Instructions to use StonyBrook-CVLab/PixCell-256-Cell-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use StonyBrook-CVLab/PixCell-256-Cell-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("StonyBrook-CVLab/PixCell-256-Cell-ControlNet", torch_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
Update README.md
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README.md
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@@ -47,7 +47,7 @@ timm_kwargs = {
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'dynamic_img_size': True
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}
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uni_model = timm.create_model("hf-hub:MahmoodLab/UNI2-h", pretrained=True, **timm_kwargs)
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-
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uni_model.eval()
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uni_model.to(device);
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```
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'dynamic_img_size': True
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}
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uni_model = timm.create_model("hf-hub:MahmoodLab/UNI2-h", pretrained=True, **timm_kwargs)
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uni_transforms = create_transform(**resolve_data_config(uni_model.pretrained_cfg, model=uni_model))
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uni_model.eval()
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uni_model.to(device);
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```
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