huggingface / huggingface/diffusers

Request to implement FreeScale, a new diffusion scheduler

Aperta
#10,281 1 commento 0 reazioni 0 assegnatari Vedi su GitHub
consider-for-modular-diffusers stale
Lingua principale
Python
Stelle
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Merge medio
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PR unite (30g)
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Descrizione

### Model/Pipeline/Scheduler description

FreeScale is a tuning-free method for higher-resolution visual generation, unlocking the 8k image generation for pre-trained SDXL! Compared to direct inference by SDXL, FreeScale brings negligible additional memory and time costs.

![fig_teaser](https://github.com/user-attachments/assets/3eef38cc-3642-42a7-b5e7-8b32c32ecc77)

![fig_diff8k](https://github.com/user-attachments/assets/8cec7c55-011e-4434-81e3-1e80dd5dd003)

### 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

- Project: http://haonanqiu.com/projects/FreeScale.html
- Paper: https://arxiv.org/abs/2412.09626
- Code: https://github.com/ali-vilab/FreeScale
- Hugging Face Demo: https://huggingface.co/spaces/MoonQiu/FreeScale

The code changes of FreeScale are not complicated, but I do not know how to integrate them into diffusers smoothly. If you have questions about FreeScale, please ask me(@arthur-qiu).

Guida per i contributori

Apri la guida per i contributori

Direzione di ricerca

Start by reading the FreeScale paper and the linked implementation to understand the scheduler changes and how they should fit into diffusers. Compare the behavior with the linked Hugging Face demo; done means FreeScale is integrated smoothly into diffusers and its high-resolution generation behavior matches the documented method.

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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