├── LICENSE ├── README.md ├── classifier ├── __pycache__ │ ├── dataloader.cpython-38.pyc │ ├── eval_dataloader.cpython-38.pyc │ ├── eval_dataloader_multi.cpython-38.pyc │ ├── fid_score.cpython-38.pyc │ ├── fid_score_flintstone.cpython-38.pyc │ ├── fid_score_pororo.cpython-38.pyc │ ├── fid_score_vlc.cpython-38.pyc │ ├── inception.cpython-38.pyc │ └── vfid_score.cpython-38.pyc ├── dataloader.py ├── eval_dataloader.py ├── eval_dataloader_multi.py ├── fid_score.py ├── fid_score_flintstone.py ├── fid_score_pororo.py ├── fid_score_vlc.py ├── inception.py └── inception____.py ├── configs ├── autoencoder │ ├── autoencoder_kl_16x16x16.yaml │ ├── autoencoder_kl_32x32x4.yaml │ ├── autoencoder_kl_64x64x3.yaml │ ├── autoencoder_kl_8x8x64.yaml │ ├── mugen_kl_32x32x4.yaml │ ├── mugen_kl_8x8x16.yaml │ ├── mugen_kl_8x8x16_test.yaml │ └── mugen_kl_8x8x64.yaml └── latent-diffusion │ ├── celebahq-ldm-vq-4.yaml │ ├── cin-ldm-vq-f8.yaml │ ├── cin256-v2.yaml │ ├── ffhq-ldm-vq-4.yaml │ ├── flintstones_txt2img-1p4B-eval.yaml │ ├── flintstones_txt2img-1p4B-train.yaml │ ├── lsun_bedrooms-ldm-vq-4.yaml │ ├── lsun_churches-ldm-kl-8.yaml │ ├── pororo_txt2img-1p4B-eval.yaml │ ├── pororo_txt2img-1p4B-train.yaml │ ├── txt2img-1p4B-autoencoder.yaml │ ├── txt2img-1p4B-eval.yaml │ └── txt2img-1p4B-train.yaml ├── data ├── DejaVuSans.ttf ├── example_conditioning │ ├── superresolution │ │ └── sample_0.jpg │ └── text_conditional │ │ └── sample_0.txt ├── imagenet_clsidx_to_label.txt ├── imagenet_train_hr_indices.p ├── imagenet_val_hr_indices.p ├── index_synset.yaml └── inpainting_examples │ ├── 6458524847_2f4c361183_k.png │ ├── 6458524847_2f4c361183_k_mask.png │ ├── 8399166846_f6fb4e4b8e_k.png │ ├── 8399166846_f6fb4e4b8e_k_mask.png │ ├── alex-iby-G_Pk4D9rMLs.png │ ├── alex-iby-G_Pk4D9rMLs_mask.png │ ├── bench2.png │ ├── bench2_mask.png │ ├── bertrand-gabioud-CpuFzIsHYJ0.png │ ├── bertrand-gabioud-CpuFzIsHYJ0_mask.png │ ├── billow926-12-Wc-Zgx6Y.png │ ├── billow926-12-Wc-Zgx6Y_mask.png │ ├── overture-creations-5sI6fQgYIuo.png │ ├── overture-creations-5sI6fQgYIuo_mask.png │ ├── photo-1583445095369-9c651e7e5d34.png │ └── photo-1583445095369-9c651e7e5d34_mask.png ├── environment.yaml ├── eval_backgound_classifier.py ├── eval_char_classifier.py ├── eval_vfid.py ├── ldm ├── __pycache__ │ └── util.cpython-38.pyc ├── data │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── base.cpython-38.pyc │ │ ├── flintstones_data.cpython-38.pyc │ │ ├── imagenet.cpython-38.pyc │ │ ├── mugen_data.cpython-38.pyc │ │ ├── mugen_data_single_clip.cpython-38.pyc │ │ └── pororo_data.cpython-38.pyc │ ├── base.py │ ├── flintstones_data.py │ ├── flintstones_data_ref.py │ ├── imagenet.py │ ├── lsun.py │ ├── mugen_data.py │ ├── mugen_data_ref_text.py │ ├── mugen_data_single_clip.py │ └── pororo_data.py ├── lr_scheduler.py ├── models │ ├── __pycache__ │ │ └── autoencoder.cpython-38.pyc │ ├── autoencoder.py │ └── diffusion │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── ddim.cpython-38.pyc │ │ ├── ddpm.cpython-38.pyc │ │ └── plms.cpython-38.pyc │ │ ├── classifier.py │ │ ├── ddim.py │ │ ├── ddpm.py │ │ ├── ddpm_bk.py │ │ └── plms.py ├── modules │ ├── __pycache__ │ │ ├── attention.cpython-38.pyc │ │ ├── ema.cpython-38.pyc │ │ └── x_transformer.cpython-38.pyc │ ├── attention.py │ ├── diffusionmodules │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── model.cpython-38.pyc │ │ │ ├── openaimodel.cpython-38.pyc │ │ │ └── util.cpython-38.pyc │ │ ├── model.py │ │ ├── openaimodel.py │ │ └── util.py │ ├── distributions │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── distributions.cpython-38.pyc │ │ └── distributions.py │ ├── ema.py │ ├── encoders │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── modules.cpython-38.pyc │ │ └── modules.py │ ├── image_degradation │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── bsrgan.cpython-38.pyc │ │ │ ├── bsrgan_light.cpython-38.pyc │ │ │ └── utils_image.cpython-38.pyc │ │ ├── bsrgan.py │ │ ├── bsrgan_light.py │ │ ├── utils │ │ │ └── test.png │ │ └── utils_image.py │ ├── losses │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── contperceptual.cpython-38.pyc │ │ ├── contperceptual.py │ │ └── vqperceptual.py │ └── x_transformer.py └── util.py ├── main.py ├── main_ours.py ├── models ├── first_stage_models │ ├── kl-f16 │ │ └── config.yaml │ ├── kl-f32 │ │ └── config.yaml │ ├── kl-f4 │ │ └── config.yaml │ ├── kl-f8 │ │ └── config.yaml │ ├── vq-f16 │ │ └── config.yaml │ ├── vq-f4-noattn │ │ └── config.yaml │ ├── vq-f4 │ │ └── config.yaml │ ├── vq-f8-n256 │ │ └── config.yaml │ └── vq-f8 │ │ └── config.yaml └── ldm │ ├── bsr_sr │ └── config.yaml │ ├── celeba256 │ └── config.yaml │ ├── cin256 │ └── config.yaml │ ├── ffhq256 │ └── config.yaml │ ├── inpainting_big │ └── config.yaml │ ├── layout2img-openimages256 │ └── config.yaml │ ├── lsun_beds256 │ └── config.yaml │ ├── lsun_churches256 │ └── config.yaml │ ├── semantic_synthesis256 │ └── config.yaml │ ├── semantic_synthesis512 │ └── config.yaml │ └── text2img256 │ └── config.yaml ├── mugen_data ├── __pycache__ │ ├── data.cpython-38.pyc │ ├── mugen_data.cpython-38.pyc │ └── video_utils.cpython-38.pyc ├── coinrun │ ├── __pycache__ │ │ ├── construct_from_json.cpython-38.pyc │ │ ├── construct_from_json.cpython-39.pyc │ │ ├── game.cpython-38.pyc │ │ └── game.cpython-39.pyc │ ├── assets │ │ ├── backgrounds │ │ │ ├── background-2 │ │ │ │ ├── Background_2.png │ │ │ │ ├── airadventurelevel1.png │ │ │ │ ├── airadventurelevel2.png │ │ │ │ ├── airadventurelevel3.png │ │ │ │ └── airadventurelevel4.png │ │ │ └── spacebackgrounds-0 │ │ │ │ └── milky_way_01.png │ │ ├── bubble_shield.png │ │ ├── kenney │ │ │ ├── Ground │ │ │ │ ├── Dirt │ │ │ │ │ ├── dirt.png │ │ │ │ │ ├── dirtCenter.png │ │ │ │ │ ├── dirtCenter_rounded.png │ │ │ │ │ ├── dirtCliffAlt_left.png │ │ │ │ │ ├── dirtCliffAlt_right.png │ │ │ │ │ ├── dirtCliff_left.png │ │ │ │ │ ├── dirtCliff_right.png │ │ │ │ │ ├── dirtCorner_left.png │ │ │ │ │ ├── dirtCorner_right.png │ │ │ │ │ ├── dirtHalf.png │ │ │ │ │ ├── dirtHalf_left.png │ │ │ │ │ ├── dirtHalf_mid.png │ │ │ │ │ ├── dirtHalf_right.png │ │ │ │ │ ├── dirtHill_left.png │ │ │ │ │ ├── dirtHill_right.png │ │ │ │ │ ├── dirtLeft.png │ │ │ │ │ ├── dirtMid.png │ │ │ │ │ └── dirtRight.png │ │ │ │ ├── Grass │ │ │ │ │ ├── grass.png │ │ │ │ │ ├── grassCenter.png │ │ │ │ │ ├── grassCenter_round.png │ │ │ │ │ ├── grassCliffAlt_left.png │ │ │ │ │ ├── grassCliffAlt_right.png │ │ │ │ │ ├── grassCliff_left.png │ │ │ │ │ ├── grassCliff_right.png │ │ │ │ │ ├── grassCorner_left.png │ │ │ │ │ ├── grassCorner_right.png │ │ │ │ │ ├── grassHalf.png │ │ │ │ │ ├── grassHalf_left.png │ │ │ │ │ ├── grassHalf_mid.png │ │ │ │ │ ├── grassHalf_right.png │ │ │ │ │ ├── grassHill_left.png │ │ │ │ │ ├── grassHill_right.png │ │ │ │ │ ├── grassLeft.png │ │ │ │ │ ├── grassMid.png │ │ │ │ │ └── grassRight.png │ │ │ │ ├── Planet │ │ │ │ │ ├── planetCenter.png │ │ │ │ │ ├── planetCliff_left.png │ │ │ │ │ ├── planetCliff_right.png │ │ │ │ │ └── planetMid.png │ │ │ │ ├── Sand │ │ │ │ │ ├── sand.png │ │ │ │ │ ├── sandCenter.png │ │ │ │ │ ├── sandCenter_rounded.png │ │ │ │ │ ├── sandCliffAlt_left.png │ │ │ │ │ ├── sandCliffAlt_right.png │ │ │ │ │ ├── sandCliff_left.png │ │ │ │ │ ├── sandCliff_right.png │ │ │ │ │ ├── sandCorner_leftg.png │ │ │ │ │ ├── sandCorner_right.png │ │ │ │ │ ├── sandHalf.png │ │ │ │ │ ├── sandHalf_left.png │ │ │ │ │ ├── sandHalf_mid.png │ │ │ │ │ ├── sandHalf_right.png │ │ │ │ │ ├── sandHill_left.png │ │ │ │ │ ├── sandHill_right.png │ │ │ │ │ ├── sandLeft.png │ │ │ │ │ ├── sandMid.png │ │ │ │ │ └── sandRight.png │ │ │ │ ├── Snow │ │ │ │ │ ├── snowCenter.png │ │ │ │ │ ├── snowCliff_left.png │ │ │ │ │ ├── snowCliff_right.png │ │ │ │ │ └── snowMid.png │ │ │ │ └── Stone │ │ │ │ │ ├── stone.png │ │ │ │ │ ├── stoneCenter.png │ │ │ │ │ ├── stoneCenter_rounded.png │ │ │ │ │ ├── stoneCliffAlt_left.png │ │ │ │ │ ├── stoneCliffAlt_right.png │ │ │ │ │ ├── stoneCliff_left.png │ │ │ │ │ ├── stoneCliff_right.png │ │ │ │ │ ├── stoneCorner_left.png │ │ │ │ │ ├── stoneCorner_right.png │ │ │ │ │ ├── stoneHalf.png │ │ │ │ │ ├── stoneHalf_left.png │ │ │ │ │ ├── stoneHalf_mid.png │ │ │ │ │ ├── stoneHalf_right.png │ │ │ │ │ ├── stoneHill_left.png │ │ │ │ │ ├── stoneHill_right.png │ │ │ │ │ ├── stoneLeft.png │ │ │ │ │ ├── stoneMid.png │ │ │ │ │ └── stoneRight.png │ │ │ └── Tiles │ │ │ │ ├── boxCrate.png │ │ │ │ ├── boxCrate_double.png │ │ │ │ ├── boxCrate_single.png │ │ │ │ ├── boxCrate_warning.png │ │ │ │ ├── ladderMid.png │ │ │ │ ├── lava.png │ │ │ │ └── lavaTop_low.png │ │ └── kenneyLarge │ │ │ ├── Enemies │ │ │ ├── barnacle.png │ │ │ ├── barnacle_attack.png │ │ │ ├── barnacle_dead.png │ │ │ ├── bee.png │ │ │ ├── bee_dead.png │ │ │ ├── bee_move.png │ │ │ ├── frog.png │ │ │ ├── frog_dead.png │ │ │ ├── frog_move.png │ │ │ ├── ladybug.png │ │ │ ├── ladybug_dead.png │ │ │ ├── ladybug_move.png │ │ │ ├── mouse.png │ │ │ ├── mouse_dead.png │ │ │ ├── mouse_move.png │ │ │ ├── sawHalf.png │ │ │ ├── sawHalf_dead.png │ │ │ ├── sawHalf_move.png │ │ │ ├── slimeBlock.png │ │ │ ├── slimeBlock_dead.png │ │ │ ├── slimeBlock_move.png │ │ │ ├── slimeBlue.png │ │ │ ├── slimeBlue_dead.png │ │ │ ├── slimeBlue_hit.png │ │ │ ├── slimeBlue_move.png │ │ │ ├── snail.png │ │ │ ├── snail_dead.png │ │ │ ├── snail_move.png │ │ │ ├── wormPink.png │ │ │ ├── wormPink_dead.png │ │ │ └── wormPink_move.png │ │ │ ├── Items │ │ │ ├── coinGold.png │ │ │ └── gemRed.png │ │ │ └── Players │ │ │ ├── 128x256 │ │ │ ├── Beige │ │ │ │ ├── alienBeige_climb1.png │ │ │ │ ├── alienBeige_climb2.png │ │ │ │ ├── alienBeige_duck.png │ │ │ │ ├── alienBeige_front.png │ │ │ │ ├── alienBeige_hit.png │ │ │ │ ├── alienBeige_jump.png │ │ │ │ ├── alienBeige_stand.png │ │ │ │ ├── alienBeige_swim1.png │ │ │ │ ├── alienBeige_swim2.png │ │ │ │ ├── alienBeige_walk1.png │ │ │ │ └── alienBeige_walk2.png │ │ │ ├── Blue │ │ │ │ ├── alienBlue_climb1.png │ │ │ │ ├── alienBlue_climb2.png │ │ │ │ ├── alienBlue_duck.png │ │ │ │ ├── alienBlue_front.png │ │ │ │ ├── alienBlue_hit.png │ │ │ │ ├── alienBlue_jump.png │ │ │ │ ├── alienBlue_stand.png │ │ │ │ ├── alienBlue_swim1.png │ │ │ │ ├── alienBlue_swim2.png │ │ │ │ ├── alienBlue_walk1.png │ │ │ │ └── alienBlue_walk2.png │ │ │ ├── Green │ │ │ │ ├── alienGreen_climb1.png │ │ │ │ ├── alienGreen_climb2.png │ │ │ │ ├── alienGreen_duck.png │ │ │ │ ├── alienGreen_front.png │ │ │ │ ├── alienGreen_hit.png │ │ │ │ ├── alienGreen_jump.png │ │ │ │ ├── alienGreen_stand.png │ │ │ │ ├── alienGreen_swim1.png │ │ │ │ ├── alienGreen_swim2.png │ │ │ │ ├── alienGreen_walk1.png │ │ │ │ └── alienGreen_walk2.png │ │ │ ├── Pink │ │ │ │ ├── alienPink_climb1.png │ │ │ │ ├── alienPink_climb2.png │ │ │ │ ├── alienPink_duck.png │ │ │ │ ├── alienPink_front.png │ │ │ │ ├── alienPink_hit.png │ │ │ │ ├── alienPink_jump.png │ │ │ │ ├── alienPink_stand.png │ │ │ │ ├── alienPink_swim1.png │ │ │ │ ├── alienPink_swim2.png │ │ │ │ ├── alienPink_walk1.png │ │ │ │ └── alienPink_walk2.png │ │ │ └── Yellow │ │ │ │ ├── alienYellow_climb1.png │ │ │ │ ├── alienYellow_climb2.png │ │ │ │ ├── alienYellow_duck.png │ │ │ │ ├── alienYellow_front.png │ │ │ │ ├── alienYellow_hit.png │ │ │ │ ├── alienYellow_jump.png │ │ │ │ ├── alienYellow_stand.png │ │ │ │ ├── alienYellow_swim1.png │ │ │ │ ├── alienYellow_swim2.png │ │ │ │ ├── alienYellow_walk1.png │ │ │ │ └── alienYellow_walk2.png │ │ │ └── 128x256_no_helmet │ │ │ ├── .DS_Store │ │ │ ├── Blue │ │ │ ├── alienBlue_climb1.png │ │ │ ├── alienBlue_climb2.png │ │ │ ├── alienBlue_duck.png │ │ │ ├── alienBlue_front.png │ │ │ ├── alienBlue_hit.png │ │ │ ├── alienBlue_jump.png │ │ │ ├── alienBlue_stand.png │ │ │ ├── alienBlue_swim1.png │ │ │ ├── alienBlue_swim2.png │ │ │ ├── alienBlue_walk1.png │ │ │ └── alienBlue_walk2.png │ │ │ ├── Pink │ │ │ ├── alienPink_climb1.png │ │ │ ├── alienPink_climb2.png │ │ │ ├── alienPink_duck.png │ │ │ ├── alienPink_front.png │ │ │ ├── alienPink_hit.png │ │ │ ├── alienPink_jump.png │ │ │ ├── alienPink_stand.png │ │ │ ├── alienPink_swim1.png │ │ │ ├── alienPink_swim2.png │ │ │ ├── alienPink_walk1.png │ │ │ └── alienPink_walk2.png │ │ │ └── Yellow │ │ │ ├── alienYellow_climb1.png │ │ │ ├── alienYellow_climb2.png │ │ │ ├── alienYellow_duck.png │ │ │ ├── alienYellow_front.png │ │ │ ├── alienYellow_hit.png │ │ │ ├── alienYellow_jump.png │ │ │ ├── alienYellow_stand.png │ │ │ ├── alienYellow_walk1.png │ │ │ └── alienYellow_walk2.png │ ├── construct_from_json.py │ ├── game.py │ └── generate_text_desc.py ├── data.py ├── models │ ├── audio_vqvae │ │ ├── __pycache__ │ │ │ └── hparams.cpython-38.pyc │ │ ├── hparams.py │ │ └── vqvae.py │ ├── fad │ │ ├── fad.py │ │ └── resnet.py │ ├── fvd │ │ ├── fvd.py │ │ └── pytorch_i3d.py │ ├── gpt │ │ ├── __pycache__ │ │ │ ├── attention.cpython-38.pyc │ │ │ ├── gpt.cpython-38.pyc │ │ │ └── utils.cpython-38.pyc │ │ ├── attention.py │ │ ├── gpt.py │ │ └── utils.py │ ├── video_vqvae │ │ ├── __pycache__ │ │ │ └── vqvae.cpython-38.pyc │ │ └── vqvae.py │ └── videoclip │ │ ├── __pycache__ │ │ ├── clip.cpython-38.pyc │ │ ├── modules.cpython-38.pyc │ │ ├── resnet.cpython-38.pyc │ │ └── s3d.cpython-38.pyc │ │ ├── clip.py │ │ ├── modules.py │ │ ├── resnet.py │ │ └── s3d.py ├── mugen_data.py └── video_utils.py ├── scripts ├── download_first_stages.sh ├── download_models.sh ├── inpaint.py ├── latent_imagenet_diffusion.ipynb ├── sample_diffusion.py ├── txt2img.py ├── txt2img_flintstone_multitxt.py ├── txt2img_mugen.py ├── txt2img_mugen_multitxt.py └── txt2img_pororo_multitxt.py └── setup.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # We are still cleaning our Code 2 | 3 | # Make-A-Story 4 | 5 | This repository contains the code for the CVPR 2023 paper titled ["Make-A-Story: Visual Memory Conditioned Consistent Story Generation"](https://arxiv.org/pdf/2211.13319.pdf). 6 | 7 | # Extended dataset 8 | 9 | We extend existing datasets (e.g. ["Mugen"](https://arxiv.org/pdf/2204.08058.pdf), ["FlintstonesSV"](https://arxiv.org/pdf/1804.03608.pdf) and ["PororoSV"](https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_StoryGAN_A_Sequential_Conditional_GAN_for_Story_Visualization_CVPR_2019_paper.pdf) dataset) to include more 10 | complex scenarios and referential text. 11 | 12 | 13 | # Acknowledgment 14 | 15 | This repository is developed on top of ["Latent Diffusion Models"](https://github.com/CompVis/latent-diffusion). Please also refer to the original License of the project. 16 | 17 | # Bibtext 18 | 19 | If you find this code is useful for your research, please cite our paper 20 | 21 | 22 | ``` 23 | @article{rahman2022make, 24 | title={Make-A-Story: Visual Memory Conditioned Consistent Story Generation}, 25 | author={Rahman, Tanzila and Lee, Hsin-Ying and Ren, Jian and Tulyakov, Sergey and Mahajan, Shweta and Sigal, Leonid}, 26 | journal={arXiv preprint arXiv:2211.13319}, 27 | year={2022} 28 | } 29 | ``` 30 | -------------------------------------------------------------------------------- /classifier/__pycache__/dataloader.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/classifier/__pycache__/dataloader.cpython-38.pyc -------------------------------------------------------------------------------- /classifier/__pycache__/eval_dataloader.cpython-38.pyc: 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/configs/autoencoder/autoencoder_kl_16x16x16.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 16 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 16 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [16] 24 | dropout: 0.0 25 | 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 12 31 | wrap: True 32 | train: 33 | target: ldm.data.imagenet.ImageNetSRTrain 34 | params: 35 | size: 256 36 | degradation: pil_nearest 37 | validation: 38 | target: ldm.data.imagenet.ImageNetSRValidation 39 | params: 40 | size: 256 41 | degradation: pil_nearest 42 | 43 | lightning: 44 | callbacks: 45 | image_logger: 46 | target: main.ImageLogger 47 | params: 48 | batch_frequency: 1000 49 | max_images: 8 50 | increase_log_steps: True 51 | 52 | trainer: 53 | benchmark: True 54 | accumulate_grad_batches: 2 55 | -------------------------------------------------------------------------------- /configs/autoencoder/autoencoder_kl_32x32x4.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 4 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 4 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [ ] 24 | dropout: 0.0 25 | 26 | data: 27 | target: main.DataModuleFromConfig 28 | params: 29 | batch_size: 12 30 | wrap: True 31 | train: 32 | target: ldm.data.imagenet.ImageNetSRTrain 33 | params: 34 | size: 256 35 | degradation: pil_nearest 36 | validation: 37 | target: ldm.data.imagenet.ImageNetSRValidation 38 | params: 39 | size: 256 40 | degradation: pil_nearest 41 | 42 | lightning: 43 | callbacks: 44 | image_logger: 45 | target: main.ImageLogger 46 | params: 47 | batch_frequency: 1000 48 | max_images: 8 49 | increase_log_steps: True 50 | 51 | trainer: 52 | benchmark: True 53 | accumulate_grad_batches: 2 54 | -------------------------------------------------------------------------------- /configs/autoencoder/autoencoder_kl_64x64x3.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 3 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 3 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [ ] 24 | dropout: 0.0 25 | 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 12 31 | wrap: True 32 | train: 33 | target: ldm.data.imagenet.ImageNetSRTrain 34 | params: 35 | size: 256 36 | degradation: pil_nearest 37 | validation: 38 | target: ldm.data.imagenet.ImageNetSRValidation 39 | params: 40 | size: 256 41 | degradation: pil_nearest 42 | 43 | lightning: 44 | callbacks: 45 | image_logger: 46 | target: main.ImageLogger 47 | params: 48 | batch_frequency: 1000 49 | max_images: 8 50 | increase_log_steps: True 51 | 52 | trainer: 53 | benchmark: True 54 | accumulate_grad_batches: 2 55 | -------------------------------------------------------------------------------- /configs/autoencoder/autoencoder_kl_8x8x64.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 64 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 64 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [16,8] 24 | dropout: 0.0 25 | 26 | data: 27 | target: main.DataModuleFromConfig 28 | params: 29 | batch_size: 12 30 | wrap: True 31 | train: 32 | target: ldm.data.imagenet.ImageNetSRTrain 33 | params: 34 | size: 256 35 | degradation: pil_nearest 36 | validation: 37 | target: ldm.data.imagenet.ImageNetSRValidation 38 | params: 39 | size: 256 40 | degradation: pil_nearest 41 | 42 | lightning: 43 | callbacks: 44 | image_logger: 45 | target: main.ImageLogger 46 | params: 47 | batch_frequency: 1000 48 | max_images: 8 49 | increase_log_steps: True 50 | 51 | trainer: 52 | benchmark: True 53 | accumulate_grad_batches: 2 54 | -------------------------------------------------------------------------------- /configs/autoencoder/mugen_kl_32x32x4.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 4 7 | ckpt_path: models/first_stage_models/kl-f8/model.ckpt 8 | lossconfig: 9 | target: ldm.modules.losses.LPIPSWithDiscriminator 10 | params: 11 | disc_start: 50001 12 | kl_weight: 0.000001 13 | disc_weight: 0.5 14 | 15 | ddconfig: 16 | double_z: True 17 | z_channels: 4 18 | resolution: 256 19 | in_channels: 3 20 | out_ch: 3 21 | ch: 128 22 | ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 23 | num_res_blocks: 2 24 | attn_resolutions: [ ] 25 | dropout: 0.0 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 2 31 | wrap: True 32 | train: 33 | target: ldm.data.mugen_data_single_clip.CustomTrain 34 | params: 35 | training_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/train.json 36 | size: 256 37 | get_game_frame: True 38 | get_text_desc: True 39 | get_audio: False 40 | split: train 41 | sample_every_n_frames: 12 42 | sequence_length: 1 43 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 44 | use_manual_annotation: True 45 | resolution: 256 46 | validation: 47 | target: ldm.data.mugen_data_single_clip.CustomTest 48 | params: 49 | test_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/test.json 50 | size: 256 51 | get_game_frame: True 52 | get_text_desc: True 53 | get_audio: False 54 | split: test 55 | sample_every_n_frames: 12 56 | sequence_length: 1 57 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 58 | use_manual_annotation: True 59 | resolution: 256 60 | 61 | lightning: 62 | callbacks: 63 | image_logger: 64 | target: main.ImageLogger 65 | params: 66 | batch_frequency: 1000 67 | max_images: 8 68 | increase_log_steps: True 69 | 70 | trainer: 71 | benchmark: True 72 | accumulate_grad_batches: 2 73 | -------------------------------------------------------------------------------- /configs/autoencoder/mugen_kl_8x8x16.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 16 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 16 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 64 #128 21 | ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [16,8] 24 | dropout: 0.0 25 | 26 | data: 27 | target: main.DataModuleFromConfig 28 | params: 29 | batch_size: 12 30 | wrap: True 31 | train: 32 | target: ldm.data.mugen_data.CustomTrain 33 | params: 34 | training_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/train.json 35 | size: 256 36 | get_game_frame: True 37 | get_text_desc: True 38 | get_audio: False 39 | split: train 40 | sample_every_n_frames: 12 41 | sequence_length: 1 42 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 43 | use_manual_annotation: True 44 | resolution: 256 45 | validation: 46 | target: ldm.data.mugen_data.CustomTest 47 | params: 48 | test_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/test.json 49 | size: 256 50 | get_game_frame: True 51 | get_text_desc: True 52 | get_audio: False 53 | split: test 54 | sample_every_n_frames: 12 55 | sequence_length: 1 56 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 57 | use_manual_annotation: True 58 | resolution: 256 59 | 60 | lightning: 61 | callbacks: 62 | image_logger: 63 | target: main.ImageLogger 64 | params: 65 | batch_frequency: 1000 66 | max_images: 8 67 | increase_log_steps: True 68 | 69 | trainer: 70 | benchmark: True 71 | accumulate_grad_batches: 2 72 | -------------------------------------------------------------------------------- /configs/autoencoder/mugen_kl_8x8x16_test.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 16 7 | ckpt_path: logs/2022-09-15T02-30-17_mugen_kl_8x8x16/checkpoints/epoch=000029.ckpt 8 | lossconfig: 9 | target: ldm.modules.losses.LPIPSWithDiscriminator 10 | params: 11 | disc_start: 50001 12 | kl_weight: 0.000001 13 | disc_weight: 0.5 14 | 15 | ddconfig: 16 | double_z: True 17 | z_channels: 16 18 | resolution: 256 19 | in_channels: 3 20 | out_ch: 3 21 | ch: 64 #128 22 | ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1 23 | num_res_blocks: 2 24 | attn_resolutions: [16,8] 25 | dropout: 0.0 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 12 31 | wrap: True 32 | train: 33 | target: ldm.data.mugen_data.CustomTrain 34 | params: 35 | training_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/train.json 36 | size: 256 37 | get_game_frame: True 38 | get_text_desc: True 39 | get_audio: False 40 | split: train 41 | sample_every_n_frames: 12 42 | sequence_length: 1 43 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 44 | use_manual_annotation: True 45 | resolution: 256 46 | validation: 47 | target: ldm.data.mugen_data.CustomTest 48 | params: 49 | test_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/test.json 50 | size: 256 51 | get_game_frame: True 52 | get_text_desc: True 53 | get_audio: False 54 | split: test 55 | sample_every_n_frames: 12 56 | sequence_length: 1 57 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 58 | use_manual_annotation: True 59 | resolution: 256 60 | 61 | lightning: 62 | callbacks: 63 | image_logger: 64 | target: main.ImageLogger 65 | params: 66 | batch_frequency: 1000 67 | max_images: 8 68 | increase_log_steps: True 69 | 70 | trainer: 71 | benchmark: True 72 | accumulate_grad_batches: 2 73 | -------------------------------------------------------------------------------- /configs/autoencoder/mugen_kl_8x8x64.