Instructions to use ModelsLab/blipdiffusion-controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/blipdiffusion-controlnet 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-controlnet", 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
- Xet hash:
- ef111d1d2905cc7b1464d81806d816ab044f74c35c1ddf1eb233785c986a9867
- Size of remote file:
- 3.44 GB
- SHA256:
- 4f789a4d491581750b69ba3d8f83560c9dd85ee93d9f80006d9e9bcf24de0da5
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