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
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| |-- guidance_encoder_normal.pth
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| |-- guidance_encoder_semantic_map.pth
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| |-- reference_unet.pth
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|-- image_encoder
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| |-- config.json
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| `-- pytorch_model.bin
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| |-- guidance_encoder_normal.pth
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| |-- guidance_encoder_semantic_map.pth
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| |-- reference_unet.pth
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|-- control_v11p_sd15_openpose
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| |-- diffusion_pytorch_model.bin
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|-- image_encoder
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| |-- config.json
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| `-- pytorch_model.bin
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