huggingface / huggingface/diffusers

Elastic-DiT support

Aperta
#13,191 2 commenti 2 reazioni 0 assegnatari Vedi su GitHub
Lingua principale
Python
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34.5k
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Merge medio
3g 3h
PR unite (30g)
91

Descrizione

### Model/Pipeline/Scheduler description

Elastic-DiT was released a few hours ago: https://github.com/wangjiangshan0725/Elastic-DiT

It's supposed to greatly accelerate (~2x speed) the diffusion process of 2D image generators like qwen image and Flux with little impact to the quality of the output. This project was leaded by the creator of Hunyuan3D who used it also for the image->3d model (but this is likely out of scope for the diffusers project as it doesn't support 3D generation).

The code is available in the Github repo, the model checkpoint is hosted on HuggingFace and the paper is available on Arxiv.

### 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

https://github.com/wangjiangshan0725/Elastic-DiT
https://github.com/wangjiangshan0725/Elastic-DiT/blob/65535c3671199b0e26e96d7493cdeb71b2bcf3d8/diffsynth/pipelines/qwen_image_elastic_dit.py
https://huggingface.co/wjs0725/Elastic-DiT

@wangjiangshan0725
@Zeqiang-Lai

Guida per i contributori

Apri la guida per i contributori

Direzione di ricerca

Start by reading the Elastic-DiT repository and its referenced diffsynth/pipelines/qwen_image_elastic_dit.py implementation, then inspect the Elastic-DiT checkpoint on Hugging Face. Determine the appropriate diffusers entry points and validation needed for Qwen Image and Flux support; done means the model is integrated and its output can be validated against the published implementation.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
python, pytorch
Ambito
machine-learning
Tipo di issue
Funzionalità
Difficoltà
5/5
Tempo stimato
Più di una settimana
Stato di attività
Ferma
Chiarezza
Da chiarire
Idoneità per principianti
25/100

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