delete Kandinsky-3 folder

Kandinsky-3/.gitignore

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-#.idea/

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Kandinsky-3/README.md

@@ -1,230 +0,0 @@
-# Kandinsky-3: Text-to-image diffusion model
-
-![](assets/title.jpg)
-
-[Kandinsky 3.0 Post](https://habr.com/ru/companies/sberbank/articles/775590/) | [Kandinsky 3.1 Post](https://habr.com/ru/companies/sberbank/articles/805337/) | [Project Page](https://ai-forever.github.io/Kandinsky-3) | [Generate](https://fusionbrain.ai) | [Telegram-bot](https://t.me/kandinsky21_bot) | [Technical Report](https://arxiv.org/pdf/2312.03511.pdf) |  [HuggingFace](https://huggingface.co/kandinsky-community/kandinsky-3) |
-
-# Kandinsky 3.1:
-
-## Description:
-
-We present Kandinsky 3.1, the follow-up to the Kandinsky 3.0 model, a large-scale text-to-image generation model based on latent diffusion, continuing the series of text-to-image Kandinsky models and reflecting our progress to achieve higher quality and realism of image generation, which we have enhanced and enriched with a variety of useful features and modes to give users more opportunities to fully utilise the power of our new model.
-
-## Kandinsky Flash (Kandinsky 3.0 Refiner)
-
-<figure>
-  <img src="assets/butterly_effect.jpg">
-</figure>
-
-
-Diffusion models have problems with fast image generation. To address this problem, we trained a Kandinksy Flash model based on the [Adversarial Diffusion Distillation](https://arxiv.org/abs/2311.17042) approach with some modifications: we trained the model on latents, which reduced the memory overhead and removed distillation loss as it did not affect the training. Also, we applied Kandinsky Flash model to images generated from Kandinsky 3.0 to improve visual quality of generated images.
-
-### Architecture
-
-For training Kandinsky Flash we used the following architecture of discriminator. It is the half of Kandinsky 3.0 U-Net encoder with additional head predictions.
-
-<img src="assets/architecture.png">
-
-### How to use:
-Check our jupyter notebooks with examples in `./examples` folder
-
-```python
-from kandinsky3 import get_T2I_Flash_pipeline
-
-device_map = torch.device('cuda:0')
-dtype_map = {
-    'unet': torch.float32,
-    'text_encoder': torch.float16,
-    'movq': torch.float32,
-}
-
-t2i_pipe = get_T2I_Flash_pipeline(
-    device_map, dtype_map
-)
-
-res = t2i_pipe("A cute corgi lives in a house made out of sushi.")
-```
-### Kandinsky Inpainting
-
-Also, we released a newer version of inpainting model, which we additionally trained the model on the object detection dataset. This allowed to get more stable generation of objects. The new weights are available at [ai-forever/Kandinsky3.1](https://huggingface.co/ai-forever/Kandinsky3.1). Check the usage [example](https://github.com/ai-forever/Kandinsky-3?tab=readme-ov-file#2-inpainting).
-
-
-## Prompt beautification
-
-<figure>
-  <img src="assets/prompt_beautifcation.png">
-</figure>
-
-
-Prompt plays crucial role in text-to-image generation. So, in Kandinsky 3.1 we decided to use language model for making prompt better. We used Intel's [neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) with the following system prompt as the LLM:
-
-```
-### System: You are a prompt engineer. Your mission is to expand prompts written by user. You should provide the best prompt for text to image generation in English.
-### User:
-{prompt}
-### Assistant:
-{answer of the model}
-```
-
-## KandiSuperRes
-
-<figure>
-  <img src="assets/superres.png">
-</figure>
-
-To learn more about KandiSuperRes, please checkout: https://github.com/ai-forever/KandiSuperRes/
-
-## Kandinsky IP-Adapter & Kandinsky ControlNet
-
-<figure>
-  <img src="assets/ip-adapter.png">
-</figure>
-
-To allow using image as condition in Kandinsky model, we trained IP-Adapter and HED-based ControlNet model. For more details please check out: https://github.com/ai-forever/kandinsky3-diffusers
-
-# Kandinsky 3.0:
-
-## Description:
-
-Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, Kandinsky 3.0 incorporates more data and specifically related to Russian culture, which allows to generate pictures related to Russin culture. Furthermore, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.
-
-For more information: details of training, example of generations check out our [post](). The english version will be released in a couple of days.
-
-## Architecture details:
-
-
-![](assets/kandinsky.jpg)
-
-
-Architecture consists of three parts:
-
-+ Text encoder Flan-UL2 (encoder part) - 8.6B
-+ Latent Diffusion U-Net - 3B
-+ MoVQ encoder/decoder - 267M
-
-
-## Models
-
-We release our two models:
-
-+ [Base](): Base text-to-image diffusion model. This model was trained over 2M steps on 400 A100
-+ [Inpainting](): Inpainting version of the model. The model was initialized from final checkpoint of base model and trained 250k steps on 300 A100.
-
-## Installing
-
-To install repo first one need to create conda environment:
-
-```
-conda create -n kandinsky -y python=3.8;
-source activate kandinsky;
-pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html;
-pip install -r requirements.txt;
-```
-The exact dependencies is got using `pip freeze` and can be found in `exact_requirements.txt`
-
-## How to use:
-
-Check our jupyter notebooks with examples in `./examples` folder
-
-### 1. text2image
-
-```python
-import sys
-sys.path.append('..')
-
-import torch
-from kandinsky3 import get_T2I_pipeline
-
-device_map = torch.device('cuda:0')
-dtype_map = {
-    'unet': torch.float32,
-    'text_encoder': torch.float16,
-    'movq': torch.float32,
-}
-
-t2i_pipe = get_T2I_pipeline(
-    device_map, dtype_map,
-)
-res = t2i_pipe("A cute corgi lives in a house made out of sushi.")
-
-res[0]
-```
-
-### 2. inpainting
-
-```python
-from kandinsky3 import get_inpainting_pipeline
-
-device_map = torch.device('cuda:0')
-dtype_map = {
-    'unet': torch.float16,
-    'text_encoder': torch.float16,
-    'movq': torch.float32,
-}
-
-pipe = get_inpainting_pipeline(
-    device_map, dtype_map,
-)
-
-image = ... # PIL Image
-mask = ... # Numpy array (HxW). Set 1 where image should be masked
-image = inp_pipe( "A cute corgi lives in a house made out of sushi.", image, mask)
-```
-
-## Examples of generations
-
-<hr>
-
-<table class="center">
-<tr>
-  <td><img src="assets/photo_8.jpg" raw=true></td>
-  <td><img src="assets/photo_15.jpg"></td>
-  <td><img src="assets/photo_16.jpg"></td>
-  <td><img src="assets/photo_17.jpg"></td>
-</tr>
-<tr>
-  <td width=25% align="center">"A beautiful landscape outdoors scene in the crochet knitting art style, drawing in style by Alfons Mucha"</td>
-  <td width=25% align="center">"gorgeous phoenix, cosmic, darkness, epic, cinematic, moonlight, stars, high - definition, texture,Oscar-Claude Monet"</td>
-  <td width=25% align="center">"a yellow house at the edge of the danish fjord, in the style of eiko ojala, ingrid baars, ad posters, mountainous vistas, george ault, realistic details, dark white and dark gray, 4k"</td>
-  <td width=25% align="center">"dragon fruit head, upper body, realistic, illustration by Joshua Hoffine Norman Rockwell, scary, creepy, biohacking, futurism, Zaha Hadid style"</td>
-</tr>
-<tr>
-  <td><img src="assets/photo_2.jpg" raw=true></td>
-  <td><img src="assets/photo_19.jpg"></td>
-  <td><img src="assets/photo_13.jpg"></td>
-  <td><img src="assets/photo_14.jpg"></td>
-</tr>
-<tr>
-  <td width=25% align="center">"Amazing playful nice cute strawberry character, dynamic poze, surreal fantazy garden background, gorgeous masterpice, award winning photo, soft natural lighting, 3d, Blender, Octane render, tilt - shift, deep field, colorful, I can't believe how beautiful this is, colorful, cute and sweet baby - loved photo"</td>
-  <td width=25% align="center">"beautiful fairy-tale desert, in the sky a wave of sand merges with the milky way, stars, cosmism, digital art, 8k"</td>
-  <td width=25% align="center">"Car, mustang, movie, person, poster, car cover, person, in the style of alessandro gottardo, gold and cyan, gerald harvey jones, reflections, highly detailed illustrations, industrial urban scenes""</td>
-  <td width=25% align="center">"cloud in blue sky, a red lip, collage art, shuji terayama, dreamy objects, surreal, criterion collection, showa era, intricate details, mirror"</td>
-</tr>
-
-</table>
-
-<hr>
-
-## Authors
-
-+ Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse)
-+ Anastasia Maltseva [Github](https://github.com/NastyaMittseva)
-+ Andrei Filatov [Github](https://github.com/anvilarth), 
-+ Igor Pavlov: [Github](https://github.com/boomb0om)
-+ Julia Agafonova 
-+ Arseniy Shakhmatov: [Github](https://github.com/cene555), [Blog](https://t.me/gradientdip)
-+ Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey), [Blog](https://t.me/complete_ai)
-+ Denis Dimitrov: [Github](https://github.com/denndimitrov), [Blog](https://t.me/dendi_math_ai)
-
-## Citation
-```
-@misc{arkhipkin2023kandinsky,
-      title={Kandinsky 3.0 Technical Report}, 
-      author={Vladimir Arkhipkin and Andrei Filatov and Viacheslav Vasilev and Anastasia Maltseva and Said Azizov and Igor Pavlov and Julia Agafonova and Andrey Kuznetsov and Denis Dimitrov},
-      year={2023},
-      eprint={2312.03511},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
-

