Instructions to use ntk1507/UFC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ntk1507/UFC with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ntk1507/UFC", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
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Improve model card: Add pipeline tag, library name, links, and usage details
Verified signatureThis PR improves the model card for **Universal Few-Shot Spatial Control for Diffusion Models (UFC)** by: - Adding the `pipeline_tag: text-to-image`, ensuring the model is discoverable under the relevant pipeline at https://huggingface.co/models?pipeline_tag=text-to-image. - Adding `library_name: diffusers` as the project's code is based on the `diffusers` library, enabling the automated "how to use" snippet. - Including a direct link to the paper: https://huggingface.co/papers/2509.07530. - Providing a link to the official GitHub repository for easier access to the code and further details: https://github.com/kietngt00/UFC. - Incorporating key information about the model, its method, available checkpoints, and usage examples for image generation from the original GitHub README. Please review these additions.162927f
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fix checkpoint densepose unet
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update ft UNet checkpoint for depth, normal
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add DiT checkpoints: pose
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add DiT checkpoints
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add DiT checkpoints
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reorganize the UNet checkpoints
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add: fine-tuned checkpoint for UNet model
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add: meta-train checkpoint for UNet model
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initial commit
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