Instructions to use ModelsLab/blipdiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/blipdiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ModelsLab/blipdiffusion", 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
Download unet/diffusion_pytorch_model.bin from ModelsLab/blipdiffusion: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/ModelsLab/blipdiffusion/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://ModelsLab/blipdiffusion/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/ModelsLab/blipdiffusion/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- 385db010fc8173b62ad4e9a76124452e0756525247555cc6ca4e6209c3157e1a
- Size of remote file:
- 3.44 GB
- SHA256:
- c42d7cf4e4f028e3c955e13f4406231c946b602329de281b67c214f5efcb5137
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