license: mit

license: mit ---vv from diffusers import FluxPipeline from safetensors.torch import load_file

prompt='The Death of Ophelia by John Everett Millais, Pre-Raphaelite painting, Ophelia floating in a river surrounded by flowers, detailed natural elements, melancholic and tragic atmosphere' pipe = FluxPipeline.from_pretrained('./data/flux', torch_dtype=torch.bfloat16, use_safetensors=True ).to("cuda") state_dict = load_file("./srpo/diffusion_pytorch_model.safetensors") pipe.transformer.load_state_dict(state_dict) image = pipe( prompt, guidance_scale=3.5, height=1024, width=1024, num_inference_steps=50, max_sequence_length=512, generator=generator ).images[0] @misc{shen2025directlyaligningdiffusiontrajectory, title={Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference}, author={Xiangwei Shen and Zhimin Li and Zhantao Yang and Shiyi Zhang and Yingfang Zhang and Donghao Li and Chunyu Wang and Qinglin Lu and Yansong Tang}, year={2025}, eprint={2509.06942}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2509.06942}, }

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