Kandinsky-3/exact_requirements.txt

@@ -1,372 +0,0 @@
-absl-py==1.0.0
-accelerate==0.20.3
-adal==1.2.7
-addict==2.4.0
-aioboto3==11.0.1
-aiobotocore==2.4.2
-aiofiles==23.2.1
-aiohttp==3.8.1
-aiohttp-cors==0.7.0
-aioitertools==0.11.0
-aioredis==1.3.1
-aiosignal==1.2.0
-albumentations==1.3.1
-alembic==1.4.1
-alt-profanity-check==1.0.1
-altair==5.0.1
-antlr4-python3-runtime==4.9.3
-anyio==3.5.0
-apex==0.1
-appdirs==1.4.4
-argon2-cffi==21.3.0
-argon2-cffi-bindings==21.2.0
-asgiref==3.4.1
-astunparse==1.6.3
-async-timeout==4.0.2
-asyncpool==1.0
-asynctest==0.13.0
-attrs==21.4.0
-audioread==2.1.9
-autopage==0.4.0
-avro==1.11.0
-awscli==1.22.38
-azure-common==1.1.27
-azure-storage-blob==2.1.0
-azure-storage-common==2.1.0
-Babel==2.9.1
-backcall==0.2.0
-basicsr==1.4.2
-beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1631087867185/work
-bezier==2021.2.12
-bitarray==2.8.3
-bitmath==1.3.3.1
-bleach==4.1.0
-blessings==1.7
-blis==0.7.11
-bokeh==2.4.2
-boto3==1.24.59
-botocore==1.27.59
-braceexpand==0.1.7
-brotlipy @ file:///home/conda/feedstock_root/build_artifacts/brotlipy_1636012184244/work
-cached-property==1.5.2
-cachetools==4.2.4
-catalogue==2.0.10
-certifi==2021.10.8
-cffi==1.15.0
-chardet @ file:///home/conda/feedstock_root/build_artifacts/chardet_1635814832679/work
-charset-normalizer==2.0.10
-click==8.0.3
-cliff==3.10.0
-clip @ git+https://github.com/openai/CLIP.git@a1d071733d7111c9c014f024669f959182114e33
-cloudevents==1.2.0
-cloudpathlib==0.16.0
-cloudpickle==2.0.0
-cmaes==0.8.2
-cmake==3.27.7
-cmd2==2.3.3
-colorama==0.4.3
-colorful==0.5.4
-colorlog==6.6.0
-conda==4.11.0
-conda-build==3.21.7
-conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1636021712360/work
-confection==0.1.3
-configparser==5.2.0
-cryptography @ file:///tmp/build/80754af9/cryptography_1639414570729/work
-cycler==0.11.0
-cymem==2.0.8
-Cython==0.29.26
-dask==2022.1.0
-databricks-cli==0.16.2
-datasets==2.13.2
-DAWG-Python==0.7.2
-debugpy==1.5.1
-decorator==5.1.1
-deep-translator==1.11.4
-defusedxml==0.7.1
-Deprecated==1.2.13
-deprecation==2.1.0
-diffusers==0.21.4
-dill==0.3.6
-distributed==2022.1.0
-docker==5.0.3
-docker-pycreds==0.4.0
-docopt==0.6.2
-docutils==0.15.2
-einops==0.6.1
-entrypoints==0.3
-fairscale==0.4.6
-fairseq==0.12.2
-fastapi==0.72.0
-fastBPE==0.1.0
-fasttext==0.9.2
-fasttext-langdetect==1.0.5
-ffmpy==0.3.1
-filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1641470428964/work
-Flask==2.0.2
-fonttools==4.28.5
-fpie==0.2.4
-frozenlist==1.3.0
-fsspec==2023.1.0
-ftfy==6.1.1
-future==0.18.2
-gitdb==4.0.9
-GitPython==3.1.26
-glob2==0.7
-google-api-core==2.4.0
-google-auth==1.35.0
-google-auth-oauthlib==0.4.6
-google-cloud-core==2.2.2
-google-cloud-language==2.3.1
-google-cloud-storage==2.0.0
-google-crc32c==1.3.0
-google-resumable-media==2.1.0
-googleapis-common-protos==1.54.0
-gorilla==0.4.0
-gpustat==0.6.0
-GPUtil==1.4.0
-gradio==3.34.0
-gradio_client==0.2.6
-grpcio==1.43.0
-grpcio-status==1.43.0
-gunicorn==20.1.0
-h11==0.14.0
-h5py==3.6.0
-HeapDict==1.0.1
-hiredis==2.0.0
-horovod==0.28.1
-httpcore==0.17.3
-httpx==0.24.1
-huggingface-hub==0.16.4
-hydra-core==1.3.2
-idna==3.3
-image-reward @ file:///home/jovyan/afilatov/Diffusion/imagen/notebooks/image_assessment/ImageReward
-imageio==2.31.2
-importlib-metadata==4.10.1
-importlib-resources==5.4.0
-inflect==5.3.0
-intervaltree==3.1.0
-ipykernel==6.7.0
-ipymarkup==0.9.0
-ipyplot==1.1.1
-ipython==7.31.1
-ipython-genutils==0.2.0
-ipywidgets==7.6.5
-itsdangerous==2.0.1
-jedi==0.18.1
-Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1636510082894/work
-jmespath==0.10.0
-joblib==1.1.0
-json5==0.9.6
-jsonschema==4.4.0
-jupyter-archive==3.2.1
-jupyter-client==7.1.2
-jupyter-core==4.9.1
-jupyter-server==1.13.4
-jupyter-server-proxy==1.3.2
-jupyter-tensorboard @ file:///tmp/mlspace/packages/jupyter_tensorboard-0.2.2a0-py2.py3-none-any.whl
-jupyterlab==3.3.0a2
-jupyterlab-nvdashboard==0.6.0
-jupyterlab-pygments==0.1.2
-jupyterlab-server==2.10.3
-jupyterlab-tensorboard @ git+https://github.com/rhangelxs/jupyterlab_tensorboard.git@8dc7b1d5f24ece0e76e61b4dbbf36c58b84cbddd
-jupyterlab-widgets==1.0.2
-kfserving==0.6.1
-kiwisolver==1.3.2
-kubernetes==21.7.0
-langcodes==3.3.0
-langid==1.1.6
-libarchive-c @ file:///home/conda/feedstock_root/build_artifacts/python-libarchive-c_1643045751069/work
-libmambapy @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1643117251182/work/libmambapy
-librosa==0.8.1
-linkify-it-py==2.0.2
-llvmlite==0.38.0
-lmdb==1.4.1
-locket==0.2.1
-Mako==1.1.6
-mamba @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1643117251182/work/mamba
-Markdown==3.3.6
-markdown-it-py==2.2.0
-MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1635833550185/work
-matplotlib==3.5.1
-matplotlib-inline==0.1.3
-mdit-py-plugins==0.3.3
-mdurl==0.1.2
-minio==6.0.2
-mistune==0.8.4
-mlflow @ file:///tmp/mlspace/packages/mlflow-1.7.2-py3-none-any.whl
-mmcv-full==1.4.3
-modin==0.12.1
-mpi4py==3.1.3
-msgpack==1.0.3
-multidict==5.2.0
-multiprocess==0.70.14
-murmurhash==1.0.10
-natasha==1.6.0
-navec==0.10.0
-nbclassic==0.3.5
-nbclient==0.5.10
-nbconvert==6.4.1
-nbformat==5.1.3
-nest-asyncio==1.5.4
-networkx==2.6.3
-nltk==3.6.7
-notebook @ file:///tmp/mlspace/packages/notebook-6.1.4-py3-none-any.whl
-npm==0.1.1
-numba==0.55.0
-numpy==1.21.5
-nvidia-ml-py3==7.352.0
-oauthlib==3.1.1
-omegaconf==2.3.0
-opencensus==0.8.0
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-opencv-python==4.5.5.62
-opencv-python-headless==4.8.1.78
-optional-django==0.1.0
-optuna==2.10.0
-orjson==3.9.7
-packaging==21.3
-pandas==1.3.5
-pandocfilters==1.5.0
-parso==0.8.3
-partd==1.2.0
-pbr==5.8.0
-pexpect==4.8.0
-pickleshare==0.7.5
-Pillow==9.0.0
-pkginfo @ file:///home/conda/feedstock_root/build_artifacts/pkginfo_1638813452194/work
-pooch==1.5.2
-portalocker==2.3.2
-preshed==3.0.9
-prettytable==3.0.0
-prometheus-client==0.13.0
-prometheus-flask-exporter==0.18.7
-prompt-toolkit==3.0.26
-proto-plus==1.19.8
-protobuf==3.19.3
-psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1640887121529/work
-ptyprocess==0.7.0
-py-spy==0.3.11
-pyarrow==12.0.1
-pyasn1==0.4.8
-pyasn1-modules==0.2.8
-pybind11==2.11.1
-pycosat @ file:///home/conda/feedstock_root/build_artifacts/pycosat_1636020357254/work
-pycparser==2.21
-pydantic==1.9.0
-pyDeprecate==0.3.2
-pydub==0.25.1
-pyee==8.2.2
-Pygments==2.16.1
-PyJWT==2.3.0
-pymorphy2==0.9.1
-pymorphy2-dicts-ru==2.4.417127.4579844
-pynvml==11.4.1
-pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1633192417276/work
-pyparsing==3.0.7
-pyperclip==1.8.2
-pyppeteer==0.2.6
-pyrsistent==0.18.1
-PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1635862409558/work
-python-dateutil==2.8.2
-python-editor==1.0.4
-python-multipart==0.0.6
-python-speech-features==0.6
-pytorch-lightning==1.7.5
-pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1633452062248/work
-PyWavelets==1.3.0
-PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1636139801027/work
-pyzmq==22.3.0
-qudida==0.0.4
-querystring-parser==1.2.4
-ray==1.6.0
-razdel==0.5.0
-redis==4.1.1
-regex==2022.1.18
-requests==2.27.1
-requests-oauthlib==1.3.0
-resampy==0.2.2
-rich==13.6.0
-rsa==4.8
-ruamel-yaml-conda @ file:///home/conda/feedstock_root/build_artifacts/ruamel_yaml_1636009153751/work
-s3fs==2023.1.0
-s3transfer==0.6.2
-sacrebleu==2.0.0
-sacremoses==0.0.53
-safetensors==0.4.0
-scikit-image==0.19.3
-scikit-learn==1.0.1
-scipy==1.7.3
-semantic-version==2.10.0
-Send2Trash==1.8.0
-sentence-transformers==2.2.2
-sentencepiece==0.1.96
-sentry-sdk==1.34.0
-setproctitle==1.3.3
-shortuuid==1.0.11
-simpervisor==0.4
-simplejson==3.17.6
-six==1.16.0
-slovnet==0.6.0
-smart-open==6.4.0
-smmap==5.0.0
-sniffio==1.2.0
-sortedcontainers==2.4.0
-SoundFile==0.10.3.post1
-soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1638550740809/work
-sox==1.4.1
-spacy==3.7.2
-spacy-legacy==3.0.12
-spacy-loggers==1.0.5
-SQLAlchemy==1.3.13
-sqlparse==0.4.2
-srsly==2.4.8
-starlette==0.17.1
-stevedore==3.5.0
-table-logger==0.3.6
-tabulate==0.8.9
-taichi==1.6.0
-tblib==1.7.0
-tensorboard==2.11.2
-tensorboard-data-server==0.6.1
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-termcolor==2.3.0
-terminado==0.13.1
-testpath==0.5.0
-thinc==8.2.1
-threadpoolctl==3.0.0
-tifffile==2021.11.2
-timm==0.9.7
-tokenizers==0.13.3
-toolz==0.11.2
-torch==1.10.1+cu111
-torchaudio==0.10.1+rocm4.1
-torchmetrics==0.11.4
-torchvision==0.11.2+cu111
-tornado==6.1
-tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1632160078689/work
-traitlets==5.1.1
-transformers==4.30.2
-typer==0.4.0
-typing_extensions==4.0.1
-uc-micro-py==1.0.2
-urllib3==1.26.18
-uvicorn==0.17.0
-wandb==0.16.0
-wasabi==1.1.2
-wcwidth==0.2.5
-weasel==0.3.4
-webdataset==0.2.74
-webencodings==0.5.1
-websocket-client==1.2.3
-websockets==11.0.3
-Werkzeug==2.0.2
-widgetsnbextension==3.5.2
-wrapt==1.13.3
-xgboost==1.5.2
-xxhash==3.4.1
-yapf==0.32.0
-yargy==0.16.0
-yarl==1.7.2
-zict==2.0.0
-zipp==3.7.0

