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Add a LatentCut node to cut latents. (#9609)

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comfyanonymous
2025-08-28 16:38:28 -07:00
committed by GitHub
parent 1c184c29eb
commit d28b39d93d
1 changed files with 37 additions and 0 deletions
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37
comfy_extras/nodes_latent.py
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@@ -1,6 +1,7 @@
import comfy.utils
import comfy_extras.nodes_post_processing
import torch
import nodes
def reshape_latent_to(target_shape, latent, repeat_batch=True):
@@ -137,6 +138,41 @@ class LatentConcat:
samples_out["samples"] = torch.cat(c, dim=dim)
return (samples_out,)
class LatentCut:
@classmethod
def INPUT_TYPES(s):
return {"required": {"samples": ("LATENT",),
"dim": (["x", "y", "t"], ),
"index": ("INT", {"default": 0, "min": -nodes.MAX_RESOLUTION, "max": nodes.MAX_RESOLUTION, "step": 1}),
"amount": ("INT", {"default": 1, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 1})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples, dim, index, amount):
samples_out = samples.copy()
s1 = samples["samples"]
if "x" in dim:
dim = s1.ndim - 1
elif "y" in dim:
dim = s1.ndim - 2
elif "t" in dim:
dim = s1.ndim - 3
if index >= 0:
index = min(index, s1.shape[dim] - 1)
amount = min(s1.shape[dim] - index, amount)
else:
index = max(index, -s1.shape[dim])
amount = min(-index, amount)
samples_out["samples"] = torch.narrow(s1, dim, index, amount)
return (samples_out,)
class LatentBatch:
@classmethod
def INPUT_TYPES(s):
@@ -312,6 +348,7 @@ NODE_CLASS_MAPPINGS = {
"LatentMultiply": LatentMultiply,
"LatentInterpolate": LatentInterpolate,
"LatentConcat": LatentConcat,
"LatentCut": LatentCut,
"LatentBatch": LatentBatch,
"LatentBatchSeedBehavior": LatentBatchSeedBehavior,
"LatentApplyOperation": LatentApplyOperation,