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 /image_encoder /pytorch_model.bin
- Xet hash:
- 7c9803ffc355f060168dba073138de06cb28ed50d2c390350f4c1a87d944c86e
- Size of remote file:
- 1.22 GB
- SHA256:
- 89d2aa29b5fdf64f3ad4f45fb4227ea98bc45156bbae673b85be1af7783dbabb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.