Kandinsky-3/kandinsky3/__init__.py

@@ -1,267 +0,0 @@
-import os
-from typing import Optional, Union
-
-import torch
-from huggingface_hub import hf_hub_download, snapshot_download
-
-from kandinsky3.model.unet import UNet
-from kandinsky3.movq import MoVQ
-from kandinsky3.condition_encoders import T5TextConditionEncoder
-from kandinsky3.condition_processors import T5TextConditionProcessor
-from kandinsky3.model.diffusion import BaseDiffusion, get_named_beta_schedule
-
-from .t2i_pipeline import Kandinsky3T2IPipeline
-from .inpainting_pipeline import Kandinsky3InpaintingPipeline
-
-
-def get_T2I_unet(
-        device: Union[str, torch.device],
-        weights_path: Optional[str] = None,
-        dtype: Union[str, torch.dtype] = torch.float32,
-) -> (UNet, Optional[torch.Tensor], Optional[dict]):
-    unet = UNet(
-        model_channels=384,
-        num_channels=4,
-        init_channels=192,
-        time_embed_dim=1536,
-        context_dim=4096,
-        groups=32,
-        head_dim=64,
-        expansion_ratio=4,
-        compression_ratio=2,
-        dim_mult=(1, 2, 4, 8),
-        num_blocks=(3, 3, 3, 3),
-        add_cross_attention=(False, True, True, True),
-        add_self_attention=(False, True, True, True),
-    )
-
-    null_embedding = None
-    if weights_path:
-        state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
-        null_embedding = state_dict['null_embedding']
-        unet.load_state_dict(state_dict['unet'])
-
-    unet.to(device=device, dtype=dtype).eval()
-    return unet, null_embedding
-
-
-def get_T5encoder(
-        device: Union[str, torch.device],
-        weights_path: str,
-        projection_name: str,
-        dtype: Union[str, torch.dtype] = torch.float32,
-        low_cpu_mem_usage: bool = True,
-        load_in_8bit: bool = False,
-        load_in_4bit: bool = False,
-) -> (T5TextConditionProcessor, T5TextConditionEncoder):
-    tokens_length = 128
-    context_dim = 4096
-    processor = T5TextConditionProcessor(tokens_length, weights_path)
-    condition_encoder = T5TextConditionEncoder(
-        weights_path, context_dim, low_cpu_mem_usage=low_cpu_mem_usage, device=device,
-        dtype=dtype, load_in_8bit=load_in_8bit, load_in_4bit=load_in_4bit
-    )
-
-    if weights_path:
-        projections_weights_path = os.path.join(weights_path, projection_name)
-        state_dict = torch.load(projections_weights_path, map_location=torch.device('cpu'))
-        condition_encoder.projection.load_state_dict(state_dict)
-
-    condition_encoder.projection.to(device=device, dtype=dtype).eval()
-    return processor, condition_encoder
-
-
-def get_movq(
-        device: Union[str, torch.device],
-        weights_path: Optional[str] = None,
-        dtype: Union[str, torch.dtype] = torch.float32,
-) -> MoVQ:
-    generator_config = {
-        'double_z': False,
-        'z_channels': 4,
-        'resolution': 256,
-        'in_channels': 3,
-        'out_ch': 3,
-        'ch': 256,
-        'ch_mult': [1, 2, 2, 4],
-        'num_res_blocks': 2,
-        'attn_resolutions': [32],
-        'dropout': 0.0
-    }
-    movq = MoVQ(generator_config)
-
-    if weights_path:
-        state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
-        movq.load_state_dict(state_dict)
-
-    movq.to(device=device, dtype=dtype).eval()
-    return movq
-
-
-def get_inpainting_unet(
-        device: Union[str, torch.device],
-        weights_path: Optional[str] = None,
-        dtype: Union[str, torch.dtype] = torch.float32,
-) -> (UNet, Optional[torch.Tensor], Optional[dict]):
-    unet = UNet(
-        model_channels=384,
-        num_channels=9,
-        init_channels=192,
-        time_embed_dim=1536,
-        context_dim=4096,
-        groups=32,
-        head_dim=64,
-        expansion_ratio=4,
-        compression_ratio=2,
-        dim_mult=(1, 2, 4, 8),
-        num_blocks=(3, 3, 3, 3),
-        add_cross_attention=(False, True, True, True),
-        add_self_attention=(False, True, True, True),
-    )
-
-    null_embedding = None
-    if weights_path:
-        state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
-        null_embedding = state_dict['null_embedding']
-        unet.load_state_dict(state_dict['unet'])
-
-    unet.to(device=device, dtype=dtype).eval()
-    return unet, null_embedding
-
-
-def get_T2I_pipeline(
-        device_map: Union[str, torch.device, dict],
-        dtype_map: Union[str, torch.dtype, dict] = torch.float32,
-        low_cpu_mem_usage: bool = True,
-        load_in_8bit: bool = False,
-        load_in_4bit: bool = False,
-        cache_dir: str = '/tmp/kandinsky3/',
-        unet_path: str = None,
-        text_encoder_path: str = None,
-        movq_path: str = None,
-) -> Kandinsky3T2IPipeline:
-    # assert ((unet_path is not None) or (text_encoder_path is not None) or (movq_path is not None))
-    if not isinstance(device_map, dict):
-        device_map = {
-            'unet': device_map, 'text_encoder': device_map, 'movq': device_map
-        }
-    if not isinstance(dtype_map, dict):
-        dtype_map = {
-            'unet': dtype_map, 'text_encoder': dtype_map, 'movq': dtype_map
-        }
-
-    if unet_path is None:
-        unet_path = hf_hub_download(
-            repo_id="ai-forever/Kandinsky3.1", filename='weights/kandinsky3.pt', cache_dir=cache_dir
-        )
-    if text_encoder_path is None:
-        text_encoder_path = snapshot_download(
-            repo_id="ai-forever/Kandinsky3.1", allow_patterns="weights/flan_ul2_encoder/*", cache_dir=cache_dir
-        )
-        text_encoder_path = os.path.join(text_encoder_path, 'weights/flan_ul2_encoder')
-    if movq_path is None:
-        movq_path = hf_hub_download(
-            repo_id="ai-forever/Kandinsky3.1", filename='weights/movq.pt', cache_dir=cache_dir
-        )
-
-    unet, null_embedding = get_T2I_unet(device_map['unet'], unet_path, dtype=dtype_map['unet'])
-    processor, condition_encoder = get_T5encoder(
-        device_map['text_encoder'], text_encoder_path, 'projection.pt', dtype=dtype_map['text_encoder'],
-        low_cpu_mem_usage=low_cpu_mem_usage, load_in_8bit=load_in_8bit, load_in_4bit=load_in_4bit
-    )
-    movq = get_movq(device_map['movq'], movq_path, dtype=dtype_map['movq'])
-    return Kandinsky3T2IPipeline(
-        device_map, dtype_map, unet, null_embedding, processor, condition_encoder, movq, False
-    )
-
-
-def get_T2I_Flash_pipeline(
-        device_map: Union[str, torch.device, dict],
-        dtype_map: Union[str, torch.dtype, dict] = torch.float32,
-        low_cpu_mem_usage: bool = True,
-        load_in_8bit: bool = False,
-        load_in_4bit: bool = False,
-        cache_dir: str = '/tmp/kandinsky3/',
-        unet_path: str = None,
-        text_encoder_path: str = None,
-        movq_path: str = None,
-) -> Kandinsky3T2IPipeline:
-    # assert ((unet_path is not None) or (text_encoder_path is not None) or (movq_path is not None))
-    if not isinstance(device_map, dict):
-        device_map = {
-            'unet': device_map, 'text_encoder': device_map, 'movq': device_map
-        }
-    if not isinstance(dtype_map, dict):
-        dtype_map = {
-            'unet': dtype_map, 'text_encoder': dtype_map, 'movq': dtype_map
-        }
-
-    if unet_path is None:
-        unet_path = hf_hub_download(
-            repo_id="ai-forever/Kandinsky3.1", filename='weights/kandinsky3_flash.pt', cache_dir=cache_dir
-        )
-    if text_encoder_path is None:
-        text_encoder_path = snapshot_download(
-            repo_id="ai-forever/Kandinsky3.1", allow_patterns="weights/flan_ul2_encoder/*", cache_dir=cache_dir
-        )
-        text_encoder_path = os.path.join(text_encoder_path, 'weights/flan_ul2_encoder')
-    if movq_path is None:
-        movq_path = hf_hub_download(
-            repo_id="ai-forever/Kandinsky3.1", filename='weights/movq.pt', cache_dir=cache_dir
-        )
-
-    unet, null_embedding = get_T2I_unet(device_map['unet'], unet_path, dtype=dtype_map['unet'])
-    processor, condition_encoder = get_T5encoder(
-        device_map['text_encoder'], text_encoder_path, 'projection_flash.pt', dtype=dtype_map['text_encoder'],
-        low_cpu_mem_usage=low_cpu_mem_usage, load_in_8bit=load_in_8bit, load_in_4bit=load_in_4bit
-    )
-    movq = get_movq(device_map['movq'], movq_path, dtype=dtype_map['movq'])
-    return Kandinsky3T2IPipeline(
-        device_map, dtype_map, unet, null_embedding, processor, condition_encoder, movq, True
-    )
-
-
-def get_inpainting_pipeline(
-        device_map: Union[str, torch.device, dict],
-        dtype_map: Union[str, torch.dtype, dict] = torch.float32,
-        low_cpu_mem_usage: bool = True,
-        load_in_8bit: bool = False,
-        load_in_4bit: bool = False,
-        cache_dir: str = '/tmp/kandinsky3/',
-        unet_path: str = None,
-        text_encoder_path: str = None,
-        movq_path: str = None,
-) -> Kandinsky3InpaintingPipeline:
-    # assert ((unet_path is not None) or (text_encoder_path is not None) or (movq_path is not None))
-    if not isinstance(device_map, dict):
-        device_map = {
-            'unet': device_map, 'text_encoder': device_map, 'movq': device_map
-        }
-    if not isinstance(dtype_map, dict):
-        dtype_map = {
-            'unet': dtype_map, 'text_encoder': dtype_map, 'movq': dtype_map
-        }
-
-    if unet_path is None:
-        unet_path = hf_hub_download(
-            repo_id="ai-forever/Kandinsky3.1", filename='weights/kandinsky3_inpainting.pt', cache_dir=cache_dir
-        )
-    if text_encoder_path is None:
-        text_encoder_path = snapshot_download(
-            repo_id="ai-forever/Kandinsky3.1", allow_patterns="weights/flan_ul2_encoder/*", cache_dir=cache_dir
-        )
-        text_encoder_path = os.path.join(text_encoder_path, 'weights/flan_ul2_encoder')
-    if movq_path is None:
-        movq_path = hf_hub_download(
-            repo_id="ai-forever/Kandinsky3.1", filename='weights/movq.pt', cache_dir=cache_dir
-        )
-
-    unet, null_embedding = get_inpainting_unet(device_map['unet'], unet_path, dtype=dtype_map['unet'])
-    processor, condition_encoder = get_T5encoder(
-        device_map['text_encoder'], text_encoder_path, 'projection_inpainting.pt', dtype=dtype_map['text_encoder'],
-        low_cpu_mem_usage=low_cpu_mem_usage, load_in_8bit=load_in_8bit, load_in_4bit=load_in_4bit
-    )
-    movq = get_movq(device_map['movq'], movq_path, dtype=dtype_map['movq'])
-    return Kandinsky3InpaintingPipeline(
-        device_map, dtype_map, unet, null_embedding, processor, condition_encoder, movq
-    )

