Instructions to use nitrosocke/archer-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/archer-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/archer-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. (#5)
- Add `clip_sample=False` to scheduler to make model compatible with DDIM. (4ee4470fe56d8837dc6251232425a2630de471fb) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>013f2c2
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Add Diffusers model (#1)
- Add Diffusers model (a726aa42047536a53bf61885448e82b95ca231de)e2c6509