https://github.com/patil-suraj/stable-diffusion-jax

https://github.com/patil-suraj/stable-diffusion-jax

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/patil-suraj/stable-diffusion-jax
• Owner: patil-suraj
• License: mit
• Created: 2022-08-12T07:04:12.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-19T17:15:04.000Z (about 2 years ago)
• Last Synced: 2024-06-08T12:34:44.555Z (3 months ago)
• Language: Python
• Size: 402 KB
• Stars: 84
• Watchers: 5
• Forks: 9
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## TODOs:

- [x] Finish implementing the `UNet2D` model in `modeling_unte2d.py`. Port weights of any existing LDM unet from diffusers and verify equivalence. I've added the skleton of modules that we need to implement in the file.

- [x] Adapt the `PNDMScheduler` from `diffusers` for JAX: Use `jnp` arrays and make it stateless.

- [x] Add the KL module from (here)[https://github.dev/CompVis/stable-diffusion] in `modeling_vae.py` file. For inference we don't really need it, but would be nice to have for completeness. Port the weights of any existing KL VAE and verify equivalence.

- [x] Add an inference loop in `pipeline_stabel_diffusion`. We should able to `jit`/`pmap` the loop to deploy on TPUs.