Instructions to use cehongw/civit_jacket_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cehongw/civit_jacket_output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cehongw/civit_jacket_output", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> jacket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download pytorch_custom_diffusion_weights.bin from cehongw/civit_jacket_output: direct link, hf CLI and curl.
- Browser
- Download file 76.7 MB
-
https://huggingface.co/cehongw/civit_jacket_output/resolve/main/pytorch_custom_diffusion_weights.bin
- Command line
-
hf download hf://cehongw/civit_jacket_output/pytorch_custom_diffusion_weights.bin
-
curl -L -o pytorch_custom_diffusion_weights.bin https://huggingface.co/cehongw/civit_jacket_output/resolve/main/pytorch_custom_diffusion_weights.bin
76.7 MB
- Xet hash:
- dce7c3c55adad6684b1aae726a4747a6d1253a1a18266dbdbafa428e3d146226
- Size of remote file:
- 76.7 MB
- SHA256:
- afc2b464da75355bc1163a9e4b57c8cb2c76e28e42d179a025090a83ba850225
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