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:
- 3a38b7118695e3340263d3c93a50f1163f3d4a60b02abbcd3b26081fce84b8cf
- Size of remote file:
- 492 MB
- SHA256:
- 2579756b867c003d1b8d06b05b8e1a0bd781cc4551c0ca2c8d43544cb2ef0d8e
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