pxlpshr/pytti-models

PyTTI Portable models

Mirror of the pretrained models that PyTTI Portable downloads on first use, and of the five Python packages its installer needs from GitHub. PyTTI Portable fetches each model from here first, checks its size and SHA-256, and falls back to the original source if this repo can't be reached. Its installer installs the packages from the wheels in wheels/, which pip checks against their SHA-256.

Every model file is an unchanged copy of the original. The SHA-256 of each CLIP model is also part of its original download link, which OpenAI's clip package checks.

Files

File	Size	Used for	Original source	License
clip/40d36571…/ViT-B-32.pt	338 MB	CLIP ViT-B/32	OpenAI	MIT (CLIP)
clip/5806e77c…/ViT-B-16.pt	335 MB	CLIP ViT-B/16	OpenAI	MIT (CLIP)
clip/b8cca3fd…/ViT-L-14.pt	890 MB	CLIP ViT-L/14	OpenAI	MIT (CLIP)
clip/3035c92b…/ViT-L-14-336px.pt	891 MB	CLIP ViT-L/14@336px	OpenAI	MIT (CLIP)
clip/afeb0e10…/RN50.pt	244 MB	CLIP RN50	OpenAI	MIT (CLIP)
clip/8fa8567b…/RN101.pt	278 MB	CLIP RN101	OpenAI	MIT (CLIP)
clip/7e526bd1…/RN50x4.pt	402 MB	CLIP RN50x4	OpenAI	MIT (CLIP)
clip/52378b40…/RN50x16.pt	630 MB	CLIP RN50x16	OpenAI	MIT (CLIP)
clip/be1cfb55…/RN50x64.pt	1.3 GB	CLIP RN50x64	OpenAI	MIT (CLIP)
adabins/AdaBins_nyu.pt	897 MB	Depth for 3D mode	deforum/AdaBins	GPL-3.0 (AdaBins)
torch_hub/checkpoints/tf_efficientnet_b5_ap-9e82fae8.pth	117 MB	AdaBins encoder	GitHub release	Apache-2.0 (gen-efficientnet-pytorch)
torch_hub/gen-efficientnet-pytorch-771ce082….zip	68 KB	AdaBins encoder code (torch.hub)	GitHub, commit 771ce08	Apache-2.0
vqgan/coco_first_stage.yaml	1 KB	VQGAN coco config	batbot.ai	MIT (taming-transformers)
vqgan/coco_first_stage.ckpt	282 MB	VQGAN coco	batbot.ai	MIT (taming-transformers)
vqgan/imagenet.yaml	1 KB	VQGAN imagenet config	Heidelberg University	MIT (taming-transformers)
vqgan/imagenet.ckpt	935 MB	VQGAN imagenet	Heidelberg University	MIT (taming-transformers)
vqgan/wikiart.yaml	1 KB	VQGAN wikiart config	pixray v1.7.1 release	see below
vqgan/wikiart.ckpt	959 MB	VQGAN wikiart	pixray v1.7.1 release	see below
vqgan/sflckr.yaml	2 KB	VQGAN sflckr config	Heidelberg University	MIT (taming-transformers)
vqgan/sflckr.ckpt	4.0 GB	VQGAN sflckr	Heidelberg University	MIT (taming-transformers)
vqgan/openimages.yaml	1 KB	VQGAN openimages config	Heidelberg University	MIT (taming-transformers)
vqgan/openimages.ckpt	359 MB	VQGAN openimages	Heidelberg University	MIT (taming-transformers)

The wikiart model's original host, eaidata.bmk.sh, no longer responds. These files are the copy the pixray project published in its v1.7.1 release (January 2022). They are byte-identical to the copy in Somnai/pytti-vqgan-checkpoints, and every other VQGAN file here also matches that repo's manifest.json. The wikiart model is a community training on WikiArt with taming-transformers; no separate license was published with it.

The license texts are in licenses/.

Package wheels

install.bat installs these five packages from the wheels below instead of from their GitHub repositories, so installing needs neither Git nor those repositories. Each wheel was built with pip wheel --no-deps from the commit shown, with the source unchanged. pytti-core is the version PyTTI Portable's patches are written for.

