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README.md

@@ -7,7 +7,7 @@ license: apache-2.0
 
 ## Model Introduction
 
-This model is an image structure control model based on [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image), with a ControlNet architecture that enables control over generated image structures using depth maps. The training framework is built on [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio), and the dataset used for training is [BLIP3o](https://modelscope.cn/datasets/BLIP3o/BLIP3o-60k).
+This model is an image structure control model based on [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image), with a ControlNet architecture that enables structural control of generated images using depth maps. The training framework is built upon [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio), and the dataset used for training is [BLIP3o](https://modelscope.cn/datasets/BLIP3o/BLIP3o-60k).
 
 ## Result Demonstration
 
@@ -50,12 +50,11 @@ dataset_snapshot_download(
 )
 
 controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
-
+```
 
 prompt = "Exquisite portrait, underwater girl, flowing blue dress, gently floating hair, translucent lighting, surrounded by bubbles, serene expression, intricate details, dreamy and ethereal."
 image = pipe(
     prompt, seed=0,
     blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
 )
-image.save("image.jpg")
-```
+image.save("image.jpg")