Merge aadccd28ca into bbe2c13a70

comfy_extras/nodes_dataset.py

@@ -644,9 +644,13 @@ class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
         if w < h:
             new_w = shorter_edge
             new_h = int(h * (shorter_edge / w))
-        else:
+        elif h < w:
             new_h = shorter_edge
             new_w = int(w * (shorter_edge / h))
+        else:
+            # Square image: set both dimensions directly to avoid precision loss from float operations
+            new_w = shorter_edge
+            new_h = shorter_edge
         img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
         return pil_to_tensor(img)
 
@@ -674,9 +678,13 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
             if w > h:
                 new_w = longer_edge
                 new_h = int(h * (longer_edge / w))
-            else:
+            elif h > w:
                 new_h = longer_edge
                 new_w = int(w * (longer_edge / h))
+            else:
+                # Square image: set both dimensions directly to avoid precision loss from float operations
+                new_w = longer_edge
+                new_h = longer_edge
             img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
             resized_images.append(pil_to_tensor(img))
         return torch.cat(resized_images, dim=0)

comfy_extras/nodes_hunyuan.py

@@ -56,7 +56,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples":latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
 
     generate = execute  # TODO: remove
 
@@ -73,7 +73,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         # Using scale factor of 16 instead of 8
         latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 16})
 
 
 class HunyuanVideo15ImageToVideo(io.ComfyNode):

comfy_extras/nodes_images.py

@@ -395,10 +395,15 @@ class ResizeAndPadImage(IO.ComfyNode):
 
         scale_w = target_width / orig_width
         scale_h = target_height / orig_height
-        scale = min(scale_w, scale_h)
 
-        new_width = int(orig_width * scale)
-        new_height = int(orig_height * scale)
+        # When aspect ratios match, scale directly to target to avoid precision loss from float multiplication
+        if scale_w == scale_h:
+            new_width = target_width
+            new_height = target_height
+        else:
+            scale = min(scale_w, scale_h)
+            new_width = int(orig_width * scale)
+            new_height = int(orig_height * scale)
 
         image_permuted = image.permute(0, 3, 1, 2)