More details in model card.

README.md

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 ## VQGAN-f16-16384
 
-Model converted to JAX from [boris/vqgan_f16_16384](https://huggingface.co/boris/vqgan_f16_16384).
-
-Model finetuned with [taming-transformers](https://github.com/CompVis/taming-transformers):
-* Training run
-  * [Part 1](https://wandb.ai/wandb/hf-flax-dalle-mini/runs/2021-07-09T15-33-11_dalle_vqgan?workspace=user-borisd13) - started from [vqgan_imagenet_f16_16384 checkpoint](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/) (pretrained on ImageNet)
-  * [Part 2](https://wandb.ai/wandb/hf-flax-dalle-mini/runs/2021-07-09T21-42-07_dalle_vqgan?workspace=user-borisd13) - continuation from Part 1
-* Dataset: subset of 2,268,720 images processed once originating from [Conceptual Captions 3M](https://ai.google.com/research/ConceptualCaptions/) and [OpenAI subset of YFCC100M](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md)
-* Checkpoint uploaded from last artifact version (see training run)
+### Model Description
+
+This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in [Taming Transformers for High-Resolution Image Synthesis](https://compvis.github.io/taming-transformers/) ([CVPR paper](https://openaccess.thecvf.com/content/CVPR2021/html/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.html)).
+
+The model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.
+
+This version of the model uses a reduction factor `f=16` and a vocabulary of `13,384` tokens.
+
+As an example of how the reduction factor works, images of size `256x256` are encoded to sequences of `256` tokens: `256/16 * 256/16`. Images of `512x512` would result in sequences of `1024` tokens.
+
+### Datasets Used for Training
+
+* ImageNet. We didn't train this model from scratch. Instead, we started from [a checkpoint pre-trained on ImageNet](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/).
+* [Conceptual Captions 3M](https://ai.google.com/research/ConceptualCaptions/) (CC3M).
+* [OpenAI subset of YFCC100M](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md).
+
+We fine-tuned on CC3M and YFCC100M to improve the encoding quality of people and faces, which are not very well represented in ImageNet. We used a subset of 2,268,720 images from CC3M and YFCC100M for this purpose.
+
+### Training Process
+
+Finetuning was performed in PyTorch using [taming-transformers](https://github.com/CompVis/taming-transformers). The full training process and model preparation includes these steps:
+
+* Pre-training on ImageNet. Previously performed. We used [this checkpoint](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887).
+* Fine-tuning, [Part 1](https://wandb.ai/wandb/hf-flax-dalle-mini/runs/2021-07-09T15-33-11_dalle_vqgan?workspace=user-borisd13).
+* Fine-tuning, [Part 2](https://wandb.ai/wandb/hf-flax-dalle-mini/runs/2021-07-09T21-42-07_dalle_vqgan?workspace=user-borisd13) – continuation from Part 1. The final checkpoint was uploaded to [boris/vqgan_f16_16384](https://huggingface.co/boris/vqgan_f16_16384).
+* Conversion to JAX, which is the model described in this card.
+
+### How to Use
+
+The checkpoint can be loaded using [Suraj Patil's implementation](https://github.com/patil-suraj/vqgan-jax) of `VQModel`.
+
+* Encoding. `coming soon`.
+* Decoding. `coming soon`.
+
+### Other
+
+This model was successfully used as part of the implementation of [DALL·E mini](https://github.com/borisdayma/dalle-mini). Our [report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA) contains more details on how to leverage it in an image encoding / generation pipeline.