList
    method pack_to_data_sample (line 119) | def pack_to_data_sample(
    method forward (line 151) | def forward(self,
    method interpolation (line 231) | def interpolation(self,

FILE: mmagic/models/editors/eg3d/eg3d_generator.py
  class TriplaneGenerator (line 17) | class TriplaneGenerator(BaseModule):
    method __init__ (line 73) | def __init__(self,
    method sample_ray (line 147) | def sample_ray(self, cond: torch.Tensor) -> Tuple[Tensor]:
    method forward (line 163) | def forward(self,

FILE: mmagic/models/editors/eg3d/eg3d_modules.py
  class TriPlaneBackbone (line 15) | class TriPlaneBackbone(StyleGAN2Generator):
    method __init__ (line 42) | def __init__(self,
    method mapping (line 68) | def mapping(self,
    method synthesis (line 128) | def synthesis(self, styles: torch.Tensor, *args, **kwargs) -> torch.Te...
  class SuperResolutionModule (line 143) | class SuperResolutionModule(BaseModule):
    method __init__ (line 161) | def __init__(self,
    method forward (line 210) | def forward(self,
  class SynthesisBlock (line 247) | class SynthesisBlock(BaseModule):
    method __init__ (line 261) | def __init__(self,
    method forward (line 296) | def forward(self,

FILE: mmagic/models/editors/eg3d/eg3d_utils.py
  function get_ray_limits_box (line 7) | def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor,
  function inverse_transform_sampling (line 87) | def inverse_transform_sampling(bins: torch.Tensor,
  function linspace_batch (line 146) | def linspace_batch(start: torch.Tensor, stop: torch.Tensor,

FILE: mmagic/models/editors/eg3d/ray_sampler.py
  function sample_rays (line 7) | def sample_rays(cam2world: torch.Tensor, intrinsics: torch.Tensor,

FILE: mmagic/models/editors/eg3d/renderer.py
  class EG3DRenderer (line 19) | class EG3DRenderer(BaseModule):
    method __init__ (line 49) | def __init__(self,
    method get_value (line 74) | def get_value(self,
    method forward (line 91) | def forward(self,
    method sample_stratified (line 197) | def sample_stratified(self, ray_origins: torch.Tensor,
    method neural_rendering (line 242) | def neural_rendering(self, planes: torch.Tensor, sample_coordinates: f...
    method sample_from_planes (line 268) | def sample_from_planes(self,
    method project_onto_planes (line 307) | def project_onto_planes(self, coordinates: torch.Tensor) -> torch.Tensor:
    method unify_samples (line 355) | def unify_samples(self, depths_c: torch.Tensor, colors_c: torch.Tensor,
    method volume_rendering (line 392) | def volume_rendering(self, colors: torch.Tensor, densities: torch.Tensor,
    method sample_importance (line 451) | def sample_importance(self, z_vals: torch.Tensor, weights: torch.Tensor,
  class EG3DDecoder (line 483) | class EG3DDecoder(BaseModule):
    method __init__ (line 496) | def __init__(self,
    method forward (line 512) | def forward(self, sampled_features: torch.Tensor) -> dict:

FILE: mmagic/models/editors/esrgan/esrgan.py
  class ESRGAN (line 9) | class ESRGAN(SRGAN):
    method g_step (line 36) | def g_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Ten...
    method d_step_real (line 78) | def d_step_real(self, batch_outputs: torch.Tensor,
    method d_step_fake (line 104) | def d_step_fake(self, batch_outputs: torch.Tensor, batch_gt_data):

FILE: mmagic/models/editors/esrgan/rrdb_net.py
  class RRDBNet (line 13) | class RRDBNet(BaseModule):
    method __init__ (line 34) | def __init__(self,
    method forward (line 66) | def forward(self, x):
    method init_weights (line 93) | def init_weights(self):
  class ResidualDenseBlock (line 108) | class ResidualDenseBlock(nn.Module):
    method __init__ (line 119) | def __init__(self, mid_channels=64, growth_channels=32):
    method init_weights (line 131) | def init_weights(self):
    method forward (line 141) | def forward(self, x):
  class RRDB (line 159) | class RRDB(nn.Module):
    method __init__ (line 169) | def __init__(self, mid_channels, growth_channels=32):
    method forward (line 175) | def forward(self, x):

FILE: mmagic/models/editors/fastcomposer/fastcomposer.py
  class FastComposer (line 21) | class FastComposer(StableDiffusion):
    method __init__ (line 23) | def __init__(self,
    method _tokenize_and_mask_noun_phrases_ends (line 89) | def _tokenize_and_mask_noun_phrases_ends(self, caption):
    method _encode_augmented_prompt (line 126) | def _encode_augmented_prompt(self, prompt: str,
    method infer (line 170) | def infer(self,

FILE: mmagic/models/editors/fastcomposer/fastcomposer_util.py
  class FastComposerModel (line 24) | class FastComposerModel(nn.Module):
    method __init__ (line 28) | def __init__(self, text_encoder, image_encoder, vae, unet, cfg):
    method _clear_cross_attention_scores (line 60) | def _clear_cross_attention_scores(self):
    method from_pretrained (line 70) | def from_pretrained(cfg, vae, unet):
    method forward (line 98) | def forward(self, batch, noise_scheduler):
  class FastComposerTextEncoder (line 203) | class FastComposerTextEncoder(CLIPPreTrainedModel):
    method from_pretrained (line 207) | def from_pretrained(model_name_or_path, **kwargs):
    method __init__ (line 213) | def __init__(self, text_model):
    method forward (line 221) | def forward(
  class FastComposerCLIPImageEncoder (line 314) | class FastComposerCLIPImageEncoder(CLIPPreTrainedModel):
    method from_pretrained (line 318) | def from_pretrained(global_model_name_or_path):
    method __init__ (line 334) | def __init__(
    method forward (line 347) | def forward(self, object_pixel_values):
  function get_object_transforms (line 375) | def get_object_transforms(cfg):
  class FastComposerPostfuseModule (line 428) | class FastComposerPostfuseModule(nn.Module):
    method __init__ (line 431) | def __init__(self, embed_dim):
    method fuse_fn (line 438) | def fuse_fn(self, text_embeds, object_embeds):
    method forward (line 457) | def forward(
  function unet_store_cross_attention_scores (line 486) | def unet_store_cross_attention_scores(unet, attention_scores, layers=5):
  class BalancedL1Loss (line 530) | class BalancedL1Loss(nn.Module):
    method __init__ (line 533) | def __init__(self, threshold=1.0, normalize=False):
    method forward (line 538) | def forward(self, object_token_attn_prob, object_segmaps):
  function get_object_localization_loss (line 566) | def get_object_localization_loss(
  function get_object_localization_loss_for_one_layer (line 585) | def get_object_localization_loss_for_one_layer(
  class RandomZoomIn (line 635) | class RandomZoomIn(nn.Module):
    method __init__ (line 638) | def __init__(self, min_zoom=1.0, max_zoom=1.5):
    method forward (line 643) | def forward(self, image: torch.Tensor):
  class PadToSquare (line 663) | class PadToSquare(nn.Module):
    method __init__ (line 667) | def __init__(self, fill=0, padding_mode='constant'):
    method forward (line 672) | def forward(self, image: torch.Tensor):
  class CropTopSquare (line 703) | class CropTopSquare(nn.Module):
    method __init__ (line 707) | def __init__(self):
    method forward (line 710) | def forward(self, image: torch.Tensor):
  class MLP (line 725) | class MLP(nn.Module):
    method __init__ (line 728) | def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True):
    method forward (line 738) | def forward(self, x):
  function fuse_object_embeddings (line 757) | def fuse_object_embeddings(
  function build_causal_attention_mask (line 795) | def build_causal_attention_mask(bsz, seq_len, dtype, device=None):

FILE: mmagic/models/editors/fba/fba_decoder.py
  class FBADecoder (line 14) | class FBADecoder(nn.Module):
    method __init__ (line 28) | def __init__(self,
    method init_weights (line 138) | def init_weights(self, pretrained=None):
    method forward (line 158) | def forward(self, inputs):

FILE: mmagic/models/editors/fba/fba_encoder.py
  class FBAResnetDilated (line 7) | class FBAResnetDilated(ResNet):
    method forward (line 10) | def forward(self, x):

FILE: mmagic/models/editors/flavr/flavr.py
  class FLAVR (line 10) | class FLAVR(BasicInterpolator):
    method merge_frames (line 38) | def merge_frames(input_tensors, output_tensors):

FILE: mmagic/models/editors/flavr/flavr_net.py
  class FLAVRNet (line 11) | class FLAVRNet(BaseModule):
    method __init__ (line 37) | def __init__(self,
    method forward (line 82) | def forward(self, images: torch.Tensor):
  class Encoder (line 120) | class Encoder(nn.Module):
    method __init__ (line 134) | def __init__(self, block, layers, stem_layer, mid_channels_list, norm_...
    method forward (line 174) | def forward(self, x):
    method _make_layer (line 192) | def _make_layer(self,
    method _initialize_weights (line 239) | def _initialize_weights(self):
  class Decoder (line 255) | class Decoder(nn.Module):
    method __init__ (line 268) | def __init__(self,
    method forward (line 318) | def forward(self, xs):
    method _join_tensors (line 345) | def _join_tensors(self, x1, x2):
  class UpConv3d (line 359) | class UpConv3d(nn.Module):
    method __init__ (line 378) | def __init__(self,
    method forward (line 417) | def forward(self, x):
  class Conv3d (line 422) | class Conv3d(nn.Module):
    method __init__ (line 441) | def __init__(self,
    method forward (line 467) | def forward(self, x):
  class BasicStem (line 472) | class BasicStem(ConvModule):
    method __init__ (line 483) | def __init__(self, out_channels=64, bias=False, norm_cfg=None):
  class BasicBlock (line 496) | class BasicBlock(nn.Module):
    method __init__ (line 514) | def __init__(
    method forward (line 549) | def forward(self, x):
  class SEGating (line 572) | class SEGating(nn.Module):
    method __init__ (line 579) | def __init__(self, in_channels):
    method forward (line 589) | def forward(self, x):

FILE: mmagic/models/editors/gca/gca.py
  class GCA (line 10) | class GCA(BaseMattor):
    method __init__ (line 28) | def __init__(self,
    method _forward (line 44) | def _forward(self, inputs):
    method _forward_test (line 57) | def _forward_test(self, inputs):
    method _forward_train (line 68) | def _forward_train(self, inputs, data_samples):

FILE: mmagic/models/editors/gca/gca_module.py
  class GCAModule (line 9) | class GCAModule(nn.Module):
    method __init__ (line 49) | def __init__(self,
    method init_weights (line 81) | def init_weights(self):
    method forward (line 87) | def forward(self, img_feat, alpha_feat, unknown=None, softmax_scale=1.):
    method extract_feature_maps_patches (line 160) | def extract_feature_maps_patches(self, img_feat, alpha_feat, unknown):
    method compute_similarity_map (line 198) | def compute_similarity_map(self, img_feat, img_ps):
    method compute_guided_attention_score (line 220) | def compute_guided_attention_score(self, similarity_map, unknown_ps, s...
    method propagate_alpha_feature (line 252) | def propagate_alpha_feature(self, gca_score, alpha_ps):
    method process_unknown_mask (line 276) | def process_unknown_mask(self, unknown, img_feat, softmax_scale):
    method extract_patches (line 317) | def extract_patches(self, x, kernel_size, stride):
    method pad (line 339) | def pad(self, x, kernel_size, stride):
    method get_self_correlation_mask (line 355) | def get_self_correlation_mask(self, img_feat):
    method l2_norm (line 375) | def l2_norm(x):

FILE: mmagic/models/editors/gca/resgca_dec.py
  class BasicBlockDec (line 14) | class BasicBlockDec(BasicBlock):
    method build_conv1 (line 22) | def build_conv1(self, in_channels, out_channels, kernel_size, stride,
    method build_conv2 (line 60) | def build_conv2(self, in_channels, out_channels, kernel_size, conv_cfg,
  class ResNetDec (line 88) | class ResNetDec(BaseModule):
    method __init__ (line 110) | def __init__(self,
    method init_weights (line 160) | def init_weights(self):
    method _make_layer (line 178) | def _make_layer(self, block, planes, num_blocks, conv_cfg, norm_cfg,
    method forward (line 217) | def forward(self, x):
  class ResShortcutDec (line 237) | class ResShortcutDec(ResNetDec):
    method forward (line 268) | def forward(self, inputs):
  class ResGCADecoder (line 302) | class ResGCADecoder(ResShortcutDec):
    method __init__ (line 337) | def __init__(self,
    method forward (line 353) | def forward(self, inputs):

