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.gitattributes

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README.md

@@ -1,10 +1,15 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: image-text-to-image
+tags:
+- multimodal
+library_name: transformers
 ---
 
 ## 🔥🔥🔥 News!!
-* Apr 25, 2025: 👋 We release the evaluation code and benchmark data of Step1X-Edit. [Download GEdit-Bench](https://huggingface.co/datasets/stepfun-ai/GEdit-Bench)
-* Apr 25, 2025: 👋 We release the inference code and model weights of Step1X-Edit. [Download Step1X-Edit model](https://huggingface.co/stepfun-ai/Step1X-Edit)
+* Apr 25, 2025: 👋 We release the inference code and model weights of Step1X-Edit. [inference code](https://github.com/stepfun-ai/Step1X-Edit)
 * Apr 25, 2025: 🎉 We have made our technical report available as open source. [Read](https://arxiv.org/abs/2504.17761)
 
 ## Image Edit Demos
@@ -15,6 +20,23 @@ license: mit
 </div>
 
 
+## Model introduction
+<div align="center">
+<img width="720" alt="demo" src="assets/arch.png">
+</div>
+
+Framework of Step1X-Edit. Step1X-Edit leverages the image understanding capabilities
+of MLLMs to parse editing instructions and generate editing tokens, which are then decoded into
+images using a DiT-based network.More details please refer to our [technical report](https://arxiv.org/abs/2504.17761).
+
+
+## Benchmark
+We release [GEdit-Bench](https://huggingface.co/datasets/stepfun-ai/GEdit-Bench) as a new benchmark, grounded in real-world usages is developed to support more authentic and comprehensive evaluation. This benchmark, which is carefully curated to reflect actual user editing needs and a wide range of editing scenarios, enables more authentic and comprehensive evaluations of image editing models.
+The evaluation process and related code can be found in [GEdit-Bench/EVAL.md](GEdit-Bench/EVAL.md). Part results of the benchmark are shown below:
+<div align="center">
+<img width="1080" alt="results" src="assets/eval_res_en.png">
+</div>
+
 ## Citation
 ```
 @article{liu2025step1x-edit,