File	Size	Built from	License
wheels/pyttitools_core-0.0.1-py3-none-any.whl	14 MB	pytti-tools/pytti-core @ b5070aa	MIT
wheels/pyttitools_adabins-0.0.1-py3-none-any.whl	34 KB	pytti-tools/AdaBins @ 9b57712, a fork of shariqfarooq123/AdaBins	GPL-3.0
wheels/pyttitools_gma-0.0.1-py3-none-any.whl	84 MB	pytti-tools/GMA @ 27e8b4e, a fork of zacjiang/GMA; includes GMA's four pretrained optical flow checkpoints	WTFPL
wheels/pyttitools_taming_transformers-0.0.1-py3-none-any.whl	66 KB	pytti-tools/taming-transformers @ f44c0b1, a fork of CompVis/taming-transformers	MIT
wheels/clip-1.0-py3-none-any.whl	1.3 MB	openai/CLIP @ d05afc4	MIT

Each wheel contains its project's full Python source and license file.

SHA-256

40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af  clip/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt
5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f  clip/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt
b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836  clip/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt
3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02  clip/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt
afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762  clip/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt
8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599  clip/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt
7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd  clip/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt
52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa  clip/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt
be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c  clip/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt
3c917d1b86d058918d4055e70b2cdb9696ec4967bb2d8f05c0051263c1ac9641  adabins/AdaBins_nyu.pt
9e82fae840d76e8d3d64d363b3dca7678a597e1d086b9affdb78eb3f38a3da16  torch_hub/checkpoints/tf_efficientnet_b5_ap-9e82fae8.pth
9d37b77cc82c794d9818ba745124e455af31308f3c4422853eeca491b9a468c1  torch_hub/gen-efficientnet-pytorch-771ce082b2ce6d033f55b3d47c1f77389ad3c180.zip
17d0c2d9fda59eccade2a6070d833804b759dc817da42c2268be8cf1eb047676  vqgan/coco_first_stage.yaml
106cc20fde571df14afc4349d62218d8213cf47177c682ecaa78a8c28b12f9de  vqgan/coco_first_stage.ckpt
00e2c6189926f1d89ecfef73e9598db77981c1982f0555fbade963ffd16143c7  vqgan/imagenet.yaml
845a68805098cb666420d5db93df53f3a3b6dd443e6dd85c05759c5b998cd663  vqgan/imagenet.ckpt
6e78241d2828ff35b8381839ce249c24a6c468ed429760d33a25e98e7f24499a  vqgan/wikiart.yaml
bd08bb46301f98be1712bb2be9f8868cea30b53137cd985d4d6e8da8b3e02c36  vqgan/wikiart.ckpt
c27f012996f3f1f02580f063be47fd60bdd8528efb04d7318b2cbe8a86505b1b  vqgan/sflckr.yaml
8a8adea3da8dab412675772831370dd948e0aa97bb11a9488a2f328b58dbc929  vqgan/sflckr.ckpt
91110bab325067b37f85d1e75895a837d59c292bae4f3097f80c518714d5caff  vqgan/openimages.yaml
5cd6c74810ab97e00e942c25403f73afc081e8b19987b31ec0d9ff5b68e7ab14  vqgan/openimages.ckpt
b8c5c4f5f3187cf5700861fc7b776f5a79a0e85e8f66ad5b7eb065c17d108267  wheels/pyttitools_core-0.0.1-py3-none-any.whl
0506d733c4cb5ca87684cafac16ee040f65c35cb377463bbbc657f9c7e2dc5d9  wheels/pyttitools_adabins-0.0.1-py3-none-any.whl
900f72586819230e05ac8fcb051ee5a0eb4cfb60de939f0bdf970a2edb63fc02  wheels/pyttitools_gma-0.0.1-py3-none-any.whl
d0bf08df0053c68932ed965396bcde74468bddb89122dc39819dcc91b4663bf3  wheels/pyttitools_taming_transformers-0.0.1-py3-none-any.whl
24d6d72bf73d81012581a3add81344ff3f05d854161fd94f18dbef6274735a40  wheels/clip-1.0-py3-none-any.whl

Credits

• CLIP: OpenAI, Learning Transferable Visual Models From Natural Language Supervision
• AdaBins: Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka, AdaBins: Depth Estimation using Adaptive Bins
• EfficientNet weights and gen-efficientnet-pytorch: Ross Wightman, ported from Google's TensorFlow EfficientNet (AdvProp)
• VQGAN: Patrick Esser, Robin Rombach, Björn Ommer, Taming Transformers for High-Resolution Image Synthesis
• wikiart VQGAN: a community training on WikiArt, preserved by pixray (Tom White) and Somnai/pytti-vqgan-checkpoints
• GMA: Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley, Learning to Estimate Hidden Motions with Global Motion Aggregation
• pytti-core: sportsracer48 and the pytti-tools contributors