add workflows, etc
This commit is contained in:
Sirshendu Ganguly
+165
-39
@@ -1,24 +1,97 @@
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import re
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from .utils import runwareUtils as rwUtils
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class vectorize:
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"""Vectorize node for converting images to vector format"""
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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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# Models grouped by architecture (provider), same pattern as video model search
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VECTORIZE_MODELS = {
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"Recraft": [
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"recraft:1@1 (Recraft 1)",
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"recraft:v4-pro@vector (Recraft V4 Pro Vector)",
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"recraft:v4@vector (Recraft V4 Vector)",
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],
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"Picsart": [
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"picsart:1@1 (Picsart 1)",
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],
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}
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MODEL_ARCHITECTURES = [
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"All",
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"Recraft",
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"Picsart",
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]
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@classmethod
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def _getAllModels(cls):
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"""Get all models from all architectures"""
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all_models = []
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for models in cls.VECTORIZE_MODELS.values():
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all_models.extend(models)
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return all_models
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@classmethod
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def INPUT_TYPES(cls):
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all_models = cls._getAllModels()
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default_model = all_models[0] if all_models else "recraft:1@1 (Recraft 1)"
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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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"Model Search": ("STRING", {
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"tooltip": "Search for a vectorization model by name or AIR code (e.g. recraft, picsart).",
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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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"Model Architecture": (cls.MODEL_ARCHITECTURES, {
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"tooltip": "Choose model architecture to filter the model list.",
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"default": "Recraft",
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}),
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"Vector Model List": (all_models, {
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"tooltip": "Select the vectorization model. Filter by Model Architecture above.",
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"default": default_model,
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}),
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"Use Search Value": ("BOOLEAN", {
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"tooltip": "When enabled, the value in Model Search is used as the model AIR code instead of the list selection.",
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"default": False,
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"label_on": "Enabled",
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"label_off": "Disabled",
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}),
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},
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"optional": {
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"Image": ("IMAGE", {
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"tooltip": "Optional input image to be vectorized."
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}),
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"dimensions": ([
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"None", "Square HD (1024x1024)", "Custom"
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], {
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"default": "None",
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"tooltip": "Output dimensions preset. Select 'None' for model default, or a preset to set width/height. Use 'Custom' with width/height inputs.",
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}),
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"width": ("INT", {
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"tooltip": "Width in pixels. Used when dimensions is not 'None' (or with Custom).",
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"default": 1024,
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"min": 128,
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"max": 6048,
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"step": 1,
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}),
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"height": ("INT", {
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"tooltip": "Height in pixels. Used when dimensions is not 'None' (or with Custom).",
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"default": 1024,
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"min": 128,
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"max": 6048,
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"step": 1,
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}),
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"Use positivePrompt": ("BOOLEAN", {
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"tooltip": "When enabled, include positivePrompt in the vectorize request.",
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"default": False,
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"label_on": "Enabled",
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"label_off": "Disabled",
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}),
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"positivePrompt": ("STRING", {
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"multiline": True,
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"tooltip": "Optional text prompt for vectorization. Only sent when Use Positive Prompt is enabled.",
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}),
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"providerSettings": ("RUNWAREPROVIDERSETTINGS", {
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"tooltip": "Connect a Runware Provider Settings Node to configure provider-specific parameters (e.g. Recraft, Picsart).",
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}),
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},
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}
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@@ -29,51 +102,104 @@ class vectorize:
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RETURN_NAMES = ("IMAGE",)
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CATEGORY = "Runware"
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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return True
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def vectorizeImage(self, **kwargs):
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"""Vectorize image and return SVG URL"""
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image = kwargs.get("Image")
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modelName = kwargs.get("Model", "recraft:1@1")
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model = self.RUNWARE_VECTORIZE_MODELS.get(modelName, "recraft:1@1")
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imageUuid = rwUtils.convertTensor2IMG(image)
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genConfig = self._buildGenConfig(model, imageUuid)
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use_search_value = kwargs.get("Use Search Value", False)
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search_input = kwargs.get("Model Search", "")
