---
license: apache-2.0
---


## Resources
- [Website](https://jialuo-li.github.io/Science-T2I-Web/)
- [arXiv: Paper](https://arxiv.org/abs/2504.13129)
- [GitHub: Code](https://github.com/Jialuo-Li/Science-T2I)
- [Huggingface: SciScore](https://huggingface.co/Jialuo21/SciScore)
- [Huggingface: Science-T2I-S&C Benchmark](https://huggingface.co/collections/Jialuo21/science-t2i-67d3bfe43253da2bc7cfaf06)
- [Huggingface: Science-T2I training set](https://huggingface.co/datasets/Jialuo21/Science-T2I-Trainset)


## 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")

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}, 
}
```