Instructions to use nitrosocke/redshift-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/redshift-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nitrosocke/redshift-diffusion", 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 Settings
- Draw Things
- DiffusionBee
Commit History
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Add `clip_sample=False` to scheduler to make model compatible with DDIM. (#17)
- Add `clip_sample=False` to scheduler to make model compatible with DDIM. (85f6ec58252f8a06c905ff3b8de8b1da8c95bdde) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>143bda7
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uploading diffusers (#3)
- Add Diffusers weights for (b09d5930227a8770e0fabe16c7b515b28460641c) Co-authored-by: Kevin <Kevin@users.noreply.huggingface.co>cfa394e