List the VQGAN imagenet, wikiart, sflckr and openimages models in the model card

README.md

@@ -34,6 +34,16 @@ Every file is an unchanged copy of the original. The SHA-256 of each CLIP model
 | `torch_hub/gen-efficientnet-pytorch-771ce082….zip` | 68 KB | AdaBins encoder code (torch.hub) | [GitHub, commit 771ce08](https://github.com/rwightman/gen-efficientnet-pytorch/tree/771ce082b2ce6d033f55b3d47c1f77389ad3c180) | Apache-2.0 |
 | `vqgan/coco_first_stage.yaml` | 1 KB | VQGAN coco config | [batbot.ai](http://batbot.ai/models/VQGAN/coco_first_stage.yaml) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
 | `vqgan/coco_first_stage.ckpt` | 282 MB | VQGAN coco | [batbot.ai](http://batbot.ai/models/VQGAN/coco_first_stage.ckpt) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+| `vqgan/imagenet.yaml` | 1 KB | VQGAN imagenet config | [Heidelberg University](https://heibox.uni-heidelberg.de/f/274fb24ed38341bfa753/?dl=1) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+| `vqgan/imagenet.ckpt` | 935 MB | VQGAN imagenet | [Heidelberg University](https://heibox.uni-heidelberg.de/f/867b05fc8c4841768640/?dl=1) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+| `vqgan/wikiart.yaml` | 1 KB | VQGAN wikiart config | [pixray v1.7.1 release](https://github.com/pixray/pixray/releases/tag/v1.7.1) | see below |
+| `vqgan/wikiart.ckpt` | 959 MB | VQGAN wikiart | [pixray v1.7.1 release](https://github.com/pixray/pixray/releases/tag/v1.7.1) | see below |
+| `vqgan/sflckr.yaml` | 2 KB | VQGAN sflckr config | [Heidelberg University](https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fconfigs%2F2020-11-09T13-31-51-project.yaml&dl=1) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+| `vqgan/sflckr.ckpt` | 4.0 GB | VQGAN sflckr | [Heidelberg University](https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fcheckpoints%2Flast.ckpt&dl=1) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+| `vqgan/openimages.yaml` | 1 KB | VQGAN openimages config | [Heidelberg University](https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+| `vqgan/openimages.ckpt` | 359 MB | VQGAN openimages | [Heidelberg University](https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fckpts%2Flast.ckpt&dl=1) | MIT ([taming-transformers](https://github.com/CompVis/taming-transformers)) |
+
+The wikiart model's original host, `eaidata.bmk.sh`, no longer responds. These files are the copy the [pixray](https://github.com/pixray/pixray) project published in its v1.7.1 release (January 2022). They are byte-identical to the copy in [Somnai/pytti-vqgan-checkpoints](https://huggingface.co/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/`](https://huggingface.co/pxlpshr/pytti-models/tree/main/licenses).
 
@@ -54,6 +64,14 @@ be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c  clip/be1cfb55d
 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
 ```
 
 ## Credits
@@ -62,4 +80,5 @@ be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c  clip/be1cfb55d
 - AdaBins: Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka, [AdaBins: Depth Estimation using Adaptive Bins](https://arxiv.org/abs/2011.14141)
 - 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](https://arxiv.org/abs/2012.09841)
+- wikiart VQGAN: a community training on WikiArt, preserved by [pixray](https://github.com/pixray/pixray) (Tom White) and [Somnai/pytti-vqgan-checkpoints](https://huggingface.co/Somnai/pytti-vqgan-checkpoints)
 - [pytti-core](https://github.com/pytti-tools/pytti-core): sportsracer48 and the pytti-tools contributors