Kandinsky-3/kandinsky3/condition_encoders.py

@@ -1,40 +0,0 @@
-import torch
-from torch import nn
-from transformers import T5EncoderModel
-from typing import Optional, Union
-
-
-class T5TextConditionEncoder(nn.Module):
-
-    def __init__(
-            self, model_path, context_dim,
-            low_cpu_mem_usage: bool = True, device: Optional[str] = None,
-            dtype: Union[str, torch.dtype] = torch.float32, load_in_4bit: bool = False, load_in_8bit: bool = False
-    ):
-        super().__init__()
-        self.encoder = T5EncoderModel.from_pretrained(
-            model_path, low_cpu_mem_usage=low_cpu_mem_usage, device_map=device,
-            torch_dtype=dtype, load_in_8bit=load_in_8bit, load_in_4bit=load_in_4bit,
-        ).encoder
-        self.projection = nn.Sequential(
-            nn.Linear(self.encoder.config.d_model, context_dim, bias=False),
-            nn.LayerNorm(context_dim)
-        )
-
-    def forward(self, model_input):
-        embeddings = self.encoder(**model_input).last_hidden_state
-        context = self.projection(embeddings)
-        if 'attention_mask' in model_input:
-            context_mask = model_input['attention_mask']
-            context[context_mask == 0] = torch.zeros_like(context[context_mask == 0])
-            max_seq_length = context_mask.sum(-1).max() + 1
-            context = context[:, :max_seq_length]
-            context_mask = context_mask[:, :max_seq_length]
-        else:
-            context_mask = torch.ones(*embeddings.shape[:-1], dtype=torch.long, device=embeddings.device)
-        return context, context_mask
-
-
-def get_condition_encoder(conf):
-    return T5TextConditionEncoder(**conf)
-

Kandinsky-3/kandinsky3/condition_processors.py

@@ -1,34 +0,0 @@
-import torch
-from transformers import T5Tokenizer
-
-
-class T5TextConditionProcessor:
-
-    def __init__(self, tokens_length, processor_path):
-        self.tokens_length = tokens_length
-        self.processor = T5Tokenizer.from_pretrained(processor_path)
-
-    def encode(self, text=None, negative_text=None):
-        encoded = self.processor(text, max_length=self.tokens_length, truncation=True)
-        pad_length = self.tokens_length - len(encoded['input_ids'])
-        input_ids = encoded['input_ids'] + [self.processor.pad_token_id] * pad_length
-        attention_mask = encoded['attention_mask'] + [0] * pad_length
-        condition_model_input = {
-            'input_ids': torch.tensor(input_ids, dtype=torch.long),
-            'attention_mask': torch.tensor(attention_mask, dtype=torch.long)
-        }
-
-        if negative_text is not None:
-            negative_encoded = self.processor(negative_text, max_length=self.tokens_length, truncation=True)
-            negative_input_ids = negative_encoded['input_ids'][:len(encoded['input_ids'])]
-            negative_input_ids[-1] = self.processor.eos_token_id
-            negative_pad_length = self.tokens_length - len(negative_input_ids)
-            negative_input_ids = negative_input_ids + [self.processor.pad_token_id] * negative_pad_length
-            negative_attention_mask = encoded['attention_mask'] + [0] * pad_length
-            negative_condition_model_input = {
-                'input_ids': torch.tensor(negative_input_ids, dtype=torch.long),
-                'attention_mask': torch.tensor(negative_attention_mask, dtype=torch.long)
-            }
-        else:
-            negative_condition_model_input = None
-        return condition_model_input, negative_condition_model_input

Kandinsky-3/kandinsky3/inpainting_pipeline.py

@@ -1,168 +0,0 @@
-from typing import Union, List
-import PIL
-import numpy as np
-
-import torch
-import torchvision.transforms as T
-from einops import repeat
-
-from kandinsky3.model.unet import UNet
-from kandinsky3.movq import MoVQ
-from kandinsky3.condition_encoders import T5TextConditionEncoder
-from kandinsky3.condition_processors import T5TextConditionProcessor
-from kandinsky3.model.diffusion import BaseDiffusion, get_named_beta_schedule
-from kandinsky3.utils import resize_image_for_diffusion, resize_mask_for_diffusion
-
-
-class Kandinsky3InpaintingPipeline:
-
-    def __init__(
-            self,
-            device_map: Union[str, torch.device, dict],
-            dtype_map: Union[str, torch.dtype, dict],
-            unet: UNet,
-            null_embedding: torch.Tensor,
-            t5_processor: T5TextConditionProcessor,
-            t5_encoder: T5TextConditionEncoder,
-            movq: MoVQ,
-    ):
-        self.device_map = device_map
-        self.dtype_map = dtype_map
-        self.to_pil = T.ToPILImage()
-        self.to_tensor = T.ToTensor()
-
-        self.unet = unet
-        self.null_embedding = null_embedding
-        self.t5_processor = t5_processor
-        self.t5_encoder = t5_encoder
-        self.movq = movq
-
-    def shared_step(self, batch: dict) -> dict:
-        image = batch['image']
-        condition_model_input = batch['text']
-        negative_condition_model_input = batch['negative_text']
-
-        bs = image.shape[0]
-
-        masked_latent = None
-        mask = batch['mask']
-
-        if 'masked_image' in batch:
-            masked_latent = batch['masked_image']
-        elif self.unet.in_layer.in_channels == 9:
-            masked_latent = image.masked_fill((1 - mask).bool(), 0)
-        else:
-            raise ValueError()
-
-        with torch.cuda.amp.autocast(dtype=self.dtype_map['movq']):
-            masked_latent = self.movq.encode(masked_latent)
-        mask = torch.nn.functional.interpolate(mask, size=(masked_latent.shape[2], masked_latent.shape[3]))
-
-        with torch.cuda.amp.autocast(dtype=self.dtype_map['text_encoder']):
-            context, context_mask = self.t5_encoder(condition_model_input)
-
-        if negative_condition_model_input is not None:
-            negative_context, negative_context_mask = self.t5_encoder(negative_condition_model_input)
-        else:
-            negative_context, negative_context_mask = None, None
-
-        return {
-            'context': context,
-            'context_mask': context_mask,
-            'negative_context': negative_context,
-            'negative_context_mask': negative_context_mask,
-            'image': image,
-            'masked_latent': masked_latent,
-            'mask': mask
-        }
-
-    def prepare_batch(
-            self,
-            text: str,
-            negative_text: str,
-            image: PIL.Image.Image,
-            mask: np.ndarray,
-    ) -> dict:
-        condition_model_input, negative_condition_model_input = self.t5_processor.encode(
-            text=text, negative_text=negative_text
-        )
-        batch = {
-            'image': self.to_tensor(resize_image_for_diffusion(image.convert("RGB"))) * 2 - 1,
-            'mask': 1 - self.to_tensor(resize_mask_for_diffusion(mask)),
-            'text': condition_model_input,
-            'negative_text': negative_condition_model_input
-        }
-        batch['mask'] = batch['mask'].type(self.dtype_map['movq'])
-
-        batch['image'] = batch['image'].unsqueeze(0).to(self.device_map['movq'])
-        batch['text']['input_ids'] = batch['text']['input_ids'].unsqueeze(0).to(self.device_map['text_encoder'])
-        batch['text']['attention_mask'] = batch['text']['attention_mask'].unsqueeze(0).to(
-            self.device_map['text_encoder'])
-        batch['mask'] = batch['mask'].unsqueeze(0).to(self.device_map['movq'])
-
-        if negative_condition_model_input is not None:
-            batch['negative_text']['input_ids'] = batch['negative_text']['input_ids'].to(
-                self.device_map['text_encoder'])
-            batch['negative_text']['attention_mask'] = batch['negative_text']['attention_mask'].to(
-                self.device_map['text_encoder'])
-
-        return batch
-
-    def __call__(
-            self,
-            text: str,
-            image: PIL.Image.Image,
-            mask: np.ndarray,
-            negative_text: str = None,
-            images_num: int = 1,
-            bs: int = 1,
-            steps: int = 50,
-            guidance_weight_text: float = 4,
-            eta=1.0
-    ) -> List[PIL.Image.Image]:
-
-        with torch.no_grad():
-            batch = self.prepare_batch(text, negative_text, image, mask)
-            processed = self.shared_step(batch)
-            betas = get_named_beta_schedule('cosine', 1000)
-            base_diffusion = BaseDiffusion(betas, percentile=0.95)
-            times = list(range(999, 0, -1000 // steps))
-
-            pil_images = []
-            k, m = images_num // bs, images_num % bs
-            for minibatch in [bs] * k + [m]:
-                if minibatch == 0:
-                    continue
-
-                bs_context = repeat(processed['context'], '1 n d -> b n d', b=minibatch)
-                bs_context_mask = repeat(processed['context_mask'], '1 n -> b n', b=minibatch)
-
-                if processed['negative_context'] is not None:
-                    bs_negative_context = repeat(processed['negative_context'], '1 n d -> b n d', b=minibatch)
-                    bs_negative_context_mask = repeat(processed['negative_context_mask'], '1 n -> b n', b=minibatch)
-                else:
-                    bs_negative_context, bs_negative_context_mask = None, None
-
-                mask = processed['mask'].repeat_interleave(minibatch, dim=0)
-                masked_latent = processed['masked_latent'].repeat_interleave(minibatch, dim=0)
-
-                minibatch = masked_latent.shape[0]
-
-                with torch.cuda.amp.autocast(dtype=self.dtype_map['unet']):
-                    with torch.no_grad():
-                        images = base_diffusion.p_sample_loop(
-                            self.unet, (minibatch, 4, masked_latent.shape[2], masked_latent.shape[3]), times,
-                            self.device_map['unet'],
-                            bs_context, bs_context_mask, self.null_embedding, guidance_weight_text, eta,
-                            negative_context=bs_negative_context, negative_context_mask=bs_negative_context_mask,
-                            mask=mask, masked_latent=masked_latent, gan=False
-                        )
-
-                with torch.cuda.amp.autocast(dtype=self.dtype_map['movq']):
-                    images = torch.cat([self.movq.decode(image) for image in images.chunk(2)])
-                    images = torch.clip((images + 1.) / 2., 0., 1.).cpu()
-
-                for images_chunk in images.chunk(1):
-                    pil_images += [self.to_pil(image) for image in images_chunk]
-
-        return pil_images

