├── .gitignore ├── LICENSE ├── README.md ├── multi-scale-blended-diffusion ├── InteractiveEditing.ipynb ├── OrignalLatentDiffusionLICENSE ├── configs │ ├── autoencoder │ │ ├── autoencoder_kl_16x16x16.yaml │ │ ├── autoencoder_kl_32x32x4.yaml │ │ ├── autoencoder_kl_64x64x3.yaml │ │ └── autoencoder_kl_8x8x64.yaml │ ├── latent-diffusion │ │ ├── celebahq-ldm-vq-4.yaml │ │ ├── cin-ldm-vq-f8.yaml │ │ ├── cin256-v2.yaml │ │ ├── ffhq-ldm-vq-4.yaml │ │ ├── lsun_bedrooms-ldm-vq-4.yaml │ │ ├── lsun_churches-ldm-kl-8.yaml │ │ └── txt2img-1p4B-eval.yaml │ ├── retrieval-augmented-diffusion │ │ └── 768x768.yaml │ └── stable-diffusion │ │ └── v1-inference.yaml ├── environment.yaml ├── inputs │ ├── bedroom_painting.jpg │ ├── clarissa_strozzi.jpg │ ├── inputs.txt │ ├── lofi_revoy.jpg │ ├── prof_pic.jpg │ └── readme.md ├── ldm │ ├── __init__.py │ ├── data │ │ ├── __init__.py │ │ ├── base.py │ │ ├── imagenet.py │ │ └── lsun.py │ ├── lr_scheduler.py │ ├── models │ │ ├── autoencoder.py │ │ └── diffusion │ │ │ ├── __init__.py │ │ │ ├── classifier.py │ │ │ ├── ddim.py │ │ │ ├── ddpm.py │ │ │ └── plms.py │ ├── modules │ │ ├── attention.py │ │ ├── diffusionmodules │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ ├── openaimodel.py │ │ │ └── util.py │ │ ├── distributions │ │ │ ├── __init__.py │ │ │ └── distributions.py │ │ ├── ema.py │ │ ├── encoders │ │ │ ├── __init__.py │ │ │ └── modules.py │ │ ├── image_degradation │ │ │ ├── __init__.py │ │ │ ├── bsrgan.py │ │ │ ├── bsrgan_light.py │ │ │ └── utils_image.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── contperceptual.py │ │ │ └── vqperceptual.py │ │ └── x_transformer.py │ └── util.py ├── msbd │ ├── BLDSampler.py │ ├── MSBDGenerator.py │ ├── __init__.py │ └── msbd_utils.py └── multi_scale_blended_diffusion.py └── overview.jpg /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | build/ 11 | develop-eggs/ 12 | dist/ 13 | downloads/ 14 | eggs/ 15 | .eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | wheels/ 22 | share/python-wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .nox/ 42 | .coverage 43 | .coverage.* 44 | .cache 45 | nosetests.xml 46 | coverage.xml 47 | *.cover 48 | *.py,cover 49 | .hypothesis/ 50 | .pytest_cache/ 51 | cover/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | local_settings.py 60 | db.sqlite3 61 | db.sqlite3-journal 62 | 63 | # Flask stuff: 64 | instance/ 65 | .webassets-cache 66 | 67 | # Scrapy stuff: 68 | .scrapy 69 | 70 | # Sphinx documentation 71 | docs/_build/ 72 | 73 | # PyBuilder 74 | .pybuilder/ 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | latent-diffusion-main/models 85 | src 86 | latent-diffusion-main/src 87 | latent-diffusion-main/outputs 88 | latent-diffusion-main/output 89 | latent-diffusion-main/data 90 | *.pt 91 | *.png 92 | RealESRGAN_x4plus.pth 93 | *.sh 94 | 95 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Preferred Networks, Inc. 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 | Open In Colab 2 | 3 | 4 | ## High-Resolution Image Editing via Multi-Stage Blended Diffusion 5 | 6 | ![Overview of Multi-Stage Blended Diffusion](overview.jpg) 7 | 8 | This repository includes our implementation of Multi-Stage Blended Diffusion, as described in our paper [High-Resolution Image Editing via Multi-Stage Blended Diffusion](https://arxiv.org/abs/2210.12965) 9 | 10 | Our implementation builds on the original implementation of Latent Diffusion, available at (https://github.com/CompVis/latent-diffusion), which is licensed under the MIT license. 11 | Specifically `multi-scale-blended-diffusion/ldm`, `multi-scale-blended-diffusion/configs`, and `multi-scale-blended-diffusion/models` are entirely from latent diffusion. 12 | 13 | ### Setup: 14 | 15 | * Install the environment specified in `multi-scale-blended-diffusion/environment.yaml`: 16 | ``` 17 | conda env create -f environment.yaml 18 | ``` 19 | * Download the stable diffusion v1.4 from the [huggingface space](https://huggingface.co/spaces/stabilityai/stable-diffusion) and copy it to `multi-scale-blended-diffusion/models/ldm/stable-diffusion-v1/model.ckpt`. 20 | This requires login and has to be done manually. 21 | * Also download RealESRGANx4plus from [here](https://github.com/xinntao/Real-ESRGAN#inference-general-images) and place it in `multi-scale-blended-diffusion/RealESRGAN_x4plus.pth`: 22 | ``` 23 | wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P multi-scale-blended-diffusion/ 24 | ``` 25 | ### Usage 26 | To try our approach for interactive editing, use the [`multi-scale-blended-diffusion/InteractiveEditing.ipynb`](multi-scale-blended-diffusion/InteractiveEditing.ipynb) notebook locally, or use our [colab demo](https://colab.research.google.com/gist/JohannesAck/2c4561a8a4d1522f752b1a86f3e24c12/multiscaleblendeddiffusioncolab.ipynb). 27 | 28 | To run, validate our approach on the examples used in our paper, use [`multi-scale-blended-diffusion/multi_scale_blended_diffusion.py`.](multi-scale-blended-diffusion/multi_scale_blended_diffusion.py) 29 | 30 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/OrignalLatentDiffusionLICENSE: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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: 320 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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/configs/retrieval-augmented-diffusion/768x768.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: 0.015 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: jpg 11 | cond_stage_key: nix 12 | image_size: 48 13 | channels: 16 14 | cond_stage_trainable: false 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_by_std: false 18 | scale_factor: 0.22765929 19 | unet_config: 20 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 21 | params: 22 | image_size: 48 23 | in_channels: 16 24 | out_channels: 16 25 | model_channels: 448 26 | attention_resolutions: 27 | - 4 28 | - 2 29 | - 1 30 | num_res_blocks: 2 31 | channel_mult: 32 | - 1 33 | - 2 34 | - 3 35 | - 4 36 | use_scale_shift_norm: false 37 | resblock_updown: false 38 | num_head_channels: 32 39 | use_spatial_transformer: true 40 | transformer_depth: 1 41 | context_dim: 768 42 | use_checkpoint: true 43 | first_stage_config: 44 | target: ldm.models.autoencoder.AutoencoderKL 45 | params: 46 | monitor: val/rec_loss 47 | embed_dim: 16 48 | ddconfig: 49 | double_z: true 50 | z_channels: 16 51 | resolution: 256 52 | in_channels: 3 53 | out_ch: 3 54 | ch: 128 55 | ch_mult: 56 | - 1 57 | - 1 58 | - 2 59 | - 2 60 | - 4 61 | num_res_blocks: 2 62 | attn_resolutions: 63 | - 16 64 | dropout: 0.0 65 | lossconfig: 66 | target: torch.nn.Identity 67 | cond_stage_config: 68 | target: torch.nn.Identity -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/configs/stable-diffusion/v1-inference.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-04 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.00085 6 | linear_end: 0.0120 7 | num_timesteps_cond: 1 8 | log_every_t: 200 9 | timesteps: 1000 10 | first_stage_key: "jpg" 11 | cond_stage_key: "txt" 12 | image_size: 64 13 | channels: 4 14 | cond_stage_trainable: false # Note: different from the one we trained before 15 | conditioning_key: crossattn 16 | monitor: val/loss_simple_ema 17 | scale_factor: 0.18215 18 | use_ema: False 19 | 20 | scheduler_config: # 10000 warmup steps 21 | target: ldm.lr_scheduler.LambdaLinearScheduler 22 | params: 23 | warm_up_steps: [ 10000 ] 24 | cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases 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 # unused 33 | in_channels: 4 34 | out_channels: 4 35 | model_channels: 320 36 | attention_resolutions: [ 4, 2, 1 ] 37 | num_res_blocks: 2 38 | channel_mult: [ 1, 2, 4, 4 ] 39 | num_heads: 8 40 | use_spatial_transformer: True 41 | transformer_depth: 1 42 | context_dim: 768 43 | use_checkpoint: True 44 | legacy: False 45 | 46 | first_stage_config: 47 | target: ldm.models.autoencoder.AutoencoderKL 48 | params: 49 | embed_dim: 4 50 | monitor: val/rec_loss 51 | ddconfig: 52 | double_z: true 53 | z_channels: 4 54 | resolution: 256 55 | in_channels: 3 56 | out_ch: 3 57 | ch: 128 58 | ch_mult: 59 | - 1 60 | - 2 61 | - 4 62 | - 4 63 | num_res_blocks: 2 64 | attn_resolutions: [] 65 | dropout: 0.0 66 | lossconfig: 67 | target: torch.nn.Identity 68 | 69 | cond_stage_config: 70 | target: ldm.modules.encoders.modules.FrozenCLIPEmbedder 71 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/environment.yaml: -------------------------------------------------------------------------------- 1 | name: msbd 2 | channels: 3 | - pytorch 4 | - defaults 5 | - conda-forge 6 | dependencies: 7 | - python=3.8.5 8 | - pip 9 | - cudatoolkit=11.3 10 | - pytorch::pytorch 11 | - pytorch::torchvision 12 | - pytorch::torchaudio 13 | - numpy 14 | - jupyterlab 15 | - ipycanvas 16 | - pip: 17 | - albumentations 18 | - pudb==2019.2 19 | - imageio 20 | - imageio-ffmpeg 21 | - pytorch-lightning 22 | - kornia 23 | - omegaconf==2.1.1 24 | - test-tube>=0.7.5 25 | - einops==0.3.0 26 | - transformers 27 | - basicsr 28 | - realesrgan 29 | - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers 30 | - -e git+https://github.com/openai/CLIP.git@main#egg=clip -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/inputs/bedroom_painting.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/inputs/bedroom_painting.jpg -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/inputs/clarissa_strozzi.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/inputs/clarissa_strozzi.jpg -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/inputs/inputs.txt: -------------------------------------------------------------------------------- 1 | # prompt; filename (assuming mask follows FOO_mask.png patter); margin_multiplier 2 | An Oil painting of a girl huggin a corgi on a pedestal; clarissa_strozzi.jpg; 1.4 3 | Statue of Roman Emperor, Canon 5D Mark 3, 35mm, flickr; marunouchi.png; 1.2 4 | Oil painting of Mt. Fuji, by Paul Sandby; river_severn.png; 1.3 5 | red hair; prof_pic.jpg; 1.1 6 | cyberpunk neon cityscape, digital painting, trending on artstation, David Revoy; lofi_revoy.jpg; 1.3 7 | a painting is hanging on the wall; bedroom_painting.jpg; 1.4 8 | fishing boat, lofi, dreamy, moody, very colorful, anime inspiration, ghibli vibe; stable_anime.png; 1.4 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/inputs/lofi_revoy.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/inputs/lofi_revoy.jpg -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/inputs/prof_pic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/inputs/prof_pic.jpg -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/inputs/readme.md: -------------------------------------------------------------------------------- 1 | All images used in our paper are used in accordance with their licenses and attributed below, in the order of Figure \ref{fig:variations}: 2 | 1. `bedroom_painting.jpg` : "white wooden dresser with mirror photo, by Minh Pham, via https://unsplash.com/photos/7pCFUybP\_P8 (3902x5853) 3 | 2. `clarissa_strozzi.jpg` : "Portrait of Clarissa Strozzi", by Titian Vecelli, (1803x2117) 4 | 3. `marunouchi.png` : "people walking on sidewalk near high rise buildings during daytime", by Nat Weearwong, via https://unsplash.com/photos/0cZgvYHirBg (4896x3264) 5 | 4. `river_severn.png` : "The River Severn at Shrewsbury, Shropshire", by Paul Sandby, via https://https://unsplash.com/photos/HEEvYhNzpEo (3999x3041) 6 | 5. `prof_pic.jpg` : Selfie by author (3456x4608) 7 | 6. `lofi_revoy.jpg` : "Lofi Cyberpunk" by David Revoy https://www.davidrevoy.com/article867/lofi-cyberpunk (2431x1930) 8 | 7. `stable_anime.png` : Anime-style image of river generated with stable diffusion by the authors (2048x2048) 9 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/ldm/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/ldm/data/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/data/base.py: -------------------------------------------------------------------------------- 1 | from abc import abstractmethod 2 | from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset 3 | 4 | 5 | class 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 -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/data/imagenet.py: -------------------------------------------------------------------------------- 1 | import os, yaml, pickle, shutil, tarfile, glob 2 | import cv2 3 | import albumentations 4 | import PIL 5 | import numpy as np 6 | import torchvision.transforms.functional as TF 7 | from omegaconf import OmegaConf 8 | from functools import partial 9 | from PIL import Image 10 | from tqdm import tqdm 11 | from torch.utils.data import Dataset, Subset 12 | 13 | import taming.data.utils as tdu 14 | from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve 15 | from taming.data.imagenet import ImagePaths 16 | 17 | from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light 18 | 19 | 20 | def synset2idx(path_to_yaml="data/index_synset.yaml"): 21 | with open(path_to_yaml) as f: 22 | di2s = yaml.load(f) 23 | return dict((v,k) for k,v in di2s.items()) 24 | 25 | 26 | class ImageNetBase(Dataset): 27 | def __init__(self, config=None): 28 | self.config = config or OmegaConf.create() 29 | if not type(self.config)==dict: 30 | self.config = OmegaConf.to_container(self.config) 31 | self.keep_orig_class_label = self.config.get("keep_orig_class_label", False) 32 | self.process_images = True # if False we skip loading & processing images and self.data contains filepaths 33 | self._prepare() 34 | self._prepare_synset_to_human() 35 | self._prepare_idx_to_synset() 36 | self._prepare_human_to_integer_label() 37 | self._load() 38 | 39 | def __len__(self): 40 | return len(self.data) 41 | 42 | def __getitem__(self, i): 43 | return self.data[i] 44 | 45 | def _prepare(self): 46 | raise NotImplementedError() 47 | 48 | def _filter_relpaths(self, relpaths): 49 | ignore = set([ 50 | "n06596364_9591.JPEG", 51 | ]) 52 | relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore] 53 | if "sub_indices" in self.config: 54 | indices = str_to_indices(self.config["sub_indices"]) 55 | synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings 56 | self.synset2idx = synset2idx(path_to_yaml=self.idx2syn) 57 | files = [] 58 | for rpath in relpaths: 59 | syn = rpath.split("/")[0] 60 | if syn in synsets: 61 | files.append(rpath) 62 | return files 63 | else: 64 | return relpaths 65 | 66 | def _prepare_synset_to_human(self): 67 | SIZE = 2655750 68 | URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1" 69 | self.human_dict = os.path.join(self.root, "synset_human.txt") 70 | if (not os.path.exists(self.human_dict) or 71 | not os.path.getsize(self.human_dict)==SIZE): 72 | download(URL, self.human_dict) 73 | 74 | def _prepare_idx_to_synset(self): 75 | URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1" 76 | self.idx2syn = os.path.join(self.root, "index_synset.yaml") 77 | if (not os.path.exists(self.idx2syn)): 78 | download(URL, self.idx2syn) 79 | 80 | def _prepare_human_to_integer_label(self): 81 | URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1" 82 | self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt") 83 | if (not os.path.exists(self.human2integer)): 84 | download(URL, self.human2integer) 85 | with open(self.human2integer, "r") as f: 86 | lines = f.read().splitlines() 87 | assert len(lines) == 1000 88 | self.human2integer_dict = dict() 89 | for line in lines: 90 | value, key = line.split(":") 91 | self.human2integer_dict[key] = int(value) 92 | 93 | def _load(self): 94 | with open(self.txt_filelist, "r") as f: 95 | self.relpaths = f.read().splitlines() 96 | l1 = len(self.relpaths) 97 | self.relpaths = self._filter_relpaths(self.relpaths) 98 | print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths))) 99 | 100 | self.synsets = [p.split("/")[0] for p in self.relpaths] 101 | self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths] 102 | 103 | unique_synsets = np.unique(self.synsets) 104 | class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets)) 105 | if not self.keep_orig_class_label: 106 | self.class_labels = [class_dict[s] for s in self.synsets] 107 | else: 108 | self.class_labels = [self.synset2idx[s] for s in self.synsets] 109 | 110 | with open(self.human_dict, "r") as f: 111 | human_dict = f.read().splitlines() 112 | human_dict = dict(line.split(maxsplit=1) for line in human_dict) 113 | 114 | self.human_labels = [human_dict[s] for s in self.synsets] 115 | 116 | labels = { 117 | "relpath": np.array(self.relpaths), 118 | "synsets": np.array(self.synsets), 119 | "class_label": np.array(self.class_labels), 120 | "human_label": np.array(self.human_labels), 121 | } 122 | 123 | if self.process_images: 124 | self.size = retrieve(self.config, "size", default=256) 125 | self.data = ImagePaths(self.abspaths, 126 | labels=labels, 127 | size=self.size, 128 | random_crop=self.random_crop, 129 | ) 130 | else: 131 | self.data = self.abspaths 132 | 133 | 134 | class ImageNetTrain(ImageNetBase): 135 | NAME = "ILSVRC2012_train" 136 | URL = "http://www.image-net.org/challenges/LSVRC/2012/" 137 | AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2" 138 | FILES = [ 139 | "ILSVRC2012_img_train.tar", 140 | ] 141 | SIZES = [ 142 | 147897477120, 143 | ] 144 | 145 | def __init__(self, process_images=True, data_root=None, **kwargs): 146 | self.process_images = process_images 147 | self.data_root = data_root 148 | super().__init__(**kwargs) 149 | 150 | def _prepare(self): 151 | if self.data_root: 152 | self.root = os.path.join(self.data_root, self.NAME) 153 | else: 154 | cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) 155 | self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) 156 | 157 | self.datadir = os.path.join(self.root, "data") 158 | self.txt_filelist = os.path.join(self.root, "filelist.txt") 159 | self.expected_length = 1281167 160 | self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop", 161 | default=True) 162 | if not tdu.is_prepared(self.root): 163 | # prep 164 | print("Preparing dataset {} in {}".format(self.NAME, self.root)) 165 | 166 | datadir = self.datadir 167 | if not os.path.exists(datadir): 168 | path = os.path.join(self.root, self.FILES[0]) 169 | if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: 170 | import academictorrents as at 171 | atpath = at.get(self.AT_HASH, datastore=self.root) 172 | assert atpath == path 173 | 174 | print("Extracting {} to {}".format(path, datadir)) 175 | os.makedirs(datadir, exist_ok=True) 176 | with tarfile.open(path, "r:") as tar: 177 | tar.extractall(path=datadir) 178 | 179 | print("Extracting sub-tars.") 180 | subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar"))) 181 | for subpath in tqdm(subpaths): 182 | subdir = subpath[:-len(".tar")] 183 | os.makedirs(subdir, exist_ok=True) 184 | with tarfile.open(subpath, "r:") as tar: 185 | tar.extractall(path=subdir) 186 | 187 | filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) 188 | filelist = [os.path.relpath(p, start=datadir) for p in filelist] 189 | filelist = sorted(filelist) 190 | filelist = "\n".join(filelist)+"\n" 191 | with open(self.txt_filelist, "w") as f: 192 | f.write(filelist) 193 | 194 | tdu.mark_prepared(self.root) 195 | 196 | 197 | class ImageNetValidation(ImageNetBase): 198 | NAME = "ILSVRC2012_validation" 199 | URL = "http://www.image-net.org/challenges/LSVRC/2012/" 200 | AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5" 201 | VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1" 202 | FILES = [ 203 | "ILSVRC2012_img_val.tar", 204 | "validation_synset.txt", 205 | ] 206 | SIZES = [ 207 | 6744924160, 208 | 1950000, 209 | ] 210 | 211 | def __init__(self, process_images=True, data_root=None, **kwargs): 212 | self.data_root = data_root 213 | self.process_images = process_images 214 | super().__init__(**kwargs) 215 | 216 | def _prepare(self): 217 | if self.data_root: 218 | self.root = os.path.join(self.data_root, self.NAME) 219 | else: 220 | cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) 221 | self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) 222 | self.datadir = os.path.join(self.root, "data") 223 | self.txt_filelist = os.path.join(self.root, "filelist.txt") 224 | self.expected_length = 50000 225 | self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop", 226 | default=False) 227 | if not tdu.is_prepared(self.root): 228 | # prep 229 | print("Preparing dataset {} in {}".format(self.NAME, self.root)) 230 | 231 | datadir = self.datadir 232 | if not os.path.exists(datadir): 233 | path = os.path.join(self.root, self.FILES[0]) 234 | if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: 235 | import academictorrents as at 236 | atpath = at.get(self.AT_HASH, datastore=self.root) 237 | assert atpath == path 238 | 239 | print("Extracting {} to {}".format(path, datadir)) 240 | os.makedirs(datadir, exist_ok=True) 241 | with tarfile.open(path, "r:") as tar: 242 | tar.extractall(path=datadir) 243 | 244 | vspath = os.path.join(self.root, self.FILES[1]) 245 | if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]: 246 | download(self.VS_URL, vspath) 247 | 248 | with open(vspath, "r") as f: 249 | synset_dict = f.read().splitlines() 250 | synset_dict = dict(line.split() for line in synset_dict) 251 | 252 | print("Reorganizing into synset folders") 253 | synsets = np.unique(list(synset_dict.values())) 254 | for s in synsets: 255 | os.makedirs(os.path.join(datadir, s), exist_ok=True) 256 | for k, v in synset_dict.items(): 257 | src = os.path.join(datadir, k) 258 | dst = os.path.join(datadir, v) 259 | shutil.move(src, dst) 260 | 261 | filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) 262 | filelist = [os.path.relpath(p, start=datadir) for p in filelist] 263 | filelist = sorted(filelist) 264 | filelist = "\n".join(filelist)+"\n" 265 | with open(self.txt_filelist, "w") as f: 266 | f.write(filelist) 267 | 268 | tdu.mark_prepared(self.root) 269 | 270 | 271 | 272 | class ImageNetSR(Dataset): 273 | def __init__(self, size=None, 274 | degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1., 275 | random_crop=True): 276 | """ 277 | Imagenet Superresolution Dataloader 278 | Performs following ops in order: 279 | 1. crops a crop of size s from image either as random or center crop 280 | 2. resizes crop to size with cv2.area_interpolation 281 | 3. degrades resized crop with degradation_fn 282 | 283 | :param size: resizing to size after cropping 284 | :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light 285 | :param downscale_f: Low Resolution Downsample factor 286 | :param min_crop_f: determines crop size s, 287 | where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f) 288 | :param max_crop_f: "" 289 | :param data_root: 290 | :param random_crop: 291 | """ 292 | self.base = self.get_base() 293 | assert size 294 | assert (size / downscale_f).is_integer() 295 | self.size = size 296 | self.LR_size = int(size / downscale_f) 297 | self.min_crop_f = min_crop_f 298 | self.max_crop_f = max_crop_f 299 | assert(max_crop_f <= 1.) 300 | self.center_crop = not random_crop 301 | 302 | self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA) 303 | 304 | self.pil_interpolation = False # gets reset later if incase interp_op is from pillow 305 | 306 | if degradation == "bsrgan": 307 | self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f) 308 | 309 | elif degradation == "bsrgan_light": 310 | self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f) 311 | 312 | else: 313 | interpolation_fn = { 314 | "cv_nearest": cv2.INTER_NEAREST, 315 | "cv_bilinear": cv2.INTER_LINEAR, 316 | "cv_bicubic": cv2.INTER_CUBIC, 317 | "cv_area": cv2.INTER_AREA, 318 | "cv_lanczos": cv2.INTER_LANCZOS4, 319 | "pil_nearest": PIL.Image.NEAREST, 320 | "pil_bilinear": PIL.Image.BILINEAR, 321 | "pil_bicubic": PIL.Image.BICUBIC, 322 | "pil_box": PIL.Image.BOX, 323 | "pil_hamming": PIL.Image.HAMMING, 324 | "pil_lanczos": PIL.Image.LANCZOS, 325 | }[degradation] 326 | 327 | self.pil_interpolation = degradation.startswith("pil_") 328 | 329 | if self.pil_interpolation: 330 | self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn) 331 | 332 | else: 333 | self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size, 334 | interpolation=interpolation_fn) 335 | 336 | def __len__(self): 337 | return len(self.base) 338 | 339 | def __getitem__(self, i): 340 | example = self.base[i] 341 | image = Image.open(example["file_path_"]) 342 | 343 | if not image.mode == "RGB": 344 | image = image.convert("RGB") 345 | 346 | image = np.array(image).astype(np.uint8) 347 | 348 | min_side_len = min(image.shape[:2]) 349 | crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None) 350 | crop_side_len = int(crop_side_len) 351 | 352 | if self.center_crop: 353 | self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len) 354 | 355 | else: 356 | self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len) 357 | 358 | image = self.cropper(image=image)["image"] 359 | image = self.image_rescaler(image=image)["image"] 360 | 361 | if self.pil_interpolation: 362 | image_pil = PIL.Image.fromarray(image) 363 | LR_image = self.degradation_process(image_pil) 364 | LR_image = np.array(LR_image).astype(np.uint8) 365 | 366 | else: 367 | LR_image = self.degradation_process(image=image)["image"] 368 | 369 | example["image"] = (image/127.5 - 1.0).astype(np.float32) 370 | example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32) 371 | 372 | return example 373 | 374 | 375 | class ImageNetSRTrain(ImageNetSR): 376 | def __init__(self, **kwargs): 377 | super().__init__(**kwargs) 378 | 379 | def get_base(self): 380 | with open("data/imagenet_train_hr_indices.p", "rb") as f: 381 | indices = pickle.load(f) 382 | dset = ImageNetTrain(process_images=False,) 383 | return Subset(dset, indices) 384 | 385 | 386 | class ImageNetSRValidation(ImageNetSR): 387 | def __init__(self, **kwargs): 388 | super().__init__(**kwargs) 389 | 390 | def get_base(self): 391 | with open("data/imagenet_val_hr_indices.p", "rb") as f: 392 | indices = pickle.load(f) 393 | dset = ImageNetValidation(process_images=False,) 394 | return Subset(dset, indices) 395 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/lr_scheduler.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class LambdaWarmUpCosineScheduler: 5 | """ 6 | note: use with a base_lr of 1.0 7 | """ 8 | def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): 9 | self.lr_warm_up_steps = warm_up_steps 10 | self.lr_start = lr_start 11 | self.lr_min = lr_min 12 | self.lr_max = lr_max 13 | self.lr_max_decay_steps = max_decay_steps 14 | self.last_lr = 0. 15 | self.verbosity_interval = verbosity_interval 16 | 17 | def schedule(self, n, **kwargs): 18 | if self.verbosity_interval > 0: 19 | if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") 20 | if n < self.lr_warm_up_steps: 21 | lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start 22 | self.last_lr = lr 23 | return lr 24 | else: 25 | t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) 26 | t = min(t, 1.0) 27 | lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( 28 | 1 + np.cos(t * np.pi)) 29 | self.last_lr = lr 30 | return lr 31 | 32 | def __call__(self, n, **kwargs): 33 | return self.schedule(n,**kwargs) 34 | 35 | 36 | class LambdaWarmUpCosineScheduler2: 37 | """ 38 | supports repeated iterations, configurable via lists 39 | note: use with a base_lr of 1.0. 40 | """ 41 | def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): 42 | assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) 43 | self.lr_warm_up_steps = warm_up_steps 44 | self.f_start = f_start 45 | self.f_min = f_min 46 | self.f_max = f_max 47 | self.cycle_lengths = cycle_lengths 48 | self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) 49 | self.last_f = 0. 50 | self.verbosity_interval = verbosity_interval 51 | 52 | def find_in_interval(self, n): 53 | interval = 0 54 | for cl in self.cum_cycles[1:]: 55 | if n <= cl: 56 | return interval 57 | interval += 1 58 | 59 | def schedule(self, n, **kwargs): 60 | cycle = self.find_in_interval(n) 61 | n = n - self.cum_cycles[cycle] 62 | if self.verbosity_interval > 0: 63 | if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " 64 | f"current cycle {cycle}") 65 | if n < self.lr_warm_up_steps[cycle]: 66 | f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] 67 | self.last_f = f 68 | return f 69 | else: 70 | t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) 71 | t = min(t, 1.0) 72 | f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( 73 | 1 + np.cos(t * np.pi)) 74 | self.last_f = f 75 | return f 76 | 77 | def __call__(self, n, **kwargs): 78 | return self.schedule(n, **kwargs) 79 | 80 | 81 | class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): 82 | 83 | def schedule(self, n, **kwargs): 84 | cycle = self.find_in_interval(n) 85 | n = n - self.cum_cycles[cycle] 86 | if self.verbosity_interval > 0: 87 | if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " 88 | f"current cycle {cycle}") 89 | 90 | if n < self.lr_warm_up_steps[cycle]: 91 | f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] 92 | self.last_f = f 93 | return f 94 | else: 95 | f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) 96 | self.last_f = f 97 | return f 98 | 99 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/models/autoencoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pytorch_lightning as pl 3 | import torch.nn.functional as F 4 | from contextlib import contextmanager 5 | 6 | from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer 7 | 8 | from ldm.modules.diffusionmodules.model import Encoder, Decoder 9 | from ldm.modules.distributions.distributions import DiagonalGaussianDistribution 10 | 11 | from ldm.util import instantiate_from_config 12 | 13 | 14 | class VQModel(pl.LightningModule): 15 | def __init__(self, 16 | ddconfig, 17 | lossconfig, 18 | n_embed, 19 | embed_dim, 20 | ckpt_path=None, 21 | ignore_keys=[], 22 | image_key="image", 23 | colorize_nlabels=None, 24 | monitor=None, 25 | batch_resize_range=None, 26 | scheduler_config=None, 27 | lr_g_factor=1.0, 28 | remap=None, 29 | sane_index_shape=False, # tell vector quantizer to return indices as bhw 30 | use_ema=False 31 | ): 32 | super().__init__() 33 | self.embed_dim = embed_dim 34 | self.n_embed = n_embed 35 | self.image_key = image_key 36 | self.encoder = Encoder(**ddconfig) 37 | self.decoder = Decoder(**ddconfig) 38 | self.loss = instantiate_from_config(lossconfig) 39 | self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, 40 | remap=remap, 41 | sane_index_shape=sane_index_shape) 42 | self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) 43 | self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) 44 | if colorize_nlabels is not None: 45 | assert type(colorize_nlabels)==int 46 | self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) 47 | if monitor is not None: 48 | self.monitor = monitor 49 | self.batch_resize_range = batch_resize_range 50 | if self.batch_resize_range is not None: 51 | print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") 52 | 53 | self.use_ema = use_ema 54 | if self.use_ema: 55 | self.model_ema = LitEma(self) 56 | print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") 57 | 58 | if ckpt_path is not None: 59 | self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) 60 | self.scheduler_config = scheduler_config 61 | self.lr_g_factor = lr_g_factor 62 | 63 | @contextmanager 64 | def ema_scope(self, context=None): 65 | if self.use_ema: 66 | self.model_ema.store(self.parameters()) 67 | self.model_ema.copy_to(self) 68 | if context is not None: 69 | print(f"{context}: Switched to EMA weights") 70 | try: 71 | yield None 72 | finally: 73 | if self.use_ema: 74 | self.model_ema.restore(self.parameters()) 75 | if context is not None: 76 | print(f"{context}: Restored training weights") 77 | 78 | def init_from_ckpt(self, path, ignore_keys=list()): 79 | sd = torch.load(path, map_location="cpu")["state_dict"] 80 | keys = list(sd.keys()) 81 | for k in keys: 82 | for ik in ignore_keys: 83 | if k.startswith(ik): 84 | print("Deleting key {} from state_dict.".format(k)) 85 | del sd[k] 86 | missing, unexpected = self.load_state_dict(sd, strict=False) 87 | print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") 88 | if len(missing) > 0: 89 | print(f"Missing Keys: {missing}") 90 | print(f"Unexpected Keys: {unexpected}") 91 | 92 | def on_train_batch_end(self, *args, **kwargs): 93 | if self.use_ema: 94 | self.model_ema(self) 95 | 96 | def encode(self, x): 97 | h = self.encoder(x) 98 | h = self.quant_conv(h) 99 | quant, emb_loss, info = self.quantize(h) 100 | return quant, emb_loss, info 101 | 102 | def encode_to_prequant(self, x): 103 | h = self.encoder(x) 104 | h = self.quant_conv(h) 105 | return h 106 | 107 | def decode(self, quant): 108 | quant = self.post_quant_conv(quant) 109 | dec = self.decoder(quant) 110 | return dec 111 | 112 | def decode_code(self, code_b): 113 | quant_b = self.quantize.embed_code(code_b) 114 | dec = self.decode(quant_b) 115 | return dec 116 | 117 | def forward(self, input, return_pred_indices=False): 118 | quant, diff, (_,_,ind) = self.encode(input) 119 | dec = self.decode(quant) 120 | if return_pred_indices: 121 | return dec, diff, ind 122 | return dec, diff 123 | 124 | def get_input(self, batch, k): 125 | x = batch[k] 126 | if len(x.shape) == 3: 127 | x = x[..., None] 128 | x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() 129 | if self.batch_resize_range is not None: 130 | lower_size = self.batch_resize_range[0] 131 | upper_size = self.batch_resize_range[1] 132 | if self.global_step <= 4: 133 | # do the first few batches with max size to avoid later oom 134 | new_resize = upper_size 135 | else: 136 | new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) 137 | if new_resize != x.shape[2]: 138 | x = F.interpolate(x, size=new_resize, mode="bicubic") 139 | x = x.detach() 140 | return x 141 | 142 | def training_step(self, batch, batch_idx, optimizer_idx): 143 | # https://github.com/pytorch/pytorch/issues/37142 144 | # try not to fool the heuristics 145 | x = self.get_input(batch, self.image_key) 146 | xrec, qloss, ind = self(x, return_pred_indices=True) 147 | 148 | if optimizer_idx == 0: 149 | # autoencode 150 | aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, 151 | last_layer=self.get_last_layer(), split="train", 152 | predicted_indices=ind) 153 | 154 | self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) 155 | return aeloss 156 | 157 | if optimizer_idx == 1: 158 | # discriminator 159 | discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, 160 | last_layer=self.get_last_layer(), split="train") 161 | self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) 162 | return discloss 163 | 164 | def validation_step(self, batch, batch_idx): 165 | log_dict = self._validation_step(batch, batch_idx) 166 | with self.ema_scope(): 167 | log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") 168 | return log_dict 169 | 170 | def _validation_step(self, batch, batch_idx, suffix=""): 171 | x = self.get_input(batch, self.image_key) 172 | xrec, qloss, ind = self(x, return_pred_indices=True) 173 | aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, 174 | self.global_step, 175 | last_layer=self.get_last_layer(), 176 | split="val"+suffix, 177 | predicted_indices=ind 178 | ) 179 | 180 | discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, 181 | self.global_step, 182 | last_layer=self.get_last_layer(), 183 | split="val"+suffix, 184 | predicted_indices=ind 185 | ) 186 | rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] 187 | self.log(f"val{suffix}/rec_loss", rec_loss, 188 | prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) 189 | self.log(f"val{suffix}/aeloss", aeloss, 190 | prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) 191 | if version.parse(pl.__version__) >= version.parse('1.4.0'): 192 | del log_dict_ae[f"val{suffix}/rec_loss"] 193 | self.log_dict(log_dict_ae) 194 | self.log_dict(log_dict_disc) 195 | return self.log_dict 196 | 197 | def configure_optimizers(self): 198 | lr_d = self.learning_rate 199 | lr_g = self.lr_g_factor*self.learning_rate 200 | print("lr_d", lr_d) 201 | print("lr_g", lr_g) 202 | opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ 203 | list(self.decoder.parameters())+ 204 | list(self.quantize.parameters())+ 205 | list(self.quant_conv.parameters())+ 206 | list(self.post_quant_conv.parameters()), 207 | lr=lr_g, betas=(0.5, 0.9)) 208 | opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), 209 | lr=lr_d, betas=(0.5, 0.9)) 210 | 211 | if self.scheduler_config is not None: 212 | scheduler = instantiate_from_config(self.scheduler_config) 213 | 214 | print("Setting up LambdaLR scheduler...") 215 | scheduler = [ 216 | { 217 | 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), 218 | 'interval': 'step', 219 | 'frequency': 1 220 | }, 221 | { 222 | 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), 223 | 'interval': 'step', 224 | 'frequency': 1 225 | }, 226 | ] 227 | return [opt_ae, opt_disc], scheduler 228 | return [opt_ae, opt_disc], [] 229 | 230 | def get_last_layer(self): 231 | return self.decoder.conv_out.weight 232 | 233 | def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): 234 | log = dict() 235 | x = self.get_input(batch, self.image_key) 236 | x = x.to(self.device) 237 | if only_inputs: 238 | log["inputs"] = x 239 | return log 240 | xrec, _ = self(x) 241 | if x.shape[1] > 3: 242 | # colorize with random projection 243 | assert xrec.shape[1] > 3 244 | x = self.to_rgb(x) 245 | xrec = self.to_rgb(xrec) 246 | log["inputs"] = x 247 | log["reconstructions"] = xrec 248 | if plot_ema: 249 | with self.ema_scope(): 250 | xrec_ema, _ = self(x) 251 | if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) 252 | log["reconstructions_ema"] = xrec_ema 253 | return log 254 | 255 | def to_rgb(self, x): 256 | assert self.image_key == "segmentation" 257 | if not hasattr(self, "colorize"): 258 | self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) 259 | x = F.conv2d(x, weight=self.colorize) 260 | x = 2.*(x-x.min())/(x.max()-x.min()) - 1. 261 | return x 262 | 263 | 264 | class VQModelInterface(VQModel): 265 | def __init__(self, embed_dim, *args, **kwargs): 266 | super().__init__(embed_dim=embed_dim, *args, **kwargs) 267 | self.embed_dim = embed_dim 268 | 269 | def encode(self, x): 270 | h = self.encoder(x) 271 | h = self.quant_conv(h) 272 | return h 273 | 274 | def decode(self, h, force_not_quantize=False): 275 | # also go through quantization layer 276 | if not force_not_quantize: 277 | quant, emb_loss, info = self.quantize(h) 278 | else: 279 | quant = h 280 | quant = self.post_quant_conv(quant) 281 | dec = self.decoder(quant) 282 | return dec 283 | 284 | 285 | class AutoencoderKL(pl.LightningModule): 286 | def __init__(self, 287 | ddconfig, 288 | lossconfig, 289 | embed_dim, 290 | ckpt_path=None, 291 | ignore_keys=[], 292 | image_key="image", 293 | colorize_nlabels=None, 294 | monitor=None, 295 | ): 296 | super().__init__() 297 | self.image_key = image_key 298 | self.encoder = Encoder(**ddconfig) 299 | self.decoder = Decoder(**ddconfig) 300 | self.loss = instantiate_from_config(lossconfig) 301 | assert ddconfig["double_z"] 302 | self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) 303 | self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) 304 | self.embed_dim = embed_dim 305 | if colorize_nlabels is not None: 306 | assert type(colorize_nlabels)==int 307 | self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) 308 | if monitor is not None: 309 | self.monitor = monitor 310 | if ckpt_path is not None: 311 | self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) 312 | 313 | def init_from_ckpt(self, path, ignore_keys=list()): 314 | sd = torch.load(path, map_location="cpu")["state_dict"] 315 | keys = list(sd.keys()) 316 | for k in keys: 317 | for ik in ignore_keys: 318 | if k.startswith(ik): 319 | print("Deleting key {} from state_dict.".format(k)) 320 | del sd[k] 321 | self.load_state_dict(sd, strict=False) 322 | print(f"Restored from {path}") 323 | 324 | def encode(self, x): 325 | h = self.encoder(x) 326 | moments = self.quant_conv(h) 327 | posterior = DiagonalGaussianDistribution(moments) 328 | return posterior 329 | 330 | def decode(self, z): 331 | z = self.post_quant_conv(z) 332 | dec = self.decoder(z) 333 | return dec 334 | 335 | def forward(self, input, sample_posterior=True): 336 | posterior = self.encode(input) 337 | if sample_posterior: 338 | z = posterior.sample() 339 | else: 340 | z = posterior.mode() 341 | dec = self.decode(z) 342 | return dec, posterior 343 | 344 | def get_input(self, batch, k): 345 | x = batch[k] 346 | if len(x.shape) == 3: 347 | x = x[..., None] 348 | x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() 349 | return x 350 | 351 | def training_step(self, batch, batch_idx, optimizer_idx): 352 | inputs = self.get_input(batch, self.image_key) 353 | reconstructions, posterior = self(inputs) 354 | 355 | if optimizer_idx == 0: 356 | # train encoder+decoder+logvar 357 | aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, 358 | last_layer=self.get_last_layer(), split="train") 359 | self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) 360 | self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) 361 | return aeloss 362 | 363 | if optimizer_idx == 1: 364 | # train the discriminator 365 | discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, 366 | last_layer=self.get_last_layer(), split="train") 367 | 368 | self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) 369 | self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) 370 | return discloss 371 | 372 | def validation_step(self, batch, batch_idx): 373 | inputs = self.get_input(batch, self.image_key) 374 | reconstructions, posterior = self(inputs) 375 | aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, 376 | last_layer=self.get_last_layer(), split="val") 377 | 378 | discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, 379 | last_layer=self.get_last_layer(), split="val") 380 | 381 | self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) 382 | self.log_dict(log_dict_ae) 383 | self.log_dict(log_dict_disc) 384 | return self.log_dict 385 | 386 | def configure_optimizers(self): 387 | lr = self.learning_rate 388 | opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ 389 | list(self.decoder.parameters())+ 390 | list(self.quant_conv.parameters())+ 391 | list(self.post_quant_conv.parameters()), 392 | lr=lr, betas=(0.5, 0.9)) 393 | opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), 394 | lr=lr, betas=(0.5, 0.9)) 395 | return [opt_ae, opt_disc], [] 396 | 397 | def get_last_layer(self): 398 | return self.decoder.conv_out.weight 399 | 400 | @torch.no_grad() 401 | def log_images(self, batch, only_inputs=False, **kwargs): 402 | log = dict() 403 | x = self.get_input(batch, self.image_key) 404 | x = x.to(self.device) 405 | if not only_inputs: 406 | xrec, posterior = self(x) 407 | if x.shape[1] > 3: 408 | # colorize with random projection 409 | assert xrec.shape[1] > 3 410 | x = self.to_rgb(x) 411 | xrec = self.to_rgb(xrec) 412 | log["samples"] = self.decode(torch.randn_like(posterior.sample())) 413 | log["reconstructions"] = xrec 414 | log["inputs"] = x 415 | return log 416 | 417 | def to_rgb(self, x): 418 | assert self.image_key == "segmentation" 419 | if not hasattr(self, "colorize"): 420 | self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) 421 | x = F.conv2d(x, weight=self.colorize) 422 | x = 2.*(x-x.min())/(x.max()-x.min()) - 1. 423 | return x 424 | 425 | 426 | class IdentityFirstStage(torch.nn.Module): 427 | def __init__(self, *args, vq_interface=False, **kwargs): 428 | self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff 429 | super().__init__() 430 | 431 | def encode(self, x, *args, **kwargs): 432 | return x 433 | 434 | def decode(self, x, *args, **kwargs): 435 | return x 436 | 437 | def quantize(self, x, *args, **kwargs): 438 | if self.vq_interface: 439 | return x, None, [None, None, None] 440 | return x 441 | 442 | def forward(self, x, *args, **kwargs): 443 | return x 444 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/models/diffusion/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/ldm/models/diffusion/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/models/diffusion/classifier.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import pytorch_lightning as pl 4 | from omegaconf import OmegaConf 5 | from torch.nn import functional as F 6 | from torch.optim import AdamW 7 | from torch.optim.lr_scheduler import LambdaLR 8 | from copy import deepcopy 9 | from einops import rearrange 10 | from glob import glob 11 | from natsort import natsorted 12 | 13 | from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel 14 | from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config 15 | 16 | __models__ = { 17 | 'class_label': EncoderUNetModel, 18 | 'segmentation': UNetModel 19 | } 20 | 21 | 22 | def disabled_train(self, mode=True): 23 | """Overwrite model.train with this function to make sure train/eval mode 24 | does not change anymore.""" 25 | return self 26 | 27 | 28 | class NoisyLatentImageClassifier(pl.LightningModule): 29 | 30 | def __init__(self, 31 | diffusion_path, 32 | num_classes, 33 | ckpt_path=None, 34 | pool='attention', 35 | label_key=None, 36 | diffusion_ckpt_path=None, 37 | scheduler_config=None, 38 | weight_decay=1.e-2, 39 | log_steps=10, 40 | monitor='val/loss', 41 | *args, 42 | **kwargs): 43 | super().__init__(*args, **kwargs) 44 | self.num_classes = num_classes 45 | # get latest config of diffusion model 46 | diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] 47 | self.diffusion_config = OmegaConf.load(diffusion_config).model 48 | self.diffusion_config.params.ckpt_path = diffusion_ckpt_path 49 | self.load_diffusion() 50 | 51 | self.monitor = monitor 52 | self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 53 | self.log_time_interval = self.diffusion_model.num_timesteps // log_steps 54 | self.log_steps = log_steps 55 | 56 | self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ 57 | else self.diffusion_model.cond_stage_key 58 | 59 | assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' 60 | 61 | if self.label_key not in __models__: 62 | raise NotImplementedError() 63 | 64 | self.load_classifier(ckpt_path, pool) 65 | 66 | self.scheduler_config = scheduler_config 67 | self.use_scheduler = self.scheduler_config is not None 68 | self.weight_decay = weight_decay 69 | 70 | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): 71 | sd = torch.load(path, map_location="cpu") 72 | if "state_dict" in list(sd.keys()): 73 | sd = sd["state_dict"] 74 | keys = list(sd.keys()) 75 | for k in keys: 76 | for ik in ignore_keys: 77 | if k.startswith(ik): 78 | print("Deleting key {} from state_dict.".format(k)) 79 | del sd[k] 80 | missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( 81 | sd, strict=False) 82 | print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") 83 | if len(missing) > 0: 84 | print(f"Missing Keys: {missing}") 85 | if len(unexpected) > 0: 86 | print(f"Unexpected Keys: {unexpected}") 87 | 88 | def load_diffusion(self): 89 | model = instantiate_from_config(self.diffusion_config) 90 | self.diffusion_model = model.eval() 91 | self.diffusion_model.train = disabled_train 92 | for param in self.diffusion_model.parameters(): 93 | param.requires_grad = False 94 | 95 | def load_classifier(self, ckpt_path, pool): 96 | model_config = deepcopy(self.diffusion_config.params.unet_config.params) 97 | model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels 98 | model_config.out_channels = self.num_classes 99 | if self.label_key == 'class_label': 100 | model_config.pool = pool 101 | 102 | self.model = __models__[self.label_key](**model_config) 103 | if ckpt_path is not None: 104 | print('#####################################################################') 105 | print(f'load from ckpt "{ckpt_path}"') 106 | print('#####################################################################') 107 | self.init_from_ckpt(ckpt_path) 108 | 109 | @torch.no_grad() 110 | def get_x_noisy(self, x, t, noise=None): 111 | noise = default(noise, lambda: torch.randn_like(x)) 112 | continuous_sqrt_alpha_cumprod = None 113 | if self.diffusion_model.use_continuous_noise: 114 | continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) 115 | # todo: make sure t+1 is correct here 116 | 117 | return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, 118 | continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) 119 | 120 | def forward(self, x_noisy, t, *args, **kwargs): 121 | return self.model(x_noisy, t) 122 | 123 | @torch.no_grad() 124 | def get_input(self, batch, k): 125 | x = batch[k] 126 | if len(x.shape) == 3: 127 | x = x[..., None] 128 | x = rearrange(x, 'b h w c -> b c h w') 129 | x = x.to(memory_format=torch.contiguous_format).float() 130 | return x 131 | 132 | @torch.no_grad() 133 | def get_conditioning(self, batch, k=None): 134 | if k is None: 135 | k = self.label_key 136 | assert k is not None, 'Needs to provide label key' 137 | 138 | targets = batch[k].to(self.device) 139 | 140 | if self.label_key == 'segmentation': 141 | targets = rearrange(targets, 'b h w c -> b c h w') 142 | for down in range(self.numd): 143 | h, w = targets.shape[-2:] 144 | targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') 145 | 146 | # targets = rearrange(targets,'b c h w -> b h w c') 147 | 148 | return targets 149 | 150 | def compute_top_k(self, logits, labels, k, reduction="mean"): 151 | _, top_ks = torch.topk(logits, k, dim=1) 152 | if reduction == "mean": 153 | return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() 154 | elif reduction == "none": 155 | return (top_ks == labels[:, None]).float().sum(dim=-1) 156 | 157 | def on_train_epoch_start(self): 158 | # save some memory 159 | self.diffusion_model.model.to('cpu') 160 | 161 | @torch.no_grad() 162 | def write_logs(self, loss, logits, targets): 163 | log_prefix = 'train' if self.training else 'val' 164 | log = {} 165 | log[f"{log_prefix}/loss"] = loss.mean() 166 | log[f"{log_prefix}/acc@1"] = self.compute_top_k( 167 | logits, targets, k=1, reduction="mean" 168 | ) 169 | log[f"{log_prefix}/acc@5"] = self.compute_top_k( 170 | logits, targets, k=5, reduction="mean" 171 | ) 172 | 173 | self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) 174 | self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) 175 | self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) 176 | lr = self.optimizers().param_groups[0]['lr'] 177 | self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) 178 | 179 | def shared_step(self, batch, t=None): 180 | x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) 181 | targets = self.get_conditioning(batch) 182 | if targets.dim() == 4: 183 | targets = targets.argmax(dim=1) 184 | if t is None: 185 | t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() 186 | else: 187 | t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() 188 | x_noisy = self.get_x_noisy(x, t) 189 | logits = self(x_noisy, t) 190 | 191 | loss = F.cross_entropy(logits, targets, reduction='none') 192 | 193 | self.write_logs(loss.detach(), logits.detach(), targets.detach()) 194 | 195 | loss = loss.mean() 196 | return loss, logits, x_noisy, targets 197 | 198 | def training_step(self, batch, batch_idx): 199 | loss, *_ = self.shared_step(batch) 200 | return loss 201 | 202 | def reset_noise_accs(self): 203 | self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in 204 | range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} 205 | 206 | def on_validation_start(self): 207 | self.reset_noise_accs() 208 | 209 | @torch.no_grad() 210 | def validation_step(self, batch, batch_idx): 211 | loss, *_ = self.shared_step(batch) 212 | 213 | for t in self.noisy_acc: 214 | _, logits, _, targets = self.shared_step(batch, t) 215 | self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) 216 | self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) 217 | 218 | return loss 219 | 220 | def configure_optimizers(self): 221 | optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) 222 | 223 | if self.use_scheduler: 224 | scheduler = instantiate_from_config(self.scheduler_config) 225 | 226 | print("Setting up LambdaLR scheduler...") 227 | scheduler = [ 228 | { 229 | 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), 230 | 'interval': 'step', 231 | 'frequency': 1 232 | }] 233 | return [optimizer], scheduler 234 | 235 | return optimizer 236 | 237 | @torch.no_grad() 238 | def log_images(self, batch, N=8, *args, **kwargs): 239 | log = dict() 240 | x = self.get_input(batch, self.diffusion_model.first_stage_key) 241 | log['inputs'] = x 242 | 243 | y = self.get_conditioning(batch) 244 | 245 | if self.label_key == 'class_label': 246 | y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) 247 | log['labels'] = y 248 | 249 | if ismap(y): 250 | log['labels'] = self.diffusion_model.to_rgb(y) 251 | 252 | for step in range(self.log_steps): 253 | current_time = step * self.log_time_interval 254 | 255 | _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) 256 | 257 | log[f'inputs@t{current_time}'] = x_noisy 258 | 259 | pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) 260 | pred = rearrange(pred, 'b h w c -> b c h w') 261 | 262 | log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) 263 | 264 | for key in log: 265 | log[key] = log[key][:N] 266 | 267 | return log 268 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/models/diffusion/ddim.py: -------------------------------------------------------------------------------- 1 | """SAMPLING ONLY.""" 2 | 3 | import torch 4 | import numpy as np 5 | from tqdm import tqdm 6 | 7 | from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like 8 | 9 | 10 | class DDIMSampler(object): 11 | def __init__(self, model, schedule="linear", **kwargs): 12 | super().