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

• DALLE-pytorch

  2 90 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/

• guided-diffusion

  1 26 3.6 Python

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

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• DALLE-pytorch

  20 5,492 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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