Kandinsky-3/kandinsky3/model/diffusion.py

@@ -1,200 +0,0 @@
-import math
-
-import torch
-from einops import rearrange
-from tqdm import tqdm
-
-from .utils import get_tensor_items
-
-
-def get_named_beta_schedule(schedule_name, timesteps):
-    if schedule_name == "linear":
-        scale = 1000 / timesteps
-        beta_start = scale * 0.0001
-        beta_end = scale * 0.02
-        return torch.linspace(
-            beta_start, beta_end, timesteps, dtype=torch.float32
-        )
-    elif schedule_name == "cosine":
-        alpha_bar = lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-        betas = []
-        for i in range(timesteps):
-            t1 = i / timesteps
-            t2 = (i + 1) / timesteps
-            betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), 0.999))
-        return torch.tensor(betas, dtype=torch.float32)
-
-
-class BaseDiffusion:
-
-    def __init__(self, betas, percentile=None, gen_noise=torch.randn_like):
-        self.betas = betas
-        self.num_timesteps = betas.shape[0]
-
-        alphas = 1. - betas
-        self.alphas_cumprod = torch.cumprod(alphas, dim=0)
-        self.alphas_cumprod_prev = torch.cat([torch.ones(1, dtype=betas.dtype), self.alphas_cumprod[:-1]])
-
-        # calculate q(x_t | x_{t-1})
-        self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - self.alphas_cumprod)
-
-        # calculate q(x_{t-1} | x_t, x_0)
-        self.posterior_mean_coef_1 = torch.sqrt(self.alphas_cumprod_prev) * betas / (1. - self.alphas_cumprod)
-        self.posterior_mean_coef_2 = torch.sqrt(alphas) * (1. - self.alphas_cumprod_prev) / (1. - self.alphas_cumprod)
-        self.posterior_variance = betas * (1. - self.alphas_cumprod_prev) / (1. - self.alphas_cumprod)
-        self.posterior_log_variance = torch.log(
-            torch.cat([self.posterior_variance[1].unsqueeze(0), self.posterior_variance[1:]])
-        )
-
-        self.percentile = percentile
-        self.time_scale = 1000 // self.num_timesteps
-        self.gen_noise = gen_noise
-        self.jump_length = 3
-
-    def process_x_start(self, x_start):
-        bs, ndims = x_start.shape[0], len(x_start.shape[1:])
-        if self.percentile is not None:
-            quantile = torch.quantile(
-                rearrange(x_start, 'b ... -> b (...)').abs(),
-                self.percentile,
-                dim=-1
-            )
-            quantile = torch.clip(quantile, min=1.)
-            quantile = quantile.reshape(bs, *((1,) * ndims))
-            return torch.clip(x_start, -quantile, quantile) / quantile
-        else:
-            return torch.clip(x_start, -1., 1.)
-
-    def get_x_start(self, x, t, noise):
-        sqrt_one_minus_alphas_cumprod = get_tensor_items(self.sqrt_one_minus_alphas_cumprod, t, noise.shape)
-        sqrt_alphas_cumprod = get_tensor_items(self.sqrt_alphas_cumprod, t, noise.shape)
-        pred_x_start = (x - sqrt_one_minus_alphas_cumprod * noise) / sqrt_alphas_cumprod
-        return pred_x_start
-
-    def get_noise(self, x, t, x_start):
-        sqrt_one_minus_alphas_cumprod = get_tensor_items(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
-        sqrt_alphas_cumprod = get_tensor_items(self.sqrt_alphas_cumprod, t, x_start.shape)
-        pred_noise = (x - sqrt_alphas_cumprod * x_start) / sqrt_one_minus_alphas_cumprod
-        return pred_noise
-
-    def q_sample(self, x_start, t, noise=None):
-        if noise is None:
-            noise = self.gen_noise(x_start)
-        sqrt_alphas_cumprod = get_tensor_items(self.sqrt_alphas_cumprod, t, x_start.shape)
-        sqrt_one_minus_alphas_cumprod = get_tensor_items(self.sqrt_one_minus_alphas_cumprod, t, noise.shape)
-        x_t = sqrt_alphas_cumprod * x_start + sqrt_one_minus_alphas_cumprod * noise
-        return x_t
-
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        posterior_mean_coef_1 = get_tensor_items(self.posterior_mean_coef_1, t, x_start.shape)
-        posterior_mean_coef_2 = get_tensor_items(self.posterior_mean_coef_2, t, x_t.shape)
-        posterior_mean = posterior_mean_coef_1 * x_start + posterior_mean_coef_2 * x_t
-
-        posterior_variance = get_tensor_items(self.posterior_variance, t, x_start.shape)
-        posterior_log_variance = get_tensor_items(self.posterior_log_variance, t, x_start.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance
-
-    def q_posterior_variance(self, t, prev_t, shape, eta=1., ):
-        alphas_cumprod = get_tensor_items(self.alphas_cumprod, t, shape)
-        prev_alphas_cumprod = get_tensor_items(self.alphas_cumprod, prev_t, shape)
-
-        posterior_variance = torch.sqrt(
-            eta * (1. - alphas_cumprod / prev_alphas_cumprod) * (1. - prev_alphas_cumprod) / (1. - alphas_cumprod)
-        )
-        return posterior_variance
-
-    def text_guidance(
-            self, model, x, t, context, context_mask, null_embedding, guidance_weight_text,
-            uncondition_context=None, uncondition_context_mask=None, mask=None, masked_latent=None
-    ):
-        large_x = x.repeat(2, 1, 1, 1)
-        large_t = t.repeat(2).to(x.dtype)
-
-        if uncondition_context is None:
-            uncondition_context = torch.zeros_like(context)
-            uncondition_context_mask = torch.zeros_like(context_mask)
-            uncondition_context[:, 0] = null_embedding
-            uncondition_context_mask[:, 0] = 1
-        large_context = torch.cat([context, uncondition_context])
-        large_context_mask = torch.cat([context_mask, uncondition_context_mask])
-
-        if mask is not None:
-            mask = mask.repeat(2, 1, 1, 1)
-        if masked_latent is not None:
-            masked_latent = masked_latent.repeat(2, 1, 1, 1)
-
-        if model.in_layer.in_channels == 9:
-            large_x = torch.cat([large_x, mask, masked_latent], dim=1)
-
-        pred_large_noise = model(large_x, large_t * self.time_scale, large_context, large_context_mask.bool())
-        pred_noise, uncond_pred_noise = torch.chunk(pred_large_noise, 2)
-        pred_noise = (guidance_weight_text + 1.) * pred_noise - guidance_weight_text * uncond_pred_noise
-        return pred_noise
-
-    def p_mean_variance(
-            self, model, x, t, prev_t, context, context_mask, null_embedding, guidance_weight_text, eta=1.,
-            negative_context=None, negative_context_mask=None, mask=None, masked_latent=None
-    ):
-
-        pred_noise = self.text_guidance(
-            model, x, t, context, context_mask, null_embedding, guidance_weight_text,
-            negative_context, negative_context_mask, mask, masked_latent
-        )
-
-        pred_x_start = self.get_x_start(x, t, pred_noise)
-        pred_x_start = self.process_x_start(pred_x_start)
-        pred_noise = self.get_noise(x, t, pred_x_start)
-        pred_var = self.q_posterior_variance(t, prev_t, x.shape, eta)
-
-        prev_alphas_cumprod = get_tensor_items(self.alphas_cumprod, prev_t, x.shape)
-        pred_mean = torch.sqrt(prev_alphas_cumprod) * pred_x_start
-        pred_mean += torch.sqrt(1. - prev_alphas_cumprod - pred_var ** 2) * pred_noise
-        return pred_mean, pred_var
-
-    @torch.no_grad()
-    def p_sample(
-            self, model, x, t, prev_t, context, context_mask, null_embedding, guidance_weight_text, eta=1.,
-            negative_context=None, negative_context_mask=None, mask=None, masked_latent=None
-    ):
-        bs = x.shape[0]
-        ndims = len(x.shape[1:])
-        pred_mean, pred_var = self.p_mean_variance(
-            model, x, t, prev_t, context, context_mask, null_embedding, guidance_weight_text, eta,
-            negative_context=negative_context, negative_context_mask=negative_context_mask,
-            mask=mask, masked_latent=masked_latent
-        )
-        noise = torch.randn_like(x)
-        mask = (prev_t != 0).reshape(bs, *((1,) * ndims))
-        sample = pred_mean + mask * pred_var * noise
-        return sample
-
-    @torch.no_grad()
-    def p_sample_loop(
-            self, model, shape, times, device, context, context_mask, null_embedding, guidance_weight_text, eta=1.,
-            negative_context=None, negative_context_mask=None, mask=None, masked_latent=None, gan=False,
-    ):
-        img = torch.randn(*shape, device=device)
-        times = times + [0, ]
-        times = list(zip(times[:-1], times[1:]))
-
-        for time, prev_time in tqdm(times):
-            time = torch.tensor([time] * shape[0], device=device)
-            if gan:
-                x_t = self.q_sample(img, time)
-                pred_noise = model(x_t, time.type(x_t.dtype), context, context_mask.bool())
-                img = self.get_x_start(x_t, time, pred_noise)
-            else:
-                prev_time = torch.tensor([prev_time] * shape[0], device=device)
-                img = self.p_sample(
-                    model, img, time, prev_time, context, context_mask, null_embedding, guidance_weight_text, eta,
-                    negative_context=negative_context, negative_context_mask=negative_context_mask,
-                    mask=mask, masked_latent=masked_latent
-                )
-        return img
-
-
-def get_diffusion(conf):
-    betas = get_named_beta_schedule(**conf.schedule_params)
-    base_diffusion = BaseDiffusion(betas, **conf.diffusion_params)
-    return base_diffusion

Kandinsky-3/kandinsky3/model/nn.py

@@ -1,84 +0,0 @@
-import math
-
-import torch
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-from .utils import exist
-
-
-class Identity(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-
-    @staticmethod
-    def forward(x, *args, **kwargs):
-        return x
-
-
-class SinusoidalPosEmb(nn.Module):
-
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=x.device, dtype=x.dtype) * -emb)
-        emb = rearrange(x, 'i -> i 1') * rearrange(emb, 'j -> 1 j')
-        return torch.cat((emb.sin(), emb.cos()), dim=-1)
-
-
-class ConditionalGroupNorm(nn.Module):
-
-    def __init__(self, groups, normalized_shape, context_dim):
-        super().__init__()
-        self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)
-        self.context_mlp = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(context_dim, 2 * normalized_shape)
-        )
-        self.context_mlp[1].weight.data.zero_()
-        self.context_mlp[1].bias.data.zero_()
-
-    def forward(self, x, context):
-        context = self.context_mlp(context)
-        ndims = ' 1' * len(x.shape[2:])
-        context = rearrange(context, f'b c -> b c{ndims}')
-
-        scale, shift = context.chunk(2, dim=1)
-        x = self.norm(x) * (scale + 1.) + shift
-        return x
-
-
-class Attention(nn.Module):
-
-    def __init__(self, in_channels, out_channels, context_dim, head_dim=64):
-        super().__init__()
-        assert out_channels % head_dim == 0
-        self.num_heads = out_channels // head_dim
-        self.scale = head_dim ** -0.5
-
-        self.to_query = nn.Linear(in_channels, out_channels, bias=False)
-        self.to_key = nn.Linear(context_dim, out_channels, bias=False)
-        self.to_value = nn.Linear(context_dim, out_channels, bias=False)
-
-        self.output_layer = nn.Linear(out_channels, out_channels, bias=False)
-
-    def forward(self, x, context, context_mask=None):
-        query = rearrange(self.to_query(x), 'b n (h d) -> b h n d', h=self.num_heads)
-        key = rearrange(self.to_key(context), 'b n (h d) -> b h n d', h=self.num_heads)
-        value = rearrange(self.to_value(context), 'b n (h d) -> b h n d', h=self.num_heads)
-
-        attention_matrix = einsum('b h i d, b h j d -> b h i j', query, key) * self.scale
-        if exist(context_mask):
-            max_neg_value = -torch.finfo(attention_matrix.dtype).max
-            context_mask = rearrange(context_mask, 'b j -> b 1 1 j')
-            attention_matrix = attention_matrix.masked_fill(~context_mask, max_neg_value)
-        attention_matrix = attention_matrix.softmax(dim=-1)
-
-        out = einsum('b h i j, b h j d -> b h i d', attention_matrix, value)
-        out = rearrange(out, 'b h n d -> b n (h d)')
-        out = self.output_layer(out)
-        return out

