Add video background removal and video upscaler nodes

- Add Runware Video Background Removal node with Bria support - Add Runware Video Upscaler node with Bria support - Add Runware Vectorize node for image vectorization - Add Lightricks Provider Settings node - Add Video Inference Inputs node for structured inputs - Add Input Audios and Reference Videos nodes - Update video inference with speech support and reference videos - Add new video models: OmniHuman 1/1.5, PixVerse LipSync, LTX - Update Bytedance provider settings with boolean controls - Add corresponding workflow examples
2025-10-23 22:36:24 -04:00
parent 1c7da18f11
commit 8d75a6933f
28 changed files with 757 additions and 34 deletions
@@ -0,0 +1,93 @@
+from .utils import runwareUtils as rwUtils
+import json
+
+class vectorize:
+    RUNWARE_VECTORIZE_MODELS = {
+        "recraft:1@1": "recraft:1@1",
+        "picsart:1@1": "picsart:1@1",
+    }
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "Image": ("IMAGE", {
+                    "tooltip": "Input image to be vectorized."
+                }),
+                "Model": (list(cls.RUNWARE_VECTORIZE_MODELS.keys()), {
+                    "tooltip": "Select the vectorization model to use.",
+                    "default": "recraft:1@1",
+                }),
+            },
+        }
+
+    DESCRIPTION = "Convert images to vector format using Runware's vectorization service."
+    FUNCTION = "vectorizeImage"
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("IMAGE", "Vector Data")
+    CATEGORY = "Runware"
+
+    def vectorizeImage(self, **kwargs):
+        image = kwargs.get("Image")
+        modelName = kwargs.get("Model", "recraft:1@1")
+        
+        # Get the model AIR code
+        model = self.RUNWARE_VECTORIZE_MODELS.get(modelName, "recraft:1@1")
+        
+        # Convert image to UUID
+        image_uuid = rwUtils.convertTensor2IMG(image)
+        
+        # Build the API request
+        genConfig = [
+            {
+                "taskType": "vectorize",
+                "taskUUID": rwUtils.genRandUUID(),
+                "model": model,
+                "inputs": {
+                    "image": image_uuid
+                },
+            }
+        ]
+        
+        try:
+            # Debug: Print the request being sent
+            print(f"[DEBUG] Sending Vectorize Request:")
+            print(f"[DEBUG] Request Payload: {json.dumps(genConfig, indent=2)}")
+            
+            genResult = rwUtils.inferenecRequest(genConfig)
+            
+            # Debug: Print the response received
+            print(f"[DEBUG] Received Vectorize Response:")
+            print(f"[DEBUG] Response: {json.dumps(genResult, indent=2)}")
+            
+            # Check for errors
+            if "errors" in genResult:
+                error_message = genResult["errors"][0]["message"]
+                raise Exception(f"Vectorization failed: {error_message}")
+            
+            # Extract the result
+            if "data" in genResult and len(genResult["data"]) > 0:
+                result_data = genResult["data"][0]
+                
+                # Return the vectorized image URL
+                if "imageURL" in result_data:
+                    vector_url = result_data["imageURL"]
+                    return (image, vector_url)
+                else:
+                    raise Exception("imageURL not found in response")
+            else:
+                raise Exception("No data returned from vectorization API")
+                
+        except Exception as e:
+            print(f"[Error] Vectorization failed: {str(e)}")
+            raise e
+
+# Node class mappings
+NODE_CLASS_MAPPINGS = {
+    "RunwareVectorize": vectorize,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RunwareVectorize": "Runware Vectorize",
+}
+