FIX 增加模型不存在的处理

FIX 修复视频剪裁问题

nodes/image_face_nodes.py

@@ -7,6 +7,7 @@ import torch
 from comfy import model_management
 from ultralytics import YOLO
 
+from ..utils.download_utils import download_file
 from ..utils.face_occu_detect import face_occu_detect
 
 
@@ -21,7 +22,7 @@ class FaceDetect:
             "required": {
                 "image": ("IMAGE",),
                 "main_seed": (
-                    "INT:seed",
+                    "INT",
                     {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
                 ),
                 "model": (["convnext_tiny", "convnext_base"],),
@@ -104,12 +105,15 @@ class FaceExtract:
     def crop(self, image):
         device = model_management.get_torch_device()
         image_np = 255.0 * image.cpu().numpy()
-        model = YOLO(
-            model=os.path.join(
-                os.path.dirname(os.path.abspath(__file__)),
+        model_path = os.path.join(
+                os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
                 "model",
                 "yolov8n-face-lindevs.pt",
             )
+        if not os.path.exists(model_path):
+            download_file("https://github.com/lindevs/yolov8-face/releases/latest/download/yolov8n-face-lindevs.pt",model_path)
+        model = YOLO(
+            model=model_path
         )
         total_images = image_np.shape[0]
         out_images = np.ndarray(shape=(total_images, 512, 512, 3))

nodes/video_nodes.py

@@ -119,7 +119,7 @@ class VideoCut:
                 os.remove(files[0])
             except:
                 pass
-            return (video, {"waveform": audio, "sample_rate": info["audio_fps"]},)
+            return (video/255.0, {"waveform": audio, "sample_rate": info["audio_fps"]} if "audio_fps" in info else None,)
         except:
             traceback.print_exc()
             raise Exception("Cut Failed")
@@ -234,7 +234,7 @@ class VideoCutByFramePoint:
                 os.remove(output)
             except:
                 pass
-            return (video, {"waveform": audio, "sample_rate": info["audio_fps"]},)
+            return (video/255.0, {"waveform": audio, "sample_rate": info["audio_fps"]},)
         except:
             traceback.print_exc()
             raise Exception("Cut Failed")

utils/download_utils.py

@@ -0,0 +1,146 @@
+import os
+import requests
+import threading
+from tqdm import tqdm
+from urllib.parse import urlparse
+
+
+def download_file(url, output_path=None, num_threads=8, chunk_size=1024 * 1024):
+    """
+    多线程下载文件
+
+    参数:
+    url (str): 下载URL
+    output_path (str, optional): 输出文件路径，默认为URL中的文件名
+    num_threads (int, optional): 线程数，默认为8
+    chunk_size (int, optional): 每个线程下载的块大小(字节)，默认为1MB
+
+    返回:
+    bool: 下载成功返回True，失败返回False
+    """
+    try:
+        # 获取文件名
+        if not output_path:
+            output_path = os.path.basename(urlparse(url).path)
+            if not output_path:
+                output_path = "downloaded_file"
+
+        # 检查服务器是否支持范围请求
+        response = requests.head(url)
+        response.raise_for_status()
+
+        # 检查是否支持断点续传
+        supports_range = 'Accept-Ranges' in response.headers and response.headers['Accept-Ranges'] == 'bytes'
+
+        if not supports_range:
+            print("服务器不支持多线程下载，将使用单线程下载")
+            return _download_single_thread(url, output_path)
+
+        # 获取文件大小
+        file_size = int(response.headers.get('Content-Length', 0))
+        if not file_size:
+            print("无法获取文件大小，将使用单线程下载")
+            return _download_single_thread(url, output_path)
+
+        # 创建临时文件
+        temp_files = [f"{output_path}.part{i}" for i in range(num_threads)]
+
+        # 计算每个线程下载的范围
+        ranges = []
+        for i in range(num_threads):
+            start = i * (file_size // num_threads)
+            end = start + (file_size // num_threads) - 1 if i < num_threads - 1 else file_size - 1
+            ranges.append((start, end))
+
+        # 创建进度条
+        progress = tqdm(total=file_size, unit='B', unit_scale=True, desc="下载中")
+
+        # 启动线程
+        threads = []
+        for i in range(num_threads):
+            start, end = ranges[i]
+            thread = threading.Thread(target=_download_chunk, args=(url, temp_files[i], start, end, progress))
+            thread.start()
+            threads.append(thread)
+
+        # 等待所有线程完成
+        for thread in threads:
+            thread.join()
+
+        progress.close()
+
+        # 合并临时文件
+        if not _merge_files(temp_files, output_path):
+            print("合并文件失败")
+            return False
+
+        # 删除临时文件
+        for temp_file in temp_files:
+            os.remove(temp_file)
+
+        print(f"文件下载完成: {output_path}")
+        return True
+
+    except Exception as e:
+        print(f"下载过程中发生错误: {e}")
+        # 清理临时文件
+        for temp_file in temp_files:
+            if os.path.exists(temp_file):
+                os.remove(temp_file)
+        return False
+
+
+def _download_single_thread(url, output_path):
+    """单线程下载文件"""
+    try:
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+
+        with open(output_path, 'wb') as f, \
+                tqdm(desc="下载中", total=int(response.headers.get('Content-Length', 0)),
+                     unit='B', unit_scale=True) as progress:
+            for chunk in response.iter_content(chunk_size=1024):
+                if chunk:
+                    f.write(chunk)
+                    progress.update(len(chunk))
+
+        print(f"文件下载完成: {output_path}")
+        return True
+    except Exception as e:
+        print(f"单线程下载失败: {e}")
+        return False
+
+
+def _download_chunk(url, output_path, start, end, progress):
+    """下载文件的指定块"""
+    headers = {'Range': f'bytes={start}-{end}'}
+    try:
+        response = requests.get(url, headers=headers, stream=True)
+        response.raise_for_status()
+
+        with open(output_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=1024):
+                if chunk:
+                    f.write(chunk)
+                    progress.update(len(chunk))
+    except Exception as e:
+        print(f"下载块失败 {start}-{end}: {e}")
+
+
+def _merge_files(temp_files, output_path):
+    """合并临时文件"""
+    try:
+        with open(output_path, 'wb') as outfile:
+            for temp_file in temp_files:
+                with open(temp_file, 'rb') as infile:
+                    outfile.write(infile.read())
+        return True
+    except Exception as e:
+        print(f"合并文件失败: {e}")
+        return False
+
+
+# 使用示例
+if __name__ == "__main__":
+    download_url = "https://example.com/large_file.zip"  # 替换为实际的下载URL
+    download_file(download_url, num_threads=4)

utils/face_occu_detect.py

@@ -63,7 +63,9 @@ def face_occu_detect(image: torch.Tensor, length=10, thres=95, model_name="convn
     }
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     model = Model(model_name, 2, False).to(device)
-    weight = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", weight_dic[model_name])
+    weight = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "model", weight_dic[model_name])
+    if not os.path.exists(weight):
+        raise Exception("请前往https://github.com/LamKser/face-occlusion-classification下载所选权重文件到model文件夹！")
     model = load_weight(model, weight)
     model.eval()
     image = image.permute(0, 3, 1, 2)