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
Add JLT-H-1 transformer weights
Browse files
JLT-H-1/transformer/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 3805232112
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