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 64 7 | ckpt_path: models/first_stage_models/kl-f32/model.ckpt 8 | lossconfig: 9 | target: ldm.modules.losses.LPIPSWithDiscriminator 10 | params: 11 | disc_start: 50001 12 | kl_weight: 0.000001 13 | disc_weight: 0.5 14 | 15 | ddconfig: 16 | double_z: True 17 | z_channels: 64 18 | resolution: 256 19 | in_channels: 3 20 | out_ch: 3 21 | ch: 128 22 | ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1 23 | num_res_blocks: 2 24 | attn_resolutions: [16,8] 25 | dropout: 0.0 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 3 31 | wrap: True 32 | train: 33 | target: ldm.data.mugen_data_single_clip.CustomTrain 34 | params: 35 | training_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/train.json 36 | size: 256 37 | get_game_frame: True 38 | get_text_desc: True 39 | get_audio: False 40 | split: train 41 | sample_every_n_frames: 12 42 | sequence_length: 1 43 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 44 | use_manual_annotation: True 45 | resolution: 256 46 | validation: 47 | target: ldm.data.mugen_data_single_clip.CustomTest 48 | params: 49 | test_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/test.json 50 | size: 256 51 | get_game_frame: True 52 | get_text_desc: True 53 | get_audio: False 54 | split: test 55 | sample_every_n_frames: 12 56 | sequence_length: 1 57 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 58 | use_manual_annotation: True 59 | resolution: 256 60 | 61 | lightning: 62 | callbacks: 63 | image_logger: 64 | target: main.ImageLogger 65 | params: 66 | batch_frequency: 1000 67 | max_images: 8 68 | increase_log_steps: True 69 | 70 | trainer: 71 | benchmark: True 72 | accumulate_grad_batches: 2 73 | -------------------------------------------------------------------------------- /configs/latent-diffusion/celebahq-ldm-vq-4.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | image_size: 64 12 | channels: 3 13 | monitor: val/loss_simple_ema 14 | 15 | unet_config: 16 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 17 | params: 18 | image_size: 64 19 | in_channels: 3 20 | out_channels: 3 21 | model_channels: 224 22 | attention_resolutions: 23 | # note: this isn\t actually the resolution but 24 | # the downsampling factor, i.e. this corresnponds to 25 | # attention on spatial resolution 8,16,32, as the 26 | # spatial reolution of the latents is 64 for f4 27 | - 8 28 | - 4 29 | - 2 30 | num_res_blocks: 2 31 | channel_mult: 32 | - 1 33 | - 2 34 | - 3 35 | - 4 36 | num_head_channels: 32 37 | first_stage_config: 38 | target: ldm.models.autoencoder.VQModelInterface 39 | params: 40 | embed_dim: 3 41 | n_embed: 8192 42 | ckpt_path: models/first_stage_models/vq-f4/model.ckpt 43 | ddconfig: 44 | double_z: false 45 | z_channels: 3 46 | resolution: 256 47 | in_channels: 3 48 | out_ch: 3 49 | ch: 128 50 | ch_mult: 51 | - 1 52 | - 2 53 | - 4 54 | num_res_blocks: 2 55 | attn_resolutions: [] 56 | dropout: 0.0 57 | lossconfig: 58 | target: torch.nn.Identity 59 | cond_stage_config: __is_unconditional__ 60 | data: 61 | target: main.DataModuleFromConfig 62 | params: 63 | batch_size: 48 64 | num_workers: 5 65 | wrap: false 66 | train: 67 | target: taming.data.faceshq.CelebAHQTrain 68 | params: 69 | size: 256 70 | validation: 71 | target: taming.data.faceshq.CelebAHQValidation 72 | params: 73 | size: 256 74 | 75 | 76 | lightning: 77 | callbacks: 78 | image_logger: 79 | target: main.ImageLogger 80 | params: 81 | batch_frequency: 5000 82 | max_images: 8 83 | increase_log_steps: False 84 | 85 | trainer: 86 | benchmark: True -------------------------------------------------------------------------------- /configs/latent-diffusion/cin-ldm-vq-f8.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: class_label 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | unet_config: 18 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 19 | params: 20 | image_size: 32 21 | in_channels: 4 22 | out_channels: 4 23 | model_channels: 256 24 | attention_resolutions: 25 | #note: this isn\t actually the resolution but 26 | # the downsampling factor, i.e. this corresnponds to 27 | # attention on spatial resolution 8,16,32, as the 28 | # spatial reolution of the latents is 32 for f8 29 | - 4 30 | - 2 31 | - 1 32 | num_res_blocks: 2 33 | channel_mult: 34 | - 1 35 | - 2 36 | - 4 37 | num_head_channels: 32 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 512 41 | first_stage_config: 42 | target: ldm.models.autoencoder.VQModelInterface 43 | params: 44 | embed_dim: 4 45 | n_embed: 16384 46 | ckpt_path: configs/first_stage_models/vq-f8/model.yaml 47 | ddconfig: 48 | double_z: false 49 | z_channels: 4 50 | resolution: 256 51 | in_channels: 3 52 | out_ch: 3 53 | ch: 128 54 | ch_mult: 55 | - 1 56 | - 2 57 | - 2 58 | - 4 59 | num_res_blocks: 2 60 | attn_resolutions: 61 | - 32 62 | dropout: 0.0 63 | lossconfig: 64 | target: torch.nn.Identity 65 | cond_stage_config: 66 | target: ldm.modules.encoders.modules.ClassEmbedder 67 | params: 68 | embed_dim: 512 69 | key: class_label 70 | data: 71 | target: main.DataModuleFromConfig 72 | params: 73 | batch_size: 64 74 | num_workers: 12 75 | wrap: false 76 | train: 77 | target: ldm.data.imagenet.ImageNetTrain 78 | params: 79 | config: 80 | size: 256 81 | validation: 82 | target: ldm.data.imagenet.ImageNetValidation 83 | params: 84 | config: 85 | size: 256 86 | 87 | 88 | lightning: 89 | callbacks: 90 | image_logger: 91 | target: main.ImageLogger 92 | params: 93 | batch_frequency: 5000 94 | max_images: 8 95 | increase_log_steps: False 96 | 97 | trainer: 98 | benchmark: True -------------------------------------------------------------------------------- /configs/latent-diffusion/cin256-v2.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 0.0001 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: class_label 12 | image_size: 64 13 | channels: 3 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss 17 | use_ema: False 18 | 19 | unet_config: 20 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 21 | params: 22 | image_size: 64 23 | in_channels: 3 24 | out_channels: 3 25 | model_channels: 192 26 | attention_resolutions: 27 | - 8 28 | - 4 29 | - 2 30 | num_res_blocks: 2 31 | channel_mult: 32 | - 1 33 | - 2 34 | - 3 35 | - 5 36 | num_heads: 1 37 | use_spatial_transformer: true 38 | transformer_depth: 1 39 | context_dim: 512 40 | 41 | first_stage_config: 42 | target: ldm.models.autoencoder.VQModelInterface 43 | params: 44 | embed_dim: 3 45 | n_embed: 8192 46 | ddconfig: 47 | double_z: false 48 | z_channels: 3 49 | resolution: 256 50 | in_channels: 3 51 | out_ch: 3 52 | ch: 128 53 | ch_mult: 54 | - 1 55 | - 2 56 | - 4 57 | num_res_blocks: 2 58 | attn_resolutions: [] 59 | dropout: 0.0 60 | lossconfig: 61 | target: torch.nn.Identity 62 | 63 | cond_stage_config: 64 | target: ldm.modules.encoders.modules.ClassEmbedder 65 | params: 66 | n_classes: 1001 67 | embed_dim: 512 68 | key: class_label 69 | -------------------------------------------------------------------------------- /configs/latent-diffusion/ffhq-ldm-vq-4.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | image_size: 64 12 | channels: 3 13 | monitor: val/loss_simple_ema 14 | unet_config: 15 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 16 | params: 17 | image_size: 64 18 | in_channels: 3 19 | out_channels: 3 20 | model_channels: 224 21 | attention_resolutions: 22 | # note: this isn\t actually the resolution but 23 | # the downsampling factor, i.e. this corresnponds to 24 | # attention on spatial resolution 8,16,32, as the 25 | # spatial reolution of the latents is 64 for f4 26 | - 8 27 | - 4 28 | - 2 29 | num_res_blocks: 2 30 | channel_mult: 31 | - 1 32 | - 2 33 | - 3 34 | - 4 35 | num_head_channels: 32 36 | first_stage_config: 37 | target: ldm.models.autoencoder.VQModelInterface 38 | params: 39 | embed_dim: 3 40 | n_embed: 8192 41 | ckpt_path: configs/first_stage_models/vq-f4/model.yaml 42 | ddconfig: 43 | double_z: false 44 | z_channels: 3 45 | resolution: 256 46 | in_channels: 3 47 | out_ch: 3 48 | ch: 128 49 | ch_mult: 50 | - 1 51 | - 2 52 | - 4 53 | num_res_blocks: 2 54 | attn_resolutions: [] 55 | dropout: 0.0 56 | lossconfig: 57 | target: torch.nn.Identity 58 | cond_stage_config: __is_unconditional__ 59 | data: 60 | target: main.DataModuleFromConfig 61 | params: 62 | batch_size: 42 63 | num_workers: 5 64 | wrap: false 65 | train: 66 | target: taming.data.faceshq.FFHQTrain 67 | params: 68 | size: 256 69 | validation: 70 | target: taming.data.faceshq.FFHQValidation 71 | params: 72 | size: 256 73 | 74 | 75 | lightning: 76 | callbacks: 77 | image_logger: 78 | target: main.ImageLogger 79 | params: 80 | batch_frequency: 5000 81 | max_images: 8 82 | increase_log_steps: False 83 | 84 | trainer: 85 | benchmark: True -------------------------------------------------------------------------------- /configs/latent-diffusion/flintstones_txt2img-1p4B-eval.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 32 24 | in_channels: 4 25 | out_channels: 4 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 4 48 | monitor: val/rec_loss 49 | ddconfig: 50 | double_z: true 51 | z_channels: 4 52 | resolution: 256 53 | in_channels: 3 54 | out_ch: 3 55 | ch: 128 56 | ch_mult: 57 | - 1 58 | - 2 59 | - 4 60 | - 4 61 | num_res_blocks: 2 62 | attn_resolutions: [] 63 | dropout: 0.0 64 | lossconfig: 65 | target: torch.nn.Identity 66 | 67 | cond_stage_config: 68 | target: ldm.modules.encoders.modules.BERTEmbedder 69 | params: 70 | n_embed: 1280 71 | n_layer: 32 72 | 73 | -------------------------------------------------------------------------------- /configs/latent-diffusion/flintstones_txt2img-1p4B-train.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 32 24 | in_channels: 4 25 | out_channels: 4 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 4 48 | ckpt_path: models/first_stage_models/kl-f8/model.ckpt 49 | monitor: val/rec_loss 50 | ddconfig: 51 | double_z: true 52 | z_channels: 4 53 | resolution: 256 54 | in_channels: 3 55 | out_ch: 3 56 | ch: 128 57 | ch_mult: 58 | - 1 59 | - 2 60 | - 4 61 | - 4 62 | num_res_blocks: 2 63 | attn_resolutions: [] 64 | dropout: 0.0 65 | lossconfig: 66 | target: torch.nn.Identity 67 | 68 | cond_stage_config: 69 | target: ldm.modules.encoders.modules.BERTEmbedder 70 | params: 71 | n_embed: 1280 72 | n_layer: 32 73 | data: 74 | target: main.DataModuleFromConfig 75 | params: 76 | batch_size: 12 77 | num_workers: 8 78 | train: 79 | target: ldm.data.flintstones_data.CustomTrain 80 | params: 81 | data_folder: /ubc/cs/research/shield/projects/trahman8/snap_research/flintstones_data 82 | cache: /ubc/cs/research/shield/projects/trahman8/snap_research/flintstones_data 83 | min_len: 4 84 | mode: train 85 | validation: 86 | target: ldm.data.flintstones_data.CustomTest 87 | params: 88 | data_folder: /ubc/cs/research/shield/projects/trahman8/snap_research/flintstones_data 89 | cache: /ubc/cs/research/shield/projects/trahman8/snap_research/flintstones_data 90 | min_len: 4 91 | mode: test 92 | 93 | -------------------------------------------------------------------------------- /configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | image_size: 64 12 | channels: 3 13 | monitor: val/loss_simple_ema 14 | unet_config: 15 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 16 | params: 17 | image_size: 64 18 | in_channels: 3 19 | out_channels: 3 20 | model_channels: 224 21 | attention_resolutions: 22 | # note: this isn\t actually the resolution but 23 | # the downsampling factor, i.e. this corresnponds to 24 | # attention on spatial resolution 8,16,32, as the 25 | # spatial reolution of the latents is 64 for f4 26 | - 8 27 | - 4 28 | - 2 29 | num_res_blocks: 2 30 | channel_mult: 31 | - 1 32 | - 2 33 | - 3 34 | - 4 35 | num_head_channels: 32 36 | first_stage_config: 37 | target: ldm.models.autoencoder.VQModelInterface 38 | params: 39 | ckpt_path: configs/first_stage_models/vq-f4/model.yaml 40 | embed_dim: 3 41 | n_embed: 8192 42 | ddconfig: 43 | double_z: false 44 | z_channels: 3 45 | resolution: 256 46 | in_channels: 3 47 | out_ch: 3 48 | ch: 128 49 | ch_mult: 50 | - 1 51 | - 2 52 | - 4 53 | num_res_blocks: 2 54 | attn_resolutions: [] 55 | dropout: 0.0 56 | lossconfig: 57 | target: torch.nn.Identity 58 | cond_stage_config: __is_unconditional__ 59 | data: 60 | target: main.DataModuleFromConfig 61 | params: 62 | batch_size: 48 63 | num_workers: 5 64 | wrap: false 65 | train: 66 | target: ldm.data.lsun.LSUNBedroomsTrain 67 | params: 68 | size: 256 69 | validation: 70 | target: ldm.data.lsun.LSUNBedroomsValidation 71 | params: 72 | size: 256 73 | 74 | 75 | lightning: 76 | callbacks: 77 | image_logger: 78 | target: main.ImageLogger 79 | params: 80 | batch_frequency: 5000 81 | max_images: 8 82 | increase_log_steps: False 83 | 84 | trainer: 85 | benchmark: True -------------------------------------------------------------------------------- /configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 5.0e-5 # set to target_lr by starting main.py with '--scale_lr False' 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0155 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | loss_type: l1 11 | first_stage_key: "image" 12 | cond_stage_key: "image" 13 | image_size: 32 14 | channels: 4 15 | cond_stage_trainable: False 16 | concat_mode: False 17 | scale_by_std: True 18 | monitor: 'val/loss_simple_ema' 19 | 20 | scheduler_config: # 10000 warmup steps 21 | target: ldm.lr_scheduler.LambdaLinearScheduler 22 | params: 23 | warm_up_steps: [10000] 24 | cycle_lengths: [10000000000000] 25 | f_start: [1.e-6] 26 | f_max: [1.] 