Create README.md

README.md

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+---
+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}, 
+}
+```