__init__() 13 | self.model = model 14 | self.ddpm_num_timesteps = model.num_timesteps 15 | self.schedule = schedule 16 | 17 | def register_buffer(self, name, attr): 18 | if type(attr) == torch.Tensor: 19 | if attr.device != torch.device("cuda"): 20 | attr = attr.to(torch.device("cuda")) 21 | setattr(self, name, attr) 22 | 23 | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): 24 | self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, 25 | num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose) 26 | alphas_cumprod = self.model.alphas_cumprod 27 | assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' 28 | def to_torch(x): return x.clone().detach().to(torch.float32).to(self.model.device) 29 | 30 | self.register_buffer('betas', to_torch(self.model.betas)) 31 | self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) 32 | self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) 33 | 34 | # calculations for diffusion q(x_t | x_{t-1}) and others 35 | self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) 36 | self.register_buffer('sqrt_one_minus_alphas_cumprod', 37 | to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) 38 | self.register_buffer('log_one_minus_alphas_cumprod', 39 | to_torch(np.log(1. - alphas_cumprod.cpu()))) 40 | self.register_buffer('sqrt_recip_alphas_cumprod', 41 | to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) 42 | self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch( 43 | np.sqrt(1. / alphas_cumprod.cpu() - 1))) 44 | 45 | # ddim sampling parameters 46 | ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), 47 | ddim_timesteps=self.ddim_timesteps, 48 | eta=ddim_eta, verbose=verbose) 49 | self.register_buffer('ddim_sigmas', ddim_sigmas) 50 | self.register_buffer('ddim_alphas', ddim_alphas) 51 | self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) 52 | self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) 53 | sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( 54 | (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 55 | 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) 56 | self.register_buffer('ddim_sigmas_for_original_num_steps', 57 | sigmas_for_original_sampling_steps) 58 | 59 | @torch.no_grad() 60 | def sample(self, 61 | S, 62 | batch_size, 63 | shape, 64 | conditioning=None, 65 | callback=None, 66 | normals_sequence=None, 67 | img_callback=None, 68 | quantize_x0=False, 69 | eta=0., 70 | mask=None, 71 | x0=None, 72 | temperature=1., 73 | noise_dropout=0., 74 | score_corrector=None, 75 | corrector_kwargs=None, 76 | verbose=True, 77 | x_T=None, 78 | log_every_t=100, 79 | unconditional_guidance_scale=1., 80 | unconditional_conditioning=None, 81 | # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... 82 | **kwargs 83 | ): 84 | if conditioning is not None: 85 | if isinstance(conditioning, dict): 86 | cbs = conditioning[list(conditioning.keys())[0]].shape[0] 87 | if cbs != batch_size: 88 | print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") 89 | else: 90 | if conditioning.shape[0] != batch_size: 91 | print( 92 | f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") 93 | 94 | self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) 95 | # sampling 96 | C, H, W = shape 97 | size = (batch_size, C, H, W) 98 | print(f'Data shape for DDIM sampling is {size}, eta {eta}') 99 | 100 | samples, intermediates = self.ddim_sampling(conditioning, size, 101 | callback=callback, 102 | img_callback=img_callback, 103 | quantize_denoised=quantize_x0, 104 | mask=mask, x0=x0, 105 | ddim_use_original_steps=False, 106 | noise_dropout=noise_dropout, 107 | temperature=temperature, 108 | score_corrector=score_corrector, 109 | corrector_kwargs=corrector_kwargs, 110 | x_T=x_T, 111 | log_every_t=log_every_t, 112 | unconditional_guidance_scale=unconditional_guidance_scale, 113 | unconditional_conditioning=unconditional_conditioning, 114 | ) 115 | return samples, intermediates 116 | 117 | 118 | @torch.no_grad() 119 | def ddim_sampling(self, cond, shape, 120 | x_T=None, ddim_use_original_steps=False, 121 | callback=None, timesteps=None, quantize_denoised=False, 122 | mask=None, x0=None, img_callback=None, log_every_t=100, 123 | temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, 124 | unconditional_guidance_scale=1., unconditional_conditioning=None,): 125 | device = self.model.betas.device 126 | b = shape[0] 127 | if x_T is None: 128 | img = torch.randn(shape, device=device) 129 | else: 130 | img = x_T 131 | 132 | if timesteps is None: 133 | timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps 134 | elif timesteps is not None and not ddim_use_original_steps: 135 | subset_end = int( 136 | min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 137 | timesteps = self.ddim_timesteps[:subset_end] 138 | 139 | intermediates = {'x_inter': [img], 'pred_x0': [img]} 140 | time_range = reversed( 141 | range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps) 142 | total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] 143 | print(f"Running DDIM Sampling with {total_steps} timesteps") 144 | 145 | iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) 146 | 147 | for i, step in enumerate(iterator): 148 | index = total_steps - i - 1 149 | ts = torch.full((b,), step, device=device, dtype=torch.long) 150 | 151 | if mask is not None: 152 | assert x0 is not None 153 | img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? 154 | img = img_orig * mask + (1. - mask) * img 155 | 156 | outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, 157 | quantize_denoised=quantize_denoised, temperature=temperature, 158 | noise_dropout=noise_dropout, score_corrector=score_corrector, 159 | corrector_kwargs=corrector_kwargs, 160 | unconditional_guidance_scale=unconditional_guidance_scale, 161 | unconditional_conditioning=unconditional_conditioning) 162 | img, pred_x0 = outs 163 | if callback: 164 | callback(i) 165 | if img_callback: 166 | img_callback(pred_x0, i) 167 | 168 | if index % log_every_t == 0 or index == total_steps - 1: 169 | intermediates['x_inter'].append(img) 170 | intermediates['pred_x0'].append(pred_x0) 171 | 172 | return img, intermediates 173 | 174 | @torch.no_grad() 175 | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, 176 | temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, 177 | unconditional_guidance_scale=1., unconditional_conditioning=None): 178 | """ 179 | Performs a single denoising step on x_t given conditioning c. Plus many options which seem to be unused in the code? 180 | Can also accept batches I think. 181 | """ 182 | b, *_, device = *x.shape, x.device 183 | 184 | if unconditional_conditioning is None or unconditional_guidance_scale == 1.: 185 | e_t = self.model.apply_model(x, t, c) 186 | else: 187 | x_in = torch.cat([x] * 2) 188 | t_in = torch.cat([t] * 2) 189 | c_in = torch.cat([unconditional_conditioning, c]) 190 | e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) 191 | e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) 192 | 193 | if score_corrector is not None: 194 | assert self.model.parameterization == "eps" 195 | e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) 196 | 197 | alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas 198 | alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev 199 | sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas 200 | sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas 201 | # select parameters corresponding to the currently considered timestep 202 | a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) 203 | a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) 204 | sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) 205 | sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device) 206 | 207 | # current prediction for x_0 208 | pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() 209 | if quantize_denoised: 210 | pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) 211 | # direction pointing to x_t 212 | dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t 213 | noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature 214 | if noise_dropout > 0.: # wtf is noise dropout???? 215 | noise = torch.nn.functional.dropout(noise, p=noise_dropout) 216 | x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise 217 | return x_prev, pred_x0 218 | 219 | 220 | 221 | @torch.no_grad() 222 | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): 223 | # fast, but does not allow for exact reconstruction 224 | # t serves as an index to gather the correct alphas 225 | if use_original_steps: 226 | sqrt_alphas_cumprod = self.sqrt_alphas_cumprod 227 | sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod 228 | else: 229 | sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) 230 | sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas 231 | 232 | if noise is None: 233 | noise = torch.randn_like(x0) 234 | return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + 235 | extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) 236 | 237 | @torch.no_grad() 238 | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, 239 | use_original_steps=False): 240 | 241 | timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps 242 | timesteps = timesteps[:t_start] 243 | 244 | time_range = np.flip(timesteps) 245 | total_steps = timesteps.shape[0] 246 | print(f"Running DDIM Sampling with {total_steps} timesteps") 247 | 248 | iterator = tqdm(time_range, desc='Decoding image', total=total_steps) 249 | x_dec = x_latent 250 | for i, step in enumerate(iterator): 251 | index = total_steps - i - 1 252 | ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) 253 | x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, 254 | unconditional_guidance_scale=unconditional_guidance_scale, 255 | unconditional_conditioning=unconditional_conditioning) 256 | return x_dec 257 | 258 | def extract_into_tensor(a, t, x_shape): 259 | b, *_ = t.shape 260 | out = a.gather(-1, t) 261 | return out.reshape(b, *((1,) * (len(x_shape) - 1))) 262 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/models/diffusion/plms.py: -------------------------------------------------------------------------------- 1 | """SAMPLING ONLY.""" 2 | 3 | import torch 4 | import numpy as np 5 | from tqdm import tqdm 6 | from functools import partial 7 | 8 | from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like 9 | 10 | 11 | class PLMSSampler(object): 12 | def __init__(self, model, schedule="linear", **kwargs): 13 | super().__init__() 14 | self.model = model 15 | self.ddpm_num_timesteps = model.num_timesteps 16 | self.schedule = schedule 17 | 18 | def register_buffer(self, name, attr): 19 | if type(attr) == torch.Tensor: 20 | if attr.device != torch.device("cuda"): 21 | attr = attr.to(torch.device("cuda")) 22 | setattr(self, name, attr) 23 | 24 | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): 25 | if ddim_eta != 0: 26 | raise ValueError('ddim_eta must be 0 for PLMS') 27 | self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, 28 | num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) 29 | alphas_cumprod = self.model.alphas_cumprod 30 | assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' 31 | to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) 32 | 33 | self.register_buffer('betas', to_torch(self.model.betas)) 34 | self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) 35 | self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) 36 | 37 | # calculations for diffusion q(x_t | x_{t-1}) and others 38 | self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) 39 | self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) 40 | self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) 41 | self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) 42 | self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) 43 | 44 | # ddim sampling parameters 45 | ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), 46 | ddim_timesteps=self.ddim_timesteps, 47 | eta=ddim_eta,verbose=verbose) 48 | self.register_buffer('ddim_sigmas', ddim_sigmas) 49 | self.register_buffer('ddim_alphas', ddim_alphas) 50 | self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) 51 | self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) 52 | sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( 53 | (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 54 | 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) 55 | self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) 56 | 57 | @torch.no_grad() 58 | def sample(self, 59 | S, 60 | batch_size, 61 | shape, 62 | conditioning=None, 63 | callback=None, 64 | normals_sequence=None, 65 | img_callback=None, 66 | quantize_x0=False, 67 | eta=0., 68 | mask=None, 69 | x0=None, 70 | temperature=1., 71 | noise_dropout=0., 72 | score_corrector=None, 73 | corrector_kwargs=None, 74 | verbose=True, 75 | x_T=None, 76 | log_every_t=100, 77 | unconditional_guidance_scale=1., 78 | unconditional_conditioning=None, 79 | # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... 80 | **kwargs 81 | ): 82 | if conditioning is not None: 83 | if isinstance(conditioning, dict): 84 | cbs = conditioning[list(conditioning.keys())[0]].shape[0] 85 | if cbs != batch_size: 86 | print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") 87 | else: 88 | if conditioning.shape[0] != batch_size: 89 | print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") 90 | 91 | self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) 92 | # sampling 93 | C, H, W = shape 94 | size = (batch_size, C, H, W) 95 | print(f'Data shape for PLMS sampling is {size}') 96 | 97 | samples, intermediates = self.plms_sampling(conditioning, size, 98 | callback=callback, 99 | img_callback=img_callback, 100 | quantize_denoised=quantize_x0, 101 | mask=mask, x0=x0, 102 | ddim_use_original_steps=False, 103 | noise_dropout=noise_dropout, 104 | temperature=temperature, 105 | score_corrector=score_corrector, 106 | corrector_kwargs=corrector_kwargs, 107 | x_T=x_T, 108 | log_every_t=log_every_t, 109 | unconditional_guidance_scale=unconditional_guidance_scale, 110 | unconditional_conditioning=unconditional_conditioning, 111 | ) 112 | return samples, intermediates 113 | 114 | @torch.no_grad() 115 | def plms_sampling(self, cond, shape, 116 | x_T=None, ddim_use_original_steps=False, 117 | callback=None, timesteps=None, quantize_denoised=False, 118 | mask=None, x0=None, img_callback=None, log_every_t=100, 119 | temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, 120 | unconditional_guidance_scale=1., unconditional_conditioning=None,): 121 | device = self.model.betas.device 122 | b = shape[0] 123 | if x_T is None: 124 | img = torch.randn(shape, device=device) 125 | else: 126 | img = x_T 127 | 128 | if timesteps is None: 129 | timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps 130 | elif timesteps is not None and not ddim_use_original_steps: 131 | subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 132 | timesteps = self.ddim_timesteps[:subset_end] 133 | 134 | intermediates = {'x_inter': [img], 'pred_x0': [img]} 135 | time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) 136 | total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] 137 | print(f"Running PLMS Sampling with {total_steps} timesteps") 138 | 139 | iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) 140 | old_eps = [] 141 | 142 | for i, step in enumerate(iterator): 143 | index = total_steps - i - 1 144 | ts = torch.full((b,), step, device=device, dtype=torch.long) 145 | ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) 146 | 147 | if mask is not None: 148 | assert x0 is not None 149 | img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? 