Kandinsky-3/kandinsky3/model/unet.py

@@ -1,516 +0,0 @@
-import torch
-from torch import nn, einsum
-from einops import rearrange
-
-from .nn import Identity, Attention, SinusoidalPosEmb, ConditionalGroupNorm
-from .utils import exist, set_default_item, set_default_layer
-import torch.nn.functional as F
-
-
-class Block(nn.Module):
-
-    def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):
-        super().__init__()
-        self.group_norm = ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)
-        self.activation = nn.SiLU()
-        self.up_sample = set_default_layer(
-            exist(up_resolution) and up_resolution,
-            nn.ConvTranspose2d, (in_channels, in_channels), {'kernel_size': 2, 'stride': 2}
-        )
-        padding = set_default_item(kernel_size == 1, 0, 1)
-        self.projection = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding)
-        self.down_sample = set_default_layer(
-            exist(up_resolution) and not up_resolution,
-            nn.Conv2d, (out_channels, out_channels), {'kernel_size': 2, 'stride': 2}
-        )
-
-    def forward(self, x, time_embed):
-        x = self.group_norm(x, time_embed)
-        x = self.activation(x)
-        x = self.up_sample(x)
-        x = self.projection(x)
-        x = self.down_sample(x)
-        return x
-
-
-class ResNetBlock(nn.Module):
-
-    def __init__(
-            self, in_channels, out_channels, time_embed_dim, norm_groups=32, compression_ratio=2, up_resolutions=4*[None]
-    ):
-        super().__init__()
-        kernel_sizes = [1, 3, 3, 1]
-        hidden_channel = max(in_channels, out_channels) // compression_ratio
-        hidden_channels = [(in_channels, hidden_channel)] + [(hidden_channel, hidden_channel)] * 2 + [(hidden_channel, out_channels)]
-        self.resnet_blocks = nn.ModuleList([
-            Block(in_channel, out_channel, time_embed_dim, kernel_size, norm_groups, up_resolution)
-            for (in_channel, out_channel), kernel_size, up_resolution in zip(hidden_channels, kernel_sizes, up_resolutions)
-        ])
-
-        self.shortcut_up_sample = set_default_layer(
-            True in up_resolutions,
-            nn.ConvTranspose2d, (in_channels, in_channels), {'kernel_size': 2, 'stride': 2}
-        )
-        self.shortcut_projection = set_default_layer(
-            in_channels != out_channels,
-            nn.Conv2d, (in_channels, out_channels), {'kernel_size': 1}
-        )
-        self.shortcut_down_sample = set_default_layer(
-            False in up_resolutions,
-            nn.Conv2d, (out_channels, out_channels), {'kernel_size': 2, 'stride': 2}
-        )
-
-    def forward(self, x, time_embed):
-        out = x
-        for resnet_block in self.resnet_blocks:
-            out = resnet_block(out, time_embed)
-
-        x = self.shortcut_up_sample(x)
-        x = self.shortcut_projection(x)
-        x = self.shortcut_down_sample(x)
-        x = x + out
-        return x
-
-
-class AttentionPolling(nn.Module):
-
-    def __init__(self, num_channels, context_dim, head_dim=64):
-        super().__init__()
-        self.attention = Attention(context_dim, num_channels, context_dim, head_dim)
-
-    def forward(self, x, context, context_mask=None):
-        context = self.attention(context.mean(dim=1, keepdim=True), context, context_mask)
-        return x + context.squeeze(1)
-
-
-class AttentionBlock(nn.Module):
-
-    def __init__(self, num_channels, time_embed_dim, context_dim=None, norm_groups=32, head_dim=64, expansion_ratio=4):
-        super().__init__()
-        self.in_norm = ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
-        self.attention = Attention(num_channels, num_channels, context_dim or num_channels, head_dim)
-
-        hidden_channels = expansion_ratio * num_channels
-        self.out_norm = ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
-        self.feed_forward = nn.Sequential(
-            nn.Conv2d(num_channels, hidden_channels, kernel_size=1, bias=False),
-            nn.SiLU(),
-            nn.Conv2d(hidden_channels, num_channels, kernel_size=1, bias=False),
-        )
-
-    def forward(self, x, time_embed, context=None, context_mask=None):
-        height, width = x.shape[-2:]
-        out = self.in_norm(x, time_embed)
-        out = rearrange(out, 'b c h w -> b (h w) c', h=height, w=width)
-        context = set_default_item(exist(context), context, out)
-        out = self.attention(out, context, context_mask)
-        out = rearrange(out, 'b (h w) c -> b c h w', h=height, w=width)
-        x = x + out
-
-        out = self.out_norm(x, time_embed)
-        out = self.feed_forward(out)
-        x = x + out
-        return x
-
-
-class DownSampleBlock(nn.Module):
-
-    def __init__(
-            self, in_channels, out_channels, time_embed_dim, context_dim=None,
-            num_blocks=3, groups=32, head_dim=64, expansion_ratio=4, compression_ratio=2,
-            down_sample=True, self_attention=True
-    ):
-        super().__init__()
-        self.self_attention_block = set_default_layer(
-            self_attention,
-            AttentionBlock,
-            (in_channels, time_embed_dim, None, groups, head_dim, expansion_ratio),
-            layer_2=Identity
-        )
-
-        up_resolutions = [[None] * 4] * (num_blocks - 1) + [[None, None, set_default_item(down_sample, False), None]]
-        hidden_channels = [(in_channels, out_channels)] + [(out_channels, out_channels)] * (num_blocks - 1)
-        self.resnet_attn_blocks = nn.ModuleList([
-            nn.ModuleList([
-                ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio),
-                set_default_layer(
-                    exist(context_dim),
-                    AttentionBlock,
-                    (out_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio),
-                    layer_2=Identity
-                ),
-                ResNetBlock(out_channel, out_channel, time_embed_dim, groups, compression_ratio, up_resolution),
-            ]) for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions)
-        ])
-
-    def forward(self, x, time_embed, context=None, context_mask=None, control_net_residual=None):
-        x = self.self_attention_block(x, time_embed)
-        for in_resnet_block, attention, out_resnet_block in self.resnet_attn_blocks:
-            x = in_resnet_block(x, time_embed)
-            x = attention(x, time_embed, context, context_mask)
-            x = out_resnet_block(x, time_embed)
-        return x
-
-
-class UpSampleBlock(nn.Module):
-
-    def __init__(
-            self, in_channels, cat_dim, out_channels, time_embed_dim, context_dim=None,
-            num_blocks=3, groups=32, head_dim=64, expansion_ratio=4, compression_ratio=2,
-            up_sample=True, self_attention=True
-    ):
-        super().__init__()
-        up_resolutions = [[None, set_default_item(up_sample, True), None, None]] + [[None] * 4] * (num_blocks - 1)
-        hidden_channels = [(in_channels + cat_dim, in_channels)] + [(in_channels, in_channels)] * (num_blocks - 2) + [(in_channels, out_channels)]
-        self.resnet_attn_blocks = nn.ModuleList([
-            nn.ModuleList([
-                ResNetBlock(in_channel, in_channel, time_embed_dim, groups, compression_ratio, up_resolution),
-                set_default_layer(
-                    exist(context_dim),
-                    AttentionBlock,
-                    (in_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio),
-                    layer_2=Identity
-                ),
-                ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio),
-            ]) for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions)
-        ])
-
-        self.self_attention_block = set_default_layer(
-            self_attention,
-            AttentionBlock,
-            (out_channels, time_embed_dim, None, groups, head_dim, expansion_ratio),
-            layer_2=Identity
-        )
-
-    def forward(self, x, time_embed, context=None, context_mask=None):
-        for in_resnet_block, attention, out_resnet_block in self.resnet_attn_blocks:
-            x = in_resnet_block(x, time_embed)
-            x = attention(x, time_embed, context, context_mask)
-            x = out_resnet_block(x, time_embed)
-        x = self.self_attention_block(x, time_embed)
-        return x
-
-class ControlNetModel(nn.Module):
-    def __init__(self,
-                 model_channels,
-                 init_channels=None,
-                 num_channels=3,
-                 out_channels=4,
-                 time_embed_dim=None,
-                 context_dim=None,
-                 groups=32,
-                 head_dim=64,
-                 expansion_ratio=4,
-                 compression_ratio=2,
-                 dim_mult=(1, 2, 4, 8),
-                 num_blocks=(3, 3, 3, 3),
-                 add_cross_attention=(False, True, True, True),
-                 add_self_attention=(False, True, True, True)
-                 ):
-        super().__init__()
-        init_channels = init_channels or model_channels
-        self.to_time_embed = nn.Sequential(
-            SinusoidalPosEmb(init_channels),
-            nn.Linear(init_channels, time_embed_dim),
-            nn.SiLU(),
-            nn.Linear(time_embed_dim, time_embed_dim)
-        )
-        self.feature_pooling = AttentionPolling(time_embed_dim, context_dim, head_dim)
-
-        self.in_layer = nn.Conv2d(num_channels, init_channels, kernel_size=3, padding=1)
-
-        hidden_dims = [init_channels, *map(lambda mult: model_channels * mult, dim_mult)]
-        in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
-        text_dims = [set_default_item(is_exist, context_dim) for is_exist in add_cross_attention]
-        layer_params = [num_blocks, text_dims, add_self_attention]
-        rev_layer_params = map(reversed, layer_params)
-
-        cat_dims = []
-        self.num_levels = len(in_out_dims)
-        self.down_samples = nn.ModuleList([])
-        for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(zip(in_out_dims, *layer_params)):
-            down_sample = level != (self.num_levels - 1)
-            cat_dims.append(set_default_item(level != (self.num_levels - 1), out_dim, 0))
-            self.down_samples.append(
-                DownSampleBlock(
-                    in_dim, out_dim, time_embed_dim, text_dim, res_block_num, groups, head_dim, expansion_ratio,
-                    compression_ratio, down_sample, self_attention
-                )
-            )
-
-    def forward(self, x, time, context=None, context_mask=None):
-        time_embed = self.to_time_embed(time)
-        if exist(context):
-            time_embed = self.feature_pooling(time_embed, context, context_mask)
-
-        hidden_states = []
-        x = self.in_layer(x)
-        for level, down_sample in enumerate(self.down_samples):
-            x = down_sample(x, time_embed, context, context_mask)
-            if level != self.num_levels - 1:
-                hidden_states.append(x)
-        return hidden_states
-
-class UNet(nn.Module):
-
-    def __init__(self,
-                 model_channels,
-                 init_channels=None,
-                 num_channels=3,
-                 out_channels=4,
-                 time_embed_dim=None,
-                 context_dim=None,
-                 groups=32,
-                 head_dim=64,
-                 expansion_ratio=4,
-                 compression_ratio=2,
-                 dim_mult=(1, 2, 4, 8),
-                 num_blocks=(3, 3, 3, 3),
-                 add_cross_attention=(False, True, True, True),
-                 add_self_attention=(False, True, True, True),
-                 *args,
-                 **kwargs,
-                 ):
-        super().__init__()
-        init_channels = init_channels or model_channels
-        self.to_time_embed = nn.Sequential(
-            SinusoidalPosEmb(init_channels),
-            nn.Linear(init_channels, time_embed_dim),
-            nn.SiLU(),
-            nn.Linear(time_embed_dim, time_embed_dim)
-        )
-        self.feature_pooling = AttentionPolling(time_embed_dim, context_dim, head_dim)
-
-        self.in_layer = nn.Conv2d(num_channels, init_channels, kernel_size=3, padding=1)
-
-        hidden_dims = [init_channels, *map(lambda mult: model_channels * mult, dim_mult)]
-        in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
-        text_dims = [set_default_item(is_exist, context_dim) for is_exist in add_cross_attention]
-        layer_params = [num_blocks, text_dims, add_self_attention]
-        rev_layer_params = map(reversed, layer_params)
-
-        cat_dims = []
-        self.num_levels = len(in_out_dims)
-        self.down_samples = nn.ModuleList([])
-        for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(zip(in_out_dims, *layer_params)):
-            down_sample = level != (self.num_levels - 1)
-            cat_dims.append(set_default_item(level != (self.num_levels - 1), out_dim, 0))
-            self.down_samples.append(
-                DownSampleBlock(
-                    in_dim, out_dim, time_embed_dim, text_dim, res_block_num, groups, head_dim, expansion_ratio,
-                    compression_ratio, down_sample, self_attention
-                )
-            )
-
-        self.up_samples = nn.ModuleList([])
-        for level, ((out_dim, in_dim), res_block_num, text_dim, self_attention) in enumerate(zip(reversed(in_out_dims), *rev_layer_params)):
-            up_sample = level != 0
-            self.up_samples.append(
-                UpSampleBlock(
-                    in_dim, cat_dims.pop(), out_dim, time_embed_dim, text_dim, res_block_num, groups, head_dim,
-                    expansion_ratio, compression_ratio, up_sample, self_attention
-                )
-            )
-
-        self.out_layer = nn.Sequential(
-            nn.GroupNorm(groups, init_channels),
-            nn.SiLU(),
-            nn.Conv2d(init_channels, out_channels, kernel_size=3, padding=1)
-        )
-
-        self.control_net = None
-
-    def forward(self, x, time, context=None, context_mask=None, control_net_residual=None):
-        time_embed = self.to_time_embed(time)
-        if exist(context):
-            time_embed = self.feature_pooling(time_embed, context, context_mask)
-
-        hidden_states = []
-        x = self.in_layer(x)
-        for level, down_sample in enumerate(self.down_samples):
-            x = down_sample(x, time_embed, context, context_mask, control_net_residual)
-            if level != self.num_levels - 1:
-                hidden_states.append(x)
-        for level, up_sample in enumerate(self.up_samples):
-            if level != 0:
-                x = torch.cat([x, hidden_states.pop()], dim=1)
-            x = up_sample(x, time_embed, context, context_mask)
-        x = self.out_layer(x)
-        return x
-
-
-class ControlNetModel(nn.Module):
-    def __init__(self,
-                 model_channels,
-                 init_channels=None,
-                 num_channels=3,
-                 out_channels=4,
-                 time_embed_dim=None,
-                 context_dim=None,
-                 groups=32,
-                 head_dim=64,
-                 expansion_ratio=4,
-                 compression_ratio=2,
-                 dim_mult=(1, 2, 4, 8),
-                 num_blocks=(3, 3, 3, 3),
-                 add_cross_attention=(False, True, True, True),
-                 add_self_attention=(False, True, True, True),
-                *args,
-                 **kwargs,
-                 ):
-        super().__init__()
-        init_channels = init_channels or model_channels
-        self.to_time_embed = nn.Sequential(
-            SinusoidalPosEmb(init_channels),
-            nn.Linear(init_channels, time_embed_dim),
-            nn.SiLU(),
-            nn.Linear(time_embed_dim, time_embed_dim)
-        )
-        self.feature_pooling = AttentionPolling(time_embed_dim, context_dim, head_dim)
-
-        self.in_layer = nn.Conv2d(num_channels, init_channels, kernel_size=3, padding=1)
-
-        hidden_dims = [init_channels, *map(lambda mult: model_channels * mult, dim_mult)]
-        in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
-        text_dims = [set_default_item(is_exist, context_dim) for is_exist in add_cross_attention]
-        layer_params = [num_blocks, text_dims, add_self_attention]
-        rev_layer_params = map(reversed, layer_params)
-
-        cat_dims = []
-        self.num_levels = len(in_out_dims)
-        self.down_samples = nn.ModuleList([])
-        for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(zip(in_out_dims, *layer_params)):
-            down_sample = level != (self.num_levels - 1)
-            cat_dims.append(set_default_item(level != (self.num_levels - 1), out_dim, 0))
-            self.down_samples.append(
-                DownSampleBlock(
-                    in_dim, out_dim, time_embed_dim, text_dim, res_block_num, groups, head_dim, expansion_ratio,
-                    compression_ratio, down_sample, self_attention
-                )
-            )
-
-    def forward(self, x, time, context=None, context_mask=None):
-        time_embed = self.to_time_embed(time)
-        if exist(context):
-            time_embed = self.feature_pooling(time_embed, context, context_mask)
-
-        hidden_states = []
-        x = self.in_layer(x)
-        for level, down_sample in enumerate(self.down_samples):
-            x = down_sample(x, time_embed, context, context_mask)
-            if level != self.num_levels - 1:
-                hidden_states.append(x)
-        return hidden_states
-
-class ControlUNet(nn.Module):
-
-    def __init__(self,
-                 model_channels,
-                 init_channels=None,
-                 num_channels=3,
-                 out_channels=4,
-                 time_embed_dim=None,
-                 context_dim=None,
-                 groups=32,
-                 head_dim=64,
-                 expansion_ratio=4,
-                 compression_ratio=2,
-                 dim_mult=(1, 2, 4, 8),
-                 num_blocks=(3, 3, 3, 3),
-                 add_cross_attention=(False, True, True, True),
-                 add_self_attention=(False, True, True, True),
-                 control_net_channels=5,
-                 *args,
-                 **kwargs,
-                 ):
-        super().__init__()
-        init_channels = init_channels or model_channels
-        self.to_time_embed = nn.Sequential(
-            SinusoidalPosEmb(init_channels),
-            nn.Linear(init_channels, time_embed_dim),
-            nn.SiLU(),
-            nn.Linear(time_embed_dim, time_embed_dim)
-        )
-        self.feature_pooling = AttentionPolling(time_embed_dim, context_dim, head_dim)
-
-        self.in_layer = nn.Conv2d(num_channels, init_channels, kernel_size=3, padding=1)
-
-        hidden_dims = [init_channels, *map(lambda mult: model_channels * mult, dim_mult)]
-        in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
-        text_dims = [set_default_item(is_exist, context_dim) for is_exist in add_cross_attention]
-        layer_params = [num_blocks, text_dims, add_self_attention]
-        rev_layer_params = map(reversed, layer_params)
-
-        cat_dims = []
-        self.num_levels = len(in_out_dims)
-        self.down_samples = nn.ModuleList([])
-        for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(zip(in_out_dims, *layer_params)):
-            down_sample = level != (self.num_levels - 1)
-            cat_dims.append(set_default_item(level != (self.num_levels - 1), out_dim, 0))
-            self.down_samples.append(
-                DownSampleBlock(
-                    in_dim, out_dim, time_embed_dim, text_dim, res_block_num, groups, head_dim, expansion_ratio,
-                    compression_ratio, down_sample, self_attention
-                )
-            )
-
-        self.up_samples = nn.ModuleList([])
-        for level, ((out_dim, in_dim), res_block_num, text_dim, self_attention) in enumerate(zip(reversed(in_out_dims), *rev_layer_params)):
-            up_sample = level != 0
-            self.up_samples.append(
-                UpSampleBlock(
-                    in_dim, cat_dims.pop(), out_dim, time_embed_dim, text_dim, res_block_num, groups, head_dim,
-                    expansion_ratio, compression_ratio, up_sample, self_attention
-                )
-            )
-
-        self.out_layer = nn.Sequential(
-            nn.GroupNorm(groups, init_channels),
-            nn.SiLU(),
-            nn.Conv2d(init_channels, out_channels, kernel_size=3, padding=1)
-        )
-
-        self.control_net = ControlNetModel(model_channels,
-                                            init_channels,
-                                            control_net_channels,
-                                            out_channels,
-                                            time_embed_dim,
-                                            context_dim,
-                                            groups,
-                                            head_dim,
-                                            expansion_ratio,
-                                            compression_ratio,
-                                            dim_mult,
-                                            num_blocks,
-                                            add_cross_attention,
-                                            add_self_attention)
-
-    def forward(self, x, time, context=None, context_mask=None, control_net_data=None):
-        time_embed = self.to_time_embed(time)
-        if exist(context):
-            time_embed = self.feature_pooling(time_embed, context, context_mask)
-
-        control_net_hiddens =  self.control_net(control_net_data, time, context, context_mask)
-        hidden_states = []
-        x = self.in_layer(x)
-        for level, down_sample in enumerate(self.down_samples):
-            x = down_sample(x, time_embed, context, context_mask)
-            if level != self.num_levels - 1:
-                x += control_net_hiddens.pop(0)
-                hidden_states.append(x)
-        for level, up_sample in enumerate(self.up_samples):
-            if level != 0:
-                x = torch.cat([x, hidden_states.pop()], dim=1)
-            x = up_sample(x, time_embed, context, context_mask)
-        x = self.out_layer(x)
-        return x
-
-
-def get_control_unet(conf):
-    unet = ControlUNet(**conf)
-    return unet
-
-
-def get_unet(conf):
-    unet = UNet(**conf)
-    return unet