27 | f_min: [ 1.] 28 | 29 | unet_config: 30 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 31 | params: 32 | image_size: 32 33 | in_channels: 4 34 | out_channels: 4 35 | model_channels: 192 36 | attention_resolutions: [ 1, 2, 4, 8 ] # 32, 16, 8, 4 37 | num_res_blocks: 2 38 | channel_mult: [ 1,2,2,4,4 ] # 32, 16, 8, 4, 2 39 | num_heads: 8 40 | use_scale_shift_norm: True 41 | resblock_updown: True 42 | 43 | first_stage_config: 44 | target: ldm.models.autoencoder.AutoencoderKL 45 | params: 46 | embed_dim: 4 47 | monitor: "val/rec_loss" 48 | ckpt_path: "models/first_stage_models/kl-f8/model.ckpt" 49 | ddconfig: 50 | double_z: True 51 | z_channels: 4 52 | resolution: 256 53 | in_channels: 3 54 | out_ch: 3 55 | ch: 128 56 | ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 57 | num_res_blocks: 2 58 | attn_resolutions: [ ] 59 | dropout: 0.0 60 | lossconfig: 61 | target: torch.nn.Identity 62 | 63 | cond_stage_config: "__is_unconditional__" 64 | 65 | data: 66 | target: main.DataModuleFromConfig 67 | params: 68 | batch_size: 96 69 | num_workers: 5 70 | wrap: False 71 | train: 72 | target: ldm.data.lsun.LSUNChurchesTrain 73 | params: 74 | size: 256 75 | validation: 76 | target: ldm.data.lsun.LSUNChurchesValidation 77 | params: 78 | size: 256 79 | 80 | lightning: 81 | callbacks: 82 | image_logger: 83 | target: main.ImageLogger 84 | params: 85 | batch_frequency: 5000 86 | max_images: 8 87 | increase_log_steps: False 88 | 89 | 90 | trainer: 91 | benchmark: True -------------------------------------------------------------------------------- /configs/latent-diffusion/pororo_txt2img-1p4B-eval.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 32 24 | in_channels: 4 25 | out_channels: 4 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 4 48 | monitor: val/rec_loss 49 | ddconfig: 50 | double_z: true 51 | z_channels: 4 52 | resolution: 256 53 | in_channels: 3 54 | out_ch: 3 55 | ch: 128 56 | ch_mult: 57 | - 1 58 | - 2 59 | - 4 60 | - 4 61 | num_res_blocks: 2 62 | attn_resolutions: [] 63 | dropout: 0.0 64 | lossconfig: 65 | target: torch.nn.Identity 66 | 67 | cond_stage_config: 68 | target: ldm.modules.encoders.modules.BERTEmbedder 69 | params: 70 | n_embed: 1280 71 | n_layer: 32 72 | 73 | -------------------------------------------------------------------------------- /configs/latent-diffusion/pororo_txt2img-1p4B-train.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 32 24 | in_channels: 4 25 | out_channels: 4 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 4 48 | ckpt_path: models/first_stage_models/kl-f8/model.ckpt 49 | monitor: val/rec_loss 50 | ddconfig: 51 | double_z: true 52 | z_channels: 4 53 | resolution: 256 54 | in_channels: 3 55 | out_ch: 3 56 | ch: 128 57 | ch_mult: 58 | - 1 59 | - 2 60 | - 4 61 | - 4 62 | num_res_blocks: 2 63 | attn_resolutions: [] 64 | dropout: 0.0 65 | lossconfig: 66 | target: torch.nn.Identity 67 | 68 | cond_stage_config: 69 | target: ldm.modules.encoders.modules.BERTEmbedder 70 | params: 71 | n_embed: 1280 72 | n_layer: 32 73 | data: 74 | target: main.DataModuleFromConfig 75 | params: 76 | batch_size: 16 77 | num_workers: 8 78 | train: 79 | target: ldm.data.pororo_data.CustomTrain 80 | params: 81 | data_folder: /ubc/cs/research/shield/projects/trahman8/snap_research/pororo_data 82 | cache: /ubc/cs/research/shield/projects/trahman8/snap_research/pororo_data 83 | min_len: 4 84 | mode: train 85 | validation: 86 | target: ldm.data.pororo_data.CustomTest 87 | params: 88 | data_folder: /ubc/cs/research/shield/projects/trahman8/snap_research/pororo_data 89 | cache: /ubc/cs/research/shield/projects/trahman8/snap_research/pororo_data 90 | min_len: 4 91 | mode: test 92 | 93 | -------------------------------------------------------------------------------- /configs/latent-diffusion/txt2img-1p4B-autoencoder.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 64 13 | channels: 3 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 64 24 | in_channels: 3 25 | out_channels: 3 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 64 48 | ckpt_path: logs/2022-10-13T15-40-49_mugen_kl_8x8x64/checkpoints/epoch=000006.ckpt #models/first_stage_models/kl-f8/model.ckpt 49 | monitor: val/rec_loss 50 | ddconfig: 51 | double_z: true 52 | z_channels: 64 53 | resolution: 256 54 | in_channels: 3 55 | out_ch: 3 56 | ch: 128 57 | ch_mult: 58 | - 1 59 | - 1 60 | - 2 61 | - 2 62 | - 4 63 | - 4 64 | num_res_blocks: 2 65 | attn_resolutions: [] 66 | dropout: 0.0 67 | lossconfig: 68 | target: torch.nn.Identity 69 | 70 | cond_stage_config: 71 | target: ldm.modules.encoders.modules.BERTEmbedder 72 | params: 73 | n_embed: 1280 74 | n_layer: 8 75 | data: 76 | target: main.DataModuleFromConfig 77 | params: 78 | batch_size: 16 #24 79 | num_workers: 8 80 | train: 81 | target: ldm.data.mugen_data.CustomTrain 82 | params: 83 | training_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/train.json 84 | size: 256 85 | get_game_frame: True 86 | get_text_desc: True 87 | get_audio: False 88 | split: train 89 | sample_every_n_frames: 12 90 | sequence_length: 1 91 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 92 | use_manual_annotation: True 93 | resolution: 256 94 | validation: 95 | target: ldm.data.mugen_data.CustomTest 96 | params: 97 | test_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/test.json 98 | size: 256 99 | get_game_frame: True 100 | get_text_desc: True 101 | get_audio: False 102 | split: test 103 | sample_every_n_frames: 12 104 | sequence_length: 1 105 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 106 | use_manual_annotation: True 107 | resolution: 256 108 | 109 | -------------------------------------------------------------------------------- /configs/latent-diffusion/txt2img-1p4B-eval.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 5.0e-05 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 32 24 | in_channels: 4 25 | out_channels: 4 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 4 48 | monitor: val/rec_loss 49 | ddconfig: 50 | double_z: true 51 | z_channels: 4 52 | resolution: 256 53 | in_channels: 3 54 | out_ch: 3 55 | ch: 128 56 | ch_mult: 57 | - 1 58 | - 2 59 | - 4 60 | - 4 61 | num_res_blocks: 2 62 | attn_resolutions: [] 63 | dropout: 0.0 64 | lossconfig: 65 | target: torch.nn.Identity 66 | 67 | cond_stage_config: 68 | target: ldm.modules.encoders.modules.BERTEmbedder 69 | params: 70 | n_embed: 1280 71 | n_layer: 32 72 | -------------------------------------------------------------------------------- /configs/latent-diffusion/txt2img-1p4B-train.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.012 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | unet_config: 21 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 22 | params: 23 | image_size: 32 24 | in_channels: 4 25 | out_channels: 4 26 | model_channels: 128 27 | attention_resolutions: 28 | - 4 29 | - 2 30 | - 1 31 | num_res_blocks: 2 32 | channel_mult: 33 | - 1 34 | - 2 35 | - 4 36 | - 4 37 | num_heads: 8 38 | use_spatial_transformer: true 39 | transformer_depth: 1 40 | context_dim: 1280 41 | use_checkpoint: true 42 | legacy: False 43 | 44 | first_stage_config: 45 | target: ldm.models.autoencoder.AutoencoderKL 46 | params: 47 | embed_dim: 4 48 | #ckpt_path: models/first_stage_models/kl-f8/model.ckpt 49 | monitor: val/rec_loss 50 | ddconfig: 51 | double_z: true 52 | z_channels: 4 53 | resolution: 256 54 | in_channels: 3 55 | out_ch: 3 56 | ch: 128 57 | ch_mult: 58 | - 1 59 | - 2 60 | - 4 61 | - 4 62 | num_res_blocks: 2 63 | attn_resolutions: [] 64 | dropout: 0.0 65 | lossconfig: 66 | target: torch.nn.Identity 67 | 68 | cond_stage_config: 69 | target: ldm.modules.encoders.modules.BERTEmbedder 70 | params: 71 | n_embed: 1280 72 | n_layer: 32 73 | data: 74 | target: main.DataModuleFromConfig 75 | params: 76 | batch_size: 24 77 | num_workers: 8 78 | train: 79 | target: ldm.data.mugen_data.CustomTrain 80 | params: 81 | training_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/train.json 82 | size: 256 83 | get_game_frame: True 84 | get_text_desc: True 85 | get_audio: False 86 | split: train 87 | sample_every_n_frames: 12 88 | sequence_length: 1 89 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 90 | use_manual_annotation: True 91 | resolution: 256 92 | validation: 93 | target: ldm.data.mugen_data.CustomTest 94 | params: 95 | test_json: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/test.json 96 | size: 256 97 | get_game_frame: True 98 | get_text_desc: True 99 | get_audio: False 100 | split: test 101 | sample_every_n_frames: 12 102 | sequence_length: 1 103 | data_path: /ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/ 104 | use_manual_annotation: True 105 | resolution: 256 106 | 107 | -------------------------------------------------------------------------------- /data/DejaVuSans.ttf: 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3 | - pytorch 4 | - defaults 5 | dependencies: 6 | - python=3.8.5 7 | - pip=20.3 8 | - cudatoolkit=11.0 9 | - pytorch=1.7.0 10 | - torchvision=0.8.1 11 | - numpy=1.19.2 12 | - pip: 13 | - albumentations==0.4.3 14 | - opencv-python==4.1.2.30 15 | - pudb==2019.2 16 | - imageio==2.9.0 17 | - imageio-ffmpeg==0.4.2 18 | - pytorch-lightning==1.4.2 19 | - omegaconf==2.1.1 20 | - test-tube>=0.7.5 21 | - streamlit>=0.73.1 22 | - einops==0.3.0 23 | - torch-fidelity==0.3.0 24 | - transformers==4.3.1 25 | - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers 26 | - -e git+https://github.com/openai/CLIP.git@main#egg=clip 27 | - -e . 28 | -------------------------------------------------------------------------------- /eval_vfid.py: -------------------------------------------------------------------------------- 1 | import torchvision.transforms as transforms 2 | import argparse 3 | import os, sys 4 | from classifier.eval_dataloader_multi import StoryImageDataset 5 | sys.path.append('.') 6 | 7 | from classifier.fid_score import fid_score 8 | 9 | def main(args): 10 | 11 | image_transforms = transforms.Compose([ 12 | transforms.Resize((args.imsize, args.imsize)), 13 | transforms.ToTensor(), 14 | transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) 15 | ]) 16 | 17 | ref_dataset = StoryImageDataset(args.img_ref_dir, 18 | args.imsize, 19 | mode=args.mode, 20 | out_img_folder=None, fid = True) 21 | 22 | fid = fid_score(ref_dataset, args.model_path, cuda=True, normalize=True, batch_size=1) 23 | print('Frechet Image Distance: ', fid) 24 | 25 | with open('result.txt', 'a') as f: 26 | f.write("\nFID-score: "+ str(fid)) 27 | 28 | 29 | if __name__ == "__main__": 30 | 31 | parser = argparse.ArgumentParser(description='Evaluate Frechet Story and Image distance') 32 | parser.add_argument('--img_ref_dir', type=str, default='/ubc/cs/research/shield/datasets/coinrun/coinrun_dataset_jsons/release/') 33 | parser.add_argument('--model_path', type=str, default='logs/2023-01-30T13-49-14_txt2img-1p4B-train/checkpoints/epoch=000001.ckpt') 34 | parser.add_argument('--mode', type=str, default='test') 35 | parser.add_argument('--imsize', type=int, default=256) 36 | args = parser.parse_args() 37 | 38 | print(args) 39 | main(args) 40 | -------------------------------------------------------------------------------- /ldm/__pycache__/util.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/__pycache__/util.cpython-38.pyc -------------------------------------------------------------------------------- /ldm/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/data/__init__.py 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Txt2ImgIterableBaseDataset(IterableDataset): 6 | ''' 7 | Define an interface to make the IterableDatasets for text2img data chainable 8 | ''' 9 | def __init__(self, num_records=0, valid_ids=None, size=256): 10 | super().__init__() 11 | self.num_records = num_records 12 | self.valid_ids = valid_ids 13 | self.sample_ids = valid_ids 14 | self.size = size 15 | 16 | print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.') 17 | 18 | def __len__(self): 19 | return self.num_records 20 | 21 | @abstractmethod 22 | def __iter__(self): 23 | pass -------------------------------------------------------------------------------- /ldm/data/lsun.