150 | img = img_orig * mask + (1. - mask) * img 151 | 152 | outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, 153 | quantize_denoised=quantize_denoised, temperature=temperature, 154 | noise_dropout=noise_dropout, score_corrector=score_corrector, 155 | corrector_kwargs=corrector_kwargs, 156 | unconditional_guidance_scale=unconditional_guidance_scale, 157 | unconditional_conditioning=unconditional_conditioning, 158 | old_eps=old_eps, t_next=ts_next) 159 | img, pred_x0, e_t = outs 160 | old_eps.append(e_t) 161 | if len(old_eps) >= 4: 162 | old_eps.pop(0) 163 | if callback: callback(i) 164 | if img_callback: img_callback(pred_x0, i) 165 | 166 | if index % log_every_t == 0 or index == total_steps - 1: 167 | intermediates['x_inter'].append(img) 168 | intermediates['pred_x0'].append(pred_x0) 169 | 170 | return img, intermediates 171 | 172 | @torch.no_grad() 173 | def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, 174 | temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, 175 | unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): 176 | b, *_, device = *x.shape, x.device 177 | 178 | def get_model_output(x, t): 179 | if unconditional_conditioning is None or unconditional_guidance_scale == 1.: 180 | e_t = self.model.apply_model(x, t, c) 181 | else: 182 | x_in = torch.cat([x] * 2) 183 | t_in = torch.cat([t] * 2) 184 | c_in = torch.cat([unconditional_conditioning, c]) 185 | e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) 186 | e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) 187 | 188 | if score_corrector is not None: 189 | assert self.model.parameterization == "eps" 190 | e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) 191 | 192 | return e_t 193 | 194 | alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas 195 | alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev 196 | sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas 197 | sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas 198 | 199 | def get_x_prev_and_pred_x0(e_t, index): 200 | # select parameters corresponding to the currently considered timestep 201 | a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) 202 | a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) 203 | sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) 204 | sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) 205 | 206 | # current prediction for x_0 207 | pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() 208 | if quantize_denoised: 209 | pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) 210 | # direction pointing to x_t 211 | dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t 212 | noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature 213 | if noise_dropout > 0.: 214 | noise = torch.nn.functional.dropout(noise, p=noise_dropout) 215 | x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise 216 | return x_prev, pred_x0 217 | 218 | e_t = get_model_output(x, t) 219 | if len(old_eps) == 0: 220 | # Pseudo Improved Euler (2nd order) 221 | x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) 222 | e_t_next = get_model_output(x_prev, t_next) 223 | e_t_prime = (e_t + e_t_next) / 2 224 | elif len(old_eps) == 1: 225 | # 2nd order Pseudo Linear Multistep (Adams-Bashforth) 226 | e_t_prime = (3 * e_t - old_eps[-1]) / 2 227 | elif len(old_eps) == 2: 228 | # 3nd order Pseudo Linear Multistep (Adams-Bashforth) 229 | e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 230 | elif len(old_eps) >= 3: 231 | # 4nd order Pseudo Linear Multistep (Adams-Bashforth) 232 | e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 233 | 234 | x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) 235 | 236 | return x_prev, pred_x0, e_t 237 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/attention.py: -------------------------------------------------------------------------------- 1 | from inspect import isfunction 2 | import math 3 | import torch 4 | import torch.nn.functional as F 5 | from torch import nn, einsum 6 | from einops import rearrange, repeat 7 | 8 | from ldm.modules.diffusionmodules.util import checkpoint 9 | 10 | 11 | def exists(val): 12 | return val is not None 13 | 14 | 15 | def uniq(arr): 16 | return{el: True for el in arr}.keys() 17 | 18 | 19 | def default(val, d): 20 | if exists(val): 21 | return val 22 | return d() if isfunction(d) else d 23 | 24 | 25 | def max_neg_value(t): 26 | return -torch.finfo(t.dtype).max 27 | 28 | 29 | def init_(tensor): 30 | dim = tensor.shape[-1] 31 | std = 1 / math.sqrt(dim) 32 | tensor.uniform_(-std, std) 33 | return tensor 34 | 35 | 36 | # feedforward 37 | class GEGLU(nn.Module): 38 | def __init__(self, dim_in, dim_out): 39 | super().__init__() 40 | self.proj = nn.Linear(dim_in, dim_out * 2) 41 | 42 | def forward(self, x): 43 | x, gate = self.proj(x).chunk(2, dim=-1) 44 | return x * F.gelu(gate) 45 | 46 | 47 | class FeedForward(nn.Module): 48 | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): 49 | super().__init__() 50 | inner_dim = int(dim * mult) 51 | dim_out = default(dim_out, dim) 52 | project_in = nn.Sequential( 53 | nn.Linear(dim, inner_dim), 54 | nn.GELU() 55 | ) if not glu else GEGLU(dim, inner_dim) 56 | 57 | self.net = nn.Sequential( 58 | project_in, 59 | nn.Dropout(dropout), 60 | nn.Linear(inner_dim, dim_out) 61 | ) 62 | 63 | def forward(self, x): 64 | return self.net(x) 65 | 66 | 67 | def zero_module(module): 68 | """ 69 | Zero out the parameters of a module and return it. 70 | """ 71 | for p in module.parameters(): 72 | p.detach().zero_() 73 | return module 74 | 75 | 76 | def Normalize(in_channels): 77 | return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) 78 | 79 | 80 | class LinearAttention(nn.Module): 81 | def __init__(self, dim, heads=4, dim_head=32): 82 | super().__init__() 83 | self.heads = heads 84 | hidden_dim = dim_head * heads 85 | self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) 86 | self.to_out = nn.Conv2d(hidden_dim, dim, 1) 87 | 88 | def forward(self, x): 89 | b, c, h, w = x.shape 90 | qkv = self.to_qkv(x) 91 | q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) 92 | k = k.softmax(dim=-1) 93 | context = torch.einsum('bhdn,bhen->bhde', k, v) 94 | out = torch.einsum('bhde,bhdn->bhen', context, q) 95 | out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) 96 | return self.to_out(out) 97 | 98 | 99 | class SpatialSelfAttention(nn.Module): 100 | def __init__(self, in_channels): 101 | super().__init__() 102 | self.in_channels = in_channels 103 | 104 | self.norm = Normalize(in_channels) 105 | self.q = torch.nn.Conv2d(in_channels, 106 | in_channels, 107 | kernel_size=1, 108 | stride=1, 109 | padding=0) 110 | self.k = torch.nn.Conv2d(in_channels, 111 | in_channels, 112 | kernel_size=1, 113 | stride=1, 114 | padding=0) 115 | self.v = torch.nn.Conv2d(in_channels, 116 | in_channels, 117 | kernel_size=1, 118 | stride=1, 119 | padding=0) 120 | self.proj_out = torch.nn.Conv2d(in_channels, 121 | in_channels, 122 | kernel_size=1, 123 | stride=1, 124 | padding=0) 125 | 126 | def forward(self, x): 127 | h_ = x 128 | h_ = self.norm(h_) 129 | q = self.q(h_) 130 | k = self.k(h_) 131 | v = self.v(h_) 132 | 133 | # compute attention 134 | b,c,h,w = q.shape 135 | q = rearrange(q, 'b c h w -> b (h w) c') 136 | k = rearrange(k, 'b c h w -> b c (h w)') 137 | w_ = torch.einsum('bij,bjk->bik', q, k) 138 | 139 | w_ = w_ * (int(c)**(-0.5)) 140 | w_ = torch.nn.functional.softmax(w_, dim=2) 141 | 142 | # attend to values 143 | v = rearrange(v, 'b c h w -> b c (h w)') 144 | w_ = rearrange(w_, 'b i j -> b j i') 145 | h_ = torch.einsum('bij,bjk->bik', v, w_) 146 | h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) 147 | h_ = self.proj_out(h_) 148 | 149 | return x+h_ 150 | 151 | 152 | class CrossAttention(nn.Module): 153 | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): 154 | super().__init__() 155 | inner_dim = dim_head * heads 156 | context_dim = default(context_dim, query_dim) 157 | 158 | self.scale = dim_head ** -0.5 159 | self.heads = heads 160 | 161 | self.to_q = nn.Linear(query_dim, inner_dim, bias=False) 162 | self.to_k = nn.Linear(context_dim, inner_dim, bias=False) 163 | self.to_v = nn.Linear(context_dim, inner_dim, bias=False) 164 | 165 | self.to_out = nn.Sequential( 166 | nn.Linear(inner_dim, query_dim), 167 | nn.Dropout(dropout) 168 | ) 169 | 170 | def forward(self, x, context=None, mask=None): 171 | h = self.heads 172 | 173 | q = self.to_q(x) 174 | context = default(context, x) 175 | k = self.to_k(context) 176 | v = self.to_v(context) 177 | 178 | q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) 179 | 180 | sim = einsum('b i d, b j d -> b i j', q, k) * self.scale 181 | 182 | if exists(mask): 183 | mask = rearrange(mask, 'b ... -> b (...)') 184 | max_neg_value = -torch.finfo(sim.dtype).max 185 | mask = repeat(mask, 'b j -> (b h) () j', h=h) 186 | sim.masked_fill_(~mask, max_neg_value) 187 | 188 | # attention, what we cannot get enough of 189 | attn = sim.softmax(dim=-1) 190 | 191 | out = einsum('b i j, b j d -> b i d', attn, v) 192 | out = rearrange(out, '(b h) n d -> b n (h d)', h=h) 193 | return self.to_out(out) 194 | 195 | 196 | class BasicTransformerBlock(nn.Module): 197 | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True): 198 | super().__init__() 199 | self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention 200 | self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) 201 | self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, 202 | heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none 203 | self.norm1 = nn.LayerNorm(dim) 204 | self.norm2 = nn.LayerNorm(dim) 205 | self.norm3 = nn.LayerNorm(dim) 206 | self.checkpoint = checkpoint 207 | 208 | def forward(self, x, context=None): 209 | return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) 210 | 211 | def _forward(self, x, context=None): 212 | x = self.attn1(self.norm1(x)) + x 213 | x = self.attn2(self.norm2(x), context=context) + x 214 | x = self.ff(self.norm3(x)) + x 215 | return x 216 | 217 | 218 | class SpatialTransformer(nn.Module): 219 | """ 220 | Transformer block for image-like data. 221 | First, project the input (aka embedding) 222 | and reshape to b, t, d. 223 | Then apply standard transformer action. 224 | Finally, reshape to image 225 | """ 226 | def __init__(self, in_channels, n_heads, d_head, 227 | depth=1, dropout=0., context_dim=None): 228 | super().__init__() 229 | self.in_channels = in_channels 230 | inner_dim = n_heads * d_head 231 | self.norm = Normalize(in_channels) 232 | 233 | self.proj_in = nn.Conv2d(in_channels, 234 | inner_dim, 235 | kernel_size=1, 236 | stride=1, 237 | padding=0) 238 | 239 | self.transformer_blocks = nn.ModuleList( 240 | [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) 241 | for d in range(depth)] 242 | ) 243 | 244 | self.proj_out = zero_module(nn.Conv2d(inner_dim, 245 | in_channels, 246 | kernel_size=1, 247 | stride=1, 248 | padding=0)) 249 | 250 | def forward(self, x, context=None): 251 | # note: if no context is given, cross-attention defaults to self-attention 252 | b, c, h, w = x.shape 253 | x_in = x 254 | x = self.norm(x) 255 | x = self.proj_in(x) 256 | x = rearrange(x, 'b c h w -> b (h w) c') 257 | for block in self.transformer_blocks: 258 | x = block(x, context=context) 259 | x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w) 260 | x = self.proj_out(x) 261 | return x + x_in -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/diffusionmodules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/ldm/modules/diffusionmodules/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/diffusionmodules/util.py: -------------------------------------------------------------------------------- 1 | # adopted from 2 | # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py 3 | # and 4 | # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py 5 | # and 6 | # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py 7 | # 8 | # thanks! 9 | 10 | 11 | import os 12 | import math 13 | import torch 14 | import torch.nn as nn 15 | import numpy as np 16 | from einops import repeat 17 | 18 | from ldm.util import instantiate_from_config 19 | 20 | 21 | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): 22 | if schedule == "linear": 23 | betas = ( 24 | torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 25 | ) 26 | 27 | elif schedule == "cosine": 28 | timesteps = ( 29 | torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s 30 | ) 31 | alphas = timesteps / (1 + cosine_s) * np.pi / 2 32 | alphas = torch.cos(alphas).pow(2) 33 | alphas = alphas / alphas[0] 34 | betas = 1 - alphas[1:] / alphas[:-1] 35 | betas = np.clip(betas, a_min=0, a_max=0.999) 36 | 37 | elif schedule == "sqrt_linear": 38 | betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) 39 | elif schedule == "sqrt": 40 | betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 41 | else: 42 | raise ValueError(f"schedule '{schedule}' unknown.") 43 | return betas.numpy() 44 | 45 | 46 | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): 47 | if ddim_discr_method == 'uniform': 48 | c = num_ddpm_timesteps // num_ddim_timesteps 49 | ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) 50 | elif ddim_discr_method == 'quad': 51 | ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) 52 | else: 53 | raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') 54 | 55 | # assert ddim_timesteps.shape[0] == num_ddim_timesteps 56 | # add one to get the final alpha values right (the ones from first scale to data during sampling) 57 | steps_out = ddim_timesteps + 1 58 | if verbose: 59 | print(f'Selected timesteps for ddim sampler: {steps_out}') 60 | return steps_out 61 | 62 | 63 | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): 64 | # select alphas for computing the variance schedule 65 | alphas = alphacums[ddim_timesteps] 66 | alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) 67 | 68 | # according the the formula provided in https://arxiv.org/abs/2010.02502 69 | sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) 70 | if verbose: 71 | print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') 72 | print(f'For the chosen value of eta, which is {eta}, ' 73 | f'this results in the following sigma_t schedule for ddim sampler {sigmas}') 74 | return sigmas, alphas, alphas_prev 75 | 76 | 77 | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): 78 | """ 79 | Create a beta schedule that discretizes the given alpha_t_bar function, 80 | which defines the cumulative product of (1-beta) over time from t = [0,1]. 81 | :param num_diffusion_timesteps: the number of betas to produce. 82 | :param alpha_bar: a lambda that takes an argument t from 0 to 1 and 83 | produces the cumulative product of (1-beta) up to that 84 | part of the diffusion process. 85 | :param max_beta: the maximum beta to use; use values lower than 1 to 86 | prevent singularities. 87 | """ 88 | betas = [] 89 | for i in range(num_diffusion_timesteps): 90 | t1 = i / num_diffusion_timesteps 91 | t2 = (i + 1) / num_diffusion_timesteps 92 | betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) 93 | return np.array(betas) 94 | 95 | 96 | def extract_into_tensor(a, t, x_shape): 97 | b, *_ = t.shape 98 | out = a.gather(-1, t) 99 | return out.reshape(b, *((1,) * (len(x_shape) - 1))) 100 | 101 | 102 | def checkpoint(func, inputs, params, flag): 103 | """ 104 | Evaluate a function without caching intermediate activations, allowing for 105 | reduced memory at the expense of extra compute in the backward pass. 106 | :param func: the function to evaluate. 107 | :param inputs: the argument sequence to pass to `func`. 108 | :param params: a sequence of parameters `func` depends on but does not 109 | explicitly take as arguments. 110 | :param flag: if False, disable gradient checkpointing. 111 | """ 112 | if flag: 113 | args = tuple(inputs) + tuple(params) 114 | return CheckpointFunction.apply(func, len(inputs), *args) 115 | else: 116 | return func(*inputs) 117 | 118 | 119 | class CheckpointFunction(torch.autograd.Function): 120 | @staticmethod 121 | def forward(ctx, run_function, length, *args): 122 | ctx.run_function = run_function 123 | ctx.input_tensors = list(args[:length]) 124 | ctx.input_params = list(args[length:]) 125 | 126 | with torch.no_grad(): 127 | output_tensors = ctx.run_function(*ctx.input_tensors) 128 | return output_tensors 129 | 130 | @staticmethod 131 | def backward(ctx, *output_grads): 132 | ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] 133 | with torch.enable_grad(): 134 | # Fixes a bug where the first op in run_function modifies the 135 | # Tensor storage in place, which is not allowed for detach()'d 136 | # Tensors. 137 | shallow_copies = [x.view_as(x) for x in ctx.input_tensors] 138 | output_tensors = ctx.run_function(*shallow_copies) 139 | input_grads = torch.autograd.grad( 140 | output_tensors, 141 | ctx.input_tensors + ctx.input_params, 142 | output_grads, 143 | allow_unused=True, 144 | ) 145 | del ctx.input_tensors 146 | del ctx.input_params 147 | del output_tensors 148 | return (None, None) + input_grads 149 | 150 | 151 | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): 152 | """ 153 | Create sinusoidal timestep embeddings. 154 | :param timesteps: a 1-D Tensor of N indices, one per batch element. 155 | These may be fractional. 156 | :param dim: the dimension of the output. 157 | :param max_period: controls the minimum frequency of the embeddings. 158 | :return: an [N x dim] Tensor of positional embeddings. 159 | """ 160 | if not repeat_only: 161 | half = dim // 2 162 | freqs = torch.exp( 163 | -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half 164 | ).to(device=timesteps.device) 165 | args = timesteps[:, None].float() * freqs[None] 166 | embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) 167 | if dim % 2: 168 | embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) 169 | else: 170 | embedding = repeat(timesteps, 'b -> b d', d=dim) 171 | return embedding 172 | 173 | 174 | def zero_module(module): 175 | """ 176 | Zero out the parameters of a module and return it. 177 | """ 178 | for p in module.parameters(): 179 | p.detach().zero_() 180 | return module 181 | 182 | 183 | def scale_module(module, scale): 184 | """ 185 | Scale the parameters of a module and return it. 186 | """ 187 | for p in module.parameters(): 188 | p.detach().mul_(scale) 189 | return module 190 | 191 | 192 | def mean_flat(tensor): 193 | """ 194 | Take the mean over all non-batch dimensions. 195 | """ 196 | return tensor.mean(dim=list(range(1, len(tensor.shape)))) 197 | 198 | 199 | def normalization(channels): 200 | """ 201 | Make a standard normalization layer. 202 | :param channels: number of input channels. 203 | :return: an nn.Module for normalization. 204 | """ 205 | return GroupNorm32(32, channels) 206 | 207 | 208 | # PyTorch 1.7 has SiLU, but we support PyTorch 1.5. 209 | class SiLU(nn.Module): 210 | def forward(self, x): 211 | return x * torch.sigmoid(x) 212 | 213 | 214 | class GroupNorm32(nn.GroupNorm): 215 | def forward(self, x): 216 | return super().forward(x.float()).type(x.dtype) 217 | 218 | def conv_nd(dims, *args, **kwargs): 219 | """ 220 | Create a 1D, 2D, or 3D convolution module. 221 | """ 222 | if dims == 1: 223 | return nn.Conv1d(*args, **kwargs) 224 | elif dims == 2: 225 | return nn.Conv2d(*args, **kwargs) 226 | elif dims == 3: 227 | return nn.Conv3d(*args, **kwargs) 228 | raise ValueError(f"unsupported dimensions: {dims}") 229 | 230 | 231 | def linear(*args, **kwargs): 232 | """ 233 | Create a linear module. 234 | """ 235 | return nn.Linear(*args, **kwargs) 236 | 237 | 238 | def avg_pool_nd(dims, *args, **kwargs): 239 | """ 240 | Create a 1D, 2D, or 3D average pooling module. 