FILE: mmagic/models/editors/gca/resgca_enc.py
  class BasicBlock (line 14) | class BasicBlock(nn.Module):
    method __init__ (line 33) | def __init__(self,
    method build_conv1 (line 60) | def build_conv1(self, in_channels, out_channels, kernel_size, stride,
    method build_conv2 (line 91) | def build_conv2(self, in_channels, out_channels, kernel_size, conv_cfg,
    method forward (line 122) | def forward(self, x):
  class ResNetEnc (line 146) | class ResNetEnc(BaseModule):
    method __init__ (line 170) | def __init__(self,
    method init_weights (line 235) | def init_weights(self):
    method _make_layer (line 257) | def _make_layer(self, block, planes, num_blocks, stride, conv_cfg,
    method forward (line 296) | def forward(self, x):
  class ResShortcutEnc (line 318) | class ResShortcutEnc(ResNetEnc):
    method __init__ (line 356) | def __init__(self,
    method _make_shortcut (line 390) | def _make_shortcut(self, in_channels, out_channels, conv_cfg, norm_cfg,
    method forward (line 414) | def forward(self, x):
  class ResGCAEncoder (line 449) | class ResGCAEncoder(ResShortcutEnc):
    method __init__ (line 490) | def __init__(self,
    method forward (line 541) | def forward(self, x):

FILE: mmagic/models/editors/ggan/ggan.py
  class GGAN (line 15) | class GGAN(BaseGAN):
    method disc_loss (line 21) | def disc_loss(self, disc_pred_fake: Tensor,
    method gen_loss (line 48) | def gen_loss(self, disc_pred_fake):
    method train_discriminator (line 68) | def train_discriminator(self, inputs: dict, data_samples: List[DataSam...
    method train_generator (line 97) | def train_generator(self, inputs: dict, data_samples: List[DataSample],

FILE: mmagic/models/editors/glean/glean_styleganv2.py
  class GLEANStyleGANv2 (line 14) | class GLEANStyleGANv2(BaseModule):
    method __init__ (line 88) | def __init__(self,
    method forward (line 196) | def forward(self, lq):
  class RRDBFeatureExtractor (line 274) | class RRDBFeatureExtractor(nn.Module):
    method __init__ (line 287) | def __init__(self,
    method forward (line 303) | def forward(self, x):

FILE: mmagic/models/editors/global_local/gl_decoder.py
  class GLDecoder (line 13) | class GLDecoder(BaseModule):
    method __init__ (line 27) | def __init__(self,
    method forward (line 100) | def forward(self, x):

FILE: mmagic/models/editors/global_local/gl_dilation.py
  class GLDilationNeck (line 11) | class GLDilationNeck(BaseModule):
    method __init__ (line 28) | def __init__(self,
    method forward (line 52) | def forward(self, x):

FILE: mmagic/models/editors/global_local/gl_disc.py
  class GLDiscs (line 11) | class GLDiscs(BaseModule):
    method __init__ (line 23) | def __init__(self, global_disc_cfg, local_disc_cfg):
    method forward (line 30) | def forward(self, x):
    method init_weights (line 49) | def init_weights(self):

FILE: mmagic/models/editors/global_local/gl_encoder.py
  class GLEncoder (line 9) | class GLEncoder(BaseModule):
    method __init__ (line 20) | def __init__(self, norm_cfg=None, act_cfg=dict(type='ReLU')):
    method forward (line 42) | def forward(self, x):

FILE: mmagic/models/editors/global_local/gl_encoder_decoder.py
  class GLEncoderDecoder (line 8) | class GLEncoderDecoder(BaseModule):
    method __init__ (line 23) | def __init__(self,
    method forward (line 35) | def forward(self, x):

FILE: mmagic/models/editors/global_local/gl_inpaintor.py
  class GLInpaintor (line 13) | class GLInpaintor(OneStageInpaintor):
    method __init__ (line 68) | def __init__(self,
    method generator_loss (line 102) | def generator_loss(self, fake_res, fake_img, fake_local, gt, mask,
    method train_step (line 150) | def train_step(self, data: List[dict], optim_wrapper):

FILE: mmagic/models/editors/guided_diffusion/adm.py
  function classifier_grad (line 20) | def classifier_grad(classifier, x, t, y=None, classifier_scale=1.0):
  class AblatedDiffusionModel (line 35) | class AblatedDiffusionModel(BaseModel):
    method __init__ (line 51) | def __init__(self,
    method load_pretrained_models (line 78) | def load_pretrained_models(self, pretrained_cfgs):
    method device (line 98) | def device(self):
    method infer (line 107) | def infer(self,
    method forward (line 190) | def forward(self,
    method val_step (line 241) | def val_step(self, data: dict) -> SampleList:
    method test_step (line 259) | def test_step(self, data: dict) -> SampleList:
    method train_step (line 273) | def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):
    method get_module (line 315) | def get_module(self, model: nn.Module, module_name: str) -> nn.Module:

FILE: mmagic/models/editors/guided_diffusion/classifier.py
  function checkpoint (line 16) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 33) | class CheckpointFunction(torch.autograd.Function):
    method forward (line 36) | def forward(ctx, run_function, length, *args):
    method backward (line 45) | def backward(ctx, *output_grads):
  function timestep_embedding (line 67) | def timestep_embedding(timesteps, dim, max_period=10000):
  function zero_module (line 89) | def zero_module(module):
  class Upsample (line 96) | class Upsample(nn.Module):
    method __init__ (line 105) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
    method forward (line 115) | def forward(self, x):
  class TimestepBlock (line 136) | class TimestepBlock(nn.Module):
    method forward (line 141) | def forward(self, x, emb):
  class AttentionBlock (line 145) | class AttentionBlock(nn.Module):
    method __init__ (line 153) | def __init__(
    method forward (line 183) | def forward(self, x):
    method _forward (line 195) | def _forward(self, x):
  class TimestepEmbedSequential (line 212) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 216) | def forward(self, x, emb):
  class GroupNorm32 (line 235) | class GroupNorm32(nn.GroupNorm):
    method forward (line 237) | def forward(self, x):
  function normalization (line 249) | def normalization(channels):
  class Downsample (line 258) | class Downsample(nn.Module):
    method __init__ (line 267) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
    method forward (line 281) | def forward(self, x):
  class ResBlock (line 294) | class ResBlock(TimestepBlock):
    method __init__ (line 310) | def __init__(
    method forward (line 373) | def forward(self, x, emb):
    method _forward (line 383) | def _forward(self, x, emb):
  class AttentionPool2d (line 415) | class AttentionPool2d(nn.Module):
    method __init__ (line 421) | def __init__(
    method forward (line 436) | def forward(self, x):
  class EncoderUNetModel (line 456) | class EncoderUNetModel(nn.Module):
    method __init__ (line 462) | def __init__(
    method convert_to_fp16 (line 623) | def convert_to_fp16(self):
    method convert_to_fp32 (line 628) | def convert_to_fp32(self):
    method forward (line 633) | def forward(self, x, timesteps):

FILE: mmagic/models/editors/iconvsr/iconvsr_net.py
  class IconVSRNet (line 20) | class IconVSRNet(BaseModule):
    method __init__ (line 45) | def __init__(self,
    method spatial_padding (line 93) | def spatial_padding(self, lrs):
    method check_if_mirror_extended (line 117) | def check_if_mirror_extended(self, lrs):
    method compute_refill_features (line 133) | def compute_refill_features(self, lrs, keyframe_idx):
    method compute_flow (line 157) | def compute_flow(self, lrs):
    method forward (line 186) | def forward(self, lrs):
  class EDVRFeatureExtractor (line 265) | class EDVRFeatureExtractor(BaseModule):
    method __init__ (line 290) | def __init__(self,
    method forward (line 346) | def forward(self, x):

FILE: mmagic/models/editors/indexnet/indexnet.py
  class IndexNet (line 8) | class IndexNet(BaseMattor):
    method __init__ (line 27) | def __init__(self,
    method _forward (line 47) | def _forward(self, inputs):
    method _forward_test (line 59) | def _forward_test(self, inputs):
    method _forward_train (line 70) | def _forward_train(self, inputs, data_samples):

FILE: mmagic/models/editors/indexnet/indexnet_decoder.py
  class IndexedUpsample (line 16) | class IndexedUpsample(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method init_weights (line 48) | def init_weights(self):
    method forward (line 57) | def forward(self, x, shortcut, dec_idx_feat=None):
  class IndexNetDecoder (line 79) | class IndexNetDecoder(BaseModule):
    method __init__ (line 94) | def __init__(self,
    method init_weights (line 131) | def init_weights(self):
    method forward (line 142) | def forward(self, inputs):

FILE: mmagic/models/editors/indexnet/indexnet_encoder.py
  function build_index_block (line 17) | def build_index_block(in_channels,
  class HolisticIndexBlock (line 81) | class HolisticIndexBlock(BaseModule):
    method __init__ (line 96) | def __init__(self,
    method forward (line 123) | def forward(self, x):
  class DepthwiseIndexBlock (line 144) | class DepthwiseIndexBlock(BaseModule):
    method __init__ (line 162) | def __init__(self,
    method forward (line 194) | def forward(self, x):
  class InvertedResidual (line 222) | class InvertedResidual(BaseModule):
    method __init__ (line 241) | def __init__(self,
    method pad (line 285) | def pad(self, inputs, kernel_size, dilation):
    method forward (line 301) | def forward(self, x):
  class IndexNetEncoder (line 319) | class IndexNetEncoder(BaseModule):
    method __init__ (line 357) | def __init__(self,
    method _make_layer (line 462) | def _make_layer(self, layer_setting, norm_cfg):
    method train (line 489) | def train(self, mode=True):
    method init_weights (line 497) | def init_weights(self):
    method forward (line 516) | def forward(self, x):

FILE: mmagic/models/editors/inst_colorization/color_utils.py
  function xyz2rgb (line 6) | def xyz2rgb(xyz):
  function lab2xyz (line 35) | def lab2xyz(lab):
  function lab2rgb (line 68) | def lab2rgb(lab_rs, color_data_opt):
  function encode_ab_ind (line 87) | def encode_ab_ind(data_ab, color_data_opt):
  function rgb2xyz (line 105) | def rgb2xyz(rgb):
  function xyz2lab (line 136) | def xyz2lab(xyz):
  function rgb2lab (line 169) | def rgb2lab(rgb, color_opt):
  function get_colorization_data (line 187) | def get_colorization_data(data_raw, color_opt, num_points=None):
  function add_color_patches_rand_gt (line 224) | def add_color_patches_rand_gt(data,

FILE: mmagic/models/editors/inst_colorization/colorization_net.py
  class ColorizationNet (line 12) | class ColorizationNet(BaseModule):
    method __init__ (line 30) | def __init__(self,
    method forward (line 258) | def forward(self, input_A, input_B, mask_B):

FILE: mmagic/models/editors/inst_colorization/fusion_net.py
  class FusionNet (line 11) | class FusionNet(BaseModule):
    method __init__ (line 29) | def __init__(self,
    method forward (line 285) | def forward(self, input_A, input_B, mask_B, instance_feature,

FILE: mmagic/models/editors/inst_colorization/inst_colorization.py
  class InstColorization (line 15) | class InstColorization(BaseModel):
    method __init__ (line 39) | def __init__(self,
    method forward (line 70) | def forward(self,
    method convert_to_datasample (line 135) | def convert_to_datasample(self, inputs, data_samples):
    method forward_train (line 152) | def forward_train(self, inputs, data_samples=None, **kwargs):
    method train_step (line 157) | def train_step(self, data: List[dict],
    method forward_inference (line 172) | def forward_inference(self, inputs, data_samples=None, **kwargs):
    method forward_tensor (line 195) | def forward_tensor(self, inputs, data_samples):

FILE: mmagic/models/editors/inst_colorization/weight_layer.py
  function get_norm_layer (line 11) | def get_norm_layer(norm_type='instance'):
  class WeightLayer (line 34) | class WeightLayer(BaseModule):
    method __init__ (line 45) | def __init__(self, input_ch, inner_ch=16):
    method resize_and_pad (line 67) | def resize_and_pad(self, feauture_maps, info_array):
    method forward (line 88) | def forward(self, instance_feature, bg_feature, box_info):

FILE: mmagic/models/editors/liif/liif.py
  class LIIF (line 12) | class LIIF(BaseEditModel):
    method forward_tensor (line 26) | def forward_tensor(self, inputs, data_samples=None, **kwargs):
    method forward_inference (line 46) | def forward_inference(self, inputs, data_samples=None, **kwargs):

