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Merge dfd64dbdd7 into 3d870ff51f

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drozbay 2026-05-15 01:27:33 +08:00 committed by GitHub
commit 175e053855
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2 changed files with 8 additions and 4 deletions
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6
comfy_extras/nodes_images.py
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@ -136,7 +136,7 @@ class ImageFromBatch(IO.ComfyNode):
category="image/batch", category="image/batch",
inputs=[ inputs=[
IO.Image.Input("image"), IO.Image.Input("image"),
IO.Int.Input("batch_index", default=0, min=0, max=4095), IO.Int.Input("batch_index", default=0, min=-MAX_RESOLUTION, max=MAX_RESOLUTION),
IO.Int.Input("length", default=1, min=1, max=4096), IO.Int.Input("length", default=1, min=1, max=4096),
], ],
outputs=[IO.Image.Output()], outputs=[IO.Image.Output()],
@ -145,7 +145,9 @@ class ImageFromBatch(IO.ComfyNode):
@classmethod @classmethod
def execute(cls, image, batch_index, length) -> IO.NodeOutput: def execute(cls, image, batch_index, length) -> IO.NodeOutput:
s_in = image s_in = image
batch_index = min(s_in.shape[0] - 1, batch_index) if batch_index < 0:
batch_index += s_in.shape[0]
batch_index = max(0, min(s_in.shape[0] - 1, batch_index))
length = min(s_in.shape[0] - batch_index, length) length = min(s_in.shape[0] - batch_index, length)
s = s_in[batch_index:batch_index + length].clone() s = s_in[batch_index:batch_index + length].clone()
return IO.NodeOutput(s) return IO.NodeOutput(s)

6
nodes.py
View File

@ -1221,7 +1221,7 @@ class LatentFromBatch:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",), return {"required": { "samples": ("LATENT",),
"batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), "batch_index": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
"length": ("INT", {"default": 1, "min": 1, "max": 64}), "length": ("INT", {"default": 1, "min": 1, "max": 64}),
}} }}
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
@ -1232,7 +1232,9 @@ class LatentFromBatch:
def frombatch(self, samples, batch_index, length): def frombatch(self, samples, batch_index, length):
s = samples.copy() s = samples.copy()
s_in = samples["samples"] s_in = samples["samples"]
batch_index = min(s_in.shape[0] - 1, batch_index) if batch_index < 0:
batch_index += s_in.shape[0]
batch_index = max(0, min(s_in.shape[0] - 1, batch_index))
length = min(s_in.shape[0] - batch_index, length) length = min(s_in.shape[0] - batch_index, length)
s["samples"] = s_in[batch_index:batch_index + length].clone() s["samples"] = s_in[batch_index:batch_index + length].clone()
if "noise_mask" in samples: if "noise_mask" in samples: