update readme

README.md

@@ -8,11 +8,9 @@
   <img src="https://huggingface.co/franciszzj/Leffa/resolve/main/assets/teaser.png" width="100%" height="100%"/>
 </div>
 
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 ## Abstract
 Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person’s appearance or pose. However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality. We attribute these distortions to inadequate attention to corresponding regions in the reference image. To address this, we thereby propose **le**arning **f**low **f**ields in **a**ttention (***Leffa***), which explicitly guides the target query to attend to the correct reference key in the attention layer during training. Specifically, it is realized via a regularization loss on top of the attention map within a diffusion-based baseline. Our extensive experiments show that *Leffa* achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion while maintaining high image quality. Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models.
 
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 ## Method
 An overview of our *Leffa* training pipeline for controllable person image generation. The left is our diffusion-based baseline; the right is our *Leffa* loss. Note that Isrc and Itgt are the same image during training.
 
@@ -28,7 +26,6 @@ Qualitative visual results comparison with other methods. The input person image
   <img src="https://huggingface.co/franciszzj/Leffa/resolve/main/assets/vis_result.png" width="100%" height="100%"/>
 </div>
 
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 ## Installation
 
 Create a conda environment and install requirements:
@@ -45,6 +42,9 @@ Run locally:
 python app.py
 ```
 
+## Evaluation
+We use this [code](https://github.com/franciszzj/VtonEval) for metric evaluation.
+
 ## Acknowledgement
 Our code is based on [Diffusers](https://github.com/huggingface/diffusers) and [Transformers](https://github.com/huggingface/transformers).
 We use [SCHP](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing/tree/master) and [DensePose](https://github.com/facebookresearch/DensePose) to generate masks and densepose in our [Demo](https://huggingface.co/spaces/franciszzj/Leffa).