FILE: mmagic/models/editors/liif/liif_net.py
  class LIIFNet (line 11) | class LIIFNet(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method forward (line 56) | def forward(self, x, coord, cell, test_mode=False):
    method query_rgb (line 77) | def query_rgb(self, feature, coord, cell=None):
    method batched_predict (line 172) | def batched_predict(self, x, coord, cell):
    method gen_feature (line 197) | def gen_feature(self, x):
  class LIIFEDSRNet (line 209) | class LIIFEDSRNet(LIIFNet):
    method __init__ (line 224) | def __init__(self,
    method gen_feature (line 244) | def gen_feature(self, x):
  class LIIFRDNNet (line 263) | class LIIFRDNNet(LIIFNet):
    method __init__ (line 278) | def __init__(self,
    method gen_feature (line 300) | def gen_feature(self, x):

FILE: mmagic/models/editors/liif/mlp_refiner.py
  class MLPRefiner (line 9) | class MLPRefiner(BaseModule):
    method __init__ (line 18) | def __init__(self, in_dim, out_dim, hidden_list):
    method forward (line 30) | def forward(self, x):

FILE: mmagic/models/editors/lsgan/lsgan.py
  class LSGAN (line 15) | class LSGAN(BaseGAN):
    method disc_loss (line 26) | def disc_loss(self, disc_pred_fake: Tensor,
    method gen_loss (line 54) | def gen_loss(self, disc_pred_fake: Tensor) -> Tuple:
    method train_discriminator (line 74) | def train_discriminator(self, inputs: dict, data_samples: DataSample,
    method train_generator (line 103) | def train_generator(self, inputs: dict, data_samples: List[DataSample],

FILE: mmagic/models/editors/lsgan/lsgan_discriminator.py
  class LSGANDiscriminator (line 11) | class LSGANDiscriminator(BaseModule):
    method __init__ (line 47) | def __init__(self,
    method forward (line 105) | def forward(self, x):

FILE: mmagic/models/editors/lsgan/lsgan_generator.py
  class LSGANGenerator (line 13) | class LSGANGenerator(BaseModule):
    method __init__ (line 51) | def __init__(self,
    method forward (line 137) | def forward(self, noise, num_batches=0, return_noise=False):

FILE: mmagic/models/editors/mspie/mspie_stylegan2.py
  class MSPIEStyleGAN2 (line 26) | class MSPIEStyleGAN2(StyleGAN2):
    method __init__ (line 38) | def __init__(self, *args, train_settings=dict(), **kwargs):
    method train_step (line 52) | def train_step(self, data: dict,
    method train_generator (line 128) | def train_generator(self, inputs: dict, data_samples: DataSample,
    method train_discriminator (line 154) | def train_discriminator(self, inputs: dict, data_samples: DataSample,

FILE: mmagic/models/editors/mspie/mspie_stylegan2_discriminator.py
  class MSStyleGAN2Discriminator (line 12) | class MSStyleGAN2Discriminator(BaseModule):
    method __init__ (line 29) | def __init__(self,
    method forward (line 81) | def forward(self, x):

FILE: mmagic/models/editors/mspie/mspie_stylegan2_generator.py
  class MSStyleGANv2Generator (line 22) | class MSStyleGANv2Generator(BaseModule):
    method __init__ (line 49) | def __init__(self,
    method train (line 192) | def train(self, mode=True):
    method make_injected_noise (line 212) | def make_injected_noise(self, chosen_scale=0):
    method get_mean_latent (line 242) | def get_mean_latent(self, num_samples=4096, **kwargs):
    method style_mixing (line 254) | def style_mixing(self,
    method forward (line 289) | def forward(self,

FILE: mmagic/models/editors/mspie/mspie_stylegan2_modules.py
  class ModulatedPEConv2d (line 22) | class ModulatedPEConv2d(BaseModule):
    method __init__ (line 62) | def __init__(self,
    method forward (line 138) | def forward(self, x, style):
  class ModulatedPEStyleConv (line 207) | class ModulatedPEStyleConv(BaseModule):
    method __init__ (line 235) | def __init__(self,
    method forward (line 263) | def forward(self, x, style, noise=None, return_noise=False):

FILE: mmagic/models/editors/mspie/pe_singan.py
  class PESinGAN (line 17) | class PESinGAN(SinGAN):
    method __init__ (line 24) | def __init__(self,
    method construct_fixed_noises (line 45) | def construct_fixed_noises(self):

FILE: mmagic/models/editors/mspie/pe_singan_generator.py
  class SinGANMSGeneratorPE (line 16) | class SinGANMSGeneratorPE(SinGANMultiScaleGenerator):
    method __init__ (line 61) | def __init__(self,
    method forward (line 133) | def forward(self,

FILE: mmagic/models/editors/mspie/positional_encoding.py
  class SinusoidalPositionalEmbedding (line 11) | class SinusoidalPositionalEmbedding(BaseModule):
    method __init__ (line 35) | def __init__(self,
    method get_embedding (line 55) | def get_embedding(num_embeddings,
    method forward (line 85) | def forward(self, input, **kwargs):
    method make_positions (line 111) | def make_positions(self, input, padding_idx):
    method make_grid2d (line 127) | def make_grid2d(self, height, width, num_batches=1, center_shift=None):
    method make_grid2d_like (line 188) | def make_grid2d_like(self, x, center_shift=None):
  class CatersianGrid (line 205) | class CatersianGrid(BaseModule):
    method forward (line 214) | def forward(self, x, **kwargs):
    method make_grid2d (line 218) | def make_grid2d(self, height, width, num_batches=1, requires_grad=False):
    method make_grid2d_like (line 231) | def make_grid2d_like(self, x, requires_grad=False):

FILE: mmagic/models/editors/nafnet/naf_avgpool2d.py
  class NAFAvgPool2d (line 9) | class NAFAvgPool2d(BaseModule):
    method __init__ (line 19) | def __init__(self,
    method extra_repr (line 37) | def extra_repr(self) -> str:
    method forward (line 41) | def forward(self, x):
  function replace_layers (line 103) | def replace_layers(model, base_size, train_size, fast_imp, **kwargs):
  class Local_Base (line 117) | class Local_Base():
    method convert (line 124) | def convert(self, *args, train_size, **kwargs):

FILE: mmagic/models/editors/nafnet/naf_layerNorm2d.py
  class LayerNormFunction (line 7) | class LayerNormFunction(torch.autograd.Function):
    method forward (line 11) | def forward(ctx, x, weight, bias, eps):
    method backward (line 22) | def backward(ctx, grad_output):
  class LayerNorm2d (line 36) | class LayerNorm2d(nn.Module):
    method __init__ (line 45) | def __init__(self, channels, eps=1e-6):
    method forward (line 51) | def forward(self, x):

FILE: mmagic/models/editors/nafnet/nafbaseline_net.py
  class NAFBaseline (line 14) | class NAFBaseline(BaseModule):
    method __init__ (line 26) | def __init__(self,
    method forward (line 89) | def forward(self, inp):
    method check_image_size (line 119) | def check_image_size(self, x):
  class NAFBaselineLocal (line 136) | class NAFBaselineLocal(Local_Base, NAFBaseline):
    method __init__ (line 148) | def __init__(self,
  class BaselineBlock (line 166) | class BaselineBlock(BaseModule):
    method __init__ (line 176) | def __init__(self,
    method forward (line 262) | def forward(self, inp):

FILE: mmagic/models/editors/nafnet/nafnet_net.py
  class NAFNet (line 14) | class NAFNet(BaseModule):
    method __init__ (line 27) | def __init__(self,
    method forward (line 81) | def forward(self, inp):
    method check_image_size (line 111) | def check_image_size(self, x):
  class NAFNetLocal (line 128) | class NAFNetLocal(Local_Base, NAFNet):
    method __init__ (line 142) | def __init__(self,
  class NAFBlock (line 162) | class NAFBlock(BaseModule):
    method __init__ (line 176) | def __init__(self,
    method forward (line 264) | def forward(self, inp):
  class SimpleGate (line 294) | class SimpleGate(BaseModule):
    method forward (line 305) | def forward(self, x):

FILE: mmagic/models/editors/pconv/mask_conv_module.py
  class MaskConvModule (line 5) | class MaskConvModule(ConvModule):
    method __init__ (line 38) | def __init__(self, *args, **kwargs):
    method forward (line 44) | def forward(self,

FILE: mmagic/models/editors/pconv/partial_conv.py
  class PartialConv2d (line 11) | class PartialConv2d(nn.Conv2d):
    method __init__ (line 24) | def __init__(self, *args, multi_channel=False, eps=1e-8, **kwargs):
    method forward (line 44) | def forward(self, input, mask=None, return_mask=True):

FILE: mmagic/models/editors/pconv/pconv_decoder.py
  class PConvDecoder (line 11) | class PConvDecoder(BaseModule):
    method __init__ (line 26) | def __init__(self,
    method forward (line 89) | def forward(self, input_dict):

FILE: mmagic/models/editors/pconv/pconv_encoder.py
  class PConvEncoder (line 10) | class PConvEncoder(BaseModule):
    method __init__ (line 28) | def __init__(self,
    method train (line 92) | def train(self, mode=True):
    method forward (line 101) | def forward(self, x, mask):

FILE: mmagic/models/editors/pconv/pconv_encoder_decoder.py
  class PConvEncoderDecoder (line 8) | class PConvEncoderDecoder(BaseModule):
    method __init__ (line 16) | def __init__(self, encoder, decoder):
    method forward (line 24) | def forward(self, x, mask_in):

FILE: mmagic/models/editors/pconv/pconv_inpaintor.py
  class PConvInpaintor (line 9) | class PConvInpaintor(OneStageInpaintor):
    method forward_tensor (line 16) | def forward_tensor(self, inputs, data_samples):
    method train_step (line 35) | def train_step(self, data: List[dict], optim_wrapper):

FILE: mmagic/models/editors/pggan/pggan.py
  class ProgressiveGrowingGAN (line 29) | class ProgressiveGrowingGAN(BaseGAN):
    method __init__ (line 57) | def __init__(self,
    method forward (line 113) | def forward(self,
    method train_discriminator (line 205) | def train_discriminator(self, inputs: Tensor,
    method disc_loss (line 246) | def disc_loss(self, disc_pred_fake: Tensor, disc_pred_real: Tensor,
    method train_generator (line 306) | def train_generator(self, inputs: Tensor, data_samples: List[DataSample],
    method gen_loss (line 338) | def gen_loss(self, disc_pred_fake: Tensor) -> Tuple[Tensor, dict]:
    method train_step (line 359) | def train_step(self, data: dict, optim_wrapper: OptimWrapperDict):

FILE: mmagic/models/editors/pggan/pggan_discriminator.py
  class PGGANDiscriminator (line 16) | class PGGANDiscriminator(BaseModule):
    method __init__ (line 69) | def __init__(self,
    method _num_out_channels (line 147) | def _num_out_channels(self, log_scale: int) -> int:
    method _get_fromrgb_layer (line 161) | def _get_fromrgb_layer(self, in_channels: int,
    method _get_convdown_block (line 176) | def _get_convdown_block(self, in_channels: int,
    method forward (line 211) | def forward(self, x, transition_weight=1., curr_scale=-1):

FILE: mmagic/models/editors/pggan/pggan_generator.py
  class PGGANGenerator (line 16) | class PGGANGenerator(BaseModule):
    method __init__ (line 61) | def __init__(self,
    method _get_torgb_layer (line 118) | def _get_torgb_layer(self, in_channels: int):
    method _num_out_channels (line 137) | def _num_out_channels(self, log_scale: int):
    method _get_upconv_block (line 151) | def _get_upconv_block(self, in_channels, log_scale):
    method forward (line 190) | def forward(self,