241 | """ 242 | if dims == 1: 243 | return nn.AvgPool1d(*args, **kwargs) 244 | elif dims == 2: 245 | return nn.AvgPool2d(*args, **kwargs) 246 | elif dims == 3: 247 | return nn.AvgPool3d(*args, **kwargs) 248 | raise ValueError(f"unsupported dimensions: {dims}") 249 | 250 | 251 | class HybridConditioner(nn.Module): 252 | 253 | def __init__(self, c_concat_config, c_crossattn_config): 254 | super().__init__() 255 | self.concat_conditioner = instantiate_from_config(c_concat_config) 256 | self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) 257 | 258 | def forward(self, c_concat, c_crossattn): 259 | c_concat = self.concat_conditioner(c_concat) 260 | c_crossattn = self.crossattn_conditioner(c_crossattn) 261 | return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} 262 | 263 | 264 | def noise_like(shape, device, repeat=False): 265 | repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) 266 | noise = lambda: torch.randn(shape, device=device) 267 | return repeat_noise() if repeat else noise() -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/distributions/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/ldm/modules/distributions/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/distributions/distributions.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | 5 | class AbstractDistribution: 6 | def sample(self): 7 | raise NotImplementedError() 8 | 9 | def mode(self): 10 | raise NotImplementedError() 11 | 12 | 13 | class DiracDistribution(AbstractDistribution): 14 | def __init__(self, value): 15 | self.value = value 16 | 17 | def sample(self): 18 | return self.value 19 | 20 | def mode(self): 21 | return self.value 22 | 23 | 24 | class DiagonalGaussianDistribution(object): 25 | def __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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/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 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/encoders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/ldm/modules/encoders/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/encoders/modules.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from functools import partial 4 | import clip 5 | from einops import rearrange, repeat 6 | import kornia 7 | from transformers import CLIPTokenizer, CLIPTextModel 8 | 9 | 10 | from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test 11 | 12 | 13 | class AbstractEncoder(nn.Module): 14 | def __init__(self): 15 | super().__init__() 16 | 17 | def encode(self, *args, **kwargs): 18 | raise NotImplementedError 19 | 20 | 21 | 22 | class ClassEmbedder(nn.Module): 23 | def __init__(self, embed_dim, n_classes=1000, key='class'): 24 | super().__init__() 25 | self.key = key 26 | self.embedding = nn.Embedding(n_classes, embed_dim) 27 | 28 | def forward(self, batch, key=None): 29 | if key is None: 30 | key = self.key 31 | # this is for use in crossattn 32 | c = batch[key][:, None] 33 | c = self.embedding(c) 34 | return c 35 | 36 | 37 | class TransformerEmbedder(AbstractEncoder): 38 | """Some transformer encoder layers""" 39 | def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): 40 | super().__init__() 41 | self.device = device 42 | self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, 43 | attn_layers=Encoder(dim=n_embed, depth=n_layer)) 44 | 45 | def forward(self, tokens): 46 | tokens = tokens.to(self.device) # meh 47 | z = self.transformer(tokens, return_embeddings=True) 48 | return z 49 | 50 | def encode(self, x): 51 | return self(x) 52 | 53 | 54 | class BERTTokenizer(AbstractEncoder): 55 | """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" 56 | def __init__(self, device="cuda", vq_interface=True, max_length=77): 57 | super().__init__() 58 | from transformers import BertTokenizerFast # TODO: add to reuquirements 59 | self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") 60 | self.device = device 61 | self.vq_interface = vq_interface 62 | self.max_length = max_length 63 | 64 | def forward(self, text): 65 | batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, 66 | return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 67 | tokens = batch_encoding["input_ids"].to(self.device) 68 | return tokens 69 | 70 | @torch.no_grad() 71 | def encode(self, text): 72 | tokens = self(text) 73 | if not self.vq_interface: 74 | return tokens 75 | return None, None, [None, None, tokens] 76 | 77 | def decode(self, text): 78 | return text 79 | 80 | 81 | class BERTEmbedder(AbstractEncoder): 82 | """Uses the BERT tokenizr model and add some transformer encoder layers""" 83 | def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, 84 | device="cuda",use_tokenizer=True, embedding_dropout=0.0): 85 | super().__init__() 86 | self.use_tknz_fn = use_tokenizer 87 | if self.use_tknz_fn: 88 | self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) 89 | self.device = device 90 | self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, 91 | attn_layers=Encoder(dim=n_embed, depth=n_layer), 92 | emb_dropout=embedding_dropout) 93 | 94 | def forward(self, text): 95 | if self.use_tknz_fn: 96 | tokens = self.tknz_fn(text)#.to(self.device) 97 | else: 98 | tokens = text 99 | z = self.transformer(tokens, return_embeddings=True) 100 | return z 101 | 102 | def encode(self, text): 103 | # output of length 77 104 | return self(text) 105 | 106 | 107 | class SpatialRescaler(nn.Module): 108 | def __init__(self, 109 | n_stages=1, 110 | method='bilinear', 111 | multiplier=0.5, 112 | in_channels=3, 113 | out_channels=None, 114 | bias=False): 115 | super().__init__() 116 | self.n_stages = n_stages 117 | assert self.n_stages >= 0 118 | assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] 119 | self.multiplier = multiplier 120 | self.interpolator = partial(torch.nn.functional.interpolate, mode=method) 121 | self.remap_output = out_channels is not None 122 | if self.remap_output: 123 | print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') 124 | self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) 125 | 126 | def forward(self,x): 127 | for stage in range(self.n_stages): 128 | x = self.interpolator(x, scale_factor=self.multiplier) 129 | 130 | 131 | if self.remap_output: 132 | x = self.channel_mapper(x) 133 | return x 134 | 135 | def encode(self, x): 136 | return self(x) 137 | 138 | 139 | class FrozenCLIPEmbedder(AbstractEncoder): 140 | """Uses the CLIP transformer encoder for text (from Hugging Face)""" 141 | def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): 142 | super().__init__() 143 | self.tokenizer = CLIPTokenizer.from_pretrained(version) 144 | self.transformer = CLIPTextModel.from_pretrained(version) 145 | self.device = device 146 | self.max_length = max_length 147 | self.freeze() 148 | 149 | def freeze(self): 150 | self.transformer = self.transformer.eval() 151 | for param in self.parameters(): 152 | param.requires_grad = False 153 | 154 | def forward(self, text): 155 | batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, 156 | return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 157 | tokens = batch_encoding["input_ids"].to(self.device) 158 | outputs = self.transformer(input_ids=tokens) 159 | 160 | z = outputs.last_hidden_state 161 | return z 162 | 163 | def encode(self, text): 164 | return self(text) 165 | 166 | 167 | class FrozenCLIPTextEmbedder(nn.Module): 168 | """ 169 | Uses the CLIP transformer encoder for text. 170 | """ 171 | def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True): 172 | super().__init__() 173 | self.model, _ = clip.load(version, jit=False, device="cpu") 174 | self.device = device 175 | self.max_length = max_length 176 | self.n_repeat = n_repeat 177 | self.normalize = normalize 178 | 179 | def freeze(self): 180 | self.model = self.model.eval() 181 | for param in self.parameters(): 182 | param.requires_grad = False 183 | 184 | def forward(self, text): 185 | tokens = clip.tokenize(text).to(self.device) 186 | z = self.model.encode_text(tokens) 187 | if self.normalize: 188 | z = z / torch.linalg.norm(z, dim=1, keepdim=True) 189 | return z 190 | 191 | def encode(self, text): 192 | z = self(text) 193 | if z.ndim==2: 194 | z = z[:, None, :] 195 | z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat) 196 | return z 197 | 198 | 199 | class FrozenClipImageEmbedder(nn.Module): 200 | """ 201 | Uses the CLIP image encoder. 202 | """ 203 | def __init__( 204 | self, 205 | model, 206 | jit=False, 207 | device='cuda' if torch.cuda.is_available() else 'cpu', 208 | antialias=False, 209 | ): 210 | super().__init__() 211 | self.model, _ = clip.load(name=model, device=device, jit=jit) 212 | 213 | self.antialias = antialias 214 | 215 | self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) 216 | self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) 217 | 218 | def preprocess(self, x): 219 | # normalize to [0,1] 220 | x = kornia.geometry.resize(x, (224, 224), 221 | interpolation='bicubic',align_corners=True, 222 | antialias=self.antialias) 223 | x = (x + 1.) / 2. 224 | # renormalize according to clip 225 | x = kornia.enhance.normalize(x, self.mean, self.std) 226 | return x 227 | 228 | def forward(self, x): 229 | # x is assumed to be in range [-1,1] 230 | return self.model.encode_image(self.preprocess(x)) 231 | 232 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/image_degradation/__init__.py: -------------------------------------------------------------------------------- 1 | from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr 2 | from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light 3 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/losses/__init__.py: -------------------------------------------------------------------------------- 1 | from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/losses/contperceptual.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no? 5 | 6 | 7 | class LPIPSWithDiscriminator(nn.Module): 8 | def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, 9 | disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, 10 | perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, 11 | disc_loss="hinge"): 12 | 13 | super().__init__() 14 | assert disc_loss in ["hinge", "vanilla"] 15 | self.kl_weight = kl_weight 16 | self.pixel_weight = pixelloss_weight 17 | self.perceptual_loss = LPIPS().eval() 18 | self.perceptual_weight = perceptual_weight 19 | # output log variance 20 | self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) 21 | 22 | self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, 23 | n_layers=disc_num_layers, 24 | use_actnorm=use_actnorm 25 | ).apply(weights_init) 26 | self.discriminator_iter_start = disc_start 27 | self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss 28 | self.disc_factor = disc_factor 29 | self.discriminator_weight = disc_weight 30 | self.disc_conditional = disc_conditional 31 | 32 | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): 33 | if last_layer is not None: 34 | nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] 35 | g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] 36 | else: 37 | nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] 38 | g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] 39 | 40 | d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) 41 | d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() 42 | d_weight = d_weight * self.discriminator_weight 43 | return d_weight 44 | 45 | def forward(self, inputs, reconstructions, posteriors, optimizer_idx, 46 | global_step, last_layer=None, cond=None, split="train", 47 | weights=None): 48 | rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) 49 | if self.perceptual_weight > 0: 50 | p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) 51 | rec_loss = rec_loss + self.perceptual_weight * p_loss 52 | 53 | nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar 54 | weighted_nll_loss = nll_loss 55 | if weights is not None: 56 | weighted_nll_loss = weights*nll_loss 57 | weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] 58 | nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] 59 | kl_loss = posteriors.kl() 60 | kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] 61 | 62 | # now the GAN part 63 | if optimizer_idx == 0: 64 | # generator update 65 | if cond is None: 66 | assert not self.disc_conditional 67 | logits_fake = self.discriminator(reconstructions.contiguous()) 68 | else: 69 | assert self.disc_conditional 70 | logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) 71 | g_loss = -torch.mean(logits_fake) 72 | 73 | if self.disc_factor > 0.0: 74 | try: 75 | d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) 76 | except RuntimeError: 77 | assert not self.training 78 | d_weight = torch.tensor(0.0) 79 | else: 80 | d_weight = torch.tensor(0.0) 81 | 82 | disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) 83 | loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss 84 | 85 | log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), 86 | "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), 87 | "{}/rec_loss".format(split): rec_loss.detach().mean(), 88 | "{}/d_weight".format(split): d_weight.detach(), 89 | "{}/disc_factor".format(split): torch.tensor(disc_factor), 90 | "{}/g_loss".format(split): g_loss.detach().mean(), 91 | } 92 | return loss, log 93 | 94 | if optimizer_idx == 1: 95 | # second pass for discriminator update 96 | if cond is None: 97 | logits_real = self.discriminator(inputs.contiguous().detach()) 98 | logits_fake = self.discriminator(reconstructions.contiguous().detach()) 99 | else: 100 | logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) 101 | logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) 102 | 103 | disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) 104 | d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) 105 | 106 | log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), 107 | "{}/logits_real".format(split): logits_real.detach().mean(), 108 | "{}/logits_fake".format(split): logits_fake.detach().mean() 109 | } 110 | return d_loss, log 111 | 112 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/modules/losses/vqperceptual.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torch.nn.functional as F 4 | from einops import repeat 5 | 6 | from taming.modules.discriminator.model import NLayerDiscriminator, weights_init 7 | from taming.modules.losses.lpips import LPIPS 8 | from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss 9 | 10 | 11 | def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): 12 | assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0] 13 | loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3]) 14 | loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3]) 15 | loss_real = (weights * loss_real).sum() / weights.sum() 16 | loss_fake = (weights * loss_fake).sum() / weights.sum() 17 | d_loss = 0.5 * (loss_real + loss_fake) 18 | return d_loss 19 | 20 | def adopt_weight(weight, global_step, threshold=0, value=0.): 21 | if global_step < threshold: 22 | weight = value 23 | return weight 24 | 25 | 26 | def measure_perplexity(predicted_indices, n_embed): 27 | # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py 28 | # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally 29 | encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed) 30 | avg_probs = encodings.mean(0) 31 | perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp() 32 | cluster_use = torch.sum(avg_probs > 0) 33 | return perplexity, cluster_use 34 | 35 | def l1(x, y): 36 | return torch.abs(x-y) 37 | 38 | 39 | def l2(x, y): 40 | return torch.pow((x-y), 2) 41 | 42 | 43 | class VQLPIPSWithDiscriminator(nn.Module): 44 | def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, 45 | disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, 46 | perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, 47 | disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips", 48 | pixel_loss="l1"): 49 | super().__init__() 50 | assert disc_loss in ["hinge", "vanilla"] 51 | assert perceptual_loss in ["lpips", "clips", "dists"] 52 | assert pixel_loss in ["l1", "l2"] 53 | self.codebook_weight = codebook_weight 54 | self.pixel_weight = pixelloss_weight 55 | if perceptual_loss == "lpips": 56 | print(f"{self.__class__.__name__}: Running with LPIPS.") 57 | self.perceptual_loss = LPIPS().eval() 58 | else: 59 | raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<") 60 | self.perceptual_weight = perceptual_weight 61 | 62 | if pixel_loss == "l1": 63 | self.pixel_loss = l1 64 | else: 65 | self.pixel_loss = l2 66 | 67 | self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, 68 | n_layers=disc_num_layers, 69 | use_actnorm=use_actnorm, 70 | ndf=disc_ndf 71 | ).apply(weights_init) 72 | self.discriminator_iter_start = disc_start 73 | if disc_loss == "hinge": 74 | self.disc_loss = hinge_d_loss 75 | elif disc_loss == "vanilla": 76 | self.disc_loss = vanilla_d_loss 77 | else: 78 | raise ValueError(f"Unknown GAN loss '{disc_loss}'.") 