Kandinsky-3/kandinsky3/model/utils.py

@@ -1,62 +0,0 @@
-from torch.nn import Identity
-from einops import rearrange
-
-
-def exist(item):
-    return item is not None
-
-
-def set_default_item(condition, item_1, item_2=None):
-    if condition:
-        return item_1
-    else:
-        return item_2
-
-
-def set_default_layer(condition, layer_1, args_1=[], kwargs_1={}, layer_2=Identity, args_2=[], kwargs_2={}):
-    if condition:
-        return layer_1(*args_1, **kwargs_1)
-    else:
-        return layer_2(*args_2, **kwargs_2)
-
-
-def get_tensor_items(x, pos, broadcast_shape):
-    device = pos.device
-    bs = pos.shape[0]
-    ndims = len(broadcast_shape[1:])
-    x = x.cpu()[pos.cpu()]
-    return x.reshape(bs, *((1,) * ndims)).to(device)
-
-
-def local_patching(x, height, width, group_size):
-    if group_size > 0:
-        x = rearrange(
-            x, 'b c (h g1) (w g2) -> b (h w) (g1 g2) c',
-            h=height//group_size, w=width//group_size, g1=group_size, g2=group_size
-        )
-    else:
-        x = rearrange(x, 'b c h w -> b (h w) c', h=height, w=width)
-    return x
-
-
-def local_merge(x, height, width, group_size):
-    if group_size > 0:
-        x = rearrange(
-            x, 'b (h w) (g1 g2) c -> b c (h g1) (w g2)',
-            h=height//group_size, w=width//group_size, g1=group_size, g2=group_size
-        )
-    else:
-        x = rearrange(x, 'b (h w) c -> b c h w', h=height, w=width)
-    return x
-
-
-def global_patching(x, height, width, group_size):
-    x = local_patching(x, height, width, height//group_size)
-    x = x.transpose(-2, -3)
-    return x
-
-
-def global_merge(x, height, width, group_size):
-    x = x.transpose(-2, -3)
-    x = local_merge(x, height, width, height//group_size)
-    return x

