Merge branch 'master' into feat/load3d-camera-info-intrinsics

comfy_api_nodes/nodes_beeble.py

@@ -206,7 +206,7 @@ class BeebleSwitchXVideoEdit(IO.ComfyNode):
         return IO.Schema(
             node_id="BeebleSwitchXVideoEdit",
             display_name="Beeble SwitchX Video Edit",
-            category="video/partner/Beeble",
+            category="api node/video/Beeble",
             description=(
                 "Edit a video with Beeble SwitchX. Switches anything in the scene (background, "
                 "lighting, costume) while preserving the original subject's pixels and motion. "
@@ -302,7 +302,7 @@ class BeebleSwitchXImageEdit(IO.ComfyNode):
         return IO.Schema(
             node_id="BeebleSwitchXImageEdit",
             display_name="Beeble SwitchX Image Edit",
-            category="image/partner/Beeble",
+            category="api node/image/Beeble",
             description=(
                 "Edit a single image with Beeble SwitchX. Switches anything in the scene "
                 "(background, lighting, costume) while preserving the original subject's pixels. "

comfy_api_nodes/nodes_bytedance.py

@@ -44,7 +44,6 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
     download_url_to_video_output,
-    downscale_image_tensor_by_max_side,
     downscale_video_to_max_pixels,
     get_number_of_images,
     image_tensor_pair_to_batch,
@@ -123,14 +122,6 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: st
         )
 
 
-def _prepare_seedance_image(image: Input.Image) -> Input.Image:
-    """Auto-downscale a Seedance image input to the per-side limits, then validate it."""
-    validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
-    image = downscale_image_tensor_by_max_side(image, max_side=6000)
-    validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
-    return image
-
-
 async def _resolve_reference_assets(
     cls: type[IO.ComfyNode],
     asset_ids: list[str],
@@ -1790,11 +1781,6 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
         if last_frame is not None and last_frame_asset_id:
             raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.")
 
-        if first_frame is not None:
-            first_frame = _prepare_seedance_image(first_frame)
-        if last_frame is not None:
-            last_frame = _prepare_seedance_image(last_frame)
-
         asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a]
         image_assets: dict[str, str] = {}
         if asset_ids_to_resolve:
@@ -1901,7 +1887,7 @@ def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16
         ),
         IO.Boolean.Input(
             "auto_downscale",
-            default=True,
+            default=False,
             optional=True,
             tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
             "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
@@ -2069,9 +2055,6 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                 f"(audios={len(reference_audios)}, audio assets={len(reference_audio_assets)}). Maximum is 3."
             )
 
-        for key in reference_images:
-            reference_images[key] = _prepare_seedance_image(reference_images[key])
-
         model_id = SEEDANCE_MODELS[model["model"]]
         has_video_input = total_videos > 0
 

comfy_extras/nodes_dataset.py

@@ -157,7 +157,7 @@ class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
         return io.NodeOutput(output_tensor, captions)
 
 
-def save_images_to_folder(image_list, output_dir, prefix="image", overwrite=True):
+def save_images_to_folder(image_list, output_dir, prefix="image"):
     """Utility function to save a list of image tensors to disk.
 
     Args:
@@ -197,11 +197,7 @@ def save_images_to_folder(image_list, output_dir, prefix="image", overwrite=True
             raise ValueError(f"Expected torch.Tensor, got {type(img_tensor)}")
 
         # Save image
-        if overwrite:
-            filename = f"{prefix}_{idx:05d}.png"
-        else:
-            _, _, counter, _, resolved_prefix = folder_paths.get_save_image_path(prefix, output_dir)
-            filename = f"{resolved_prefix}_{counter:05}_{idx:05d}.png"
+        filename = f"{prefix}_{idx:05d}.png"
         filepath = os.path.join(output_dir, filename)
         img.save(filepath)
         saved_files.append(filename)
@@ -234,26 +230,19 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
                     tooltip="Prefix for saved image filenames.",
                     advanced=True,
                 ),
-                io.Combo.Input(
-                    "mode",
-                    default="overwrite",
-                    options=["overwrite", "increment"],
-                    tooltip="Whether to overwrite existing files or increment filenames to avoid overwriting."
-                ),
             ],
             outputs=[],
             is_deprecated=True,  # This node is redundant and superseded by existing Save Image nodes where the target folder can be specified in the filename_prefix
         )
 
     @classmethod
-    def execute(cls, images, folder_name, filename_prefix, mode):
+    def execute(cls, images, folder_name, filename_prefix):
         # Extract scalar values
         folder_name = folder_name[0]
         filename_prefix = filename_prefix[0]
-        mode = mode[0]
 
         output_dir = os.path.join(folder_paths.get_output_directory(), folder_name)
-        saved_files = save_images_to_folder(images, output_dir, filename_prefix, mode=='overwrite')
+        saved_files = save_images_to_folder(images, output_dir, filename_prefix)
 
         logging.info(f"Saved {len(saved_files)} images to {output_dir}.")
         return io.NodeOutput()
@@ -289,25 +278,18 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
                     tooltip="Prefix for saved image filenames.",
                     advanced=True,
                 ),
-                io.Combo.Input(
-                    "mode",
-                    default="overwrite",
-                    options=["overwrite", "increment"],
-                    tooltip="Whether to overwrite existing files or increment filenames to avoid overwriting."
-                ),
             ],
             outputs=[],
         )
 
     @classmethod
-    def execute(cls, images, folder_name, filename_prefix, mode, texts=None):
+    def execute(cls, images, folder_name, filename_prefix, texts=None):
         # Extract scalar values
         folder_name = folder_name[0]
         filename_prefix = filename_prefix[0]
-        mode = mode[0]
 
         output_dir = os.path.join(folder_paths.get_output_directory(), folder_name)
-        saved_files = save_images_to_folder(images, output_dir, filename_prefix, mode=='overwrite')
+        saved_files = save_images_to_folder(images, output_dir, filename_prefix)
 
         # Save captions
         if texts:

comfy_extras/nodes_load_3d.py

@@ -34,7 +34,7 @@ class Load3D(IO.ComfyNode):
             essentials_category="Basics",
             is_experimental=True,
             inputs=[
-                IO.Combo.Input("model_file", options=["none"] + sorted(files), upload=IO.UploadType.model),
+                IO.Combo.Input("model_file", options=sorted(files), upload=IO.UploadType.model),
                 IO.Load3D.Input("image"),
                 IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
                 IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
@@ -68,12 +68,8 @@ class Load3D(IO.ComfyNode):
 
             video = InputImpl.VideoFromFile(recording_video_path)
 
-        file_3d = None
-        mesh_path = ""
-        if model_file and model_file != "none":
-            file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file))
-            mesh_path = model_file
-        return IO.NodeOutput(output_image, output_mask, mesh_path, normal_image, image['camera_info'], video, file_3d)
+        file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file))
+        return IO.NodeOutput(output_image, output_mask, model_file, normal_image, image['camera_info'], video, file_3d)
 
     process = execute  # TODO: remove