huggingface / huggingface/diffusers
Modular Pipeline: support for PixArtAlphaPipeline
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Beschreibung
### Model/Pipeline/Scheduler description
I would like to implement a modular version of the PixArt pipeline family, as discussed in [#13295](https://github.com/huggingface/diffusers/issues/13295#issuecomment-4103395537).
The initial scope includes the `PixArtAlphaPipeline` under [src/diffusers/pipelines/pixart_alpha/](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/pixart_alpha). Currently, the focus is on PixArt-α only. The implementation would be structured so that `PixArtSigmaPipeline` support can be added later in a follow-up.
## Current plan:
Add a new modular PixArt package under `src/diffusers/modular_pipelines/`, following the structure of the existing built-in modular pipelines.
- `encoders` for prompt encoding
- `before_denoise` for input / latent preparation and timestep setup
- `denoise` for the main PixArt denoising loop
- `decoders` for VAE decoding and post-processing
I’m happy to implement this myself, and this would be my first contribution to Diffusers.
### Open source status
- [x] The model implementation is available.
- [x] The model weights are available (Only relevant if addition is not a scheduler).
### Provide useful links for the implementation
## Relevant references:
- Current Diffusers implementation:
- `src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py`
- `src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py`
- PixArt docs / weights:
- https://huggingface.co/docs/diffusers/api/pipelines/pixart
- https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS
Beitragsleitfaden
Rechercherichtung
Start with src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py and review the existing built-in modular pipeline structure. Organize a new package under src/diffusers/modular_pipelines/ with encoders, before_denoise, denoise, and decoders for PixArt-α. Done means PixArtAlphaPipeline has modular support while PixArtSigmaPipeline remains out of scope.
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Bewertung
- Tech-Stack
- python, pytorch
- Bereich
- machine-learning
- Issue-Typ
- Feature
- Schwierigkeit
- 4/5
- Geschätzter Aufwand
- 3-5 Tage
- Aktivitätsstatus
- Ruhig
- Klarheit
- Größtenteils klar
- Anfängerfreundlichkeit
- 48/100