Kandinsky-3/kandinsky3/movq.py

@@ -1,431 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-import torch.nn.functional as F
-
-from .utils import freeze
-
-
-def nonlinearity(x):
-    return x*torch.sigmoid(x)
-
-
-class SpatialNorm(nn.Module):
-    def __init__(
-        self, f_channels, zq_channels=None, norm_layer=nn.GroupNorm, freeze_norm_layer=False, add_conv=False, **norm_layer_params
-    ):
-        super().__init__()
-        self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
-        if zq_channels is not None:
-            if freeze_norm_layer:
-                for p in self.norm_layer.parameters:
-                    p.requires_grad = False
-            self.add_conv = add_conv
-            if self.add_conv:
-                self.conv = nn.Conv2d(zq_channels, zq_channels, kernel_size=3, stride=1, padding=1)
-            self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
-            self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
-    def forward(self, f, zq=None):
-        norm_f = self.norm_layer(f)
-        if zq is not None:
-            f_size = f.shape[-2:]
-            zq = torch.nn.functional.interpolate(zq, size=f_size, mode="nearest")
-            if self.add_conv:
-                zq = self.conv(zq)
-            norm_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
-        return norm_f
-
-
-def Normalize(in_channels, zq_ch=None, add_conv=None):
-    return SpatialNorm(
-            in_channels, zq_ch, norm_layer=nn.GroupNorm,
-            freeze_norm_layer=False, add_conv=add_conv, num_groups=32, eps=1e-6, affine=True
-        )
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512, zq_ch=None, add_conv=False):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels, zq_ch, add_conv=add_conv)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels, zq_ch, add_conv=add_conv)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb, zq=None):
-        h = x
-        h = self.norm1(h, zq)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h, zq)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels, zq_ch=None, add_conv=False):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-
-    def forward(self, x, zq=None):
-        h_ = x
-        h_ = self.norm(h_, zq)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-    
-    
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, **ignore_kwargs):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-
-    def forward(self, x):
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, zq_ch=None, add_conv=False, **ignorekwargs):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout,
-                                       zq_ch=zq_ch,
-                                       add_conv=add_conv)
-        self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout,
-                                       zq_ch=zq_ch,
-                                       add_conv=add_conv)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout,
-                                         zq_ch=zq_ch,
-                                         add_conv=add_conv))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in, zq_ch, add_conv=add_conv)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z, zq):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb, zq)
-        h = self.mid.attn_1(h, zq)
-        h = self.mid.block_2(h, temb, zq)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb, zq)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h, zq)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h, zq)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    
-class MoVQ(nn.Module):
-    
-    def __init__(self, generator_params):
-        super().__init__()
-        z_channels = generator_params["z_channels"]
-        self.encoder = Encoder(**generator_params)
-        self.quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
-        self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
-        self.decoder = Decoder(zq_ch=z_channels, **generator_params)
-        
-    @torch.no_grad()
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    @torch.no_grad()
-    def decode(self, quant):
-        decoder_input = self.post_quant_conv(quant)
-        decoded = self.decoder(decoder_input, quant)
-        return decoded
-    
-    
-def get_vae(conf):
-    movq = MoVQ(conf.params)
-    if conf.checkpoint is not None:
-        movq_state_dict = torch.load(conf.checkpoint)
-        movq.load_state_dict(movq_state_dict)
-    movq = freeze(movq)
-    return movq

Kandinsky-3/kandinsky3/setup.py

@@ -1,38 +0,0 @@
-from setuptools import setup
-
-setup(
-    name="kandinsky3",
-    packages=[
-        "kandinsky3",
-        "kandinsky3/model"
-    ],
-    install_requires=[
-                    "timm",
-                    "torch==1.10.1+cu111",
-                    "torchvision==0.11.2+cu111",
-                    "torchaudio==0.10.1",
-                    "pytorch_lightning==1.7.5",
-                    "transformers",
-                    "accelerate",
-                    "diffusers",
-                    "setuptools==59.5.0",
-                    "omegaconf",
-                    "datasets",
-                    "einops",
-                    "webdataset",
-                    "fsspec",
-                    "s3fs",
-                    "hydra-core",
-                    "scikit-image",
-                    "matplotlib",
-                    "wandb",
-                    "albumentations",
-                    "bezier",
-                    "scipy",
-                    "Pillow",
-                    "tqdm",
-                    "huggingface_hub"
-  
-    ],
-    author="",
-)

Kandinsky-3/kandinsky3/t2i_pipeline.py

@@ -1,106 +0,0 @@
-from typing import Union, List
-import PIL
-
-import torch
-import torchvision.transforms as T
-from einops import repeat
-
-from kandinsky3.model.unet import UNet
-from kandinsky3.movq import MoVQ
-from kandinsky3.condition_encoders import T5TextConditionEncoder
-from kandinsky3.condition_processors import T5TextConditionProcessor
-from kandinsky3.model.diffusion import BaseDiffusion, get_named_beta_schedule
-
-
-class Kandinsky3T2IPipeline:
-
-    def __init__(
-            self,
-            device_map: Union[str, torch.device, dict],
-            dtype_map: Union[str, torch.dtype, dict],
-            unet: UNet,
-            null_embedding: torch.Tensor,
-            t5_processor: T5TextConditionProcessor,
-            t5_encoder: T5TextConditionEncoder,
-            movq: MoVQ,
-            gan: bool,
-    ):
-        self.device_map = device_map
-        self.dtype_map = dtype_map
-        self.to_pil = T.ToPILImage()
-
-        self.unet = unet
-        self.null_embedding = null_embedding
-        self.t5_processor = t5_processor
-        self.t5_encoder = t5_encoder
-        self.movq = movq
-
-        self.gan = gan
-
-    def __call__(
-            self,
-            text: str,
-            negative_text: str = None,
-            images_num: int = 1,
-            bs: int = 1,
-            width: int = 1024,
-            height: int = 1024,
-            guidance_scale: float = 3.0,
-            steps: int = 50,
-            eta: float = 1.0
-    ) -> List[PIL.Image.Image]:
-
-        betas = get_named_beta_schedule('cosine', 1000)
-        base_diffusion = BaseDiffusion(betas, 0.99)
-        times = list(range(999, 0, -1000 // steps))
-        if self.gan:
-            times = list(range(979, 0, -250))
-
-        condition_model_input, negative_condition_model_input = self.t5_processor.encode(text, negative_text)
-        for input_type in condition_model_input:
-            condition_model_input[input_type] = condition_model_input[input_type][None].to(
-                self.device_map['text_encoder']
-            )
-
-        if negative_condition_model_input is not None:
-            for input_type in negative_condition_model_input:
-                negative_condition_model_input[input_type] = negative_condition_model_input[input_type][None].to(
-                    self.device_map['text_encoder']
-                )
-
-        pil_images = []
-        with torch.no_grad():
-            with torch.cuda.amp.autocast(dtype=self.dtype_map['text_encoder']):
-                context, context_mask = self.t5_encoder(condition_model_input)
-                if negative_condition_model_input is not None:
-                    negative_context, negative_context_mask = self.t5_encoder(negative_condition_model_input)
-                else:
-                    negative_context, negative_context_mask = None, None
-
-            k, m = images_num // bs, images_num % bs
-            for minibatch in [bs] * k + [m]:
-                if minibatch == 0:
-                    continue
-                bs_context = repeat(context, '1 n d -> b n d', b=minibatch)
-                bs_context_mask = repeat(context_mask, '1 n -> b n', b=minibatch)
-                if negative_context is not None:
-                    bs_negative_context = repeat(negative_context, '1 n d -> b n d', b=minibatch)
-                    bs_negative_context_mask = repeat(negative_context_mask, '1 n -> b n', b=minibatch)
-                else:
-                    bs_negative_context, bs_negative_context_mask = None, None
-
-                with torch.cuda.amp.autocast(dtype=self.dtype_map['unet']):
-                    images = base_diffusion.p_sample_loop(
-                        self.unet, (minibatch, 4, height // 8, width // 8), times, self.device_map['unet'],
-                        bs_context, bs_context_mask, self.null_embedding, guidance_scale, eta,
-                        negative_context=bs_negative_context, negative_context_mask=bs_negative_context_mask,
-                        gan=self.gan
-                    )
-
-                with torch.cuda.amp.autocast(dtype=self.dtype_map['movq']):
-                    images = torch.cat([self.movq.decode(image) for image in images.chunk(2)])
-                    images = torch.clip((images + 1.) / 2., 0., 1.)
-                    for images_chunk in images.chunk(1):
-                        pil_images += [self.to_pil(image) for image in images_chunk]
-
-        return pil_images

Kandinsky-3/kandinsky3/utils.py

@@ -1,71 +0,0 @@
-from omegaconf import OmegaConf
-import numpy as np
-from scipy import ndimage
-import torch.nn as nn
-from skimage.transform import resize
-
-
-def load_conf(config_path):
-    conf = OmegaConf.load(config_path)
-    conf.data.tokens_length = conf.common.tokens_length
-    conf.data.processor_names = conf.model.encoders.model_names
-    conf.data.dataset.seed = conf.common.seed
-    conf.data.dataset.image_size = conf.common.image_size
-
-    conf.trainer.trainer_params.max_steps = conf.common.train_steps
-    conf.scheduler.params.total_steps = conf.common.train_steps
-    conf.logger.tensorboard.name = conf.common.experiment_name
-
-    conf.model.encoders.context_dim = conf.model.unet_params.context_dim
-    return conf
-
-
-def freeze(model):
-    for p in model.parameters():
-        p.requires_grad = False
-    return model
-
-def unfreeze(model):
-    for p in model.parameters():
-        p.requires_grad = True
-    return model
-
-def zero_module(module):
-    for p in module.parameters():
-        nn.init.zeros_(p)
-    return module
-
-def resize_mask_for_diffusion(mask):
-    reduce_factor = max(1, (mask.size / 1024**2)**0.5)
-    resized_mask = resize(
-        mask,
-        (
-            (round(mask.shape[0] / reduce_factor) // 64) * 64,
-            (round(mask.shape[1] / reduce_factor) // 64) * 64
-        ),
-        preserve_range=True,
-        anti_aliasing=False
-    )
-
-    return resized_mask
-
-def resize_image_for_diffusion(image):
-    reduce_factor = max(1, (image.size[0] * image.size[1] / 1024**2)**0.5)
-    image = image.resize((
-        (round(image.size[0] / reduce_factor) // 64) * 64, (round(image.size[1] / reduce_factor) // 64) * 64
-    ))
-
-    return image
-
-def prepare_mask(mask):
-    ker = np.array([[1, 1,  1, 1, 1],
-        [1, 5,  5, 5, 1],
-        [1, 5, 44, 5, 1],
-        [1, 5,  5, 5, 1],
-        [1, 1,  1, 1, 1]]) / 100
-    out = ndimage.convolve(mask, ker)
-    out = ndimage.convolve(out, ker)
-    out = ndimage.convolve(out, ker)
-
-    mask = (out > 0).astype(int)
-    return mask

Kandinsky-3/requirements.txt

@@ -1,23 +0,0 @@
-timm
-
-pytorch_lightning==1.7.5
-transformers
-accelerate
-diffusers
-setuptools==59.5.0
-omegaconf
-datasets
-einops
-webdataset
-fsspec
-s3fs
-hydra-core
-scikit-image
-matplotlib
-wandb
-albumentations
-bezier
-scipy
-Pillow
-tqdm
-huggingface_hub