huggingface / huggingface/diffusers

SD3 dreambooth training - caching of embeddings and vae representations

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Langage dominant
Python
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Forks
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Merge moyen
3 j 3 h
PR mergées (30 j)
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Description

**Is your feature request related to a problem? Please describe.**
The current implementation of the dreambooth trainings for both loras and finetuning is very memory intensive.

**Describe the solution you'd like.**
I would like the option to pre-cache the VAE representations of the images and the text encoder representations so that training could be done without the text encoders being in VRAM.

**Describe alternatives you've considered.**
I don't believe there are alternatives to reducing the VRAM requirement for training.

Guide de contribution

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Piste de recherche

Start by locating the SD3 DreamBooth training entry points and how they currently encode images and text during training. The requested result is optional pre-caching of VAE and text-encoder representations so training can run without the text encoders in VRAM; the issue names no specific files or tests.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Évaluation

Stack technique
python, pytorch
Domaine
machine-learning
Type d'issue
Fonctionnalité
Difficulté
4/5
Temps estimé
3-5 jours
Activité
À l'abandon
Clarté
Plutôt claire
Accessibilité débutants
35/100

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