Instructions to use xixircc/MetaRigCapture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xixircc/MetaRigCapture with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xixircc/MetaRigCapture", 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
MetaRigCapture / pretrained_weights /sd-image-variations-diffusers /unet /diffusion_pytorch_model.bin
- Xet hash:
- 46d9b51999fd8af0ecd4d3f7137796530b1dd3474e47dae2bb41b57471f4cc0e
- Size of remote file:
- 3.44 GB
- SHA256:
- ee23e3368e4e7c0e4ef636ed61923609c97fcaa583f8bb416e3e0986d4a0cfc6
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