Unconditional Image Generation
Diffusers
Safetensors
English
priming
image-generation
class-conditional
one-step
imagenet
Instructions to use BiliSakura/Priming-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/Priming-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/Priming-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "golden retriever" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download demo.png from BiliSakura/Priming-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 131 kB
-
https://huggingface.co/BiliSakura/Priming-diffusers/resolve/main/demo.png
- Command line
-
hf download hf://BiliSakura/Priming-diffusers/demo.png
-
curl -L -o demo.png https://huggingface.co/BiliSakura/Priming-diffusers/resolve/main/demo.png
131 kB

- Xet hash:
- a02e8f7e58284a9d180b26cf722b0b6b83b2f0c962649ec56ebf70dc62b66202
- Size of remote file:
- 131 kB
- SHA256:
- 8b0f34bbd320b2f03c941b73f48449f03827514f9aca868feb5e1e886bb40f6f
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