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README.md

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+---
+license: apache-2.0
+---
+# Qwen-Image Image Structure Control Model
+
+![](./assets/title.png)
+
+## Model Introduction
+
+This model is an image structure control model trained based on [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image), with the ControlNet architecture. It can control the structure of generated images using edge detection (Canny) maps. The training framework is built on [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio), and the dataset used is [BLIP3o](https://modelscope.cn/datasets/BLIP3o/BLIP3o-60k).
+
+## Result Demonstration
+
+|Canny Edge Map|Generated Image 1|Generated Image 2|
+|-|-|-|
+|![](./assets/canny_3.png)|![](./assets/image_3_1.png)|![](./assets/image_3_2.png)|
+|![](./assets/canny_2.png)|![](./assets/image_2_1.png)|![](./assets/image_2_2.png)|
+|![](./assets/canny_1.png)|![](./assets/image_1_1.png)|![](./assets/image_1_2.png)|
+
+## Inference Code
+```
+git clone https://github.com/modelscope/DiffSynth-Studio.git  
+cd DiffSynth-Studio
+pip install -e .
+```
+
+```python
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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-Canny", 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="canny/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+```
+
+prompt = "A little dog with shiny, soft fur and lively eyes, set in a spring courtyard with cherry blossoms falling, creating a beautiful and warm atmosphere."
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
+```

README_from_modelscope.md

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+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+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 图像结构控制模型
+
+![](./assets/title.png)
+
+## 模型介绍
+
+本模型是基于 [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) 训练的图像结构控制模型，模型结构为 ControlNet，可根据边缘检测（Canny）图控制生成的图像结构。训练框架基于 [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 构建，采用的数据集是 [BLIP3o](https://modelscope.cn/datasets/BLIP3o/BLIP3o-60k)。
+
+
+## 效果展示
+
+|结构图|生成图1|生成图2|
+|-|-|-|
+|![](./assets/canny_3.png)|![](./assets/image_3_1.png)|![](./assets/image_3_2.png)|
+|![](./assets/canny_2.png)|![](./assets/image_2_1.png)|![](./assets/image_2_2.png)|
+|![](./assets/canny_1.png)|![](./assets/image_1_1.png)|![](./assets/image_1_2.png)|
+
+## 推理代码
+```
+git clone https://github.com/modelscope/DiffSynth-Studio.git  
+cd DiffSynth-Studio
+pip install -e .
+```
+
+```python
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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-Canny", 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="canny/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
+```

configuration.json

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+{"framework":"Pytorch","task":"text-to-image-synthesis"}

model.safetensors

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