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Jialuo21/
Science-T2I-Flux-SFT-OFT

Name: Science-T2I-Flux-SFT-OFT
Availability: InStock

2504.13129

apache-2.0

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Quick Start

You can use FluxPipeline to run the model

python

import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipe.load_lora_weights("Jialuo21/Science-T2I-Flux-SFT-OFT")

prompt = "An unripe grape in the garden"
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=0.0,
    num_inference_steps=50,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("example.png")

Citation

@misc{li2025sciencet2iaddressingscientificillusions,
  title={Science-T2I: Addressing Scientific Illusions in Image Synthesis}, 
  author={Jialuo Li and Wenhao Chai and Xingyu Fu and Haiyang Xu and Saining Xie},
  year={2025},
  eprint={2504.13129},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2504.13129}, 
}