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import PIL 4 | from PIL import Image 5 | from torch.utils.data import Dataset 6 | from torchvision import transforms 7 | 8 | 9 | class LSUNBase(Dataset): 10 | def __init__(self, 11 | txt_file, 12 | data_root, 13 | size=None, 14 | interpolation="bicubic", 15 | flip_p=0.5 16 | ): 17 | self.data_paths = txt_file 18 | self.data_root = data_root 19 | with open(self.data_paths, "r") as f: 20 | self.image_paths = f.read().splitlines() 21 | self._length = len(self.image_paths) 22 | self.labels = { 23 | "relative_file_path_": [l for l in self.image_paths], 24 | "file_path_": [os.path.join(self.data_root, l) 25 | for l in self.image_paths], 26 | } 27 | 28 | self.size = size 29 | self.interpolation = {"linear": PIL.Image.LINEAR, 30 | "bilinear": PIL.Image.BILINEAR, 31 | "bicubic": PIL.Image.BICUBIC, 32 | "lanczos": PIL.Image.LANCZOS, 33 | }[interpolation] 34 | self.flip = transforms.RandomHorizontalFlip(p=flip_p) 35 | 36 | def __len__(self): 37 | return self._length 38 | 39 | def __getitem__(self, i): 40 | example = dict((k, self.labels[k][i]) for k in self.labels) 41 | image = Image.open(example["file_path_"]) 42 | if not image.mode == "RGB": 43 | image = image.convert("RGB") 44 | 45 | # default to score-sde preprocessing 46 | img = np.array(image).astype(np.uint8) 47 | crop = min(img.shape[0], img.shape[1]) 48 | h, w, = img.shape[0], img.shape[1] 49 | img = img[(h - crop) // 2:(h + crop) // 2, 50 | (w - crop) // 2:(w + crop) // 2] 51 | 52 | image = Image.fromarray(img) 53 | if self.size is not None: 54 | image = image.resize((self.size, self.size), resample=self.interpolation) 55 | 56 | image = self.flip(image) 57 | image = np.array(image).astype(np.uint8) 58 | example["image"] = (image / 127.5 - 1.0).astype(np.float32) 59 | return example 60 | 61 | 62 | class LSUNChurchesTrain(LSUNBase): 63 | def __init__(self, **kwargs): 64 | super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs) 65 | 66 | 67 | class LSUNChurchesValidation(LSUNBase): 68 | def __init__(self, flip_p=0., **kwargs): 69 | super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches", 70 | flip_p=flip_p, **kwargs) 71 | 72 | 73 | class LSUNBedroomsTrain(LSUNBase): 74 | def __init__(self, **kwargs): 75 | super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs) 76 | 77 | 78 | class LSUNBedroomsValidation(LSUNBase): 79 | def __init__(self, flip_p=0.0, **kwargs): 80 | super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms", 81 | flip_p=flip_p, **kwargs) 82 | 83 | 84 | class LSUNCatsTrain(LSUNBase): 85 | def __init__(self, **kwargs): 86 | super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs) 87 | 88 | 89 | class LSUNCatsValidation(LSUNBase): 90 | def __init__(self, flip_p=0., **kwargs): 91 | super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats", 92 | flip_p=flip_p, **kwargs) 93 | -------------------------------------------------------------------------------- /ldm/models/__pycache__/autoencoder.cpython-38.pyc: -------------------------------------------------------------------------------- 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__init__(self, parameters, deterministic=False): 26 | self.parameters = parameters 27 | self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) 28 | self.logvar = torch.clamp(self.logvar, -30.0, 20.0) 29 | self.deterministic = deterministic 30 | self.std = torch.exp(0.5 * self.logvar) 31 | self.var = torch.exp(self.logvar) 32 | if self.deterministic: 33 | self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) 34 | 35 | def sample(self): 36 | x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) 37 | return x 38 | 39 | def kl(self, other=None): 40 | if self.deterministic: 41 | return torch.Tensor([0.]) 42 | else: 43 | if other is None: 44 | return 0.5 * torch.sum(torch.pow(self.mean, 2) 45 | + self.var - 1.0 - self.logvar, 46 | dim=[1, 2, 3]) 47 | else: 48 | return 0.5 * torch.sum( 49 | torch.pow(self.mean - other.mean, 2) / other.var 50 | + self.var / other.var - 1.0 - self.logvar + other.logvar, 51 | dim=[1, 2, 3]) 52 | 53 | def nll(self, sample, dims=[1,2,3]): 54 | if self.deterministic: 55 | return torch.Tensor([0.]) 56 | logtwopi = np.log(2.0 * np.pi) 57 | return 0.5 * torch.sum( 58 | logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, 59 | dim=dims) 60 | 61 | def mode(self): 62 | return self.mean 63 | 64 | 65 | def normal_kl(mean1, logvar1, mean2, logvar2): 66 | """ 67 | source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 68 | Compute the KL divergence between two gaussians. 69 | Shapes are automatically broadcasted, so batches can be compared to 70 | scalars, among other use cases. 71 | """ 72 | tensor = None 73 | for obj in (mean1, logvar1, mean2, logvar2): 74 | if isinstance(obj, torch.Tensor): 75 | tensor = obj 76 | break 77 | assert tensor is not None, "at least one argument must be a Tensor" 78 | 79 | # Force variances to be Tensors. Broadcasting helps convert scalars to 80 | # Tensors, but it does not work for torch.exp(). 81 | logvar1, logvar2 = [ 82 | x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) 83 | for x in (logvar1, logvar2) 84 | ] 85 | 86 | return 0.5 * ( 87 | -1.0 88 | + logvar2 89 | - logvar1 90 | + torch.exp(logvar1 - logvar2) 91 | + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) 92 | ) 93 | -------------------------------------------------------------------------------- /ldm/modules/ema.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | 5 | class LitEma(nn.Module): 6 | def __init__(self, model, decay=0.9999, use_num_upates=True): 7 | super().__init__() 8 | if decay < 0.0 or decay > 1.0: 9 | raise ValueError('Decay must be between 0 and 1') 10 | 11 | self.m_name2s_name = {} 12 | self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) 13 | self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates 14 | else torch.tensor(-1,dtype=torch.int)) 15 | 16 | for name, p in model.named_parameters(): 17 | if p.requires_grad: 18 | #remove as '.'-character is not allowed in buffers 19 | s_name = name.replace('.','') 20 | self.m_name2s_name.update({name:s_name}) 21 | self.register_buffer(s_name,p.clone().detach().data) 22 | 23 | self.collected_params = [] 24 | 25 | def forward(self,model): 26 | decay = self.decay 27 | 28 | if self.num_updates >= 0: 29 | self.num_updates += 1 30 | decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) 31 | 32 | one_minus_decay = 1.0 - decay 33 | 34 | with torch.no_grad(): 35 | m_param = dict(model.named_parameters()) 36 | shadow_params = dict(self.named_buffers()) 37 | 38 | for key in m_param: 39 | if m_param[key].requires_grad: 40 | sname = self.m_name2s_name[key] 41 | shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) 42 | shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) 43 | else: 44 | assert not key in self.m_name2s_name 45 | 46 | def copy_to(self, model): 47 | m_param = dict(model.named_parameters()) 48 | shadow_params = dict(self.named_buffers()) 49 | for key in m_param: 50 | if m_param[key].requires_grad: 51 | m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) 52 | else: 53 | assert not key in self.m_name2s_name 54 | 55 | def store(self, parameters): 56 | """ 57 | Save the current parameters for restoring later. 58 | Args: 59 | parameters: Iterable of `torch.nn.Parameter`; the parameters to be 60 | temporarily stored. 61 | """ 62 | self.collected_params = [param.clone() for param in parameters] 63 | 64 | def restore(self, parameters): 65 | """ 66 | Restore the parameters stored with the `store` method. 67 | Useful to validate the model with EMA parameters without affecting the 68 | original optimization process. Store the parameters before the 69 | `copy_to` method. After validation (or model saving), use this to 70 | restore the former parameters. 71 | Args: 72 | parameters: Iterable of `torch.nn.Parameter`; the parameters to be 73 | updated with the stored parameters. 74 | """ 75 | for c_param, param in zip(self.collected_params, parameters): 76 | param.data.copy_(c_param.data) 77 | -------------------------------------------------------------------------------- /ldm/modules/encoders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/modules/encoders/__init__.py -------------------------------------------------------------------------------- /ldm/modules/encoders/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/modules/image_degradation/__pycache__/bsrgan_light.cpython-38.pyc -------------------------------------------------------------------------------- /ldm/modules/image_degradation/__pycache__/utils_image.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/modules/image_degradation/__pycache__/utils_image.cpython-38.pyc -------------------------------------------------------------------------------- /ldm/modules/image_degradation/utils/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/modules/image_degradation/utils/test.png -------------------------------------------------------------------------------- /ldm/modules/losses/__init__.py: -------------------------------------------------------------------------------- 1 | from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator -------------------------------------------------------------------------------- /ldm/modules/losses/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/modules/losses/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /ldm/modules/losses/__pycache__/contperceptual.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/ldm/modules/losses/__pycache__/contperceptual.cpython-38.pyc -------------------------------------------------------------------------------- /ldm/util.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | 3 | import torch 4 | import numpy as np 5 | 6 | from inspect import isfunction 7 | from PIL import Image, ImageDraw, ImageFont 8 | 9 | 10 | def log_txt_as_img(wh, xc, size=10): 11 | # wh a tuple of (width, height) 12 | # xc a list of captions to plot 13 | b = len(xc) 14 | txts = list() 15 | for bi in range(b): 16 | txt = Image.new("RGB", wh, color="white") 17 | draw = ImageDraw.Draw(txt) 18 | font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) 19 | nc = int(40 * (wh[0] / 256)) 20 | lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) 21 | 22 | try: 23 | draw.text((0, 0), lines, fill="black", font=font) 24 | except UnicodeEncodeError: 25 | print("Cant encode string for logging. Skipping.") 26 | 27 | txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 28 | txts.append(txt) 29 | txts = np.stack(txts) 30 | txts = torch.tensor(txts) 31 | return txts 32 | 33 | 34 | def ismap(x): 35 | if not isinstance(x, torch.Tensor): 36 | return False 37 | return (len(x.shape) == 4) and (x.shape[1] > 3) 38 | 39 | 40 | def isimage(x): 41 | if not isinstance(x,torch.Tensor): 42 | return False 43 | return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) 44 | 45 | 46 | def exists(x): 47 | return x is not None 48 | 49 | 50 | def default(val, d): 51 | if exists(val): 52 | return val 53 | return d() if isfunction(d) else d 54 | 55 | 56 | def mean_flat(tensor): 57 | """ 58 | https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 59 | Take the mean over all non-batch dimensions. 60 | """ 61 | return tensor.mean(dim=list(range(1, len(tensor.shape)))) 62 | 63 | 64 | def count_params(model, verbose=False): 65 | total_params = sum(p.numel() for p in model.parameters()) 66 | if verbose: 67 | print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") 68 | return total_params 69 | 70 | 71 | def instantiate_from_config(config): 72 | if not "target" in config: 73 | if config == '__is_first_stage__': 74 | return None 75 | elif config == "__is_unconditional__": 76 | return None 77 | raise KeyError("Expected key `target` to instantiate.") 