aljaz/DALLE2-pytorch
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inform what is possible

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Phil Wang
2022-04-27 08:25:16 -07:00
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@@ -499,10 +499,12 @@ loss.backward()
### DALL-E2 with Latent Diffusion
This repository decides to take the next step and offer DALL-E2 combined with <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a>, from Rombach et al.
This repository decides to take the next step and offer DALL-E v2 combined with <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a>, from Rombach et al.
You can use it as follows. Latent diffusion can be limited to just the first U-Net in the cascade, or to any number you wish.
The repository also comes equipped with all the necessary settings to recreate `ViT-VQGan` from the <a href="https://arxiv.org/abs/2110.04627">Improved VQGans</a> paper. Furthermore, the <a href="https://github.com/lucidrains/vector-quantize-pytorch">vector quantization</a> library also comes equipped to do <a href="https://arxiv.org/abs/2203.01941">residual or multi-headed quantization</a>, which I believe will give an even further boost in performance to the autoencoder.
```python
import torch
from dalle2_pytorch import Unet, Decoder, CLIP, VQGanVAE