v1.1.1: Fix VITMatte processing quality and optimize image size handling

README.md

@@ -58,13 +58,18 @@ The following models are supported:
 - **Input**: Mask
 - **Output**: Processed Mask
 - **Parameters**:
-  - Blur Radius: Gaussian blur radius for mask smoothing
-  - Feather Radius: Edge feathering radius
-  - Contrast: Mask contrast adjustment
-  - Brightness: Mask brightness adjustment
+  - Detail Method: Choose from VITMatte, PyMatting, or GuidedFilter
+  - Erode/Dilate: Control the trimap generation
+  - Black/White Point: Adjust mask levels
+  - Max Megapixels: Control processing resolution
 
 ## Changelog
 
+### v1.1.1
+- Fixed VITMatte processing quality issues
+- Optimized image size handling for different processing methods
+- Improved mask processing workflow
+
 ### v1.1.0
 - Optimized dependency management
 - Removed version constraints for better compatibility

__init__.py

@@ -2,4 +2,4 @@ from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
 
-__version__ = 'v1.1.0' 
+__version__ = 'v1.1.1' 

imagefunc.py

@@ -61,6 +61,18 @@ def mask_edge_detail(image, mask, radius, black_point, white_point):
     mask = tensor2pil(mask)
     image = np.array(image)
     mask = np.array(mask)
+    # 修正 image 类型和通道数
+    if image.dtype not in [np.uint8, np.float32]:
+        image = image.astype(np.uint8)
+    if image.ndim == 2:
+        pass  # 灰度
+    elif image.shape[2] > 3:
+        image = image[:, :, :3]  # 只保留前三通道
+    # 修正 mask 类型和通道数
+    if mask.dtype not in [np.uint8, np.float32]:
+        mask = mask.astype(np.uint8)
+    if mask.ndim == 3 and mask.shape[2] > 1:
+        mask = mask[:, :, 0]  # 只保留单通道
     mask = cv2.ximgproc.guidedFilter(image, mask, radius, 1e-6)
     mask = adjust_levels(Image.fromarray(mask), black_point, white_point)
     return torch.from_numpy(np.array(mask)).unsqueeze(0)

nodes.py

@@ -267,22 +267,52 @@ class ProcessDetails_RBS:
                 local_files_only = detail_method == "VITMatte(local)"
                 trimap = generate_VITMatte_trimap(orig_mask, detail_erode, detail_dilate)
                 processed_mask = generate_VITMatte(orig_image, trimap, local_files_only, device, max_megapixels)
-            elif detail_method == "PyMatting":
-                trimap = generate_VITMatte_trimap(orig_mask, detail_erode, detail_dilate)
+            else:
+                # 计算目标尺寸
+                width, height = orig_image.size
+                max_pixels = int(max_megapixels * 1_048_576)
+                orig_pixels = width * height
+                patch_size = 32  # 保证分辨率为32的倍数
+                
+                if orig_pixels > max_pixels:
+                    scale = (max_pixels / orig_pixels) ** 0.5
+                    new_width = max(1, int(width * scale))
+                    new_height = max(1, int(height * scale))
+                else:
+                    new_width, new_height = width, height
+                    
+                # 向下取整为patch_size的倍数
+                new_width = (new_width // patch_size) * patch_size
+                new_height = (new_height // patch_size) * patch_size
+                new_width = max(patch_size, new_width)
+                new_height = max(patch_size, new_height)
+                inference_image_size = (new_width, new_height)
+                
+                log(f"[ProcessDetails_RBS] 原始尺寸: {width}x{height}, 实际推理尺寸: {inference_image_size[0]}x{inference_image_size[1]}", message_type='info')
+                
+                # 调整图像大小
+                resized_image = orig_image.resize(inference_image_size, Image.BILINEAR)
+                resized_mask = orig_mask.resize(inference_image_size, Image.BILINEAR)
+                
+                if detail_method == "PyMatting":
+                    trimap = generate_VITMatte_trimap(resized_mask, detail_erode, detail_dilate)
                     try:
                         from pymatting import estimate_alpha_lkm
                     except ImportError:
                         raise RuntimeError("请先安装 pymatting 库: pip install pymatting scikit-image")
                     import numpy as np
-                image_np = np.array(orig_image.convert('RGB'))
+                    image_np = np.array(resized_image.convert('RGB'))
                     trimap_np = np.array(trimap.convert('L')) / 255.0
                     # PyMatting只用LKM算法，参数风格与LayerStyle_Advance一致
                     alpha = estimate_alpha_lkm(image_np, trimap_np)
                     processed_mask = Image.fromarray((alpha * 255).astype(np.uint8))
                 else:  # GuidedFilter
-                processed_mask = mask_edge_detail(i, m, detail_erode, black_point, white_point)
+                    processed_mask = mask_edge_detail(pil2tensor(resized_image), image2mask(resized_mask), detail_erode, black_point, white_point)
                     processed_mask = tensor2pil(processed_mask)
                 
+                # 还原到原始尺寸
+                processed_mask = processed_mask.resize(orig_image.size, Image.BILINEAR)
+            
             # 应用处理后的蒙版到图像
             processed_image = RGB2RGBA(orig_image, processed_mask)
             

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "removebackgroundsuite"
 description = "A matting toolkit based on ComfyUI, supporting multiple matting models and detail processing methods."
-version = "1.1.0"
+version = "1.1.1"
 license = {file = "LICENSE"}
 dependencies = ["torch", "numpy", "Pillow", "torchvision", "opencv-python", "scipy", "transformers", "tqdm"]