Instructions to use openbmb/VisCPM-Paint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use openbmb/VisCPM-Paint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("openbmb/VisCPM-Paint", 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:
- 4e16c077e102b161b4e91437c75900948adbc3a8f32c5267eccac0d4053b708f
- Size of remote file:
- 3.46 GB
- SHA256:
- 2f47319f8c12bd06960e1cde98a1e588e0e0eee6041914a20fe495cb8b46a761
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