Update README.md

README.md

@@ -1,3 +1,90 @@
----
-license: cc-by-nc-4.0
----
+---
+library_name: diffusers
+license: cc-by-nc-2.0
+base_model:
+- black-forest-labs/FLUX.1-Fill-dev
+pipeline_tag: image-to-image
+tags:
+- tryon
+- vto
+---
+
+# Model Card for CAT-Tryoff-Flux
+
+CAT-Tryoff-Flux is an advanced tryoff model. It used the same method of (CATVTON-FLUX)[https://huggingface.co/xiaozaa/catvton-flux-alpha]. This model can extract and reconstruct the front view of clothing items from images of people wearing them.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [X/Twitter:Black Magic An](https://x.com/MrsZaaa)
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [github](https://github.com/nftblackmagic/catvton-flux)
+
+## Uses
+
+The model is designed for virtual try-off applications, allowing users to visualize how different garments would look on a person. It can be used directly through command-line interface with the following parameters:
+
+Input person image
+Person mask
+Garment image
+Random seed (optional)
+
+## How to Get Started with the Model
+
+```
+transformer = FluxTransformer2DModel.from_pretrained(
+    "xiaozaa/cat-tryoff-flux", 
+    torch_dtype=torch.bfloat16
+)
+pipe = FluxFillPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+
+
+```
+
+## Training Details
+
+### Training Data
+
+VITON-HD dataset
+
+### Training Procedure
+
+Finetuning Flux1-dev-fill
+
+
+## Evaluation
+
+#### Summary
+
+
+
+**BibTeX:**
+```
+@misc{chong2024catvtonconcatenationneedvirtual,
+ title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models}, 
+ author={Zheng Chong and Xiao Dong and Haoxiang Li and Shiyue Zhang and Wenqing Zhang and Xujie Zhang and Hanqing Zhao and Xiaodan Liang},
+ year={2024},
+ eprint={2407.15886},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV},
+ url={https://arxiv.org/abs/2407.15886}, 
+}
+@article{lhhuang2024iclora,
+  title={In-Context LoRA for Diffusion Transformers},
+  author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Dou, Huanzhang and Liang, Chen and Feng, Yutong and Liu, Yu and Zhou, Jingren},
+  journal={arXiv preprint arxiv:2410.23775},
+  year={2024}
+}
+```