FILE: mmagic/models/editors/pggan/pggan_modules.py
  class EqualizedLR (line 16) | class EqualizedLR:
    method __init__ (line 36) | def __init__(self, name='weight', gain=2**0.5, mode='fan_in', lr_mul=1...
    method compute_weight (line 42) | def compute_weight(self, module):
    method __call__ (line 64) | def __call__(self, module, inputs):
    method apply (line 69) | def apply(module, name, gain=2**0.5, mode='fan_in', lr_mul=1.):
  function equalized_lr (line 112) | def equalized_lr(module, name='weight', gain=2**0.5, mode='fan_in', lr_m...
  function pixel_norm (line 140) | def pixel_norm(x, eps=1e-6):
  class PixelNorm (line 165) | class PixelNorm(BaseModule):
    method __init__ (line 177) | def __init__(self, in_channels=None, eps=1e-6):
    method forward (line 181) | def forward(self, x):
  class EqualizedLRConvModule (line 194) | class EqualizedLRConvModule(ConvModule):
    method __init__ (line 210) | def __init__(self, *args, equalized_lr_cfg=dict(mode='fan_in'), **kwar...
    method _init_conv_weights (line 218) | def _init_conv_weights(self):
  class EqualizedLRConvUpModule (line 224) | class EqualizedLRConvUpModule(EqualizedLRConvModule):
    method __init__ (line 242) | def __init__(self,
    method forward (line 256) | def forward(self, x, **kwargs):
    method fused_nn_hook (line 270) | def fused_nn_hook(module, inputs):
  class EqualizedLRConvDownModule (line 281) | class EqualizedLRConvDownModule(EqualizedLRConvModule):
    method __init__ (line 300) | def __init__(self, *args, downsample=dict(type='fused_pool'), **kwargs):
    method forward (line 318) | def forward(self, x, **kwargs):
    method fused_avgpool_hook (line 333) | def fused_avgpool_hook(module, inputs):
  class EqualizedLRLinearModule (line 344) | class EqualizedLRLinearModule(nn.Linear):
    method __init__ (line 360) | def __init__(self, *args, equalized_lr_cfg=dict(mode='fan_in'), **kwar...
    method _init_linear_weights (line 373) | def _init_linear_weights(self):
  class PGGANNoiseTo2DFeat (line 381) | class PGGANNoiseTo2DFeat(BaseModule):
    method __init__ (line 383) | def __init__(self,
    method forward (line 417) | def forward(self, x):
  class PGGANDecisionHead (line 443) | class PGGANDecisionHead(BaseModule):
    method __init__ (line 445) | def __init__(self,
    method forward (line 484) | def forward(self, x):
  class MiniBatchStddevLayer (line 508) | class MiniBatchStddevLayer(BaseModule):
    method __init__ (line 520) | def __init__(self, group_size=4, eps=1e-8, gather_all_batch=False):
    method forward (line 529) | def forward(self, x):

FILE: mmagic/models/editors/pix2pix/pix2pix.py
  class Pix2Pix (line 14) | class Pix2Pix(BaseTranslationModel):
    method __init__ (line 21) | def __init__(self, *args, **kwargs):
    method forward_test (line 25) | def forward_test(self, img, target_domain, **kwargs):
    method _get_disc_loss (line 43) | def _get_disc_loss(self, outputs):
    method _get_gen_loss (line 74) | def _get_gen_loss(self, outputs):
    method train_step (line 105) | def train_step(self, data, optim_wrapper=None):
    method test_step (line 168) | def test_step(self, data: dict) -> SampleList:
    method val_step (line 198) | def val_step(self, data: dict) -> SampleList:

FILE: mmagic/models/editors/pix2pix/pix2pix_generator.py
  class UnetGenerator (line 10) | class UnetGenerator(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method forward (line 96) | def forward(self, x):
    method init_weights (line 107) | def init_weights(self):

FILE: mmagic/models/editors/pix2pix/pix2pix_modules.py
  class UnetSkipConnectionBlock (line 7) | class UnetSkipConnectionBlock(nn.Module):
    method __init__ (line 28) | def __init__(self,
    method forward (line 110) | def forward(self, x):

FILE: mmagic/models/editors/plain/plain_decoder.py
  class MaxUnpool2dop (line 16) | class MaxUnpool2dop(Function):
    method forward (line 24) | def forward(ctx, input, indices, kernel_size, stride, padding,
    method symbolic (line 42) | def symbolic(g, input, indices, kernel_size, stride, padding, output_s...
  class MaxUnpool2d (line 121) | class MaxUnpool2d(_MaxUnpoolNd):
    method __init__ (line 132) | def __init__(self, kernel_size, stride=None, padding=0):
    method forward (line 138) | def forward(self, input, indices, output_size=None):
  class PlainDecoder (line 154) | class PlainDecoder(BaseModule):
    method __init__ (line 162) | def __init__(self, in_channels, init_cfg: Optional[dict] = None):
    method init_weights (line 178) | def init_weights(self):
    method forward (line 188) | def forward(self, inputs):

FILE: mmagic/models/editors/plain/plain_refiner.py
  class PlainRefiner (line 11) | class PlainRefiner(BaseModule):
    method __init__ (line 20) | def __init__(self, conv_channels=64, init_cfg=None):
    method init_weights (line 36) | def init_weights(self):
    method forward (line 42) | def forward(self, x, raw_alpha):

FILE: mmagic/models/editors/rdn/rdn_net.py
  class RDNNet (line 9) | class RDNNet(BaseModule):
    method __init__ (line 37) | def __init__(self,
    method forward (line 101) | def forward(self, x):
  class DenseLayer (line 127) | class DenseLayer(BaseModule):
    method __init__ (line 135) | def __init__(self, in_channels, out_channels):
    method forward (line 141) | def forward(self, x):
  class RDB (line 153) | class RDB(BaseModule):
    method __init__ (line 162) | def __init__(self, in_channels, channel_growth, num_layers):
    method forward (line 175) | def forward(self, x):

FILE: mmagic/models/editors/real_basicvsr/real_basicvsr.py
  class RealBasicVSR (line 14) | class RealBasicVSR(RealESRGAN):
    method __init__ (line 54) | def __init__(self,
    method extract_gt_data (line 88) | def extract_gt_data(self, data_samples):
    method g_step (line 117) | def g_step(self, batch_outputs, batch_gt_data):
    method train_step (line 141) | def train_step(self, data: List[dict],
    method forward_train (line 199) | def forward_train(self, batch_inputs, data_samples=None):

FILE: mmagic/models/editors/real_basicvsr/real_basicvsr_net.py
  class RealBasicVSRNet (line 11) | class RealBasicVSRNet(BaseModule):
    method __init__ (line 37) | def __init__(self,
    method forward (line 65) | def forward(self, lqs, return_lqs=False):

FILE: mmagic/models/editors/real_esrgan/real_esrgan.py
  class RealESRGAN (line 11) | class RealESRGAN(SRGAN):
    method __init__ (line 54) | def __init__(self,
    method forward_tensor (line 95) | def forward_tensor(self, inputs, data_samples=None, training=False):
    method g_step (line 116) | def g_step(self, batch_outputs, batch_gt_data):
    method d_step_real (line 151) | def d_step_real(self, batch_outputs, batch_gt_data: torch.Tensor):
    method d_step_fake (line 171) | def d_step_fake(self, batch_outputs, batch_gt_data):
    method extract_gt_data (line 189) | def extract_gt_data(self, data_samples):

FILE: mmagic/models/editors/real_esrgan/unet_disc.py
  class UNetDiscriminatorWithSpectralNorm (line 10) | class UNetDiscriminatorWithSpectralNorm(BaseModule):
    method __init__ (line 21) | def __init__(self, in_channels, mid_channels=64, skip_connection=True):
    method forward (line 57) | def forward(self, img):

FILE: mmagic/models/editors/restormer/restormer_net.py
  function to_3d (line 14) | def to_3d(x):
  function to_4d (line 19) | def to_4d(x, h, w):
  class BiasFree_LayerNorm (line 24) | class BiasFree_LayerNorm(BaseModule):
    method __init__ (line 31) | def __init__(self, normalized_shape):
    method forward (line 42) | def forward(self, x):
  class WithBias_LayerNorm (line 55) | class WithBias_LayerNorm(BaseModule):
    method __init__ (line 62) | def __init__(self, normalized_shape):
    method forward (line 74) | def forward(self, x):
  class LayerNorm (line 88) | class LayerNorm(BaseModule):
    method __init__ (line 98) | def __init__(self, dim, LayerNorm_type):
    method forward (line 105) | def forward(self, x):
  class FeedForward (line 118) | class FeedForward(BaseModule):
    method __init__ (line 130) | def __init__(self, dim, ffn_expansion_factor, bias):
    method forward (line 150) | def forward(self, x):
  class Attention (line 166) | class Attention(BaseModule):
    method __init__ (line 178) | def __init__(self, dim, num_heads, bias):
    method forward (line 194) | def forward(self, x):
  class TransformerBlock (line 234) | class TransformerBlock(BaseModule):
    method __init__ (line 248) | def __init__(self, dim, num_heads, ffn_expansion_factor, bias,
    method forward (line 257) | def forward(self, x):
  class OverlapPatchEmbed (line 272) | class OverlapPatchEmbed(BaseModule):
    method __init__ (line 281) | def __init__(self, in_c=3, embed_dim=48, bias=False):
    method forward (line 287) | def forward(self, x):
  class Downsample (line 301) | class Downsample(BaseModule):
    method __init__ (line 308) | def __init__(self, n_feat):
    method forward (line 320) | def forward(self, x):
  class Upsample (line 332) | class Upsample(BaseModule):
    method __init__ (line 339) | def __init__(self, n_feat):
    method forward (line 351) | def forward(self, x):
  class Restormer (line 364) | class Restormer(BaseModule):
    method __init__ (line 391) | def __init__(self,
    method forward (line 506) | def forward(self, inp_img):

FILE: mmagic/models/editors/sagan/sagan.py
  class SAGAN (line 21) | class SAGAN(BaseConditionalGAN):
    method __init__ (line 52) | def __init__(self,
    method disc_loss (line 65) | def disc_loss(self, disc_pred_fake: Tensor,
    method gen_loss (line 92) | def gen_loss(self, disc_pred_fake: Tensor) -> Tuple[Tensor, dict]:
    method train_discriminator (line 112) | def train_discriminator(self, inputs: dict, data_samples: DataSample,
    method train_generator (line 146) | def train_generator(self, inputs: dict, data_samples: DataSample,

FILE: mmagic/models/editors/sagan/sagan_discriminator.py
  class ProjDiscriminator (line 19) | class ProjDiscriminator(BaseModule):
    method __init__ (line 120) | def __init__(self,
    method forward (line 248) | def forward(self, x, label=None):
    method init_weights (line 273) | def init_weights(self, pretrained=None, strict=True):

FILE: mmagic/models/editors/sagan/sagan_generator.py
  class SNGANGenerator (line 23) | class SNGANGenerator(BaseModule):
    method __init__ (line 122) | def __init__(self,
    method forward (line 240) | def forward(self, noise, num_batches=0, label=None, return_noise=False):
    method init_weights (line 320) | def init_weights(self, pretrained=None, strict=True):

FILE: mmagic/models/editors/sagan/sagan_modules.py
  class SNGANGenResBlock (line 19) | class SNGANGenResBlock(BaseModule):
    method __init__ (line 68) | def __init__(self,
    method forward (line 144) | def forward(self, x, y=None):
    method forward_shortcut (line 169) | def forward_shortcut(self, x):
    method init_weights (line 185) | def init_weights(self):
  class SNGANDiscResBlock (line 216) | class SNGANDiscResBlock(BaseModule):
    method __init__ (line 247) | def __init__(self,
    method forward (line 302) | def forward(self, x):
    method forward_shortcut (line 322) | def forward_shortcut(self, x: Tensor) -> Tensor:
    method init_weights (line 338) | def init_weights(self):
  class SNGANDiscHeadResBlock (line 369) | class SNGANDiscHeadResBlock(BaseModule):
    method __init__ (line 398) | def __init__(self,
    method forward (line 445) | def forward(self, x: Tensor) -> Tensor:
    method forward_shortcut (line 462) | def forward_shortcut(self, x: Tensor) -> Tensor:
    method init_weights (line 475) | def init_weights(self):
  class SNConditionNorm (line 499) | class SNConditionNorm(BaseModule):
    method __init__ (line 538) | def __init__(self,
    method forward (line 599) | def forward(self, x, y=None):
    method init_weights (line 621) | def init_weights(self):

FILE: mmagic/models/editors/singan/singan.py
  class SinGAN (line 28) | class SinGAN(BaseGAN):
    method __init__ (line 79) | def __init__(self,
    method load_test_pkl (line 111) | def load_test_pkl(self):
    method _from_numpy (line 123) | def _from_numpy(self,
    method get_module (line 146) | def get_module(self, model: nn.Module, module_name: str) -> nn.Module:
    method construct_fixed_noises (line 162) | def construct_fixed_noises(self):
    method forward (line 173) | def forward(self,
    method gen_loss (line 323) | def gen_loss(self, disc_pred_fake: Tensor,
    method disc_loss (line 348) | def disc_loss(self, disc_pred_fake: Tensor, disc_pred_real: Tensor,
    method train_generator (line 402) | def train_generator(self, inputs: dict, data_samples: List[DataSample],
    method train_discriminator (line 436) | def train_discriminator(self, inputs: dict, data_samples: List[DataSam...
    method train_gan (line 467) | def train_gan(self, inputs_dict: dict, data_sample: List[DataSample],
    method train_step (line 543) | def train_step(self, data: dict,
    method test_step (line 640) | def test_step(self, data: dict) -> SampleList:

FILE: mmagic/models/editors/singan/singan_discriminator.py
  class SinGANMultiScaleDiscriminator (line 12) | class SinGANMultiScaleDiscriminator(BaseModule):
    method __init__ (line 36) | def __init__(self,
    method forward (line 63) | def forward(self, x, curr_scale):
    method check_and_load_prev_weight (line 77) | def check_and_load_prev_weight(self, curr_scale):

FILE: mmagic/models/editors/singan/singan_generator.py
  class SinGANMultiScaleGenerator (line 17) | class SinGANMultiScaleGenerator(BaseModule):
    method __init__ (line 50) | def __init__(self,
    method forward (line 91) | def forward(self,
    method check_and_load_prev_weight (line 169) | def check_and_load_prev_weight(self, curr_scale):

FILE: mmagic/models/editors/singan/singan_modules.py
  class GeneratorBlock (line 10) | class GeneratorBlock(nn.Module):
    method __init__ (line 36) | def __init__(self,
    method forward (line 94) | def forward(self, x, prev):
    method init_weights (line 114) | def init_weights(self, pretrained=None):
  class DiscriminatorBlock (line 129) | class DiscriminatorBlock(nn.Module):
    method __init__ (line 149) | def __init__(self,
    method forward (line 202) | def forward(self, x):
    method init_weights (line 218) | def init_weights(self, pretrained=None):

FILE: mmagic/models/editors/srcnn/srcnn_net.py
  class SRCNNNet (line 9) | class SRCNNNet(BaseModule):
    method __init__ (line 27) | def __init__(self,
    method forward (line 61) | def forward(self, x):

FILE: mmagic/models/editors/srgan/modified_vgg.py
  class ModifiedVGG (line 9) | class ModifiedVGG(BaseModule):
    method __init__ (line 20) | def __init__(self, in_channels, mid_channels):
    method forward (line 62) | def forward(self, x):

FILE: mmagic/models/editors/srgan/sr_resnet.py
  class MSRResNet (line 11) | class MSRResNet(BaseModule):
    method __init__ (line 31) | def __init__(self,
    method forward (line 83) | def forward(self, x):
    method init_weights (line 107) | def init_weights(self):

FILE: mmagic/models/editors/srgan/srgan.py
  class SRGAN (line 13) | class SRGAN(BaseEditModel):
    method __init__ (line 35) | def __init__(self,
    method forward_train (line 76) | def forward_train(self, inputs, data_samples=None, **kwargs):
    method forward_tensor (line 92) | def forward_tensor(self, inputs, data_samples=None, training=False):
    method if_run_g (line 110) | def if_run_g(self):
    method if_run_d (line 116) | def if_run_d(self):
    method g_step (line 121) | def g_step(self, batch_outputs: torch.Tensor, batch_gt_data: torch.Ten...
    method d_step_real (line 155) | def d_step_real(self, batch_outputs, batch_gt_data: torch.Tensor):
    method d_step_fake (line 173) | def d_step_fake(self, batch_outputs: torch.Tensor, batch_gt_data):
    method g_step_with_optim (line 191) | def g_step_with_optim(self, batch_outputs: torch.Tensor,
    