78 | return get_obj_from_str(config["target"])(**config.get("params", dict())) 79 | 80 | 81 | def get_obj_from_str(string, reload=False): 82 | module, cls = string.rsplit(".", 1) 83 | if reload: 84 | module_imp = importlib.import_module(module) 85 | importlib.reload(module_imp) 86 | return getattr(importlib.import_module(module, package=None), cls) -------------------------------------------------------------------------------- /models/first_stage_models/kl-f16/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 16 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 16 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 1 23 | - 2 24 | - 2 25 | - 4 26 | num_res_blocks: 2 27 | attn_resolutions: 28 | - 16 29 | dropout: 0.0 30 | data: 31 | target: main.DataModuleFromConfig 32 | params: 33 | batch_size: 6 34 | wrap: true 35 | train: 36 | target: ldm.data.openimages.FullOpenImagesTrain 37 | params: 38 | size: 384 39 | crop_size: 256 40 | validation: 41 | target: ldm.data.openimages.FullOpenImagesValidation 42 | params: 43 | size: 384 44 | crop_size: 256 45 | -------------------------------------------------------------------------------- /models/first_stage_models/kl-f32/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 64 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 64 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 1 23 | - 2 24 | - 2 25 | - 4 26 | - 4 27 | num_res_blocks: 2 28 | attn_resolutions: 29 | - 16 30 | - 8 31 | dropout: 0.0 32 | data: 33 | target: main.DataModuleFromConfig 34 | params: 35 | batch_size: 6 36 | wrap: true 37 | train: 38 | target: ldm.data.openimages.FullOpenImagesTrain 39 | params: 40 | size: 384 41 | crop_size: 256 42 | validation: 43 | target: ldm.data.openimages.FullOpenImagesValidation 44 | params: 45 | size: 384 46 | crop_size: 256 47 | -------------------------------------------------------------------------------- /models/first_stage_models/kl-f4/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 3 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 3 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 2 23 | - 4 24 | num_res_blocks: 2 25 | attn_resolutions: [] 26 | dropout: 0.0 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 10 31 | wrap: true 32 | train: 33 | target: ldm.data.openimages.FullOpenImagesTrain 34 | params: 35 | size: 384 36 | crop_size: 256 37 | validation: 38 | target: ldm.data.openimages.FullOpenImagesValidation 39 | params: 40 | size: 384 41 | crop_size: 256 42 | -------------------------------------------------------------------------------- /models/first_stage_models/kl-f8/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 4 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 4 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 2 23 | - 4 24 | - 4 25 | num_res_blocks: 2 26 | attn_resolutions: [] 27 | dropout: 0.0 28 | data: 29 | target: main.DataModuleFromConfig 30 | params: 31 | batch_size: 4 32 | wrap: true 33 | train: 34 | target: ldm.data.openimages.FullOpenImagesTrain 35 | params: 36 | size: 384 37 | crop_size: 256 38 | validation: 39 | target: ldm.data.openimages.FullOpenImagesValidation 40 | params: 41 | size: 384 42 | crop_size: 256 43 | -------------------------------------------------------------------------------- /models/first_stage_models/vq-f16/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 8 6 | n_embed: 16384 7 | ddconfig: 8 | double_z: false 9 | z_channels: 8 10 | resolution: 256 11 | in_channels: 3 12 | out_ch: 3 13 | ch: 128 14 | ch_mult: 15 | - 1 16 | - 1 17 | - 2 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: 22 | - 16 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_start: 250001 30 | disc_weight: 0.75 31 | disc_num_layers: 2 32 | codebook_weight: 1.0 33 | 34 | data: 35 | target: main.DataModuleFromConfig 36 | params: 37 | batch_size: 14 38 | num_workers: 20 39 | wrap: true 40 | train: 41 | target: ldm.data.openimages.FullOpenImagesTrain 42 | params: 43 | size: 384 44 | crop_size: 256 45 | validation: 46 | target: ldm.data.openimages.FullOpenImagesValidation 47 | params: 48 | size: 384 49 | crop_size: 256 50 | -------------------------------------------------------------------------------- /models/first_stage_models/vq-f4-noattn/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 3 6 | n_embed: 8192 7 | monitor: val/rec_loss 8 | 9 | ddconfig: 10 | attn_type: none 11 | double_z: false 12 | z_channels: 3 13 | resolution: 256 14 | in_channels: 3 15 | out_ch: 3 16 | ch: 128 17 | ch_mult: 18 | - 1 19 | - 2 20 | - 4 21 | num_res_blocks: 2 22 | attn_resolutions: [] 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_start: 11 30 | disc_weight: 0.75 31 | codebook_weight: 1.0 32 | 33 | data: 34 | target: main.DataModuleFromConfig 35 | params: 36 | batch_size: 8 37 | num_workers: 12 38 | wrap: true 39 | train: 40 | target: ldm.data.openimages.FullOpenImagesTrain 41 | params: 42 | crop_size: 256 43 | validation: 44 | target: ldm.data.openimages.FullOpenImagesValidation 45 | params: 46 | crop_size: 256 47 | -------------------------------------------------------------------------------- /models/first_stage_models/vq-f4/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 3 6 | n_embed: 8192 7 | monitor: val/rec_loss 8 | 9 | ddconfig: 10 | double_z: false 11 | z_channels: 3 12 | resolution: 256 13 | in_channels: 3 14 | out_ch: 3 15 | ch: 128 16 | ch_mult: 17 | - 1 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: [] 22 | dropout: 0.0 23 | lossconfig: 24 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 25 | params: 26 | disc_conditional: false 27 | disc_in_channels: 3 28 | disc_start: 0 29 | disc_weight: 0.75 30 | codebook_weight: 1.0 31 | 32 | data: 33 | target: main.DataModuleFromConfig 34 | params: 35 | batch_size: 8 36 | num_workers: 16 37 | wrap: true 38 | train: 39 | target: ldm.data.openimages.FullOpenImagesTrain 40 | params: 41 | crop_size: 256 42 | validation: 43 | target: ldm.data.openimages.FullOpenImagesValidation 44 | params: 45 | crop_size: 256 46 | -------------------------------------------------------------------------------- /models/first_stage_models/vq-f8-n256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 4 6 | n_embed: 256 7 | monitor: val/rec_loss 8 | ddconfig: 9 | double_z: false 10 | z_channels: 4 11 | resolution: 256 12 | in_channels: 3 13 | out_ch: 3 14 | ch: 128 15 | ch_mult: 16 | - 1 17 | - 2 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: 22 | - 32 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_start: 250001 30 | disc_weight: 0.75 31 | codebook_weight: 1.0 32 | 33 | data: 34 | target: main.DataModuleFromConfig 35 | params: 36 | batch_size: 10 37 | num_workers: 20 38 | wrap: true 39 | train: 40 | target: ldm.data.openimages.FullOpenImagesTrain 41 | params: 42 | size: 384 43 | crop_size: 256 44 | validation: 45 | target: ldm.data.openimages.FullOpenImagesValidation 46 | params: 47 | size: 384 48 | crop_size: 256 49 | -------------------------------------------------------------------------------- /models/first_stage_models/vq-f8/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 4 6 | n_embed: 16384 7 | monitor: val/rec_loss 8 | ddconfig: 9 | double_z: false 10 | z_channels: 4 11 | resolution: 256 12 | in_channels: 3 13 | out_ch: 3 14 | ch: 128 15 | ch_mult: 16 | - 1 17 | - 2 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: 22 | - 32 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_num_layers: 2 30 | disc_start: 1 31 | disc_weight: 0.6 32 | codebook_weight: 1.0 33 | data: 34 | target: main.DataModuleFromConfig 35 | params: 36 | batch_size: 10 37 | num_workers: 20 38 | wrap: true 39 | train: 40 | target: ldm.data.openimages.FullOpenImagesTrain 41 | params: 42 | size: 384 43 | crop_size: 256 44 | validation: 45 | target: ldm.data.openimages.FullOpenImagesValidation 46 | params: 47 | size: 384 48 | crop_size: 256 49 | -------------------------------------------------------------------------------- /models/ldm/bsr_sr/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0155 7 | log_every_t: 100 8 | timesteps: 1000 9 | loss_type: l2 10 | first_stage_key: image 11 | cond_stage_key: LR_image 12 | image_size: 64 13 | channels: 3 14 | concat_mode: true 15 | cond_stage_trainable: false 16 | unet_config: 17 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 18 | params: 19 | image_size: 64 20 | in_channels: 6 21 | out_channels: 3 22 | model_channels: 160 23 | attention_resolutions: 24 | - 16 25 | - 8 26 | num_res_blocks: 2 27 | channel_mult: 28 | - 1 29 | - 2 30 | - 2 31 | - 4 32 | num_head_channels: 32 33 | first_stage_config: 34 | target: ldm.models.autoencoder.VQModelInterface 35 | params: 36 | embed_dim: 3 37 | n_embed: 8192 38 | monitor: val/rec_loss 39 | ddconfig: 40 | double_z: false 41 | z_channels: 3 42 | resolution: 256 43 | in_channels: 3 44 | out_ch: 3 45 | ch: 128 46 | ch_mult: 47 | - 1 48 | - 2 49 | - 4 50 | num_res_blocks: 2 51 | attn_resolutions: [] 52 | dropout: 0.0 53 | lossconfig: 54 | target: torch.nn.Identity 55 | cond_stage_config: 56 | target: torch.nn.Identity 57 | data: 58 | target: main.DataModuleFromConfig 59 | params: 60 | batch_size: 64 61 | wrap: false 62 | num_workers: 12 63 | train: 64 | target: ldm.data.openimages.SuperresOpenImagesAdvancedTrain 65 | params: 66 | size: 256 67 | degradation: bsrgan_light 68 | downscale_f: 4 69 | min_crop_f: 0.5 70 | max_crop_f: 1.0 71 | random_crop: true 72 | validation: 73 | target: ldm.data.openimages.SuperresOpenImagesAdvancedValidation 74 | params: 75 | size: 256 76 | degradation: bsrgan_light 77 | downscale_f: 4 78 | min_crop_f: 0.5 79 | max_crop_f: 1.0 80 | random_crop: true 81 | -------------------------------------------------------------------------------- /models/ldm/celeba256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: class_label 12 | image_size: 64 13 | channels: 3 14 | cond_stage_trainable: false 15 | concat_mode: false 16 | monitor: val/loss 17 | unet_config: 18 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 19 | params: 20 | image_size: 64 21 | in_channels: 3 22 | out_channels: 3 23 | model_channels: 224 24 | attention_resolutions: 25 | - 8 26 | - 4 27 | - 2 28 | num_res_blocks: 2 29 | channel_mult: 30 | - 1 31 | - 2 32 | - 3 33 | - 4 34 | num_head_channels: 32 35 | first_stage_config: 36 | target: ldm.models.autoencoder.VQModelInterface 37 | params: 38 | embed_dim: 3 39 | n_embed: 8192 40 | ddconfig: 41 | double_z: false 42 | z_channels: 3 43 | resolution: 256 44 | in_channels: 3 45 | out_ch: 3 46 | ch: 128 47 | ch_mult: 48 | - 1 49 | - 2 50 | - 4 51 | num_res_blocks: 2 52 | attn_resolutions: [] 53 | dropout: 0.0 54 | lossconfig: 55 | target: torch.nn.Identity 56 | cond_stage_config: __is_unconditional__ 57 | data: 58 | target: main.DataModuleFromConfig 59 | params: 60 | batch_size: 48 61 | num_workers: 5 62 | wrap: false 63 | train: 64 | target: ldm.data.faceshq.CelebAHQTrain 65 | params: 66 | size: 256 67 | validation: 68 | target: ldm.data.faceshq.CelebAHQValidation 69 | params: 70 | size: 256 71 | -------------------------------------------------------------------------------- /models/ldm/cin256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: class_label 12 | image_size: 32 13 | channels: 4 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | unet_config: 18 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 19 | params: 20 | image_size: 32 21 | in_channels: 4 22 | out_channels: 4 23 | model_channels: 256 24 | attention_resolutions: 25 | - 4 26 | - 2 27 | - 1 28 | num_res_blocks: 2 29 | channel_mult: 30 | - 1 31 | - 2 32 | - 4 33 | num_head_channels: 32 34 | use_spatial_transformer: true 35 | transformer_depth: 1 36 | context_dim: 512 37 | first_stage_config: 38 | target: ldm.models.autoencoder.VQModelInterface 39 | params: 40 | embed_dim: 4 41 | n_embed: 16384 42 | ddconfig: 43 | double_z: false 44 | z_channels: 4 45 | resolution: 256 46 | in_channels: 3 47 | out_ch: 3 48 | ch: 128 49 | ch_mult: 50 | - 1 51 | - 2 52 | - 2 53 | - 4 54 | num_res_blocks: 2 55 | attn_resolutions: 56 | - 32 57 | dropout: 0.0 58 | lossconfig: 59 | target: torch.nn.Identity 60 | cond_stage_config: 61 | target: ldm.modules.encoders.modules.ClassEmbedder 62 | params: 63 | embed_dim: 512 64 | key: class_label 65 | data: 66 | target: main.DataModuleFromConfig 67 | params: 68 | batch_size: 64 69 | num_workers: 12 70 | wrap: false 71 | train: 72 | target: ldm.data.imagenet.ImageNetTrain 73 | params: 74 | config: 75 | size: 256 76 | validation: 77 | target: ldm.data.imagenet.ImageNetValidation 78 | params: 79 | config: 80 | size: 256 81 | -------------------------------------------------------------------------------- /models/ldm/ffhq256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: class_label 12 | image_size: 64 13 | channels: 3 14 | cond_stage_trainable: false 15 | concat_mode: false 16 | monitor: val/loss 17 | unet_config: 18 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 19 | params: 20 | image_size: 64 21 | in_channels: 3 22 | out_channels: 3 23 | model_channels: 224 24 | attention_resolutions: 25 | - 8 26 | - 4 27 | - 2 28 | num_res_blocks: 2 29 | channel_mult: 30 | - 1 31 | - 2 32 | - 3 33 | - 4 34 | num_head_channels: 32 35 | first_stage_config: 36 | target: ldm.models.autoencoder.VQModelInterface 37 | params: 38 | embed_dim: 3 39 | n_embed: 8192 40 | ddconfig: 41 | double_z: false 42 | z_channels: 