[Project] DALL-3 - generate better images with fewer tokens through clip guided diffusion

1. DALLE-pytorch

   1 2 89 4.7 Python

   Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch (by Jack000)

   

   link to images and code: https://github.com/Jack000/DALLE-pytorch/

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3. guided-diffusion

   2 1 28 3.6 Python

   

   link to diffusion model: https://github.com/Jack000/guided-diffusion

4. DALLE-pytorch

   3 20 5,598 2.5 Python

   Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

   

   If in general DDPM > GAN > VAE, why do transformer image generators all use VQVAE to decode images? Wouldn't it be better to use a diffusion model? I was wondering about this and started experimenting with different ways to decode vector-quantized embeddings with a diffusion model - see discussion here After a lot of trial and error I got something that works pretty well.

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