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  {
    "path": ".circleci/config.yml",
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    "preview": "version: 2.1\n\n# this allows you to use CircleCI's dynamic configuration feature\nsetup: true\n\n# the path-filtering orb is"
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  {
    "path": ".circleci/docker/Dockerfile",
    "chars": 531,
    "preview": "ARG PYTORCH=\"1.8.1\"\nARG CUDA=\"10.2\"\nARG CUDNN=\"7\"\n\nFROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel\n\n# To"
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  {
    "path": ".circleci/test.yml",
    "chars": 5521,
    "preview": "version: 2.1\n\n# the default pipeline parameters, which will be updated according to\n# the results of the path-filtering "
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    "path": ".dele.yml",
    "chars": 1128,
    "preview": "dele_rules:\n  issue:\n    create:\n      - name: \"english needs\"\n        conditions:\n          - \"issue.title ~= ANSI\"\n   "
  },
  {
    "path": ".dev_scripts/README.md",
    "chars": 8469,
    "preview": "# Scripts for developing MMagic\n\n- [1. Check UT](#1-check-ut)\n- [2. Test all the models](#2-test-all-the-models)\n- [3. T"
  },
  {
    "path": ".dev_scripts/create_ceph_configs.py",
    "chars": 13197,
    "preview": "import glob\nimport os.path as osp\nimport shutil\nfrom argparse import ArgumentParser\nfrom copy import deepcopy\n\nfrom mmen"
  },
  {
    "path": ".dev_scripts/doc_link_checker.py",
    "chars": 3487,
    "preview": "# Copyright (c) MegFlow. All rights reserved.\n# Copyright (c) OpenMMLab. All rights reserved.\n# /bin/python3\n\nimport arg"
  },
  {
    "path": ".dev_scripts/download_models.py",
    "chars": 5443,
    "preview": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\nimport argparse\nimport os\nimport platform\nimport "
  },
  {
    "path": ".dev_scripts/inference_benchmark.sh",
    "chars": 5061,
    "preview": "python demo/download_inference_resources.py\n\n# Text-to-Image\npython demo/mmagic_inference_demo.py \\\n        --model-name"
  },
  {
    "path": ".dev_scripts/job_watcher.py",
    "chars": 5689,
    "preview": "import os\nimport os.path as osp\nimport time\nfrom argparse import ArgumentParser\nfrom functools import partial\n\nfrom pret"
  },
  {
    "path": ".dev_scripts/metric_mapping.py",
    "chars": 4529,
    "preview": "# key-in-metafile: key-in-results.pkl\nMETRICS_MAPPING = {\n    'FID': {\n        'keys': ['FID-Full-50k/fid'],\n        'to"
  },
  {
    "path": ".dev_scripts/task_mapping.py",
    "chars": 680,
    "preview": "TASK_MAPPING = {\n    'Inpainting': [\n        'aot-gan',\n        'deepfillv1',\n        'deepfillv2',\n        'global_loca"
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  {
    "path": ".dev_scripts/test_benchmark.py",
    "chars": 14872,
    "preview": "import argparse\nimport os\nimport os.path as osp\nimport pickle\nimport re\nfrom collections import OrderedDict, defaultdict"
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    "path": ".dev_scripts/test_benchmark.yml",
    "chars": 2996,
    "preview": "cases:\n- name: controlnet-1xb1-fill50k\n  params:\n    config: configs/controlnet/controlnet-1xb1-fill50k.py\n    cpus_per_"
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  {
    "path": ".dev_scripts/train_benchmark.py",
    "chars": 15834,
    "preview": "import argparse\nimport os\nimport os.path as osp\nimport pickle\nimport re\nfrom collections import OrderedDict\nfrom datetim"
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    "path": ".dev_scripts/train_benchmark.yml",
    "chars": 2997,
    "preview": "cases:\n- name: controlnet-1xb1-fill50k\n  params:\n    config: configs/controlnet/controlnet-1xb1-fill50k.py\n    cpus_per_"
  },
  {
    "path": ".dev_scripts/update_config_readme.py",
    "chars": 2384,
    "preview": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\n# This tool is used to update README.md and READM"
  },
  {
    "path": ".dev_scripts/update_model_index.py",
    "chars": 10870,
    "preview": "#!/usr/bin/env python\n# Copyright (c) OpenMMLab. All rights reserved.\n\"\"\"This tool is used to update model-index.yml whi"
  },
  {
    "path": ".dev_scripts/update_ut.py",
    "chars": 2302,
    "preview": "import os\nimport os.path as osp\nfrom argparse import ArgumentParser\nfrom fnmatch import fnmatch\nfrom glob import glob\n\nf"
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    "chars": 448,
    "preview": "from .job_util import (filter_jobs, get_info_from_id, parse_job_list,\n                       parse_job_list_from_file)\nf"
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    "chars": 3973,
    "preview": "import os\nimport os.path as osp\nfrom typing import Tuple\n\nfrom rich import print as pprint\nfrom rich.table import Table\n"
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  {
    "path": ".dev_scripts/utils/modelindex.py",
    "chars": 2405,
    "preview": "import os.path as osp\n\nimport mmengine\nfrom modelindex.models.Collection import Collection\nfrom modelindex.models.Model "
  },
  {
    "path": ".github/CODE_OF_CONDUCT.md",
    "chars": 3355,
    "preview": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, w"
  },
  {
    "path": ".github/CONTRIBUTING.md",
    "chars": 2265,
    "preview": "# Contributing to MMagic\n\nAll kinds of contributions are welcome, including but not limited to the following.\n\n- Fix typ"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/1-bug-report.yml",
    "chars": 3891,
    "preview": "name: \"🐞 Bug report\"\ndescription: \"Create a report to help us reproduce and fix the bug\"\nlabels: \"kind/bug,status/unconf"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/2-feature-request.yml",
    "chars": 1072,
    "preview": "name: 🚀 Feature request\ndescription: Suggest an idea for this project\nlabels: \"kind/enhancement,status/unconfirmed\"\ntitl"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/3-new-model.yml",
    "chars": 1084,
    "preview": "name: \"\\U0001F31F New model/dataset/scheduler addition\"\ndescription: Submit a proposal/request to implement a new model "
  },
  {
    "path": ".github/ISSUE_TEMPLATE/4-documentation.yml",
    "chars": 822,
    "preview": "name: 📚 Documentation\ndescription: Report an issue related to the documentation.\nlabels: \"kind/doc,status/unconfirmed\"\nt"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/config.yml",
    "chars": 313,
    "preview": "blank_issues_enabled: false\n\ncontact_links:\n  - name: 💬 Forum\n    url: https://github.com/open-mmlab/mmagic/discussions\n"
  },
  {
    "path": ".github/pull_request_template.md",
    "chars": 1862,
    "preview": "Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more he"
  },
  {
    "path": ".github/workflows/lint.yml",
    "chars": 731,
    "preview": "name: lint\n\non: [push, pull_request]\n\nconcurrency:\n  group: ${{ github.workflow }}-${{ github.ref }}\n  cancel-in-progres"
  },
  {
    "path": ".github/workflows/merge_stage_test.yml",
    "chars": 8227,
    "preview": "name: merge_stage_test\n\non:\n  push:\n    paths-ignore:\n      - 'README.md'\n      - 'README_zh-CN.md'\n      - 'docs/**'\n  "
  },
  {
    "path": ".github/workflows/pr_stage_test.yml",
    "chars": 5013,
    "preview": "name: pr_stage_test\n\non:\n  pull_request:\n    paths-ignore:\n      - 'README.md'\n      - 'README_zh-CN.md'\n      - '.owner"
  },
  {
    "path": ".github/workflows/publish-to-pypi.yml",
    "chars": 767,
    "preview": "name: deploy\n\non: push\n\nconcurrency:\n  group: ${{ github.workflow }}-${{ github.ref }}\n  cancel-in-progress: true\n\njobs:"
  },
  {
    "path": ".github/workflows/test_mim.yml",
    "chars": 1169,
    "preview": "name: test-mim\n\non:\n  push:\n    paths:\n      - 'model-index.yml'\n      - 'configs/**'\n\n  pull_request:\n    paths:\n      "
  },
  {
    "path": ".gitignore",
    "chars": 1669,
    "preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n**/*.pyc\n\n# C extensions\n*.so\n\n# Distribution "
  },
  {
    "path": ".owners.yml",
    "chars": 435,
    "preview": "# assign issues to owners automatically\nassign:\n  issues: enabled # or disabled\n  pull_requests: enabled # or disabled\n "
  },
  {
    "path": ".pre-commit-config.yaml",
    "chars": 2472,
    "preview": "exclude: ^tests/data/\nrepos:\n  - repo: https://github.com/PyCQA/flake8\n    rev: 4.0.1\n    hooks:\n      - id: flake8\n  - "
  },
  {
    "path": ".pylintrc",
    "chars": 18931,
    "preview": "[MASTER]\n\n# A comma-separated list of package or module names from where C extensions may\n# be loaded. Extensions are lo"
  },
  {
    "path": ".readthedocs.yml",
    "chars": 198,
    "preview": "version: 2\n\nformats: [pdf, epub]\n\nbuild:\n  os: ubuntu-22.04\n  tools:\n    python: \"3.7\"\n\npython:\n  install:\n    - require"
  },
  {
    "path": "CITATION.cff",
    "chars": 343,
    "preview": "cff-version: 1.2.0\nmessage: \"If you use this software, please cite it as below.\"\nauthors:\n  - family-names: MMagic\n    g"
  },
  {
    "path": "LICENSE",
    "chars": 12024,
    "preview": "Copyright (c) OpenMMLab. All rights reserved.\n\n                                 Apache License\n                         "
  },
  {
    "path": "MANIFEST.in",
    "chars": 281,
    "preview": "include requirements/*.txt\ninclude mmagic/.mim/VERSION\ninclude mmagic/.mim/model-index.yml\ninclude mmagic/evaluation/met"
  },
  {
    "path": "README.md",
    "chars": 22919,
    "preview": "<div id=\"top\" align=\"center\">\n  <img src=\"docs/en/_static/image/mmagic-logo.png\" width=\"500px\"/>\n  <div>&nbsp;</div>\n  <"
  },
  {
    "path": "README_zh-CN.md",
    "chars": 18840,
    "preview": "<div id=\"top\" align=\"center\">\n  <img src=\"docs/zh_cn/_static/image/mmagic-logo.png\" width=\"500px\"/>\n  <div>&nbsp;</div>\n"
  },
  {
    "path": "configs/_base_/datasets/basicvsr_test_config.py",
    "chars": 5616,
    "preview": "# configs for REDS4\nreds_data_root = 'data/REDS'\n\nreds_pipeline = [\n    dict(type='GenerateSegmentIndices', interval_lis"
  },
  {
    "path": "configs/_base_/datasets/celeba.py",
    "chars": 1080,
    "preview": "# Base config for CelebA-HQ dataset\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data/CelebA-HQ'\n"
  },
  {
    "path": "configs/_base_/datasets/cifar10_noaug.py",
    "chars": 692,
    "preview": "cifar_pipeline = [dict(type='PackInputs')]\ncifar_dataset = dict(\n    type='CIFAR10',\n    data_root='./data',\n    data_pr"
  },
  {
    "path": "configs/_base_/datasets/cifar10_nopad.py",
    "chars": 1017,
    "preview": "cifar_pipeline = [\n    dict(type='Flip', keys=['gt'], flip_ratio=0.5, direction='horizontal'),\n    dict(type='PackInputs"
  },
  {
    "path": "configs/_base_/datasets/comp1k.py",
    "chars": 925,
    "preview": "# Base config for Composition-1K dataset\n\n# dataset settings\ndataset_type = 'AdobeComp1kDataset'\ndata_root = 'data/adobe"
  },
  {
    "path": "configs/_base_/datasets/deblurring-defocus_test_config.py",
    "chars": 2847,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/deblurring-motion_test_config.py",
    "chars": 2858,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/decompression_test_config.py",
    "chars": 1732,
    "preview": "quality = 10\ntest_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color'"
  },
  {
    "path": "configs/_base_/datasets/denoising-gaussian_color_test_config.py",
    "chars": 2927,
    "preview": "sigma = 15\ntest_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n"
  },
  {
    "path": "configs/_base_/datasets/denoising-gaussian_gray_test_config.py",
    "chars": 2399,
    "preview": "sigma = 15\ntest_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n"
  },
  {
    "path": "configs/_base_/datasets/denoising-real_test_config.py",
    "chars": 1618,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/deraining_test_config.py",
    "chars": 3579,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/ffhq_flip.py",
    "chars": 1246,
    "preview": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n    dict(type='LoadImageFromFile', key='gt'),\n    dict(type='Flip"
  },
  {
    "path": "configs/_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py",
    "chars": 1180,
    "preview": "dataset_type = 'GrowScaleImgDataset'\n\npipeline = [\n    dict(type='LoadImageFromFile', key='gt'),\n    dict(type='Flip', k"
  },
  {
    "path": "configs/_base_/datasets/imagenet_128.py",
    "chars": 1382,
    "preview": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `Ran"
  },
  {
    "path": "configs/_base_/datasets/imagenet_256.py",
    "chars": 1387,
    "preview": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `Ran"
  },
  {
    "path": "configs/_base_/datasets/imagenet_512.py",
    "chars": 1384,
    "preview": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `Ran"
  },
  {
    "path": "configs/_base_/datasets/imagenet_64.py",
    "chars": 1378,
    "preview": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# We use `Ran"
  },
  {
    "path": "configs/_base_/datasets/imagenet_noaug_128.py",
    "chars": 1321,
    "preview": "# dataset settings\ndataset_type = 'ImageNet'\n\n# different from mmcls, we adopt the setting used in BigGAN.\n# Remove `Ran"
  },
  {
    "path": "configs/_base_/datasets/liif_test_config.py",
    "chars": 2830,
    "preview": "scale_test_list = [2, 3, 4, 6, 18, 30]\n\ntest_pipelines = [[\n    dict(\n        type='LoadImageFromFile',\n        key='gt'"
  },
  {
    "path": "configs/_base_/datasets/lsun_stylegan.py",
    "chars": 1162,
    "preview": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n    dict(type='LoadImageFromFile', key='gt'),\n    dict(type='Pack"
  },
  {
    "path": "configs/_base_/datasets/paired_imgs_256x256_crop.py",
    "chars": 2930,
    "preview": "dataset_type = 'PairedImageDataset'\n# domain_a = None  # set by user\n# domain_b = None  # set by user\n\ntrain_pipeline = "
  },
  {
    "path": "configs/_base_/datasets/places.py",
    "chars": 1229,
    "preview": "# Base config for places365 dataset\n\n# dataset settings\ndataset_type = 'BasicImageDataset'\ndata_root = 'data/Places'\n\ntr"
