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 vision_encoder/pytorch_model.bin from ModelsLab/blipdiffusion: direct link, hf CLI and curl.
- Browser
- Download file 1.16 GB
-
https://huggingface.co/ModelsLab/blipdiffusion/resolve/1ac3ae3fd6895b8ee5ac445c50b8b09aa7215d0e/vision_encoder/pytorch_model.bin
- Command line
-
hf download hf://ModelsLab/blipdiffusion@1ac3ae3fd6895b8ee5ac445c50b8b09aa7215d0e/vision_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ModelsLab/blipdiffusion/resolve/1ac3ae3fd6895b8ee5ac445c50b8b09aa7215d0e/vision_encoder/pytorch_model.bin
1.16 GB
- Xet hash:
- 35f3dd2f07ffabc38b982c552ecee4df147858a0564775b7eeb6b40975c52b8a
- Size of remote file:
- 1.16 GB
- SHA256:
- 84e3623a20ab1912527e682ca0467f1260599e845a69731335e5482f10482150
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