Fix: Remove Pipeline inheritance for standalone usage

pipeline.py

@@ -25,21 +25,24 @@ if HF_AVAILABLE:
 
 class GarmentMaskPipeline:
     """
-    Hugging Face Pipeline for generating garment masks for image inpainting.
+    Standalone Pipeline for generating garment masks for image inpainting.
 
     This pipeline combines DensePose and SCHP models to detect human body parts
     and garment regions, then generates variable-shaped masks suitable for inpainting.
 
     Example:
         ```python
-        from transformers import pipeline
+        from huggingface_hub import snapshot_download
+        import sys
 
-        # Load the pipeline
-        pipe = pipeline("image-segmentation",
-                       model="your-username/wearit-garment-mask",
-                       trust_remote_code=True)
+        # Download from HF Hub
+        repo_path = snapshot_download("your-username/wearit-garment-mask")
+        sys.path.insert(0, repo_path)
 
-        # Generate masks
+        # Import and use
+        from pipeline import GarmentMaskPipeline
+
+        pipe = GarmentMaskPipeline(model=repo_path, device="cuda")
         results = pipe("person.jpg", garment_types=["upper", "lower"])
 
         # Access masks
@@ -79,9 +82,6 @@ class GarmentMaskPipeline:
             allowed_strategies: List of allowed mask strategies ["ellipse", "box", "poly"]
             save_images: Whether to save intermediate images (default: False)
         """
-        if HF_AVAILABLE:
-            super().__init__(model=model, **kwargs)
-
         # Store config
         self.device = device
         self.output_height = output_height
@@ -243,26 +243,12 @@ class GarmentMaskPipeline:
         Returns:
             List of results with masks for each image
         """
-        if HF_AVAILABLE:
-            return super().__call__(inputs, **kwargs)
-        else:
-            # Fallback for non-HF usage
-            preprocess_params, forward_params, postprocess_params = self._sanitize_parameters(**kwargs)
-            model_inputs = self.preprocess(inputs)
-            model_outputs = self._forward(model_inputs, **forward_params)
-            return self.postprocess(model_outputs)
+        # Direct processing without Pipeline inheritance
+        preprocess_params, forward_params, postprocess_params = self._sanitize_parameters(**kwargs)
+        model_inputs = self.preprocess(inputs)
+        model_outputs = self._forward(model_inputs, **forward_params)
+        return self.postprocess(model_outputs)
 
 
-# Register the pipeline if transformers is available
-if HF_AVAILABLE:
-    try:
-        from transformers.pipelines import PIPELINE_REGISTRY
-
-        PIPELINE_REGISTRY.register_pipeline(
-            "garment-mask-generation",
-            pipeline_class=GarmentMaskPipeline,
-            pt_model=None,  # Custom pipeline without standard model
-        )
-    except Exception as e:
-        if HF_AVAILABLE:
-            logger.warning(f"Could not register pipeline: {e}")
+# Note: This pipeline is standalone and does not inherit from transformers.Pipeline
+# Use it directly: pipe = GarmentMaskPipeline(model="repo_path", device="cuda")