  },
  {
    "path": "configs/_base_/datasets/sisr_x2_test_config.py",
    "chars": 2573,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/sisr_x3_test_config.py",
    "chars": 2522,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/sisr_x4_test_config.py",
    "chars": 2522,
    "preview": "test_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n        color_type='color',\n        cha"
  },
  {
    "path": "configs/_base_/datasets/tdan_test_config.py",
    "chars": 3744,
    "preview": "# configs for SPMCS-30\nSPMC_data_root = 'data/SPMCS'\n\nSPMC_pipeline = [\n    dict(type='GenerateFrameIndiceswithPadding',"
  },
  {
    "path": "configs/_base_/datasets/unconditional_imgs_128x128.py",
    "chars": 1172,
    "preview": "# dataset_type = 'BasicImageDataset'\ndataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n    dict(type='LoadImageFrom"
  },
  {
    "path": "configs/_base_/datasets/unconditional_imgs_64x64.py",
    "chars": 1133,
    "preview": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n    dict(type='LoadImageFromFile', key='gt'),\n    dict(type='Resi"
  },
  {
    "path": "configs/_base_/datasets/unconditional_imgs_flip_512x512.py",
    "chars": 1212,
    "preview": "dataset_type = 'BasicImageDataset'\n\n# TODO:\ntrain_pipeline = [\n    dict(type='LoadImageFromFile', key='gt'),\n    dict(ty"
  },
  {
    "path": "configs/_base_/datasets/unconditional_imgs_flip_lanczos_resize_256x256.py",
    "chars": 1279,
    "preview": "dataset_type = 'BasicImageDataset'\n\ntrain_pipeline = [\n    dict(type='LoadImageFromFile', key='gt'),\n    dict(\n        t"
  },
  {
    "path": "configs/_base_/datasets/unpaired_imgs_256x256.py",
    "chars": 3146,
    "preview": "dataset_type = 'UnpairedImageDataset'\ndomain_a = None  # set by user\ndomain_b = None  # set by user\ntrain_pipeline = [\n "
  },
  {
    "path": "configs/_base_/default_runtime.py",
    "chars": 1030,
    "preview": "default_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n    timer=dict(type='IterTimerHook'),\n    logge"
  },
  {
    "path": "configs/_base_/gen_default_runtime.py",
    "chars": 2027,
    "preview": "default_scope = 'mmagic'\n\nrandomness = dict(seed=2022, diff_rank_seed=True)\n# env settings\ndist_params = dict(backend='n"
  },
  {
    "path": "configs/_base_/inpaint_default_runtime.py",
    "chars": 964,
    "preview": "default_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n    timer=dict(type='IterTimerHook'),\n    logge"
  },
  {
    "path": "configs/_base_/matting_default_runtime.py",
    "chars": 989,
    "preview": "default_scope = 'mmagic'\nsave_dir = './work_dirs'\n\ndefault_hooks = dict(\n    timer=dict(type='IterTimerHook'),\n    logge"
  },
  {
    "path": "configs/_base_/models/base_cyclegan.py",
    "chars": 779,
    "preview": "_domain_a = None  # set by user\n_domain_b = None  # set by user\nmodel = dict(\n    type='CycleGAN',\n    data_preprocessor"
  },
  {
    "path": "configs/_base_/models/base_deepfillv1.py",
    "chars": 3206,
    "preview": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n    type='De"
  },
  {
    "path": "configs/_base_/models/base_deepfillv2.py",
    "chars": 3257,
    "preview": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n    type='Tw"
  },
  {
    "path": "configs/_base_/models/base_edvr.py",
    "chars": 3216,
    "preview": "_base_ = '../default_runtime.py'\n\nscale = 4\n\ntrain_pipeline = [\n    dict(type='GenerateFrameIndices', interval_list=[1],"
  },
  {
    "path": "configs/_base_/models/base_gl.py",
    "chars": 1510,
    "preview": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n    type='GL"
  },
  {
    "path": "configs/_base_/models/base_glean.py",
    "chars": 1366,
    "preview": "_base_ = '../default_runtime.py'\n\n# DistributedDataParallel\nmodel_wrapper_cfg = dict(\n    type='MMSeparateDistributedDat"
  },
  {
    "path": "configs/_base_/models/base_liif.py",
    "chars": 3518,
    "preview": "_base_ = '../default_runtime.py'\nwork_dir = './work_dirs/liif'\nsave_dir = './work_dirs'\n\nscale_min, scale_max = 1, 4\nsca"
  },
  {
    "path": "configs/_base_/models/base_pconv.py",
    "chars": 1267,
    "preview": "# DistributedDataParallel\nmodel_wrapper_cfg = dict(type='MMSeparateDistributedDataParallel')\n\nmodel = dict(\n    type='PC"
  },
  {
    "path": "configs/_base_/models/base_pix2pix.py",
    "chars": 813,
    "preview": "source_domain = None  # set by user\ntarget_domain = None  # set by user\n# model settings\nmodel = dict(\n    type='Pix2Pix"
  },
  {
    "path": "configs/_base_/models/base_styleganv1.py",
    "chars": 275,
    "preview": "model = dict(\n    type='StyleGANv1',\n    data_preprocessor=dict(type='DataPreprocessor'),\n    style_channels=512,\n    ge"
  },
  {
    "path": "configs/_base_/models/base_styleganv2.py",
    "chars": 797,
    "preview": "# define GAN model\n\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ra"
  },
  {
    "path": "configs/_base_/models/base_styleganv3.py",
    "chars": 842,
    "preview": "# define GAN model\n\nd_reg_interval = 16\ng_reg_interval = 4\n\ng_reg_ratio = g_reg_interval / (g_reg_interval + 1)\nd_reg_ra"
  },
  {
    "path": "configs/_base_/models/base_tof.py",
    "chars": 2828,
    "preview": "_base_ = '../default_runtime.py'\n\ntrain_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        key='img',\n    "
  },
  {
    "path": "configs/_base_/models/biggan/base_biggan_128x128.py",
    "chars": 812,
    "preview": "model = dict(\n    type='BigGAN',\n    num_classes=1000,\n    data_preprocessor=dict(type='DataPreprocessor'),\n    generato"
  },
  {
    "path": "configs/_base_/models/dcgan/base_dcgan_128x128.py",
    "chars": 349,
    "preview": "# define GAN model\nmodel = dict(\n    type='DCGAN',\n    noise_size=100,\n    data_preprocessor=dict(type='DataPreprocessor"
  },
  {
    "path": "configs/_base_/models/dcgan/base_dcgan_64x64.py",
    "chars": 336,
    "preview": "# define GAN model\nmodel = dict(\n    type='DCGAN',\n    noise_size=100,\n    data_preprocessor=dict(type='DataPreprocessor"
  },
  {
    "path": "configs/_base_/models/sagan/base_sagan_128x128.py",
    "chars": 621,
    "preview": "model = dict(\n    type='SAGAN',\n    num_classes=1000,\n    data_preprocessor=dict(type='DataPreprocessor'),\n    generator"
  },
  {
    "path": "configs/_base_/models/sagan/base_sagan_32x32.py",
    "chars": 667,
    "preview": "model = dict(\n    type='SAGAN',\n    data_preprocessor=dict(type='DataPreprocessor'),\n    num_classes=10,\n    generator=d"
  },
  {
    "path": "configs/_base_/models/sngan_proj/base_sngan_proj_128x128.py",
    "chars": 326,
    "preview": "# define GAN model\nmodel = dict(\n    type='SNGAN',\n    num_classes=1000,\n    data_preprocessor=dict(type='DataPreprocess"
  },
  {
    "path": "configs/_base_/models/sngan_proj/base_sngan_proj_32x32.py",
    "chars": 324,
    "preview": "# define GAN model\nmodel = dict(\n    type='SNGAN',\n    num_classes=10,\n    data_preprocessor=dict(type='DataPreprocessor"
  },
  {
    "path": "configs/_base_/schedules/.gitkeep",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "configs/animatediff/README.md",
    "chars": 16588,
    "preview": "# AnimateDiff (2023)\n\n> [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning]("
  },
  {
    "path": "configs/animatediff/README_zh-CN.md",
    "chars": 15113,
    "preview": "# AnimateDiff (2023)\n\n> [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning]("
  },
  {
    "path": "configs/animatediff/animatediff_Lyriel.py",
    "chars": 2065,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'"
  },
  {
    "path": "configs/animatediff/animatediff_MajicMix.py",
    "chars": 2089,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'"
  },
  {
    "path": "configs/animatediff/animatediff_RcnzCartoon.py",
    "chars": 2072,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'"
  },
  {
    "path": "configs/animatediff/animatediff_RealisticVision.py",
    "chars": 2086,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'"
  },
  {
    "path": "configs/animatediff/animatediff_RealisticVision_v2.py",
    "chars": 2152,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'"
  },
  {
    "path": "configs/animatediff/animatediff_ToonYou.py",
    "chars": 2847,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\nmodels_path = '/home/AnimateDiff/models/'"
  },
  {
    "path": "configs/animatediff/metafile.yml",
    "chars": 2387,
    "preview": "Collections:\n- Name: AnimateDiff\n  Paper:\n    Title: 'AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Mod"
  },
  {
    "path": "configs/aot_gan/README.md",
    "chars": 6475,
    "preview": "# AOT-GAN (TVCG'2021)\n\n> [AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://a"
  },
  {
    "path": "configs/aot_gan/README_zh-CN.md",
    "chars": 3876,
    "preview": "# AOT-GAN (TVCG'2021)\n\n> [AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://a"
  },
  {
    "path": "configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py",
    "chars": 3866,
    "preview": "_base_ = [\n    '../_base_/inpaint_default_runtime.py', '../_base_/datasets/places.py'\n]\n\nexperiment_name = 'aot-gan_smpg"
  },
  {
    "path": "configs/aot_gan/metafile.yml",
    "chars": 655,
    "preview": "Collections:\n- Name: AOT-GAN\n  Paper:\n    Title: 'AOT-GAN: Aggregated Contextual Transformations for High-Resolution Ima"
  },
  {
    "path": "configs/basicvsr/README.md",
    "chars": 10685,
    "preview": "# BasicVSR (CVPR'2021)\n\n> [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://a"
  },
  {
    "path": "configs/basicvsr/README_zh-CN.md",
    "chars": 3811,
    "preview": "# BasicVSR (CVPR'2021)\n\n> **任务**: 视频超分辨率\n\n<!-- [ALGORITHM] -->\n\n<details>\n<summary align=\"right\">BasicVSR (CVPR'2021)</s"
  },
  {
    "path": "configs/basicvsr/basicvsr_2xb4_reds4.py",
    "chars": 3567,
    "preview": "_base_ = [\n    '../_base_/default_runtime.py',\n    '../_base_/datasets/basicvsr_test_config.py'\n]\n\nexperiment_name = 'ba"
  },
  {
    "path": "configs/basicvsr/basicvsr_2xb4_vimeo90k-bd.py",
    "chars": 2548,
    "preview": "_base_ = './basicvsr_2xb4_reds4.py'\n\nscale = 4\nexperiment_name = 'basicvsr_2xb4_vimeo90k-bd'\nwork_dir = f'./work_dirs/{e"
  },
  {
    "path": "configs/basicvsr/basicvsr_2xb4_vimeo90k-bi.py",
    "chars": 425,
    "preview": "_base_ = './basicvsr_2xb4_vimeo90k-bd.py'\n\nexperiment_name = 'basicvsr_2xb4_vimeo90k-bi'\nwork_dir = f'./work_dirs/{exper"
  },
  {
    "path": "configs/basicvsr/metafile.yml",
    "chars": 3143,
    "preview": "Collections:\n- Name: BasicVSR\n  Paper:\n    Title: 'BasicVSR: The Search for Essential Components in Video Super-Resoluti"
  },
  {
    "path": "configs/basicvsr_pp/README.md",
    "chars": 14837,
    "preview": "# BasicVSR++ (CVPR'2022)\n\n> [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https"
  },
  {
    "path": "configs/basicvsr_pp/README_zh-CN.md",
    "chars": 6085,
    "preview": "# BasicVSR++ (CVPR'2022)\n\n> **任务**: 视频超分辨率\n\n<!-- [ALGORITHM] -->\n\n<details>\n<summary align=\"right\"><a href=\"https://arxi"
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track1.py",
    "chars": 1881,
    "preview": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'basicvsr-pp_c128n25_600k_ntire-decompress-track1'\nwork_dir ="
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track2.py",
    "chars": 384,
    "preview": "_base_ = './basicvsr-pp_c128n25_600k_ntire-decompress-track1.py'\n\nexperiment_name = 'basicvsr-pp_c128n25_600k_ntire-deco"
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track3.py",
    "chars": 384,
    "preview": "_base_ = './basicvsr-pp_c128n25_600k_ntire-decompress-track1.py'\n\nexperiment_name = 'basicvsr-pp_c128n25_600k_ntire-deco"
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-vsr.py",
    "chars": 1776,
    "preview": "experiment_name = 'basicvsr-pp_c128n25_600k_ntire-vsr'\n\n# model settings\nmodel = dict(\n    type='BasicVSR',\n    generato"
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py",
    "chars": 1274,
    "preview": "_base_ = '../basicvsr/basicvsr_2xb4_vimeo90k-bd.py'\n\nexperiment_name = 'basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd'\nwork_di"
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py",
    "chars": 1274,
    "preview": "_base_ = '../basicvsr/basicvsr_2xb4_vimeo90k-bi.py'\n\nexperiment_name = 'basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi'\nwork_di"
  },
  {
    "path": "configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py",
    "chars": 1363,
    "preview": "_base_ = '../basicvsr/basicvsr_2xb4_reds4.py'\n\nexperiment_name = 'basicvsr-pp_c64n7_8xb1-600k_reds4'\nwork_dir = f'./work"
  },
  {
    "path": "configs/basicvsr_pp/metafile.yml",
    "chars": 5065,
    "preview": "Collections:\n- Name: BasicVSR++\n  Paper:\n    Title: 'BasicVSR++: Improving Video Super-Resolution with Enhanced Propagat"
  },
  {
    "path": "configs/biggan/README.md",
    "chars": 9198,
    "preview": "# BigGAN (ICLR'2019)\n\n> [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://openreview.net/foru"
  },
  {
    "path": "configs/biggan/README_zh-CN.md",
    "chars": 8164,
    "preview": "# BigGAN (ICLR'2019)\n\n> [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://openreview.net/foru"
  },
  {
    "path": "configs/biggan/biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py",
    "chars": 1577,
    "preview": "_base_ = [\n    '../_base_/datasets/imagenet_noaug_128.py',\n    '../_base_/gen_default_runtime.py',\n]\n\nema_config = dict("
  },
  {
    "path": "configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py",
    "chars": 1796,
    "preview": "_base_ = [\n    '../_base_/datasets/imagenet_noaug_128.py',\n    '../_base_/gen_default_runtime.py',\n]\n\n# setting image si"
  },
  {
    "path": "configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py",
    "chars": 1796,
    "preview": "_base_ = [\n    '../_base_/datasets/imagenet_noaug_128.py',\n    '../_base_/gen_default_runtime.py',\n]\n\n# setting image si"
  },
  {
    "path": "configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py",
    "chars": 2325,
