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--- |
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library_name: diffusers |
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license: mit |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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--- |
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## FLUX.1-dev-PrefGRPO |
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This model is trained using [Pref-GRPO](https://codegoat24.github.io/UnifiedReward/Pref-GRPO) on the training dataset of [UniGenBench](https://github.com/CodeGoat24/UniGenBench). |
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For further details, please refer to the following resources: |
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- 📰 Paper: |
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- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/Pref-GRPO |
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- 🤗 UniGenBench: https://github.com/CodeGoat24/UniGenBench |
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- 🤗 Leaderboard: https://huggingface.co/spaces/CodeGoat24/UniGenBench_Leaderboard |
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- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io) |
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### Quick Start |
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~~~python |
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pip install -U diffusers |
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~~~ |
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~~~python |
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import torch |
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from diffusers import FluxPipeline |
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pipe = FluxPipeline.from_pretrained("CodeGoat24/FLUX.1-dev-PrefGRPO", torch_dtype=torch.bfloat16) |
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pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power |
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prompt = "A cat holding a sign that says hello world" |
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image = pipe( |
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prompt, |
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height=1024, |
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width=1024, |
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guidance_scale=3.5, |
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num_inference_steps=50, |
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max_sequence_length=512, |
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generator=torch.Generator("cpu").manual_seed(0) |
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).images[0] |
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image.save("flux-dev.png") |
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~~~ |
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## Citation |
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``` |
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``` |
