Sirshendu Ganguly
8d75a6933f
- 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
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
from .utils import runwareUtils as rwUtils
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import json
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class vectorize:
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RUNWARE_VECTORIZE_MODELS = {
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"recraft:1@1": "recraft:1@1",
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"picsart:1@1": "picsart:1@1",
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}
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"Image": ("IMAGE", {
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"tooltip": "Input image to be vectorized."
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}),
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"Model": (list(cls.RUNWARE_VECTORIZE_MODELS.keys()), {
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"tooltip": "Select the vectorization model to use.",
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"default": "recraft:1@1",
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}),
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},
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}
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DESCRIPTION = "Convert images to vector format using Runware's vectorization service."
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FUNCTION = "vectorizeImage"
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("IMAGE", "Vector Data")
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CATEGORY = "Runware"
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def vectorizeImage(self, **kwargs):
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image = kwargs.get("Image")
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modelName = kwargs.get("Model", "recraft:1@1")
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# Get the model AIR code
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model = self.RUNWARE_VECTORIZE_MODELS.get(modelName, "recraft:1@1")
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# Convert image to UUID
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image_uuid = rwUtils.convertTensor2IMG(image)
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# Build the API request
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genConfig = [
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{
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"taskType": "vectorize",
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"taskUUID": rwUtils.genRandUUID(),
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"model": model,
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"inputs": {
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"image": image_uuid
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},
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}
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]
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try:
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# Debug: Print the request being sent
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print(f"[DEBUG] Sending Vectorize Request:")
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print(f"[DEBUG] Request Payload: {json.dumps(genConfig, indent=2)}")
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genResult = rwUtils.inferenecRequest(genConfig)
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# Debug: Print the response received
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print(f"[DEBUG] Received Vectorize Response:")
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print(f"[DEBUG] Response: {json.dumps(genResult, indent=2)}")
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# Check for errors
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if "errors" in genResult:
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error_message = genResult["errors"][0]["message"]
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raise Exception(f"Vectorization failed: {error_message}")
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# Extract the result
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if "data" in genResult and len(genResult["data"]) > 0:
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result_data = genResult["data"][0]
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# Return the vectorized image URL
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if "imageURL" in result_data:
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vector_url = result_data["imageURL"]
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return (image, vector_url)
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else:
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raise Exception("imageURL not found in response")
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else:
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raise Exception("No data returned from vectorization API")
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except Exception as e:
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print(f"[Error] Vectorization failed: {str(e)}")
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raise e
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# Node class mappings
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NODE_CLASS_MAPPINGS = {
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"RunwareVectorize": vectorize,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RunwareVectorize": "Runware Vectorize",
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}
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