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:
- 310504b7eb5dc9d27464440b7466eca32f7e5a7aac3813536b535ba7a47ff4b4
- Size of remote file:
- 1.45 GB
- SHA256:
- da137629679b3714e8a47009ec8f657e48ee6b7d741c05c9c9607a218621f9df
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