79 | print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") 80 | self.disc_factor = disc_factor 81 | self.discriminator_weight = disc_weight 82 | self.disc_conditional = disc_conditional 83 | self.n_classes = n_classes 84 | 85 | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): 86 | if last_layer is not None: 87 | nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] 88 | g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] 89 | else: 90 | nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] 91 | g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] 92 | 93 | d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) 94 | d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() 95 | d_weight = d_weight * self.discriminator_weight 96 | return d_weight 97 | 98 | def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, 99 | global_step, last_layer=None, cond=None, split="train", predicted_indices=None): 100 | if not exists(codebook_loss): 101 | codebook_loss = torch.tensor([0.]).to(inputs.device) 102 | #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) 103 | rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous()) 104 | if self.perceptual_weight > 0: 105 | p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) 106 | rec_loss = rec_loss + self.perceptual_weight * p_loss 107 | else: 108 | p_loss = torch.tensor([0.0]) 109 | 110 | nll_loss = rec_loss 111 | #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] 112 | nll_loss = torch.mean(nll_loss) 113 | 114 | # now the GAN part 115 | if optimizer_idx == 0: 116 | # generator update 117 | if cond is None: 118 | assert not self.disc_conditional 119 | logits_fake = self.discriminator(reconstructions.contiguous()) 120 | else: 121 | assert self.disc_conditional 122 | logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) 123 | g_loss = -torch.mean(logits_fake) 124 | 125 | try: 126 | d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) 127 | except RuntimeError: 128 | assert not self.training 129 | d_weight = torch.tensor(0.0) 130 | 131 | disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) 132 | loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() 133 | 134 | log = {"{}/total_loss".format(split): loss.clone().detach().mean(), 135 | "{}/quant_loss".format(split): codebook_loss.detach().mean(), 136 | "{}/nll_loss".format(split): nll_loss.detach().mean(), 137 | "{}/rec_loss".format(split): rec_loss.detach().mean(), 138 | "{}/p_loss".format(split): p_loss.detach().mean(), 139 | "{}/d_weight".format(split): d_weight.detach(), 140 | "{}/disc_factor".format(split): torch.tensor(disc_factor), 141 | "{}/g_loss".format(split): g_loss.detach().mean(), 142 | } 143 | if predicted_indices is not None: 144 | assert self.n_classes is not None 145 | with torch.no_grad(): 146 | perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) 147 | log[f"{split}/perplexity"] = perplexity 148 | log[f"{split}/cluster_usage"] = cluster_usage 149 | return loss, log 150 | 151 | if optimizer_idx == 1: 152 | # second pass for discriminator update 153 | if cond is None: 154 | logits_real = self.discriminator(inputs.contiguous().detach()) 155 | logits_fake = self.discriminator(reconstructions.contiguous().detach()) 156 | else: 157 | logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) 158 | logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) 159 | 160 | disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) 161 | d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) 162 | 163 | log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), 164 | "{}/logits_real".format(split): logits_real.detach().mean(), 165 | "{}/logits_fake".format(split): logits_fake.detach().mean() 166 | } 167 | return d_loss, log 168 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/ldm/util.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | 3 | import torch 4 | import numpy as np 5 | from collections import abc 6 | from einops import rearrange 7 | from functools import partial 8 | 9 | import multiprocessing as mp 10 | from threading import Thread 11 | from queue import Queue 12 | 13 | from inspect import isfunction 14 | from PIL import Image, ImageDraw, ImageFont 15 | 16 | 17 | def log_txt_as_img(wh, xc, size=10): 18 | # wh a tuple of (width, height) 19 | # xc a list of captions to plot 20 | b = len(xc) 21 | txts = list() 22 | for bi in range(b): 23 | txt = Image.new("RGB", wh, color="white") 24 | draw = ImageDraw.Draw(txt) 25 | font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) 26 | nc = int(40 * (wh[0] / 256)) 27 | lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) 28 | 29 | try: 30 | draw.text((0, 0), lines, fill="black", font=font) 31 | except UnicodeEncodeError: 32 | print("Cant encode string for logging. Skipping.") 33 | 34 | txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 35 | txts.append(txt) 36 | txts = np.stack(txts) 37 | txts = torch.tensor(txts) 38 | return txts 39 | 40 | 41 | def ismap(x): 42 | if not isinstance(x, torch.Tensor): 43 | return False 44 | return (len(x.shape) == 4) and (x.shape[1] > 3) 45 | 46 | 47 | def isimage(x): 48 | if not isinstance(x, torch.Tensor): 49 | return False 50 | return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) 51 | 52 | 53 | def exists(x): 54 | return x is not None 55 | 56 | 57 | def default(val, d): 58 | if exists(val): 59 | return val 60 | return d() if isfunction(d) else d 61 | 62 | 63 | def mean_flat(tensor): 64 | """ 65 | https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 66 | Take the mean over all non-batch dimensions. 67 | """ 68 | return tensor.mean(dim=list(range(1, len(tensor.shape)))) 69 | 70 | 71 | def count_params(model, verbose=False): 72 | total_params = sum(p.numel() for p in model.parameters()) 73 | if verbose: 74 | print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") 75 | return total_params 76 | 77 | 78 | def instantiate_from_config(config): 79 | if not "target" in config: 80 | if config == '__is_first_stage__': 81 | return None 82 | elif config == "__is_unconditional__": 83 | return None 84 | raise KeyError("Expected key `target` to instantiate.") 85 | return get_obj_from_str(config["target"])(**config.get("params", dict())) 86 | 87 | 88 | def get_obj_from_str(string, reload=False): 89 | module, cls = string.rsplit(".", 1) 90 | if reload: 91 | module_imp = importlib.import_module(module) 92 | importlib.reload(module_imp) 93 | return getattr(importlib.import_module(module, package=None), cls) 94 | 95 | 96 | def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): 97 | # create dummy dataset instance 98 | 99 | # run prefetching 100 | if idx_to_fn: 101 | res = func(data, worker_id=idx) 102 | else: 103 | res = func(data) 104 | Q.put([idx, res]) 105 | Q.put("Done") 106 | 107 | 108 | def parallel_data_prefetch( 109 | func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False 110 | ): 111 | # if target_data_type not in ["ndarray", "list"]: 112 | # raise ValueError( 113 | # "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray." 114 | # ) 115 | if isinstance(data, np.ndarray) and target_data_type == "list": 116 | raise ValueError("list expected but function got ndarray.") 117 | elif isinstance(data, abc.Iterable): 118 | if isinstance(data, dict): 119 | print( 120 | f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' 121 | ) 122 | data = list(data.values()) 123 | if target_data_type == "ndarray": 124 | data = np.asarray(data) 125 | else: 126 | data = list(data) 127 | else: 128 | raise TypeError( 129 | f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." 130 | ) 131 | 132 | if cpu_intensive: 133 | Q = mp.Queue(1000) 134 | proc = mp.Process 135 | else: 136 | Q = Queue(1000) 137 | proc = Thread 138 | # spawn processes 139 | if target_data_type == "ndarray": 140 | arguments = [ 141 | [func, Q, part, i, use_worker_id] 142 | for i, part in enumerate(np.array_split(data, n_proc)) 143 | ] 144 | else: 145 | step = ( 146 | int(len(data) / n_proc + 1) 147 | if len(data) % n_proc != 0 148 | else int(len(data) / n_proc) 149 | ) 150 | arguments = [ 151 | [func, Q, part, i, use_worker_id] 152 | for i, part in enumerate( 153 | [data[i: i + step] for i in range(0, len(data), step)] 154 | ) 155 | ] 156 | processes = [] 157 | for i in range(n_proc): 158 | p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) 159 | processes += [p] 160 | 161 | # start processes 162 | print(f"Start prefetching...") 163 | import time 164 | 165 | start = time.time() 166 | gather_res = [[] for _ in range(n_proc)] 167 | try: 168 | for p in processes: 169 | p.start() 170 | 171 | k = 0 172 | while k < n_proc: 173 | # get result 174 | res = Q.get() 175 | if res == "Done": 176 | k += 1 177 | else: 178 | gather_res[res[0]] = res[1] 179 | 180 | except Exception as e: 181 | print("Exception: ", e) 182 | for p in processes: 183 | p.terminate() 184 | 185 | raise e 186 | finally: 187 | for p in processes: 188 | p.join() 189 | print(f"Prefetching complete. [{time.time() - start} sec.]") 190 | 191 | if target_data_type == 'ndarray': 192 | if not isinstance(gather_res[0], np.ndarray): 193 | return np.concatenate([np.asarray(r) for r in gather_res], axis=0) 194 | 195 | # order outputs 196 | return np.concatenate(gather_res, axis=0) 197 | elif target_data_type == 'list': 198 | out = [] 199 | for r in gather_res: 200 | out.extend(r) 201 | return out 202 | else: 203 | return gather_res 204 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/msbd/BLDSampler.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module is based on the original latent diffusion models code base, specifically on `ldm/models/diffusion/ddim.py`. 3 | It mainly just implements the sampler for blended latent diffusion by changing the sampling in `blended_ddim_sampling` to incorporate the mask and 4 | replace the unmasked area by a noisy version of the original image after each timestep. 5 | It also implements SDEdit and repaint by starting at an intermediate timestep and repeating diffusion steps multiple times. 6 | """ 7 | import logging 8 | 9 | import numpy as np 10 | import torch 11 | from einops import rearrange 12 | from ldm.models.diffusion.ddim import DDIMSampler 13 | from ldm.models.diffusion.ddpm import LatentDiffusion 14 | from PIL import Image 15 | from tqdm.auto import tqdm 16 | 17 | from msbd.msbd_utils import get_dilated_mask, get_repaint_schedule 18 | 19 | logger = logging.getLogger() 20 | 21 | 22 | class BlendedDiffusionSampler(DDIMSampler): 23 | def __init__(self, model: LatentDiffusion, schedule="linear"): # this just adds the typehint for easier coding 24 | super().__init__(model, schedule) 25 | 26 | @torch.no_grad() 27 | def blended_diffusion_sampling( 28 | self, 29 | source_img, # LATENT, not pixels 30 | mask, 31 | num_ddim_steps, 32 | batch_size, 33 | shape, 34 | conditioning=None, 35 | callback=None, 36 | img_callback=None, 37 | quantize_x0=False, 38 | eta=0., 39 | x0=None, 40 | temperature=1., 41 | noise_dropout=0., 42 | score_corrector=None, 43 | corrector_kwargs=None, 44 | verbose=True, 45 | x_T=None, 46 | log_every_t=100, 47 | unconditional_guidance_scale=1., 48 | unconditional_conditioning=None, 49 | dilate_mask=False, 50 | repaint_steps=4, 51 | repaint_jump=1, 52 | start_timestep=1.0, 53 | # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... 54 | ): 55 | if conditioning is not None: 56 | if isinstance(conditioning, dict): 57 | cbs = conditioning[list(conditioning.keys())[0]].shape[0] 58 | if cbs != batch_size: 59 | print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") 60 | else: 61 | if conditioning.shape[0] != batch_size: 62 | print( 63 | f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") 64 | 65 | self.make_schedule(ddim_num_steps=num_ddim_steps, ddim_eta=eta, verbose=verbose) 66 | # sampling 67 | C, H, W = shape 68 | size = (batch_size, C, H, W) 69 | print(f'Data shape for DDIM sampling is {size}, eta {eta}') 70 | 71 | samples, intermediates = self.blended_ddim_sampling( 72 | conditioning, size, 73 | source_img=source_img, 74 | callback=callback, 75 | img_callback=img_callback, 76 | quantize_denoised=quantize_x0, 77 | mask=mask, x0=x0, 78 | ddim_use_original_steps=False, 79 | noise_dropout=noise_dropout, 80 | temperature=temperature, 81 | score_corrector=score_corrector, 82 | corrector_kwargs=corrector_kwargs, 83 | x_T=x_T, 84 | log_every_t=log_every_t, 85 | unconditional_guidance_scale=unconditional_guidance_scale, 86 | unconditional_conditioning=unconditional_conditioning, 87 | dilate_mask=dilate_mask, 88 | repaint_steps=repaint_steps, 89 | repaint_jump=repaint_jump, 90 | start_timestep=start_timestep 91 | ) 92 | return samples, intermediates 93 | 94 | @torch.no_grad() 95 | def decode_and_save_latent(self, latent: torch.Tensor, fp: str): 96 | """ 97 | Function for debugging to decode and save any input image latent tensor. 98 | """ 99 | if len(latent.shape) == 3: 100 | latent = latent[None] 101 | if latent.device != self.model.device: 102 | latent = latent.to(self.model.device) 103 | decoded = self.model.decode_first_stage(latent)[0] 104 | 105 | decoded = torch.clamp((decoded+1.0)/2.0, min=0.0, max=1.0).cpu().numpy() 106 | decoded = 255. * rearrange(decoded, 'c h w -> h w c') 107 | Image.fromarray(decoded.astype(np.uint8)).save(fp) 108 | logging.info(f'Saved sample to {fp}') 109 | 110 | def q_sample_start_end(self, x_t_start: torch.Tensor, t_end: int, t_start: int = 0, noise: torch.Tensor = None) -> torch.Tensor: 111 | """ 112 | Samples the forward diffusion process from any starting point in the diffusion process, i.e. x_t_end ~ q(x_t_end|x_t_start). 113 | 114 | Samples from the forward process as defined in DDPM, but should be fine to work with DDIM sampling. 115 | """ 116 | if noise is None: 117 | noise = torch.randn_like(x_t_start) 118 | 119 | alpha_cumprod_start_end = self.model.alphas_cumprod[t_end] / \ 120 | self.model.alphas_cumprod[t_start] 121 | return torch.sqrt(alpha_cumprod_start_end) * x_t_start + torch.sqrt(1 - alpha_cumprod_start_end) * noise 122 | 123 | @torch.no_grad() 124 | def blended_ddim_sampling( 125 | self, cond, shape, source_img, 126 | x_T=None, ddim_use_original_steps=False, 127 | callback=None, timesteps=None, quantize_denoised=False, 128 | mask=None, x0=None, img_callback=None, log_every_t=100, 129 | temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, 130 | unconditional_guidance_scale=1., unconditional_conditioning=None, 131 | dilate_mask=False, repaint_steps=0, repaint_jump=1, start_timestep=1.0): 132 | """ 133 | `source_img` is the image that we wil edit, such that the content of `mask` matches `cond`. 134 | This is done by starting at gaussian noise and then doing the following iteratively: 135 | 136 | 1. do one denoising step on img_t 137 | 2. edit_img = denoise(img_t, cond, t) 138 | 3. noised_source_img = forward_process(source_img, t) 139 | 4. img_{t-1} = mask * edit_img + (1 - mask) * noised_source_img 140 | 5. goto 2 till t=0 141 | 142 | `start_timestep` is the ratio of where to start in the diffusion process as in SDEdit. 143 | 1.0 means do the full diffusion from gaussian noise 144 | """ 145 | device = self.model.betas.device 146 | b = shape[0] 147 | 148 | mask = mask.to(device) 149 | 150 | if timesteps is None: 151 | timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps 152 | elif timesteps is not None and not ddim_use_original_steps: 153 | subset_end = int( 154 | min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 155 | timesteps = self.ddim_timesteps[:subset_end] 156 | 157 | if x_T is not None: 158 | edit_img = x_T 159 | logger.warn('starting diffusion from preset x_T, ignoring sdedit') 160 | else: 161 | if 0.0 < start_timestep < 1.0 and int(len(self.ddim_timesteps) * start_timestep): # second condition ensures at least one timestep 162 | #SDEdit 163 | if source_img.shape[0] == 1: 164 | # if a single image is given this function will make b different samples 165 | source_img = source_img.repeat(b, 1, 1, 1) 166 | 167 | timesteps = timesteps[:int(len(timesteps) * start_timestep)] 168 | logger.info(f'Using SDEdit ratio {start_timestep}, starting at {timesteps[-1]}/1000') 169 | edit_img = self.q_sample_start_end(source_img, t_end=timesteps[-1], t_start=0) 170 | elif start_timestep == 1.0: 171 | edit_img = torch.randn(shape, device=device) 172 | elif start_timestep == 0.0 or int(len(self.ddim_timesteps) * start_timestep): 173 | logger.warn('Start timestep is 0.0, or rounded down to zero, returning original image') 174 | return source_img, [source_img] 175 | 176 | 177 | intermediates = {'x_inter': [edit_img], 'pred_x0': [edit_img]} 178 | time_range = reversed( 179 | range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps) 180 | 181 | total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] 182 | print( 183 | f"Running blended DDIM Sampling with {total_steps} timesteps with {repaint_steps} repaint steps and {repaint_jump} repaint jumps") 184 | 185 | if repaint_steps: 186 | # overwrites timerange with a zigzag line 187 | index_schedule = get_repaint_schedule( 188 | np.arange(len(time_range) - 1, -1, -1), repaint_steps, repaint_jump) 189 | time_range = get_repaint_schedule(time_range, repaint_steps, repaint_jump) 190 | logger.info(f'repaint schedule: {time_range}', ) 191 | else: 192 | index_schedule = np.arange(len(time_range) - 1, -1, -1) 193 | 194 | for i, step in enumerate(tqdm(time_range, desc='DDIM Sampler', total=len(time_range))): 195 | index = index_schedule[i] 196 | ts = torch.full((b,), step, device=device, dtype=torch.long) 197 | 198 | # ts is the timestep in the original 1000 step DDPM model 199 | # index is the timestep for the DDIM sampler using only as many timesteps as specified 200 | edit_img, pred_x0 = self.p_sample_ddim(edit_img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, 201 | quantize_denoised=quantize_denoised, temperature=temperature, 202 | noise_dropout=noise_dropout, score_corrector=score_corrector, 203 | corrector_kwargs=corrector_kwargs, 204 | unconditional_guidance_scale=unconditional_guidance_scale, 205 | unconditional_conditioning=unconditional_conditioning) 206 | 207 | source_img_noised = self.model.q_sample(source_img, ts) 208 | # maybe this should not be ts, but the next step? 209 | 210 | mask_expanded = self.get_mask(mask, dilate_mask, shape, total_steps, i) 211 | edit_img = (1 - mask_expanded) * source_img_noised + mask_expanded * edit_img 212 | 213 | if repaint_steps and i > 0 and i < len(time_range) - 1: 214 | # if doing repaint, jump back if the next element in the schedule is noisier than the current one 215 | # this is the noise level/timestep AFTER the most recent denoising step 216 | t_current = timesteps[index_schedule[i] - 1] 217 | # this is the noise level/timestep we need for the next denoising step 218 | t_next_step = timesteps[index_schedule[i + 1]] 219 | if t_next_step > t_current: 220 | edit_img = self.q_sample_start_end( 221 | edit_img, t_end=t_next_step, t_start=t_current) 222 | 223 | if callback: 224 | callback(i) 225 | if img_callback: 226 | img_callback(pred_x0, i) 227 | 228 | # logger.warn('doing a lot of logging of intermediates!') 