3 43 | resolution: 256 44 | in_channels: 3 45 | out_ch: 3 46 | ch: 128 47 | ch_mult: 48 | - 1 49 | - 2 50 | - 4 51 | num_res_blocks: 2 52 | attn_resolutions: [] 53 | dropout: 0.0 54 | lossconfig: 55 | target: torch.nn.Identity 56 | cond_stage_config: __is_unconditional__ 57 | data: 58 | target: main.DataModuleFromConfig 59 | params: 60 | batch_size: 42 61 | num_workers: 5 62 | wrap: false 63 | train: 64 | target: ldm.data.faceshq.FFHQTrain 65 | params: 66 | size: 256 67 | validation: 68 | target: ldm.data.faceshq.FFHQValidation 69 | params: 70 | size: 256 71 | -------------------------------------------------------------------------------- /models/ldm/inpainting_big/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0205 7 | log_every_t: 100 8 | timesteps: 1000 9 | loss_type: l1 10 | first_stage_key: image 11 | cond_stage_key: masked_image 12 | image_size: 64 13 | channels: 3 14 | concat_mode: true 15 | monitor: val/loss 16 | scheduler_config: 17 | target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler 18 | params: 19 | verbosity_interval: 0 20 | warm_up_steps: 1000 21 | max_decay_steps: 50000 22 | lr_start: 0.001 23 | lr_max: 0.1 24 | lr_min: 0.0001 25 | unet_config: 26 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 27 | params: 28 | image_size: 64 29 | in_channels: 7 30 | out_channels: 3 31 | model_channels: 256 32 | attention_resolutions: 33 | - 8 34 | - 4 35 | - 2 36 | num_res_blocks: 2 37 | channel_mult: 38 | - 1 39 | - 2 40 | - 3 41 | - 4 42 | num_heads: 8 43 | resblock_updown: true 44 | first_stage_config: 45 | target: ldm.models.autoencoder.VQModelInterface 46 | params: 47 | embed_dim: 3 48 | n_embed: 8192 49 | monitor: val/rec_loss 50 | ddconfig: 51 | attn_type: none 52 | double_z: false 53 | z_channels: 3 54 | resolution: 256 55 | in_channels: 3 56 | out_ch: 3 57 | ch: 128 58 | ch_mult: 59 | - 1 60 | - 2 61 | - 4 62 | num_res_blocks: 2 63 | attn_resolutions: [] 64 | dropout: 0.0 65 | lossconfig: 66 | target: ldm.modules.losses.contperceptual.DummyLoss 67 | cond_stage_config: __is_first_stage__ 68 | -------------------------------------------------------------------------------- /models/ldm/layout2img-openimages256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0205 7 | log_every_t: 100 8 | timesteps: 1000 9 | loss_type: l1 10 | first_stage_key: image 11 | cond_stage_key: coordinates_bbox 12 | image_size: 64 13 | channels: 3 14 | conditioning_key: crossattn 15 | cond_stage_trainable: true 16 | unet_config: 17 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 18 | params: 19 | image_size: 64 20 | in_channels: 3 21 | out_channels: 3 22 | model_channels: 128 23 | attention_resolutions: 24 | - 8 25 | - 4 26 | - 2 27 | num_res_blocks: 2 28 | channel_mult: 29 | - 1 30 | - 2 31 | - 3 32 | - 4 33 | num_head_channels: 32 34 | use_spatial_transformer: true 35 | transformer_depth: 3 36 | context_dim: 512 37 | first_stage_config: 38 | target: ldm.models.autoencoder.VQModelInterface 39 | params: 40 | embed_dim: 3 41 | n_embed: 8192 42 | monitor: val/rec_loss 43 | ddconfig: 44 | double_z: false 45 | z_channels: 3 46 | resolution: 256 47 | in_channels: 3 48 | out_ch: 3 49 | ch: 128 50 | ch_mult: 51 | - 1 52 | - 2 53 | - 4 54 | num_res_blocks: 2 55 | attn_resolutions: [] 56 | dropout: 0.0 57 | lossconfig: 58 | target: torch.nn.Identity 59 | cond_stage_config: 60 | target: ldm.modules.encoders.modules.BERTEmbedder 61 | params: 62 | n_embed: 512 63 | n_layer: 16 64 | vocab_size: 8192 65 | max_seq_len: 92 66 | use_tokenizer: false 67 | monitor: val/loss_simple_ema 68 | data: 69 | target: main.DataModuleFromConfig 70 | params: 71 | batch_size: 24 72 | wrap: false 73 | num_workers: 10 74 | train: 75 | target: ldm.data.openimages.OpenImagesBBoxTrain 76 | params: 77 | size: 256 78 | validation: 79 | target: ldm.data.openimages.OpenImagesBBoxValidation 80 | params: 81 | size: 256 82 | -------------------------------------------------------------------------------- /models/ldm/lsun_beds256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: class_label 12 | image_size: 64 13 | channels: 3 14 | cond_stage_trainable: false 15 | concat_mode: false 16 | monitor: val/loss 17 | unet_config: 18 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 19 | params: 20 | image_size: 64 21 | in_channels: 3 22 | out_channels: 3 23 | model_channels: 224 24 | attention_resolutions: 25 | - 8 26 | - 4 27 | - 2 28 | num_res_blocks: 2 29 | channel_mult: 30 | - 1 31 | - 2 32 | - 3 33 | - 4 34 | num_head_channels: 32 35 | first_stage_config: 36 | target: ldm.models.autoencoder.VQModelInterface 37 | params: 38 | embed_dim: 3 39 | n_embed: 8192 40 | ddconfig: 41 | double_z: false 42 | z_channels: 3 43 | resolution: 256 44 | in_channels: 3 45 | out_ch: 3 46 | ch: 128 47 | ch_mult: 48 | - 1 49 | - 2 50 | - 4 51 | num_res_blocks: 2 52 | attn_resolutions: [] 53 | dropout: 0.0 54 | lossconfig: 55 | target: torch.nn.Identity 56 | cond_stage_config: __is_unconditional__ 57 | data: 58 | target: main.DataModuleFromConfig 59 | params: 60 | batch_size: 48 61 | num_workers: 5 62 | wrap: false 63 | train: 64 | target: ldm.data.lsun.LSUNBedroomsTrain 65 | params: 66 | size: 256 67 | validation: 68 | target: ldm.data.lsun.LSUNBedroomsValidation 69 | params: 70 | size: 256 71 | -------------------------------------------------------------------------------- /models/ldm/lsun_churches256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 5.0e-05 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0155 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | loss_type: l1 11 | first_stage_key: image 12 | cond_stage_key: image 13 | image_size: 32 14 | channels: 4 15 | cond_stage_trainable: false 16 | concat_mode: false 17 | scale_by_std: true 18 | monitor: val/loss_simple_ema 19 | scheduler_config: 20 | target: ldm.lr_scheduler.LambdaLinearScheduler 21 | params: 22 | warm_up_steps: 23 | - 10000 24 | cycle_lengths: 25 | - 10000000000000 26 | f_start: 27 | - 1.0e-06 28 | f_max: 29 | - 1.0 30 | f_min: 31 | - 1.0 32 | unet_config: 33 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 34 | params: 35 | image_size: 32 36 | in_channels: 4 37 | out_channels: 4 38 | model_channels: 192 39 | attention_resolutions: 40 | - 1 41 | - 2 42 | - 4 43 | - 8 44 | num_res_blocks: 2 45 | channel_mult: 46 | - 1 47 | - 2 48 | - 2 49 | - 4 50 | - 4 51 | num_heads: 8 52 | use_scale_shift_norm: true 53 | resblock_updown: true 54 | first_stage_config: 55 | target: ldm.models.autoencoder.AutoencoderKL 56 | params: 57 | embed_dim: 4 58 | monitor: val/rec_loss 59 | ddconfig: 60 | double_z: true 61 | z_channels: 4 62 | resolution: 256 63 | in_channels: 3 64 | out_ch: 3 65 | ch: 128 66 | ch_mult: 67 | - 1 68 | - 2 69 | - 4 70 | - 4 71 | num_res_blocks: 2 72 | attn_resolutions: [] 73 | dropout: 0.0 74 | lossconfig: 75 | target: torch.nn.Identity 76 | 77 | cond_stage_config: '__is_unconditional__' 78 | 79 | data: 80 | target: main.DataModuleFromConfig 81 | params: 82 | batch_size: 96 83 | num_workers: 5 84 | wrap: false 85 | train: 86 | target: ldm.data.lsun.LSUNChurchesTrain 87 | params: 88 | size: 256 89 | validation: 90 | target: ldm.data.lsun.LSUNChurchesValidation 91 | params: 92 | size: 256 93 | -------------------------------------------------------------------------------- /models/ldm/semantic_synthesis256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0205 7 | log_every_t: 100 8 | timesteps: 1000 9 | loss_type: l1 10 | first_stage_key: image 11 | cond_stage_key: segmentation 12 | image_size: 64 13 | channels: 3 14 | concat_mode: true 15 | cond_stage_trainable: true 16 | unet_config: 17 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 18 | params: 19 | image_size: 64 20 | in_channels: 6 21 | out_channels: 3 22 | model_channels: 128 23 | attention_resolutions: 24 | - 32 25 | - 16 26 | - 8 27 | num_res_blocks: 2 28 | channel_mult: 29 | - 1 30 | - 4 31 | - 8 32 | num_heads: 8 33 | first_stage_config: 34 | target: ldm.models.autoencoder.VQModelInterface 35 | params: 36 | embed_dim: 3 37 | n_embed: 8192 38 | ddconfig: 39 | double_z: false 40 | z_channels: 3 41 | resolution: 256 42 | in_channels: 3 43 | out_ch: 3 44 | ch: 128 45 | ch_mult: 46 | - 1 47 | - 2 48 | - 4 49 | num_res_blocks: 2 50 | attn_resolutions: [] 51 | dropout: 0.0 52 | lossconfig: 53 | target: torch.nn.Identity 54 | cond_stage_config: 55 | target: ldm.modules.encoders.modules.SpatialRescaler 56 | params: 57 | n_stages: 2 58 | in_channels: 182 59 | out_channels: 3 60 | -------------------------------------------------------------------------------- /models/ldm/semantic_synthesis512/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0205 7 | log_every_t: 100 8 | timesteps: 1000 9 | loss_type: l1 10 | first_stage_key: image 11 | cond_stage_key: segmentation 12 | image_size: 128 13 | channels: 3 14 | concat_mode: true 15 | cond_stage_trainable: true 16 | unet_config: 17 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 18 | params: 19 | image_size: 128 20 | in_channels: 6 21 | out_channels: 3 22 | model_channels: 128 23 | attention_resolutions: 24 | - 32 25 | - 16 26 | - 8 27 | num_res_blocks: 2 28 | channel_mult: 29 | - 1 30 | - 4 31 | - 8 32 | num_heads: 8 33 | first_stage_config: 34 | target: ldm.models.autoencoder.VQModelInterface 35 | params: 36 | embed_dim: 3 37 | n_embed: 8192 38 | monitor: val/rec_loss 39 | ddconfig: 40 | double_z: false 41 | z_channels: 3 42 | resolution: 256 43 | in_channels: 3 44 | out_ch: 3 45 | ch: 128 46 | ch_mult: 47 | - 1 48 | - 2 49 | - 4 50 | num_res_blocks: 2 51 | attn_resolutions: [] 52 | dropout: 0.0 53 | lossconfig: 54 | target: torch.nn.Identity 55 | cond_stage_config: 56 | target: ldm.modules.encoders.modules.SpatialRescaler 57 | params: 58 | n_stages: 2 59 | in_channels: 182 60 | out_channels: 3 61 | data: 62 | target: main.DataModuleFromConfig 63 | params: 64 | batch_size: 8 65 | wrap: false 66 | num_workers: 10 67 | train: 68 | target: ldm.data.landscapes.RFWTrain 69 | params: 70 | size: 768 71 | crop_size: 512 72 | segmentation_to_float32: true 73 | validation: 74 | target: ldm.data.landscapes.RFWValidation 75 | params: 76 | size: 768 77 | crop_size: 512 78 | segmentation_to_float32: true 79 | -------------------------------------------------------------------------------- /models/ldm/text2img256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 2.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0195 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: image 11 | cond_stage_key: caption 12 | image_size: 64 13 | channels: 3 14 | cond_stage_trainable: true 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | unet_config: 18 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 19 | params: 20 | image_size: 64 21 | in_channels: 3 22 | out_channels: 3 23 | model_channels: 192 24 | attention_resolutions: 25 | - 8 26 | - 4 27 | - 2 28 | num_res_blocks: 2 29 | channel_mult: 30 | - 1 31 | - 2 32 | - 3 33 | - 5 34 | num_head_channels: 32 35 | use_spatial_transformer: true 36 | transformer_depth: 1 37 | context_dim: 640 38 | first_stage_config: 39 | target: ldm.models.autoencoder.VQModelInterface 40 | params: 41 | embed_dim: 3 42 | n_embed: 8192 43 | ddconfig: 44 | double_z: false 45 | z_channels: 3 46 | resolution: 256 47 | in_channels: 3 48 | out_ch: 3 49 | ch: 128 50 | ch_mult: 51 | - 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All Right reserved. 3 | # ------------------------------------------------------------------------------ 4 | 5 | import torch.utils.data as data 6 | import torch.distributed as dist 7 | import pytorch_lightning as pl 8 | from .mugen_data import MUGENDataset 9 | 10 | class VideoData(pl.LightningDataModule): 11 | 12 | def __init__(self, args, shuffle=True): 13 | super().__init__() 14 | self.args = args 15 | self.shuffle = shuffle 16 | 17 | @property 18 | def n_classes(self): 19 | dataset = self._dataset(True) 20 | return dataset.n_classes 21 | 22 | def _dataset(self, split): 23 | 24 | Dataset = MUGENDataset(args=self.args, split=split) 25 | return Dataset 26 | 27 | def _dataloader(self, split): 28 | dataset = self._dataset(split) 29 | if dist.is_initialized(): 30 | sampler = data.distributed.DistributedSampler( 31 | dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank() 32 | ) 33 | else: 34 | sampler = None 35 | dataloader = data.DataLoader( 36 | dataset, 37 | batch_size=self.args.batch_size, 38 | num_workers=self.args.num_workers, 39 | pin_memory=True, 40 | sampler=sampler, 41 | shuffle=sampler is None and self.shuffle is True, 42 | collate_fn=None 43 | ) 44 | return dataloader 45 | 46 | def train_dataloader(self): 47 | return self._dataloader('train') 48 | 49 | def val_dataloader(self): 50 | return self._dataloader('val') 51 | 52 | def test_dataloader(self): 53 | return self._dataloader('test') -------------------------------------------------------------------------------- /mugen_data/models/audio_vqvae/__pycache__/hparams.