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Code Issues Actions 20 Packages Projects Releases Wiki Activity

Cleanups to the last PR. (#12646)

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comfyanonymous 2026-02-25 22:30:31 -08:00 committed by GitHub
parent a4522017c5
commit 8a4d85c708
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2 changed files with 22 additions and 39 deletions
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21
comfy/conds.py
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@ -4,6 +4,25 @@ import comfy.utils
import logging import logging
def is_equal(x, y):
if torch.is_tensor(x) and torch.is_tensor(y):
return torch.equal(x, y)
elif isinstance(x, dict) and isinstance(y, dict):
if x.keys() != y.keys():
return False
return all(is_equal(x[k], y[k]) for k in x)
elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
if type(x) is not type(y) or len(x) != len(y):
return False
return all(is_equal(a, b) for a, b in zip(x, y))
else:
try:
return x == y
except Exception:
logging.warning("comparison issue with COND")
return False
class CONDRegular: class CONDRegular:
def __init__(self, cond): def __init__(self, cond):
self.cond = cond self.cond = cond
@ -84,7 +103,7 @@ class CONDConstant(CONDRegular):
return self._copy_with(self.cond) return self._copy_with(self.cond)
def can_concat(self, other): def can_concat(self, other):
if self.cond != other.cond: if not is_equal(self.cond, other.cond):
return False return False
return True return True

40
comfy/model_base.py
View File

@ -65,42 +65,6 @@ from typing import TYPE_CHECKING
if TYPE_CHECKING: if TYPE_CHECKING:
from comfy.model_patcher import ModelPatcher from comfy.model_patcher import ModelPatcher
class _CONDGuideEntries(comfy.conds.CONDConstant):
"""CONDConstant subclass that safely compares guide_attention_entries.
guide_attention_entries may contain ``pixel_mask`` tensors. The default
``CONDConstant.can_concat`` uses ``!=`` which triggers a ``ValueError``
on tensors. This subclass performs a structural comparison instead.
"""
def can_concat(self, other):
if not isinstance(other, _CONDGuideEntries):
return False
a, b = self.cond, other.cond
if len(a) != len(b):
return False
for ea, eb in zip(a, b):
if ea["pre_filter_count"] != eb["pre_filter_count"]:
return False
if ea["strength"] != eb["strength"]:
return False
if ea.get("latent_shape") != eb.get("latent_shape"):
return False
a_has = ea.get("pixel_mask") is not None
b_has = eb.get("pixel_mask") is not None
if a_has != b_has:
return False
if a_has:
pm_a, pm_b = ea["pixel_mask"], eb["pixel_mask"]
if pm_a is not pm_b:
if (pm_a.shape != pm_b.shape
or pm_a.device != pm_b.device
or pm_a.dtype != pm_b.dtype
or not torch.equal(pm_a, pm_b)):
return False
return True
class ModelType(Enum): class ModelType(Enum):
EPS = 1 EPS = 1
V_PREDICTION = 2 V_PREDICTION = 2
@ -1012,7 +976,7 @@ class LTXV(BaseModel):
guide_attention_entries = kwargs.get("guide_attention_entries", None) guide_attention_entries = kwargs.get("guide_attention_entries", None)
if guide_attention_entries is not None: if guide_attention_entries is not None:
out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries) out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
return out return out
@ -1068,7 +1032,7 @@ class LTXAV(BaseModel):
guide_attention_entries = kwargs.get("guide_attention_entries", None) guide_attention_entries = kwargs.get("guide_attention_entries", None)
if guide_attention_entries is not None: if guide_attention_entries is not None:
out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries) out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
return out return out