Merge branch 'master' into feat/api-nodes/bria-replace-background

Signed-off-by: bigcat88 <bigcat88@icloud.com>

comfy_api_nodes/nodes_bria.py

@@ -289,7 +289,7 @@ class BriaRemoveVideoBackground(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+                expr="""{"type":"usd","usd":0.0044,"format":{"suffix":"/second"}}""",
             ),
         )
 
@@ -357,7 +357,7 @@ class BriaVideoGreenScreen(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+                expr="""{"type":"usd","usd":0.0044,"format":{"suffix":"/second"}}""",
             ),
         )
 
@@ -433,7 +433,7 @@ class BriaVideoReplaceBackground(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+                expr="""{"type":"usd","usd":0.0044,"format":{"suffix":"/second"}}""",
             ),
         )
 
@@ -452,7 +452,10 @@ class BriaVideoReplaceBackground(IO.ComfyNode):
             validate_video_duration(background_video, max_duration=60.0)
             background_url = await upload_video_to_comfyapi(cls, background_video, wait_label="Uploading background")
         else:
-            background_url = await upload_image_to_comfyapi(cls, background_image, wait_label="Uploading background")
+            # Bria's replace_background 500s on RGBA, so drop the alpha channel before upload.
+            background_url = await upload_image_to_comfyapi(
+                cls, background_image[:, :, :, :3], wait_label="Uploading background"
+            )
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/bria/v2/video/edit/replace_background", method="POST"),
@@ -530,7 +533,7 @@ class BriaTransparentVideoBackground(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+                expr="""{"type":"usd","usd":0.0044,"format":{"suffix":"/second"}}""",
             ),
         )
 
@@ -571,7 +574,7 @@ class BriaExtension(ComfyExtension):
             BriaRemoveImageBackground,
             BriaRemoveVideoBackground,
             BriaVideoGreenScreen,
-            # BriaVideoReplaceBackground,  # server returns Status 500 when we pass background video
+            BriaVideoReplaceBackground,
             BriaTransparentVideoBackground,
         ]
 

comfy_api_nodes/nodes_openai.py

@@ -9,7 +9,6 @@ from PIL import Image
 from typing_extensions import override
 
 import folder_paths
-from comfy.utils import common_upscale
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.openai import (
     InputFileContent,
@@ -63,8 +62,7 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten
         timeout: Request timeout in seconds. Defaults to None (no timeout).
 
     Returns:
-        A torch.Tensor of shape (N, H, W, C) with all returned images; images whose
-        dimensions differ from the first image's are resized to match it.
+        A torch.Tensor representing the image (1, H, W, C).
 
     Raises:
         ValueError: If the response is not valid.
@@ -91,14 +89,6 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten
         arr = np.asarray(pil_img).astype(np.float32) / 255.0
         image_tensors.append(torch.from_numpy(arr))
 
-    # With size="auto" the API can return images whose dimensions differ by a few pixels within a single response
-    # resize them to the first image's dimensions so they can be stacked into one batch.
-    ref_h, ref_w = image_tensors[0].shape[:2]
-    for i, t in enumerate(image_tensors):
-        if t.shape[:2] != (ref_h, ref_w):
-            samples = t.unsqueeze(0).movedim(-1, 1)
-            samples = common_upscale(samples, ref_w, ref_h, "bilinear", "center")
-            image_tensors[i] = samples.movedim(1, -1).squeeze(0)
     return torch.stack(image_tensors, dim=0)
 
 

comfy_extras/nodes_scail.py

@@ -267,8 +267,7 @@ class SCAIL2ColoredMask(io.ComfyNode):
                 io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
                                tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
                 io.Boolean.Input("replacement_mode", default=False,
-                                 tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
-                                         "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
+                                 tooltip="False = mask_video has black bg (Animation Mode). True = white bg (Replacement Mode). Set the matching replacement_mode on WanSCAILToVideo. reference_image_mask is always black-bg regardless."),
             ],
             outputs=[
                 io.Image.Output("pose_video_mask"),
@@ -297,17 +296,14 @@ class SCAIL2ColoredMask(io.ComfyNode):
             return td
 
         drv = _prep(driving_track_data)
-        # Animation: driving=black, ref=white. Replacement: driving=white, ref=black.
         mask_video = _render_colored_masks(drv, "white" if replacement_mode else "black")
-        ref_bg = "black" if replacement_mode else "white"
 
         if ref_track_data is not None:
             ref = _prep(ref_track_data)
-            reference_image_mask = _render_colored_masks(ref, ref_bg)
+            reference_image_mask = _render_colored_masks(ref, "black")
         else:
             H, W = drv["orig_size"]
-            fill_value = 1.0 if ref_bg == "white" else 0.0
-            reference_image_mask = torch.full((1, H, W, 3), fill_value, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+            reference_image_mask = torch.zeros(1, H, W, 3, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
 
         return io.NodeOutput(mask_video, reference_image_mask)