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:
- b0190907430b5638da035a2ae1d374eed70277bec747710a58352b3168921daa
- Size of remote file:
- 1.45 GB
- SHA256:
- 40c80b93aea10c31de2d282adbe8bbb945611a037ca36e0cd55d3ee7d59fedce
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