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
- Xet hash:
- 4a2b68ce9f3015b6966550c25ed29564b33d460c1b309b552e36e023ef6faa38
- Size of remote file:
- 916 MB
- SHA256:
- 8427f8b660647834d6cbfcce7400ce7f48119c823ef6aad470a6d3a5b3720fd2
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