Instructions to use JingyeChen22/textdiffuser2-full-ft-inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JingyeChen22/textdiffuser2-full-ft-inpainting with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JingyeChen22/textdiffuser2-full-ft-inpainting", 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
Download unet/diffusion_pytorch_model.bin from JingyeChen22/textdiffuser2-full-ft-inpainting: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/JingyeChen22/textdiffuser2-full-ft-inpainting/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://JingyeChen22/textdiffuser2-full-ft-inpainting/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/JingyeChen22/textdiffuser2-full-ft-inpainting/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- f324d606875133ebdb8c6e2d916d52e6457c8ad83757b263e1d47a75789b422e
- Size of remote file:
- 3.44 GB
- SHA256:
- 23270c5f20cd3c33156183d8e8fe1cff1b14e70959390d5c0cb0eb60303bef9c
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