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current_model = kwargs.get("Vector Model List", "recraft:1@1 (Recraft 1)")
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if use_search_value and search_input and str(search_input).strip():
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model_air_code = str(search_input).strip()
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else:
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# Extract AIR code from "airId (Display Name)" format
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model_air_code = current_model.split(" (")[0].strip()
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image_uuid = rwUtils.convertTensor2IMG(image) if image is not None else None
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width = kwargs.get("width")
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height = kwargs.get("height")
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dimensions = kwargs.get("dimensions", "None")
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use_positive_prompt = kwargs.get("Use positivePrompt", False)
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positive_prompt = kwargs.get("positivePrompt")
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provider_settings = kwargs.get("providerSettings")
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gen_config = self._buildGenConfig(
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model_air_code, image_uuid,
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width=width, height=height, dimensions=dimensions,
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use_positive_prompt=use_positive_prompt, positive_prompt=positive_prompt,
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provider_settings=provider_settings,
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)
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print(f"[DEBUG] Sending Vectorize Request:")
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print(f"[DEBUG] Request Payload: {rwUtils.safe_json_dumps(genConfig, indent=2)}")
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genResult = rwUtils.inferenecRequest(genConfig)
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print(f"[DEBUG] Request Payload: {rwUtils.safe_json_dumps(gen_config, indent=2)}")
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gen_result = rwUtils.inferenecRequest(gen_config)
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print(f"[DEBUG] Received Vectorize Response:")
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print(f"[DEBUG] Response: {rwUtils.safe_json_dumps(genResult, indent=2)}")
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self._validateResponse(genResult)
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svg_url = rwUtils.extractImageURLs(genResult)
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print(f"[DEBUG] Response: {rwUtils.safe_json_dumps(gen_result, indent=2)}")
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self._validateResponse(gen_result)
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svg_url = rwUtils.extractImageURLs(gen_result)
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return (svg_url,)
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def _buildGenConfig(self, model, imageUuid):
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"""Build generation configuration for API request"""
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return [{
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@staticmethod
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def _parseDimensionsPreset(dimensions):
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"""Parse 'Label (WxH)' to (width, height) or None if not a preset."""
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if not dimensions or dimensions in ("None", "Custom"):
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return None, None
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m = re.search(r"\((\d+)x(\d+)\)", dimensions)
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if m:
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return int(m.group(1)), int(m.group(2))
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return None, None
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def _buildGenConfig(self, model, image_uuid, width=None, height=None, dimensions="None",
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use_positive_prompt=False, positive_prompt=None, provider_settings=None):
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"""Build generation configuration for API request. image_uuid, width, height, positivePrompt, providerSettings are optional."""
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inputs = {}
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if image_uuid is not None:
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inputs["image"] = image_uuid
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payload = {
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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": imageUuid
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},
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"inputs": inputs,
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"outputType": "URL",
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"outputFormat": "svg",
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}]
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}
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if dimensions and dimensions != "None":
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w, h = width, height
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if w is None or h is None:
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pw, ph = self._parseDimensionsPreset(dimensions)
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if pw is not None and ph is not None:
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w, h = pw, ph
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if w is not None and h is not None:
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payload["width"] = w
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payload["height"] = h
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if use_positive_prompt and positive_prompt is not None and str(positive_prompt).strip():
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payload["positivePrompt"] = str(positive_prompt).strip()
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# Handle providerSettings - extract provider name from model and merge (same as image inference)
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if provider_settings is not None:
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provider_name = model.split(":")[0] if ":" in model else model
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if isinstance(provider_settings, dict):
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if any(key in provider_settings for key in ["alibaba", "bytedance", "klingai", "openai", "pixverse",
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"bria", "lightricks", "luma", "minimax", "runway", "vidu",
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"elevenlabs", "bfl", "xai", "recraft", "picsart", "sourceful"]):
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payload["providerSettings"] = provider_settings
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else:
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payload["providerSettings"] = {provider_name: provider_settings}
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else:
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payload["providerSettings"] = provider_settings
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return [payload]
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def _validateResponse(self, genResult):
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def _validateResponse(self, gen_result):
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"""Validate API response"""
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if "errors" in genResult:
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errorMessage = genResult["errors"][0]["message"]
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raise Exception(f"Vectorization failed: {errorMessage}")
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if "errors" in gen_result:
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error_message = gen_result["errors"][0]["message"]
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raise Exception(f"Vectorization failed: {error_message}")
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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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