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/audio_vqvae/__pycache__/hparams.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/audio_vqvae/vqvae.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------------ 2 | # Copyright (c) Meta Platforms, Inc. All Right reserved. 3 | # ------------------------------------------------------------------------------ 4 | 5 | from jukebox.vqvae.vqvae import VQVAE as Jukebox_VQVAE 6 | from jukebox.utils.audio_utils import audio_preprocess 7 | 8 | class VQVAE(Jukebox_VQVAE): 9 | def __init__(self, hps): 10 | block_kwargs = dict(width=hps.width, depth=hps.depth, m_conv=hps.m_conv, 11 | dilation_growth_rate=hps.dilation_growth_rate, 12 | dilation_cycle=hps.dilation_cycle, 13 | reverse_decoder_dilation=hps.vqvae_reverse_decoder_dilation) 14 | 15 | super().__init__( 16 | input_shape = (hps.sample_length, 1), levels = hps.levels, downs_t = hps.downs_t, strides_t = hps.strides_t, 17 | emb_width = hps.emb_width, l_bins = hps.l_bins, mu = hps.l_mu, commit = hps.commit, spectral = hps.spectral, 18 | multispectral = hps.multispectral, multipliers = hps.hvqvae_multipliers, use_bottleneck = hps.use_bottleneck, 19 | **block_kwargs) 20 | self.n_codes = hps.l_bins 21 | self.embedding_dim = hps.emb_width 22 | self.hps = hps 23 | 24 | def decode(self, zs, start_level=0, end_level=None): 25 | return self._decode([zs], start_level, end_level) 26 | 27 | def encode(self, x, start_level=0, end_level=None, include_embeddings=False): 28 | x = audio_preprocess(x, self.hps) 29 | zs = self._encode(x, start_level, end_level) 30 | if include_embeddings: 31 | embeddings = self.bottleneck.decode(zs) 32 | return zs[start_level], embeddings[start_level] 33 | return zs[start_level] 34 | -------------------------------------------------------------------------------- /mugen_data/models/fad/fad.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------------ 2 | # Copyright (c) Meta Platforms, Inc. All Right reserved. 3 | # ------------------------------------------------------------------------------ 4 | import torch 5 | from .resnet import resnet18 6 | 7 | def load_fad_model(device): 8 | model = resnet18(num_classes=309) 9 | model_path="checkpoints/pretrained/H.pth.tar" 10 | weight_dict = torch.load(model_path)['model_state_dict'] 11 | model_dict = model.state_dict() 12 | for name, param in weight_dict.items(): 13 | if 'audnet' in name: 14 | name = '.'.join(name.split('.')[1:]) 15 | model_dict[name].copy_(param) 16 | model.eval() 17 | model.to(device) 18 | return model 19 | -------------------------------------------------------------------------------- /mugen_data/models/fvd/fvd.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------------ 2 | # Copyright (c) Meta Platforms, Inc. All Right reserved. 3 | # ------------------------------------------------------------------------------ 4 | 5 | import torch 6 | from .pytorch_i3d import InceptionI3d 7 | import os 8 | 9 | def load_fvd_model(device): 10 | i3d = InceptionI3d(num_classes=400, in_channels=3).to(device) 11 | i3d_path = os.path.join('checkpoints/pretrained/i3d_pretrained_400.pt') 12 | i3d.load_state_dict(torch.load(i3d_path, map_location=device)) 13 | i3d.eval() 14 | return i3d 15 | 16 | # https://github.com/tensorflow/gan/blob/de4b8da3853058ea380a6152bd3bd454013bf619/tensorflow_gan/python/eval/classifier_metrics.py#L161 17 | def _symmetric_matrix_square_root(mat, eps=1e-10): 18 | u, s, v = torch.svd(mat) 19 | si = torch.where(s < eps, s, torch.sqrt(s)) 20 | return torch.matmul(torch.matmul(u, torch.diag(si)), v.t()) 21 | 22 | 23 | # https://github.com/tensorflow/gan/blob/de4b8da3853058ea380a6152bd3bd454013bf619/tensorflow_gan/python/eval/classifier_metrics.py#L400 24 | def trace_sqrt_product(sigma, sigma_v): 25 | sqrt_sigma = _symmetric_matrix_square_root(sigma) 26 | sqrt_a_sigmav_a = torch.matmul(sqrt_sigma, torch.matmul(sigma_v, sqrt_sigma)) 27 | return torch.trace(_symmetric_matrix_square_root(sqrt_a_sigmav_a)) 28 | 29 | 30 | # https://discuss.pytorch.org/t/covariance-and-gradient-support/16217/2 31 | def cov(m, rowvar=False): 32 | '''Estimate a covariance matrix given data. 33 | 34 | Covariance indicates the level to which two variables vary together. 35 | If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`, 36 | then the covariance matrix element `C_{ij}` is the covariance of 37 | `x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`. 38 | 39 | Args: 40 | m: A 1-D or 2-D array containing multiple variables and observations. 41 | Each row of `m` represents a variable, and each column a single 42 | observation of all those variables. 43 | rowvar: If `rowvar` is True, then each row represents a 44 | variable, with observations in the columns. Otherwise, the 45 | relationship is transposed: each column represents a variable, 46 | while the rows contain observations. 47 | 48 | Returns: 49 | The covariance matrix of the variables. 50 | ''' 51 | if m.dim() > 2: 52 | raise ValueError('m has more than 2 dimensions') 53 | if m.dim() < 2: 54 | m = m.view(1, -1) 55 | if not rowvar and m.size(0) != 1: 56 | m = m.t() 57 | 58 | fact = 1.0 / (m.size(1) - 1) # unbiased estimate 59 | m -= torch.mean(m, dim=1, keepdim=True) 60 | mt = m.t() # if complex: mt = m.t().conj() 61 | return fact * m.matmul(mt).squeeze() 62 | 63 | 64 | def frechet_distance(x1, x2): 65 | x1 = x1.flatten(start_dim=1) 66 | x2 = x2.flatten(start_dim=1) 67 | m, m_w = x1.mean(dim=0), x2.mean(dim=0) 68 | sigma, sigma_w = cov(x1, rowvar=False), cov(x2, rowvar=False) 69 | 70 | sqrt_trace_component = trace_sqrt_product(sigma, sigma_w) 71 | trace = torch.trace(sigma + sigma_w) - 2.0 * sqrt_trace_component 72 | 73 | mean = torch.sum((m - m_w) ** 2) 74 | fd = trace + mean 75 | return fd 76 | -------------------------------------------------------------------------------- /mugen_data/models/gpt/__pycache__/attention.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/gpt/__pycache__/attention.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/gpt/__pycache__/gpt.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/gpt/__pycache__/gpt.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/gpt/__pycache__/utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/gpt/__pycache__/utils.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/video_vqvae/__pycache__/vqvae.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/video_vqvae/__pycache__/vqvae.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/videoclip/__pycache__/clip.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/videoclip/__pycache__/clip.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/videoclip/__pycache__/modules.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/videoclip/__pycache__/modules.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/videoclip/__pycache__/resnet.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/videoclip/__pycache__/resnet.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/models/videoclip/__pycache__/s3d.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ubc-vision/Make-A-Story/593ee0406b34516cc4cb15b1407449c73bfe4c50/mugen_data/models/videoclip/__pycache__/s3d.cpython-38.pyc -------------------------------------------------------------------------------- /mugen_data/video_utils.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------------ 2 | # Copyright (c) Meta Platforms, Inc. All Right reserved. 3 | # ------------------------------------------------------------------------------ 4 | import torch 5 | from tqdm import tqdm 6 | 7 | 8 | label_color_map = { 9 | 0: torch.FloatTensor((0, 0, 0)), 10 | 1: torch.FloatTensor((128, 0, 0)), 11 | 2: torch.FloatTensor((255, 0, 0)), 12 | 3: torch.FloatTensor((139, 69, 19)), 13 | 4: torch.FloatTensor((0, 255, 0)), 14 | 5: torch.FloatTensor((0, 128, 0)), 15 | 6: torch.FloatTensor((0, 100, 0)), 16 | 7: torch.FloatTensor((244, 164, 96)), 17 | 8: torch.FloatTensor((205, 133, 63)), 18 | 9: torch.FloatTensor((255, 192, 203)), 19 | 10: torch.FloatTensor((210, 105, 30)), 20 | 11: torch.FloatTensor((255, 0, 255)), 21 | 12: torch.FloatTensor((230, 230, 250)), 22 | 13: torch.FloatTensor((0, 191, 255)), 23 | 14: torch.FloatTensor((154, 205, 50)), 24 | 15: torch.FloatTensor((255, 215, 0)), 25 | 16: torch.FloatTensor((169, 169, 169)), 26 | 17: torch.FloatTensor((148, 0, 211)), 27 | 18: torch.FloatTensor((127, 255, 212)), 28 | 19: torch.FloatTensor((255, 255, 0)), 29 | 20: torch.FloatTensor((255, 69, 0)), 30 | 21: torch.FloatTensor((255, 255, 255)), 31 | 22: torch.FloatTensor((0, 0, 255)), 32 | } 33 | 34 | 35 | def convert_grayscale_to_color_label(input_tensor): 36 | b_in, t_in, h_in, w_in = input_tensor.shape 37 | 38 | input_tensor = input_tensor.reshape(-1) 39 | output_tensor = torch.zeros(input_tensor.shape[0], 3) 40 | for i, t in tqdm(enumerate(input_tensor.cpu().numpy()), total=input_tensor.shape[0]): 41 | output_tensor[i] = label_color_map[t] 42 | 43 | output_tensor = output_tensor.reshape(b_in, t_in, h_in, w_in, 3).permute(0, 4, 1, 2, 3) 44 | 45 | return output_tensor 46 | -------------------------------------------------------------------------------- /scripts/download_first_stages.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip 3 | wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip 4 | wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip 5 | wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip 6 | wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip 7 | wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip 8 | wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip 9 | wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip 10 | wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip 11 | 12 | 13 | 14 | cd models/first_stage_models/kl-f4 15 | unzip -o model.zip 16 | 17 | cd ../kl-f8 18 | unzip -o model.zip 19 | 20 | cd ../kl-f16 21 | unzip -o model.zip 22 | 23 | cd ../kl-f32 24 | unzip -o model.zip 25 | 26 | cd ../vq-f4 27 | unzip -o model.zip 28 | 29 | cd ../vq-f4-noattn 30 | unzip -o model.zip 31 | 32 | cd ../vq-f8 33 | unzip -o model.zip 34 | 35 | cd ../vq-f8-n256 36 | unzip -o model.zip 37 | 38 | cd ../vq-f16 39 | unzip -o model.zip 40 | 41 | cd ../.. -------------------------------------------------------------------------------- /scripts/download_models.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | wget -O models/ldm/celeba256/celeba-256.zip https://ommer-lab.com/files/latent-diffusion/celeba.zip 3 | wget -O models/ldm/ffhq256/ffhq-256.zip https://ommer-lab.com/files/latent-diffusion/ffhq.zip 4 | wget -O models/ldm/lsun_churches256/lsun_churches-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip 5 | wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip 6 | wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip 7 | wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip 8 | wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip 9 | wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip 10 | wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip 11 | wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip 12 | wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip 13 | 14 | 15 | 16 | cd models/ldm/celeba256 17 | unzip -o celeba-256.zip 18 | 19 | cd ../ffhq256 20 | unzip -o ffhq-256.zip 21 | 22 | cd ../lsun_churches256 23 | unzip -o lsun_churches-256.zip 24 | 25 | cd ../lsun_beds256 26 | unzip -o lsun_beds-256.zip 27 | 28 | cd ../text2img256 29 | unzip -o model.zip 30 | 31 | cd ../cin256 32 | unzip -o model.zip 33 | 34 | cd ../semantic_synthesis512 35 | unzip -o model.zip 36 | 37 | cd ../semantic_synthesis256 38 | unzip -o model.zip 39 | 40 | cd ../bsr_sr 41 | unzip -o model.zip 42 | 43 | cd ../layout2img-openimages256 44 | unzip -o model.zip 45 | 46 | cd ../inpainting_big 47 | unzip -o model.zip 48 | 49 | cd ../.. 50 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name='latent-diffusion', 5 | version='0.0.1', 6 | description='', 7 | packages=find_packages(), 8 | install_requires=[ 9 | 'torch', 10 | 'numpy', 11 | 'tqdm', 12 | ], 13 | ) 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