Instructions to use levihsu/OOTDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use levihsu/OOTDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("levihsu/OOTDiffusion", 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
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Our paper is coming soon!
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> We use checkpoints of [humanparsing](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing) and [openpose](https://huggingface.co/lllyasviel/ControlNet/tree/main/annotator/ckpts) in preprocess<br>
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> Please download [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) into ***checkpoints*** folder
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Our paper is coming soon!
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> We use checkpoints of [humanparsing](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing) and [openpose](https://huggingface.co/lllyasviel/ControlNet/tree/main/annotator/ckpts) in preprocess. Please refer to their guidance if you encounter relevant environmental issues<br>
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> Please download [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) into ***checkpoints*** folder
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