Update mask_sp_app.py

mask_sp_app.py

@@ -1,3 +1,77 @@
+'''
+import os
+from datasets import load_dataset
+from PIL import Image
+from gradio_client import Client, handle_file
+from shutil import copy2
+
+# 1. 加载HuggingFace数据集
+dataset = load_dataset("svjack/Xiang_guitar_DreamO_Images")
+
+# 2. 创建本地目录结构
+os.makedirs("input_images", exist_ok=True)
+os.makedirs("xiang_output_videos", exist_ok=True)
+
+# 3. 初始化Gradio客户端
+client = Client("http://localhost:7860/")
+
+# 5. 定义生成空白图片的函数
+def generate_blank_image(image_path, output_path, color='black'):
+    with Image.open(image_path) as img:
+        width, height = img.size
+    
+    fill_color = (255, 255, 255) if color.lower() == 'white' else (0, 0, 0)
+    new_img = Image.new('RGB', (width, height), fill_color)
+    new_img.save(output_path)
+
+# 4. 定义处理函数
+def process_image(idx, image):
+    try:
+        # 保存原始图片
+        input_path = f"input_images/{idx:04d}.png"
+        image.save(input_path)
+        
+        # 生成空白掩码图片
+        mask_path = f"input_images/{idx:04d}_mask.png"
+        generate_blank_image(input_path, mask_path, color='white')
+        
+        # 调用API生成视频
+        result = client.predict(
+            input_image=handle_file(input_path),
+            input_mask=handle_file(mask_path),
+            prompt="a man playing guitar",
+            t2v=False,
+            n_prompt="",
+            seed=31337,
+            total_second_length=5,
+            latent_window_size=9,
+            steps=25,
+            cfg=1,
+            gs=10,
+            rs=0,
+            gpu_memory_preservation=4,
+            use_teacache=False,
+            mp4_crf=16,
+            api_name="/process"
+        )
+        
+        # 复制并重命名视频文件
+        output_filename = f"{idx:04d}.mp4"
+        copy2(result[0]["video"], os.path.join("xiang_output_videos", output_filename))
+        
+        print(f"已处理第{idx}张图片，生成视频: {output_filename}")
+        
+    except Exception as e:
+        print(f"处理第{idx}张图片时出错: {str(e)}")
+
+# 6. 遍历数据集并处理每张图片
+for idx, item in enumerate(dataset["train"]):
+    process_image(idx, item["image"])
+
+print("所有图片处理完成！")
+
+'''
+
 from diffusers_helper.hf_login import login
 import os
 os.environ['HF_HOME'] = os.path.abspath(os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download')))