229 | if index % log_every_t == 0 or index == total_steps - 1: 230 | intermediates['x_inter'].append(edit_img) 231 | intermediates['pred_x0'].append(pred_x0) 232 | 233 | return edit_img, intermediates 234 | 235 | def get_mask(self, mask, dilate_mask, shape, total_steps, current_step): 236 | """Implements mask dilation, extending the mask at the start of the diffusion process but slowly shrinking it to the original size. 237 | Not really used for our multi-stage blended diffusion. 238 | """ 239 | if dilate_mask: 240 | sampling_progress_ratio = current_step / total_steps 241 | if sampling_progress_ratio < 0.25: 242 | kernel_size = 7 243 | elif sampling_progress_ratio < 0.5: 244 | kernel_size = 5 245 | elif sampling_progress_ratio < 0.75: 246 | kernel_size = 3 247 | else: 248 | kernel_size = 1 249 | 250 | mask_dilated = get_dilated_mask(mask, kernel_size) 251 | mask_expanded = mask_dilated.expand(*shape) 252 | else: 253 | mask_expanded = mask.expand(*shape) 254 | return mask_expanded 255 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/msbd/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/multi-scale-blended-diffusion/msbd/__init__.py -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/msbd/msbd_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | Some utils for blended latent diffusion. 3 | 4 | I just wanted to keep them separate from the utils.py in ldm and such. 5 | 6 | @author Johannes Ackermann 7 | """ 8 | 9 | import logging 10 | import os 11 | from typing import Tuple 12 | 13 | import numpy as np 14 | import torch 15 | from einops import rearrange 16 | from PIL import Image 17 | from torchvision.utils import make_grid 18 | import kornia 19 | 20 | logger = logging.getLogger() 21 | 22 | 23 | def get_dilated_mask( 24 | mask: torch.Tensor, 25 | dilation_kernel_size: int, 26 | ) -> torch.Tensor: 27 | """ 28 | Dilates (extends) the mask by convultion with a kernel of size `dilation_kernel_size`x`dilation_kernel_size`. 29 | If applied to the downsampled mask (as is done in the paper), this should be done with sizes, 7->5->3->1 for 30 | equal parts of the diffusion process. (of course, dilation with size 1 leaves the mask unchanged) 31 | """ 32 | kernel = torch.ones([dilation_kernel_size, dilation_kernel_size], 33 | dtype=mask.dtype).to(mask.device) 34 | if len(mask.shape) == 2: 35 | mask = mask[None][None] 36 | dilated_mask = torch.nn.functional.conv2d(mask, kernel[None][None], padding='same') 37 | dilated_mask[dilated_mask >= 1.0] = 1.0 38 | return dilated_mask 39 | 40 | 41 | def get_repaint_schedule(original_schedule: int, repaint_steps: int, repaint_jump: int): 42 | """ 43 | Generates a schedule as in repaint, that takes a given denoising schedule and repeats 44 | it in such a way that each set of `repaint_jump` denoising steps is repeated `repaint_steps` times. 45 | example if we had 10 steps in total, repaint_steps = 1 and repaint_jump = 2: 46 | 10-9-8-10-9-8-7-6-5-7-6-5-4-3-5-4-3-5-4-3... 47 | 48 | This code became pretty ugly, but essentially it's just zigzagging through `original_schedule`. 49 | """ 50 | # i thought this could be done with repeat(reshape()), but not quite :'( 51 | 52 | if repaint_jump == 0: 53 | schedule = np.repeat(original_schedule, repaint_steps + 1) 54 | else: 55 | n_step_orig = len(original_schedule) 56 | schedule = [original_schedule[:repaint_jump - 1]] 57 | for idx_jump_level in range(1, n_step_orig // repaint_jump): 58 | for idx_rep in range(repaint_steps): 59 | if idx_rep == repaint_steps - 1: 60 | schedule.append( 61 | original_schedule[idx_jump_level * (repaint_jump) - 1:(idx_jump_level + 1) * (repaint_jump) - 1]) 62 | else: 63 | schedule.append( 64 | original_schedule[idx_jump_level * (repaint_jump) - 1:(idx_jump_level + 1) * (repaint_jump)]) 65 | schedule.append(original_schedule[repaint_jump * (n_step_orig // repaint_jump) - 1:]) 66 | schedule = np.concatenate(schedule) 67 | return schedule 68 | 69 | 70 | def tensor_to_pil(tensor: torch.Tensor): 71 | # if not tensor.min() < 0.0: 72 | # logger.warn('Image should be scaled to [-1.0, 1.0]') 73 | tensor = tensor.cpu().numpy()[0] 74 | tensor = (tensor + 1.0) / 2.0 75 | tensor = 255. * rearrange(tensor, 'c h w -> h w c') 76 | return Image.fromarray(tensor.astype(np.uint8)) 77 | 78 | 79 | def get_alpha_masks(crops_x: Tuple[int], crops_y: Tuple[int], target_imagesize: Tuple[int], overlap: int): 80 | """ 81 | Generates the alpha masks later used for blending. 82 | """ 83 | alpha_mask_full = np.zeros([len(crops_x)] + list(target_imagesize)) 84 | for idx, (crop_x, crop_y) in enumerate(zip(crops_x, crops_y)): 85 | alpha_mask_full[idx, crop_x[0]:crop_x[1], crop_y[0]:crop_y[1]] = 1.0 86 | alpha_func = 1.0 / (1 + np.exp(-np.linspace(-5.0, 5.0, overlap // 2))) # sigmoid blending 87 | alpha_mask_x = np.tile(alpha_func, [crop_y[1] - crop_y[0], 1]).T 88 | alpha_mask_y = np.tile(alpha_func, [crop_x[1] - crop_x[0], 1]) 89 | if not crop_x[0] == 0: 90 | alpha_mask_full[idx, crop_x[0]:crop_x[0] + overlap // 2, crop_y[0]:crop_y[1] 91 | ] = alpha_mask_full[idx, crop_x[0]:crop_x[0] + overlap // 2, crop_y[0]:crop_y[1]] * alpha_mask_x 92 | if not crop_y[0] == 0: 93 | alpha_mask_full[idx, crop_x[0]:crop_x[1], crop_y[0]:crop_y[0] + overlap // 94 | 2] = alpha_mask_full[idx, crop_x[0]:crop_x[1], crop_y[0]:crop_y[0] + overlap // 2] * alpha_mask_y 95 | if not crop_x[1] == target_imagesize[0]: 96 | alpha_mask_full[idx, crop_x[1] - overlap // 2:crop_x[1], crop_y[0]:crop_y[1]] = alpha_mask_full[idx, 97 | crop_x[1] - overlap // 2:crop_x[1], crop_y[0]:crop_y[1]] * alpha_mask_x[::-1, :] 98 | if not crop_y[1] == target_imagesize[1]: 99 | alpha_mask_full[idx, crop_x[0]:crop_x[1], crop_y[1] - overlap // 2:crop_y[1]] = alpha_mask_full[idx, 100 | crop_x[0]:crop_x[1], crop_y[1]-overlap // 2:crop_y[1]] * alpha_mask_y[:, ::-1] 101 | 102 | alpha_mask_seg = [] 103 | for idx, (crop_x, crop_y) in enumerate(zip(crops_x, crops_y)): 104 | alpha_mask_seg.append(alpha_mask_full[idx, crop_x[0]:crop_x[1], crop_y[0]:crop_y[1]]) 105 | 106 | return alpha_mask_seg 107 | 108 | 109 | def get_result_grid(source_img, all_samples, mask) -> Image: 110 | """ 111 | Saves a grid of images visualizing the original image, the mask and multiple samples 112 | """ 113 | mask = mask.cpu() 114 | h = all_samples[0].shape[2] 115 | w = all_samples[0].shape[3] 116 | source_img_mask_vis = (0.8 * source_img.clone().cpu() + 1.0) / 2.0 117 | source_img_mask_vis[0, 1] += mask[0, 0] * 0.2 118 | source_img_mask_vis = source_img_mask_vis.clamp(-1.0, 1.0) 119 | source_img = torch.nn.functional.interpolate(source_img, size=[h, w]) 120 | source_img_mask_vis = torch.nn.functional.interpolate(source_img_mask_vis, size=[h, w]) 121 | # additionally, save as grid 122 | vis_rows = [] 123 | for sample_row in all_samples: 124 | vis_rows.append(torch.cat([(source_img.cpu() + 1.0) / 2.0, 125 | source_img_mask_vis, all_samples[0].cpu()], 0)) 126 | 127 | grid = torch.stack(vis_rows, 0) 128 | 129 | grid = rearrange(grid, 'n b c h w -> (n b) c h w') 130 | grid = make_grid(grid, nrow=(len(all_samples[0]) + 2)) 131 | 132 | # to image 133 | grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() 134 | 135 | pil_result = Image.fromarray(grid.astype(np.uint8)) 136 | return pil_result 137 | 138 | 139 | def debug_save_img(image: torch.Tensor, name='debug'): 140 | assert len(image.shape) == 4 141 | # if not image.min() < 0.0: 142 | # logger.warn('Image should be scaled to [-1.0, 1.0]') 143 | image = torch.clamp((image[0] + 1.0) / 2.0, min=0.0, max=1.0) 144 | image = 255.0 * rearrange(image.cpu().numpy(), 'c h w -> h w c') 145 | Image.fromarray(image.astype(np.uint8)).save(f'{name}.png') # debugging 146 | logger.info(f'saved debug output to {name}.png') 147 | 148 | 149 | def is_notebook() -> bool: 150 | """ 151 | Based on 152 | https://stackoverflow.com/questions/15411967/how-can-i-check-if-code-is-executed-in-the-ipython-notebook 153 | """ 154 | try: 155 | from IPython import get_ipython 156 | shell = get_ipython().__class__.__name__ 157 | if shell == 'ZMQInteractiveShell': 158 | return True # Jupyter notebook or qtconsole 159 | elif shell == 'TerminalInteractiveShell': 160 | return False # Terminal running IPython 161 | else: 162 | return False # Other type (?) 163 | except NameError: 164 | return False # Probably standard Python interpreter 165 | 166 | 167 | def display_or_save(pil_img: Image, folder: str, name: str) -> None: 168 | """ 169 | If running in a jupyter notebook uses `display` to show the image, if not running in a jupyter notebook 170 | saves the image to the given folder with the given name. 171 | """ 172 | if is_notebook(): 173 | from IPython.display import display 174 | print(name) 175 | display(pil_img) 176 | else: 177 | if folder is not None: 178 | fp = os.path.join(folder, name + '.png') 179 | pil_img.save(fp) 180 | logger.info(f'saved image to {fp}') 181 | 182 | def sharpen(image: torch.Tensor, unsharpen_masking: bool = True, kernel_size: int = 11, sigma = 7.0) -> torch.Tensor: 183 | if not unsharpen_masking: 184 | raise NotImplementedError('Only unsharp masking is supported currently.') 185 | else: 186 | logger.info(f'sharpening with sigma {sigma}') 187 | return kornia.filters.unsharp_mask(image, (kernel_size,kernel_size), (sigma,sigma)).clamp(-1.0,1.0) 188 | 189 | -------------------------------------------------------------------------------- /multi-scale-blended-diffusion/multi_scale_blended_diffusion.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module can be used to batch process the files in a given input folder, along with a txt file containing the prompts and filenames. 3 | See inputs/inputs.txt for an example of the samples we use in our publication. 4 | 5 | For interactive use we recommend to use the notebook `InteractiveEditing.ipynb`. 6 | 7 | @author: Johannes Ackermann 8 | """ 9 | 10 | import argparse 11 | import sys 12 | import os 13 | import logging 14 | 15 | from PIL import Image 16 | 17 | sys.path.append(os.getcwd()) 18 | from msbd.MSBDGenerator import MSBDGenerator 19 | 20 | logger = logging.getLogger() 21 | logging.getLogger().setLevel(logging.INFO) 22 | 23 | 24 | def main(args): 25 | 26 | if args.input_list is not None and not args.input_list == 'None': 27 | prompts = [] 28 | image_fps = [] 29 | margin_mults = [] 30 | with open(args.input_list, 'r') as f: 31 | lines = f.readlines() 32 | print('Reading from ', args.input_list) 33 | for line in lines: 34 | line = line.strip() # remove newline 35 | if line.startswith('#'): 36 | print('skipping line ', line) 37 | continue 38 | prompts.append(line.split(';')[0]) 39 | margin_mults.append(float((line.split(';')[2]))) 40 | image_fp = os.path.join( 41 | os.path.dirname(args.input_list), 42 | line.split(';')[1].replace(' ', '') 43 | ) 44 | assert os.path.exists(image_fp), f'could not find image {image_fp}' 45 | mask_fp = os.path.splitext(image_fp)[0] + '_mask.png' 46 | assert os.path.exists(mask_fp), f'could not find mask {mask_fp}' 47 | image_fps.append(image_fp) 48 | print(image_fps[-1], prompts[-1], margin_mults[-1]) 49 | else: 50 | image_fps = ['inputs/marunouchi.png'] 51 | assert os.path.exists(image_fps[0]) 52 | prompts = ['Statue of Roman Emperor, Canon 5D Mark 3, 35mm, flickr'] 53 | margin_mults = [1.2] 54 | 55 | generator = MSBDGenerator( 56 | use_fp16=args.fp16, 57 | stable_diffusion=True, 58 | max_edgelen=args.max_edgelen, 59 | first_stage_batchsize=args.first_stage_batch 60 | ) 61 | 62 | for prompt, image_fp, margin_mult in zip(prompts, image_fps, margin_mults): 63 | result = generator.multi_scale_generation( 64 | pil_img = Image.open(image_fp).convert('RGB'), 65 | pil_mask = Image.open(os.path.splitext(image_fp)[0] + '_mask.png'), 66 | prompt=prompt, 67 | ddim_steps=50, 68 | decoder_optimization=args.decoder_optimization, 69 | clip_reranking=args.clip_reranking, 70 | margin=margin_mult, 71 | seed=args.seed, 72 | repaint_steps=args.repaint_steps, 73 | start_timestep=args.start_timestep, 74 | upscaling_start_step=args.upscale_startstep, 75 | upscaling_mode=args.upscaling_mode, 76 | straight_to_grid=args.straight_to_grid, 77 | grid_upscaling_start_step=args.grid_startstep, 78 | log_folder=args.outdir, 79 | lowpass_reference=args.lowpass_reference, 80 | blended_upscale=args.blended_upscale, 81 | conditional_upscale=args.conditional_upscale, 82 | grid_overlap=args.grid_overlap, 83 | first_stage_size=args.first_stage_size 84 | ) 85 | out_fp = os.path.splitext(image_fp)[0] + '_output.jpg' 86 | result.save(out_fp) 87 | print(f'Output saved to{out_fp}.') 88 | 89 | 90 | if __name__ == "__main__": 91 | parser = argparse.ArgumentParser() 92 | parser.add_argument("--input-list", type=str, nargs="?", 93 | default='inputs/inputs.txt', help="path to a list of prompts and file_paths ") 94 | def str2bool(v): 95 | # from https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse 96 | if isinstance(v, bool): 97 | return v 98 | if v.lower() in ('yes', 'true', 't', 'y', '1'): 99 | return True 100 | elif v.lower() in ('no', 'false', 'f', 'n', '0'): 101 | return False 102 | else: 103 | raise argparse.ArgumentTypeError('Boolean value expected.') 104 | 105 | 106 | parser.add_argument("--prompt", type=str, nargs="?", 107 | default='Oil painting of Mt. Fuji, by Paul Sandby', help="the prompt to render") 108 | parser.add_argument("--outdir", type=str, nargs="?", 109 | help="dir to write results to", default="outputs") 110 | parser.add_argument("--ddim_steps", type=int, default=50, 111 | help="number of ddim sampling steps",) 112 | 113 | # Added to original LDM 114 | parser.add_argument("--fp16", type=str2bool, default=True, 115 | help="run inference in mixed precision",) 116 | parser.add_argument("--use-stablediffusion", type=str2bool, default=True, 117 | help="load the stable diffusion model, if False uses the LDM `text2img-large` model instead",) 118 | 119 | # decoder optimization 120 | parser.add_argument("--decoder-optimization", type=str2bool, default=True, 121 | help="optimize the weights of the decoder for each image",) 122 | parser.add_argument("--decoderopt-it", type=int, default=100, 123 | help="iterations of decoder finetuning, ignored if not using decoder optimization",) 124 | 125 | parser.add_argument("--dilate-mask", type=str2bool, default=False, 126 | help="dilates the mask and shrinks it to original size over the diffusion timesteps, use for small or masks with fine details",) 127 | 128 | parser.add_argument("--seed", type=int, default=-1, 129 | help="seed for everything, set to -1 to seed randomly",) 130 | 131 | # repaint 132 | parser.add_argument("--repaint-steps", type=int, default=5, 133 | help="repetitions of each denoising step for repainting, set to 0 to disable repaint",) 134 | parser.add_argument("--repaint-jump", type=int, default=0, 135 | help="jumps size in the repaint steps, jump size in DDIM steps, not DDPM steps, default=0, i.e. repeats the current step `repaint-step` times,",) 136 | 137 | parser.add_argument("--start-timestep", type=float, default=1.0, 138 | help="SDEdit-like relative timestep to start the diffusion process from, i.e. 1.0 to start from pure noise, 0.5 = T/2",) 139 | parser.add_argument("--upscale-startstep", type=float, default=0.4, 140 | help="start step for upscaling stages except final gridlike upscaling stage",) 141 | parser.add_argument("--grid-startstep", type=float, default=0.25, 142 | help="start step for upscaling in the grid stage",) 143 | parser.add_argument("--clip-reranking", type=str2bool, default=True, 144 | help="re-rerank first-stage outputs by clip similarity.",) 145 | parser.add_argument("--upscaling-mode", type=str, default='esrgan', 146 | help="interpolation mode in the upscaling, 'esrgan', 'sharpen', 'bilinear', or 'bicubic' (or anything supported by torch functional interpolation)",) 147 | parser.add_argument("--straight-to-grid", type=str2bool, default=False, 148 | help="after the first stage immediately go to the grid stage without intermediate steps",) 149 | parser.add_argument("--lowpass-reference", type=str, default='matching', 150 | help=" 'matching', 'half', or 'no' .",) 151 | parser.add_argument("--conditional-upscale", type=str2bool, default=True, 152 | help="do the upscaling with text conditioning",) 153 | parser.add_argument("--blended-upscale", type=str2bool, default=True, 154 | help="do the upscaling with a reference image",) 155 | parser.add_argument("--grid-overlap", type=int, default=128, 156 | help="overlap between different grid regions in pixels. Must be multiple of 64.",) 157 | parser.add_argument("--max-edgelen", type=int, default=12 * 64, 158 | help="Maximum edge length of square images processable by the used GPU. Default value requires 25GB of VRAM.",) 159 | parser.add_argument("--first-stage-size", type=int, default=512, 160 | help="Resolution to be used in the first stage. Default is 512 for stable diffusion, largest possible on V100 is 960",) 161 | parser.add_argument("--first-stage-batch", type=int, default=5, 162 | help="Batch size for first stage. If clip-reranking is enabled, the image with the highest clip similarity is chosen, else the first one is used in subsequent stages",) 163 | 164 | arglist = parser.parse_args() 165 | main(arglist) 166 | -------------------------------------------------------------------------------- /overview.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pfnet-research/multi-stage-blended-diffusion/b933b3d395edcbe266dbb51abf8f45d3b4b66f81/overview.jpg --------------------------------------------------------------------------------