Unconditional Image Generation
Diffusers
Safetensors
English
jlt
image-generation
class-conditional
flux2
clean-latent
Instructions to use BiliSakura/JLT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/JLT-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/JLT-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "golden retriever" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download demo.png from BiliSakura/JLT-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 89.5 kB
-
https://huggingface.co/BiliSakura/JLT-diffusers/resolve/main/demo.png
- Command line
-
hf download hf://BiliSakura/JLT-diffusers/demo.png
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curl -L -o demo.png https://huggingface.co/BiliSakura/JLT-diffusers/resolve/main/demo.png
89.5 kB
