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Merge upstream/master, keep local README.md

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GitHub Actions 2025-12-31 00:38:15 +00:00
commit 9886d3ff63
3 changed files with 21 additions and 11 deletions
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6
comfy/context_windows.py
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@ -188,6 +188,12 @@ class IndexListContextHandler(ContextHandlerABC):
audio_cond = cond_value.cond
if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
# Handle vace_context (temporal dim is 3)
elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
vace_cond = cond_value.cond
if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):
sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)
new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)
# if has cond that is a Tensor, check if needs to be subset
elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
if (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \

3
comfy_api_nodes/nodes_bytedance.py
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@ -229,6 +229,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
is_deprecated=True,
)
@classmethod
@ -269,7 +270,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceSeedreamNode",
display_name="ByteDance Seedream 4",
display_name="ByteDance Seedream 4.5",
category="api node/image/ByteDance",
description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
inputs=[

23
comfy_extras/nodes_dataset.py
View File

@ -667,16 +667,19 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
@classmethod
def _process(cls, image, longer_edge):
img = tensor_to_pil(image)
w, h = img.size
if w > h:
new_w = longer_edge
new_h = int(h * (longer_edge / w))
else:
new_h = longer_edge
new_w = int(w * (longer_edge / h))
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
return pil_to_tensor(img)
resized_images = []
for image_i in image:
img = tensor_to_pil(image_i)
w, h = img.size
if w > h:
new_w = longer_edge
new_h = int(h * (longer_edge / w))
else:
new_h = longer_edge
new_w = int(w * (longer_edge / h))
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
resized_images.append(pil_to_tensor(img))
return torch.cat(resized_images, dim=0)
class CenterCropImagesNode(ImageProcessingNode):