Update README.md

README.md

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----
-license: apache-2.0
----
+---
+frameworks:
+- Pytorch
+tasks:
+- text-to-image-synthesis
+
+#model-type:
+##如 gpt、phi、llama、chatglm、baichuan 等
+#- gpt
+
+#domain:
+##如 nlp、cv、audio、multi-modal
+#- nlp
+
+#language:
+##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
+#- cn
+
+#metrics:
+##如 CIDEr、Blue、ROUGE 等
+#- CIDEr
+
+#tags:
+##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
+#- pretrained
+
+#tools:
+##如 vllm、fastchat、llamacpp、AdaSeq 等
+#- vllm
+base_model:
+  - Qwen/Qwen-Image
+base_model_relation: adapter
+---
+# Qwen-Image Image Structure Control Model
+
+![](./assets/cover.png)
+
+## Model Introduction
+
+This model is a local image redraw model trained based on [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) , with a model structure of ControlNet, capable of redrawing local areas of an image. The training framework is built on [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) , and the dataset used is [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset)。
+
+This model is compatible with both [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) and [Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)，It can perform local redrawing on Qwen-Image and edit specified areas on Qwen-Image-Edit.
+
+## Effect Demonstration
+
+|Input Prompt|Input Image|Redrawn Image|
+|-|-|-|
+|A robot with wings and a hat standing in a colorful garden with flowers and butterflies.|![](./assets/image_1_1.jpg)|![](./assets/image_1_2.jpg)|
+|A girl in a school uniform stands gracefully in front of a vibrant stained glass window with colorful geometric patterns.|![](./assets/image_2_1.jpg)|![](./assets/image_2_2.jpg)|
+|A small wooden boat battles against towering, crashing waves in a stormy sea.|![](./assets/image_3_1.png)|![](./assets/image_3_2.png)|
+
+## Limitations
+- Inpaint models based on the ControlNet structure may result in disharmonious boundaries between the redrawn and non-redrawn areas.
+
+- The model is trained on rectangular area redraw data, so its generalization to non-rectangular areas might not be optimal.
+
+
+## Inference Code
+```
+git clone https://github.com/modelscope/DiffSynth-Studio.git  
+cd DiffSynth-Studio
+pip install -e .
+```
+
+Qwen-Image：
+
+```python
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="inpaint/*.jpg"
+)
+prompt = "a cat with sunglasses"
+controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328))
+inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328))
+image = pipe(
+    prompt, seed=0,
+    input_image=controlnet_image, inpaint_mask=inpaint_mask,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
+    num_inference_steps=40,
+)
+image.save("image.jpg")
+```
+
+Qwen-Image-Edit：
+
+```python
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="inpaint/*.jpg"
+)
+prompt = "Put sunglasses on this cat"
+controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328))
+inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328))
+image = pipe(
+    prompt, seed=0,
+    input_image=controlnet_image, inpaint_mask=inpaint_mask,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
+    num_inference_steps=40,
+    edit_image=controlnet_image, # add edit_image here.
+)
+image.save("image.jpg")
+```
+
+---
+license: apache-2.0
+---