Instructions to use R-J/StainFuser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use R-J/StainFuser with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("R-J/StainFuser", 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
UPD: upload training SD
Browse files
training/sd21b_unet2dcondition.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:1939ef0f3ff90123aaf90f83a3fcf45ff05c8b305e39b19365a20aa3a015abca
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size 3463926085
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