huggingface / huggingface/diffusers

SD3 dreambooth training - caching of embeddings and vae representations

Abierto
#8,540 2 comentarios 0 reacciones 0 asignados Ver en GitHub
stale
Lenguaje dominante
Python
Estrellas
34.5k
Forks
7.3k
Merge medio
3 d 3 h
PR fusionados (30 d)
91

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

Abrir la 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

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.