Fixing image preprocessing

stable_3d.py

@@ -123,9 +123,9 @@ class Stable3DPreprocessImage:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "background_removal_model": (
-                    "STABLE3D_BIREFNET",
-                    {"tooltip": "The model to remove background (BiRefNet)."}
+                "hi3dgen_pipeline": (
+                    "HI3DGEN_PIPELINE",
+                    {"tooltip": "The hi3dgen pipeline containing the remove background setup (BiRefNet)."}
                 ),
                 "normal_predictor": (
                     "STABLE3D_NORMAL",
@@ -140,33 +140,36 @@ class Stable3DPreprocessImage:
     FUNCTION = "preprocess_image"
     INPUT_IS_LIST = False
     OUTPUT_NODE = True
-    RETURN_NAMES = ("normal_image",)
+    RETURN_NAMES = ("normal_images",)
     RETURN_TYPES = ("IMAGE",)
 
+    def pil_to_tensor(self, image):
+        image = numpy.array(image).astype(numpy.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        return image
+
     def save_normal_image(self, normal_image):
         output_id = datetime.datetime.now().strftime("%Y-%m-%d-%H%M%S")
         filename = f"{output_id}_normal.png"
         path = os.path.join(self.temp_directory, filename)
 
         normal_image.save(path)
+        log.info(f"normal_image saved as {path}")
+        return path
 
-    def preprocess_image(self, background_removal_model, normal_predictor, image):
-
-        hi3dgen_pipeline = Hi3DGenPipeline.from_pretrained("custom_nodes/ComfyUI-Stable3DGen/weights/trellis-normal-v0-1")
-        hi3dgen_pipeline.cuda()
-
-        image = torch.rand(1, 512, 512, 3) # Example tensor
+    def preprocess_image(self, hi3dgen_pipeline, normal_predictor, image):
+        # FIXME We should support properly batch mode here.
         numpy_image = image.squeeze(0).cpu().numpy()
         numpy_image_scaled = numpy.clip(numpy_image * 255, 0, 255).astype(numpy.uint8)
-        pil_image = Image.fromarray(numpy_image_scaled)
+        image = Image.fromarray(numpy_image_scaled)
 
         # FIXME We should properly handle batch here.
-        image = hi3dgen_pipeline.preprocess_image(pil_image, resolution=512)
-        normal_image = normal_predictor(pil_image)
+        image = hi3dgen_pipeline.preprocess_image(image, resolution=1024)
+        normal_image = normal_predictor(image, resolution=768, match_input_resolution=True, data_type='object')
 
         self.save_normal_image(normal_image)
 
-        return normal_image
+        return (self.pil_to_tensor(normal_image),)
 
 
 class Stable3DGenerate3D: