Instructions to use lambda/miniSD-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lambda/miniSD-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lambda/miniSD-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
How do I finetune the attention layers of the unet?
#3
by Meeeeq - opened
There are optimized dreambooth repositories, but I don't know if I have access to this feature. Do you have any published code that demontrates this finetuning the attention layers? Thx
Also curious...
@Meeeeq check my implementation
https://github.com/kopyl/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
Just add --train_attention_only argument when run train_text_to_image.py
Now i'm curious why did the author trained attention-only first then full unet instrad of training full unet from the beginning...