feat(api-nodes): add Meshy 3D nodes (#11843)

* feat(api-nodes): add Meshy 3D nodes

* rebased, added JSONata price badges

comfy_api_nodes/util/upload_helpers.py

@@ -43,7 +43,7 @@ class UploadResponse(BaseModel):
 
 async def upload_images_to_comfyapi(
     cls: type[IO.ComfyNode],
-    image: torch.Tensor,
+    image: torch.Tensor | list[torch.Tensor],
     *,
     max_images: int = 8,
     mime_type: str | None = None,
@@ -55,15 +55,28 @@ async def upload_images_to_comfyapi(
     Uploads images to ComfyUI API and returns download URLs.
     To upload multiple images, stack them in the batch dimension first.
     """
+    tensors: list[torch.Tensor] = []
+    if isinstance(image, list):
+        for img in image:
+            is_batch = len(img.shape) > 3
+            if is_batch:
+                tensors.extend(img[i] for i in range(img.shape[0]))
+            else:
+                tensors.append(img)
+    else:
+        is_batch = len(image.shape) > 3
+        if is_batch:
+            tensors.extend(image[i] for i in range(image.shape[0]))
+        else:
+            tensors.append(image)
+
     # if batched, try to upload each file if max_images is greater than 0
     download_urls: list[str] = []
-    is_batch = len(image.shape) > 3
-    batch_len = image.shape[0] if is_batch else 1
-    num_to_upload = min(batch_len, max_images)
+    num_to_upload = min(len(tensors), max_images)
     batch_start_ts = time.monotonic()
 
     for idx in range(num_to_upload):
-        tensor = image[idx] if is_batch else image
+        tensor = tensors[idx]
         img_io = tensor_to_bytesio(tensor, total_pixels=total_pixels, mime_type=mime_type)
 
         effective_label = wait_label