huggingface / huggingface/diffusers
SD3 dreambooth training - caching of embeddings and vae representations
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Descripción
**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.
Guía de contribución
Línea de trabajo
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.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- python, pytorch
- Área
- machine-learning
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 35/100