add examples

examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-ICEdit.py

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+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import snapshot_download
+from PIL import Image
+import torch
+
+# Load models
+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-Edit-2511", 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),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+lora = ModelConfig(
+    model_id="DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA",
+    origin_file_pattern="model.safetensors"
+)
+pipe.load_lora(pipe.dit, lora)
+
+# Load images
+snapshot_download(
+    "DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA",
+    local_dir="./data",
+    allow_file_pattern="assets/*"
+)
+edit_image = [
+    Image.open("data/assets/image1_original.png"),
+    Image.open("data/assets/image1_edit_1.png"),
+    Image.open("data/assets/image2_original.png")
+]
+prompt = "Edit image 3 based on the transformation from image 1 to image 2."
+negative_prompt = "泛黄，AI感，不真实，丑陋，油腻的皮肤，异常的肢体，不协调的肢体"
+
+# Generate
+image_4 = pipe(
+    prompt=prompt, negative_prompt=negative_prompt,
+    edit_image=edit_image,
+    seed=1,
+    num_inference_steps=50,
+    height=1280,
+    width=720,
+    zero_cond_t=True,
+)
+image_4.save("image.png")