    "preview": "_base_ = [\n    '../_base_/datasets/cifar10_noaug.py',\n    '../_base_/gen_default_runtime.py',\n]\n\n# define model\nema_conf"
  },
  {
    "path": "configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py",
    "chars": 1703,
    "preview": "_base_ = [\n    '../_base_/models/biggan/base_biggan_128x128.py',\n    '../_base_/datasets/imagenet_noaug_128.py',\n    '.."
  },
  {
    "path": "configs/biggan/biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py",
    "chars": 1514,
    "preview": "_base_ = [\n    '../_base_/datasets/imagenet_noaug_128.py',\n    '../_base_/gen_default_runtime.py',\n]\n\nema_config = dict("
  },
  {
    "path": "configs/biggan/metafile.yml",
    "chars": 2818,
    "preview": "Collections:\n- Name: BigGAN\n  Paper:\n    Title: Large Scale GAN Training for High Fidelity Natural Image Synthesis\n    U"
  },
  {
    "path": "configs/cain/README.md",
    "chars": 4521,
    "preview": "# CAIN (AAAI'2020)\n\n> [Channel Attention Is All You Need for Video Frame Interpolation](https://aaai.org/ojs/index.php/A"
  },
  {
    "path": "configs/cain/README_zh-CN.md",
    "chars": 2704,
    "preview": "# CAIN (AAAI'2020)\n\n> **任务**: 视频插帧\n\n<!-- [ALGORITHM] -->\n\n<details>\n<summary align=\"right\"><a href=\"https://aaai.org/ojs"
  },
  {
    "path": "configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py",
    "chars": 4132,
    "preview": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'cain_g1b32_1xb5_vimeo90k-triplet'\nwork_dir = f'./work_dirs/{"
  },
  {
    "path": "configs/cain/metafile.yml",
    "chars": 632,
    "preview": "Collections:\n- Name: CAIN\n  Paper:\n    Title: Channel Attention Is All You Need for Video Frame Interpolation\n    URL: h"
  },
  {
    "path": "configs/controlnet/README.md",
    "chars": 11088,
    "preview": "# Control Net (2023)\n\n> [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)"
  },
  {
    "path": "configs/controlnet/controlnet-1xb1-fill50k.py",
    "chars": 2446,
    "preview": "_base_ = '../_base_/gen_default_runtime.py'\n\n# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1"
  },
  {
    "path": "configs/controlnet/controlnet-canny.py",
    "chars": 1113,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-con"
  },
  {
    "path": "configs/controlnet/controlnet-pose.py",
    "chars": 1117,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-con"
  },
  {
    "path": "configs/controlnet/controlnet-seg.py",
    "chars": 1111,
    "preview": "# config for model\nstable_diffusion_v15_url = 'runwayml/stable-diffusion-v1-5'\ncontrolnet_canny_url = 'lllyasviel/sd-con"
  },
  {
    "path": "configs/controlnet/metafile.yml",
    "chars": 1208,
    "preview": "Collections:\n- Name: Control Net\n  Paper:\n    Title: Adding Conditional Control to Text-to-Image Diffusion Models\n    UR"
  },
  {
    "path": "configs/controlnet_animation/README.md",
    "chars": 4417,
    "preview": "# Controlnet Animation (2023)\n\n> [Controlnet](https://github.com/lllyasviel/ControlNet) Application\n\n> **Task**: control"
  },
  {
    "path": "configs/controlnet_animation/anythingv3_config.py",
    "chars": 1331,
    "preview": "# config for model\nstable_diffusion_v15_url = 'Linaqruf/anything-v3.0'\ncontrolnet_hed_url = 'lllyasviel/sd-controlnet-he"
  },
  {
    "path": "configs/controlnet_animation/metafile.yml",
    "chars": 492,
    "preview": "Collections:\n- Name: Controlnet Animation\n  Paper:\n    Title: Controlnet\n    URL: https://github.com/lllyasviel/ControlN"
  },
  {
    "path": "configs/cyclegan/README.md",
    "chars": 7956,
    "preview": "# CycleGAN (ICCV'2017)\n\n> [CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks](ht"
  },
  {
    "path": "configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py",
    "chars": 3163,
    "preview": "_base_ = [\n    '../_base_/models/base_cyclegan.py',\n    '../_base_/datasets/unpaired_imgs_256x256.py',\n    '../_base_/ge"
  },
  {
    "path": "configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py",
    "chars": 3109,
    "preview": "_base_ = [\n    '../_base_/models/base_cyclegan.py',\n    '../_base_/datasets/unpaired_imgs_256x256.py',\n    '../_base_/ge"
  },
  {
    "path": "configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py",
    "chars": 2555,
    "preview": "_base_ = [\n    '../_base_/models/base_cyclegan.py',\n    '../_base_/datasets/unpaired_imgs_256x256.py',\n    '../_base_/ge"
  },
  {
    "path": "configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py",
    "chars": 3140,
    "preview": "_base_ = [\n    '../_base_/models/base_cyclegan.py',\n    '../_base_/datasets/unpaired_imgs_256x256.py',\n    '../_base_/ge"
  },
  {
    "path": "configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py",
    "chars": 3110,
    "preview": "_base_ = [\n    '../_base_/models/base_cyclegan.py',\n    '../_base_/datasets/unpaired_imgs_256x256.py',\n    '../_base_/ge"
  },
  {
    "path": "configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py",
    "chars": 2556,
    "preview": "_base_ = [\n    '../_base_/models/base_cyclegan.py',\n    '../_base_/datasets/unpaired_imgs_256x256.py',\n    '../_base_/ge"
  },
  {
    "path": "configs/cyclegan/metafile.yml",
    "chars": 3355,
    "preview": "Collections:\n- Name: CycleGAN\n  Paper:\n    Title: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent "
  },
  {
    "path": "configs/dcgan/README.md",
    "chars": 3497,
    "preview": "# Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (ICLR'2016)\n\n> [Unsupervi"
  },
  {
    "path": "configs/dcgan/dcgan_1xb128-300kiters_celeba-cropped-64.py",
    "chars": 1429,
    "preview": "_base_ = [\n    '../_base_/models/dcgan/base_dcgan_64x64.py',\n    '../_base_/datasets/unconditional_imgs_64x64.py',\n    '"
  },
  {
    "path": "configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py",
    "chars": 1386,
    "preview": "_base_ = [\n    '../_base_/models/dcgan/base_dcgan_64x64.py',\n    '../_base_/datasets/unconditional_imgs_64x64.py',\n    '"
  },
  {
    "path": "configs/dcgan/dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py",
    "chars": 1887,
    "preview": "_base_ = [\n    '../_base_/models/dcgan/base_dcgan_64x64.py',\n    '../_base_/datasets/unconditional_imgs_64x64.py',\n    '"
  },
  {
    "path": "configs/dcgan/metafile.yml",
    "chars": 1753,
    "preview": "Collections:\n- Name: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial\n    Networks\n  "
  },
  {
    "path": "configs/deblurganv2/README.md",
    "chars": 4755,
    "preview": "# DeblurGAN-v2 (ICCV'2019)\n\n> [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://arxiv.org/abs/1"
  },
  {
    "path": "configs/deblurganv2/README_zh-CN.md",
    "chars": 3621,
    "preview": "# DeblurGAN-v2 (ICCV'2019)\n\n> [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://arxiv.org/abs/1"
  },
  {
    "path": "configs/deblurganv2/deblurganv2_fpn-inception_1xb1_gopro.py",
    "chars": 5207,
    "preview": "# Copyright (c) OpenMMLab. All rights reserved.\n_base_ = ['../_base_/default_runtime.py']\n\nsave_dir = './work_dir/'\n\nmod"
  },
  {
    "path": "configs/deblurganv2/deblurganv2_fpn-mobilenet_1xb1_gopro.py",
    "chars": 5206,
    "preview": "# Copyright (c) OpenMMLab. All rights reserved.\n_base_ = ['../_base_/default_runtime.py']\n\nsave_dir = './work_dir/'\n\nmod"
  },
  {
    "path": "configs/deblurganv2/metafile.yml",
    "chars": 1029,
    "preview": "Collections:\n- Name: DeblurGAN-v2\n  Paper:\n    Title: 'DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better'"
  },
  {
    "path": "configs/deepfillv1/README.md",
    "chars": 5444,
    "preview": "# DeepFillv1 (CVPR'2018)\n\n> [Generative Image Inpainting with Contextual Attention](https://arxiv.org/abs/1801.07892)\n\n>"
  },
  {
    "path": "configs/deepfillv1/README_zh-CN.md",
    "chars": 3492,
    "preview": "# DeepFillv1 (CVPR'2018)\n\n> **任务**: 图像修复\n\n<!-- [ALGORITHM] -->\n\n<details>\n<summary align=\"right\">DeepFillv1 (CVPR'2018)<"
  },
  {
    "path": "configs/deepfillv1/deepfillv1_4xb4_celeba-256x256.py",
    "chars": 1446,
    "preview": "_base_ = [\n    '../_base_/models/base_deepfillv1.py',\n    '../_base_/inpaint_default_runtime.py', '../_base_/datasets/ce"
  },
  {
    "path": "configs/deepfillv1/deepfillv1_8xb2_places-256x256.py",
    "chars": 1439,
    "preview": "_base_ = [\n    '../_base_/models/base_deepfillv1.py',\n    '../_base_/inpaint_default_runtime.py', '../_base_/datasets/pl"
  },
  {
    "path": "configs/deepfillv1/metafile.yml",
    "chars": 1003,
    "preview": "Collections:\n- Name: DeepFillv1\n  Paper:\n    Title: Generative Image Inpainting with Contextual Attention\n    URL: https"
  },
  {
    "path": "configs/deepfillv2/README.md",
    "chars": 5248,
    "preview": "# DeepFillv2 (CVPR'2019)\n\n> [Free-Form Image Inpainting with Gated Convolution](https://arxiv.org/abs/1806.03589)\n\n> **T"
  },
  {
    "path": "configs/deepfillv2/README_zh-CN.md",
    "chars": 3468,
    "preview": "# DeepFillv2 (CVPR'2019)\n\n> **任务**: 图像修复\n\n<!-- [ALGORITHM] -->\n\n<details>\n<summary align=\"right\">DeepFillv2 (CVPR'2019)<"
  },
  {
    "path": "configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py",
    "chars": 1493,
    "preview": "_base_ = [\n    '../_base_/models/base_deepfillv2.py',\n    '../_base_/inpaint_default_runtime.py', '../_base_/datasets/ce"
  },
  {
    "path": "configs/deepfillv2/deepfillv2_8xb2_places-256x256.py",
    "chars": 1494,
    "preview": "_base_ = [\n    '../_base_/models/base_deepfillv2.py',\n    '../_base_/inpaint_default_runtime.py', '../_base_/datasets/pl"
  },
  {
    "path": "configs/deepfillv2/metafile.yml",
    "chars": 998,
    "preview": "Collections:\n- Name: DeepFillv2\n  Paper:\n    Title: Free-Form Image Inpainting with Gated Convolution\n    URL: https://a"
  },
  {
    "path": "configs/dic/README.md",
    "chars": 5293,
    "preview": "# DIC (CVPR'2020)\n\n> [Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Es"
  },
  {
    "path": "configs/dic/README_zh-CN.md",
    "chars": 3139,
    "preview": "# DIC (CVPR'2020)\n\n> **任务**: 图像超分辨率\n\n<!-- [ALGORITHM] -->\n\n<details>\n\n<summary align=\"right\">DIC (CVPR'2020)</summary>\n\n"
  },
  {
    "path": "configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py",
    "chars": 1760,
    "preview": "_base_ = './dic_x8c48b6_4xb2-150k_celeba-hq.py'\n\nexperiment_name = 'dic_gan-x8c48b6_4xb2-500k_celeba-hq'\nwork_dir = f'./"
  },
  {
    "path": "configs/dic/dic_x8c48b6_4xb2-150k_celeba-hq.py",
    "chars": 4392,
    "preview": "_base_ = '../_base_/default_runtime.py'\n\nexperiment_name = 'dic_x8c48b6_4xb2-150k_celeba-hq'\nwork_dir = f'./work_dirs/{e"
  },
  {
    "path": "configs/dic/metafile.yml",
    "chars": 1006,
    "preview": "Collections:\n- Name: DIC\n  Paper:\n    Title: Deep Face Super-Resolution with Iterative Collaboration between Attentive\n "
  },
  {
    "path": "configs/diffusers_pipeline/README.md",
    "chars": 1775,
    "preview": "# Diffusers Pipeline (2023)\n\n> [Diffusers Pipeline](https://github.com/huggingface/diffusers)\n\n> **Task**: Diffusers Pip"
  },
  {
    "path": "configs/diffusers_pipeline/metafile.yml",
    "chars": 415,
    "preview": "Collections:\n- Name: Diffusers Pipeline\n  Paper:\n    Title: Diffusers Pipeline\n    URL: https://github.com/huggingface/d"
  },
  {
    "path": "configs/diffusers_pipeline/sd_xl_pipeline.py",
    "chars": 128,
    "preview": "# config for model\n\nmodel = dict(\n    type='DiffusionPipeline',\n    from_pretrained='stabilityai/stable-diffusion-xl-bas"
  },
  {
    "path": "configs/dim/README.md",
    "chars": 7120,
    "preview": "# DIM (CVPR'2017)\n\n> [Deep Image Matting](https://arxiv.org/abs/1703.03872)\n\n> **Task**: Matting\n\n<!-- [ALGORITHM] -->\n\n"
  },
  {
    "path": "configs/dim/README_zh-CN.md",
    "chars": 4850,
    "preview": "# DIM (CVPR'2017)\n\n> **任务**: 图像抠图\n\n<!-- [ALGORITHM] -->\n\n<details>\n<summary align=\"right\">DIM (CVPR'2017)</summary>\n\n```"
  },
  {
    "path": "configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py",
    "chars": 2649,
    "preview": "_base_ = [\n    '../_base_/datasets/comp1k.py', '../_base_/matting_default_runtime.py'\n]\n\nsave_dir = './work_dirs/'\nexper"
  },
  {
    "path": "configs/dim/dim_stage1-v16_1xb1-1000k_comp1k_online-merge.py",
    "chars": 847,
    "preview": "_base_ = ['./dim_stage1-v16_1xb1-1000k_comp1k.py']\nsave_dir = './work_dirs/'\nexperiment_name = 'dim_stage1-v16_1xb1-1000"
  },
  {
    "path": "configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py",
    "chars": 505,
    "preview": "_base_ = ['./dim_stage1-v16_1xb1-1000k_comp1k.py']\nsave_dir = './work_dirs/'\nexperiment_name = 'dim_stage2-v16-pln_1xb1-"
  },
  {
    "path": "configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py",
    "chars": 508,
    "preview": "_base_ = ['./dim_stage1-v16_1xb1-1000k_comp1k.py']\n\nsave_dir = './work_dirs'\nexperiment_name = 'dim_stage3-v16-pln_1xb1-"
  },
  {
    "path": "configs/dim/metafile.yml",
    "chars": 1595,
    "preview": "Collections:\n- Name: DIM\n  Paper:\n    Title: Deep Image Matting\n    URL: https://arxiv.org/abs/1703.03872\n  README: conf"
  },
  {
    "path": "configs/disco_diffusion/README.md",
    "chars": 7415,
    "preview": "# Disco Diffusion (2022)\n\n> [Disco Diffusion](https://github.com/alembics/disco-diffusion)\n\n> **Task**: Text2Image, Imag"
  },
  {
    "path": "configs/disco_diffusion/README_zh-CN.md",
    "chars": 6781,
    "preview": "# Disco Diffusion (2022)\n\n> [Disco Diffusion](https://github.com/alembics/disco-diffusion)\n\n> **任务**: 图文生成, 图像到图像的翻译, 扩散"
  },
  {
    "path": "configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-256x256.py",
    "chars": 1574,
    "preview": "unet = dict(\n    type='DenoisingUnet',\n    image_size=256,\n    in_channels=3,\n    base_channels=256,\n    resblocks_per_d"
  },
  {
    "path": "configs/disco_diffusion/disco-diffusion_adm-u-finetuned_imagenet-512x512.py",
    "chars": 1566,
    "preview": "unet = dict(\n    type='DenoisingUnet',\n    image_size=512,\n    in_channels=3,\n    base_channels=256,\n    resblocks_per_d"
  }
]

// ... and 1487 more files (download for full content)