Update README.md

README.md

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 base_model:
 - Qwen/Qwen3-VL-8B-Instruct
 pipeline_tag: image-text-to-text
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+---
+# StableI2I-PLUS
+
+Official score-supported implementation of **StableI2I: Spotting Unintended Changes in Image-to-Image Transition**  
+**ICML 2026**
+
+> This HuggingFace repository provides the **score-supported checkpoint** of StableI2I.  
+> Compared with the paper checkpoint, this version supports explicit fidelity score prediction.  
+> Please note that the score-supported version may show a slight degradation in some fine-grained evaluation abilities, such as semantic / structure / low-level diagnosis accuracy.
+
+For the latest code, demo, inference scripts, and usage examples, please refer to the official GitHub repository:  
+https://github.com/Henry-Lee-real/StableI2I
+
+Any questions can be consulted via email: **lijiayang.cs@gmail.com**
+
+Looking forward to your ⭐!
+
+---
+
+## 📌 TODOs
+
+- [x] Release code
+- [x] Release checkpoint
+- [x] Release score-supported checkpoint
+- [ ] Release pip package
+- [ ] Release arXiv version
+- [ ] Release ICML camera-ready paper
+- [ ] Release HuggingFace project page
+
+---
+
+## 🔥 News
+
+- **StableI2I** is accepted by **ICML 2026**.
+- This repository hosts the **score-supported StableI2I checkpoint**.
+- This version can directly output a fidelity score for image-to-image transitions.
+- The latest codebase is maintained in the official GitHub repository.
+
+---
+
+## Core Concept
+
+In most real-world image-to-image (I2I) scenarios, existing evaluations primarily focus on instruction following and perceptual quality or aesthetics of the generated images. However, they often fail to assess whether the output image faithfully preserves the semantic correspondence, spatial structure, and low-level appearance of the input image.
+
+To address this limitation, we propose **StableI2I**, a unified and dynamic evaluation framework for measuring content fidelity and pre--post consistency in image-to-image transitions. StableI2I does not require reference images and can be applied to a wide range of I2I tasks, including image editing and image restoration.
+
+StableI2I evaluates unintended changes from three complementary perspectives:
+
+1. **Semantic Level**  
+   Checks whether the output introduces unintended object-level or meaning-level changes, such as object addition, removal, replacement, or identity drift.
+
+2. **Structure Level**  
+   Checks whether the output preserves spatial layout and geometric consistency, including misalignment, deformation, repainting, and structural distortion.
+
+3. **Low-level Appearance**  
+   Checks whether the output introduces unintended visual degradation, such as blur, noise, color cast, exposure degradation, or artifacts.
+
+In addition to these diagnostic outputs, this score-supported version can produce an explicit **fidelity score**, which provides a compact numerical assessment of the overall content consistency between the input and output images.
+
+---
+
+## Model Checkpoint
+
+This HuggingFace repository provides the **score-supported checkpoint** of StableI2I.
+
+Please note:
+
+- This checkpoint supports explicit score output.
+- The score is intended to summarize overall content fidelity and pre--post consistency.
+- Compared with the original paper checkpoint, this version may have slightly weaker fine-grained diagnostic performance.
+- If you prioritize the most accurate semantic / structure / low-level diagnosis, please consider using the paper checkpoint.
+- If you need direct numerical scoring, please use this score-supported checkpoint.
+- For the latest inference pipeline and API interface, please refer to the official GitHub repository.
+
+---