diff --git a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py
new file mode 100644
index 0000000..f8af9e8
--- /dev/null
+++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py
@@ -0,0 +1,49 @@
+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"),
+        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/*.jpg"
+)
+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。"
+
+controlnet_canny_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+controlnet_inpaint_image = Image.open("./data/example_image_dataset/canny/image_2.jpg").convert("RGB").resize((1328, 1328))
+# generate a centered square mask
+inpaint_mask = Image.new("L", controlnet_inpaint_image.size, 0)
+mask_size = 512
+left = (controlnet_inpaint_image.width - mask_size) // 2
+top = (controlnet_inpaint_image.height - mask_size) // 2
+right = left + mask_size
+bottom = top + mask_size
+inpaint_mask.paste(255, (left, top, right, bottom))
+inpaint_mask = inpaint_mask.resize((1328, 1328)).convert("RGB")
+
+image = pipe(
+    prompt, seed=0,
+    input_image=controlnet_inpaint_image, inpaint_mask=inpaint_mask,
+    blockwise_controlnet_inputs=[
+        ControlNetInput(image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, controlnet_id=0),
+        ControlNetInput(image=controlnet_canny_image, controlnet_id=1),
+    ],
+    num_inference_steps=40,
+)
+image.save("image.jpg")
diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py
new file mode 100644
index 0000000..38fdcc1
--- /dev/null
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-InpaintCanny.py
@@ -0,0 +1,59 @@
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+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", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="canny/*.jpg"
+)
+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。"
+
+controlnet_canny_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+controlnet_inpaint_image = Image.open("./data/example_image_dataset/canny/image_2.jpg").convert("RGB").resize((1328, 1328))
+# generate a centered square mask
+inpaint_mask = Image.new("L", controlnet_inpaint_image.size, 0)
+mask_size = 512
+left = (controlnet_inpaint_image.width - mask_size) // 2
+top = (controlnet_inpaint_image.height - mask_size) // 2
+right = left + mask_size
+bottom = top + mask_size
+inpaint_mask.paste(255, (left, top, right, bottom))
+inpaint_mask = inpaint_mask.resize((1328, 1328)).convert("RGB")
+
+image = pipe(
+    prompt, seed=0,
+    input_image=controlnet_inpaint_image, inpaint_mask=inpaint_mask,
+    blockwise_controlnet_inputs=[
+        ControlNetInput(image=controlnet_inpaint_image, inpaint_mask=inpaint_mask, controlnet_id=0),
+        ControlNetInput(image=controlnet_canny_image, controlnet_id=1),
+    ],
+    num_inference_steps=40,
+)
+image.save("image.jpg")