Instructions to use DGSpitzer/DGSpitzer-Art-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DGSpitzer/DGSpitzer-Art-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("DGSpitzer/DGSpitzer-Art-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 `scale_factor` to vae config. (#1)
- Add `scale_factor` to vae config. (2ee9c4c9034331cb882ea1c9942c5da1ab6ea4cc) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>a58069e
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Upload diffusion_pytorch_model.bin
8c7d75e -
Model Config Files
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