Instructions to use d-wang26/wd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/wd with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/wd", 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
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Download diffusers/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
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https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md
- Command line
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hf download hf://d-wang26/wd/diffusers/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md
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curl -L -o k_diffusion.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md
1.35 kB
K-Diffusion
k-diffusion is a popular library created by Katherine Crowson. We provide StableDiffusionKDiffusionPipeline and StableDiffusionXLKDiffusionPipeline that allow you to run Stable DIffusion with samplers from k-diffusion.
Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers here
StableDiffusionKDiffusionPipeline
[[autodoc]] StableDiffusionKDiffusionPipeline
StableDiffusionXLKDiffusionPipeline
[[autodoc]] StableDiffusionXLKDiffusionPipeline