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 +1,46 @@
+from .utils import runwareUtils as rwUtils
+
+class videoInferenceInputs:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+            "optional": {
+                "Image": ("IMAGE", {
+                    "tooltip": "Portrait image for video generation. This will be the main subject that will speak and move in the generated video."
+                }),
+                "Audio": ("STRING", {
+                    "tooltip": "Connect the mediaUUID output from Runware Media Upload node with audio file. This audio will be synchronized with the generated video."
+                }),
+                "Mask": ("IMAGE", {
+                    "tooltip": "Mask image to specify a specific subject in the image to speak. Use white/black mask format."
+                }),
+            }
+        }
+
+    DESCRIPTION = "Configure custom inputs for Runware Video Inference with OmniHuman 1.5 support, including image, audio, and mask inputs."
+    FUNCTION = "createInputs"
+    RETURN_TYPES = ("RUNWAREVIDEOINFERENCEINPUTS",)
+    RETURN_NAMES = ("Video Inference Inputs",)
+    CATEGORY = "Runware"
+
+    def createInputs(self, **kwargs):
+        image = kwargs.get("Image", None)
+        audio = kwargs.get("Audio", None)
+        mask = kwargs.get("Mask", None)
+
+        inputs = {}
+
+        if image is not None:
+            image_uuid = rwUtils.convertTensor2IMG(image)
+            inputs["image"] = image_uuid
+
+        if audio is not None and audio.strip() != "":
+            inputs["audio"] = audio.strip()
+
+        if mask is not None:
+            mask_uuid = rwUtils.convertTensor2IMG(mask)
+            inputs["mask"] = mask_uuid
+
+        return (inputs,)
+