support controlnet depth

examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py

@@ -0,0 +1,32 @@
+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-Depth", 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="depth/image_1.jpg"
+)
+
+controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
+
+prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")