Unconditional Image Generation
Diffusers
Safetensors
English
w-flow
image-generation
class-conditional
one-step
imagenet
Instructions to use BiliSakura/W-Flow-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/W-Flow-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/W-Flow-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "golden retriever" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download demo.png from BiliSakura/W-Flow-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 126 kB
-
https://huggingface.co/BiliSakura/W-Flow-diffusers/resolve/main/demo.png
- Command line
-
hf download hf://BiliSakura/W-Flow-diffusers/demo.png
-
curl -L -o demo.png https://huggingface.co/BiliSakura/W-Flow-diffusers/resolve/main/demo.png
126 kB

- Xet hash:
- 6eb239e48b09cef3dc2407a8e2ca51ceca676f17a80e0f24e6a8755d1b028f9f
- Size of remote file:
- 126 kB
- SHA256:
- ce3795096ea6b53c0c3a3b758bee8736ba5498175f1be1aebf91ac6d59ca6713
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