Instructions to use lycui/CFSynthesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lycui/CFSynthesis with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lycui/CFSynthesis", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Champ
How to use lycui/CFSynthesis with Champ:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin from lycui/CFSynthesis: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/lycui/CFSynthesis/resolve/main/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://lycui/CFSynthesis/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/lycui/CFSynthesis/resolve/main/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- 34f68a728a5c3d0d1066e80e206980b1f6dc94738a8b5917cdcc9dc5a947a31f
- Size of remote file:
- 3.44 GB
- SHA256:
- c7da0e21ba7ea50637bee26e81c220844defdf01aafca02b2c42ecdadb813de4
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