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Fix Conditioning masks on 3d latents. (#9506)

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comfyanonymous
2025-08-22 20:15:44 -07:00
committed by GitHub
parent fc247150fe
commit 41048c69b4
1 changed files with 7 additions and 3 deletions
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10
comfy/samplers.py
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@@ -17,6 +17,7 @@ import comfy.model_patcher
import comfy.patcher_extension
import comfy.hooks
import comfy.context_windows
import comfy.utils
import scipy.stats
import numpy
@@ -61,7 +62,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
if "mask_strength" in conds:
mask_strength = conds["mask_strength"]
mask = conds['mask']
assert (mask.shape[1:] == x_in.shape[2:])
# assert (mask.shape[1:] == x_in.shape[2:])
mask = mask[:input_x.shape[0]]
if area is not None:
@@ -69,7 +70,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
mask = mask * mask_strength
mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
mask = mask.unsqueeze(1).repeat((input_x.shape[0] // mask.shape[0], input_x.shape[1]) + (1, ) * (mask.ndim - 1))
else:
mask = torch.ones_like(input_x)
mult = mask * strength
@@ -553,7 +554,10 @@ def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
if len(mask.shape) == len(dims):
mask = mask.unsqueeze(0)
if mask.shape[1:] != dims:
mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
if mask.ndim < 4:
mask = comfy.utils.common_upscale(mask.unsqueeze(1), dims[-1], dims[-2], 'bilinear', 'none').squeeze(1)
else:
mask = comfy.utils.common_upscale(mask, dims[-1], dims[-2], 'bilinear', 'none')
if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)