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Merge branch 'comfyanonymous:master' into master

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patientx 2025-08-04 12:38:56 +03:00 committed by GitHub
commit 88b7fe87ff
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2 changed files with 15 additions and 6 deletions
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7
comfy/conds.py
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@ -1,6 +1,7 @@
import torch
import math
import comfy.utils
import logging
class CONDRegular:
@ -16,6 +17,9 @@ class CONDRegular:
def can_concat(self, other):
if self.cond.shape != other.cond.shape:
return False
if self.cond.device != other.cond.device:
logging.warning("WARNING: conds not on same device, skipping concat.")
return False
return True
def concat(self, others):
@ -51,6 +55,9 @@ class CONDCrossAttn(CONDRegular):
diff = mult_min // min(s1[1], s2[1])
if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
return False
if self.cond.device != other.cond.device:
logging.warning("WARNING: conds not on same device: skipping concat.")
return False
return True
def concat(self, others):

14
comfy/model_base.py
View File

@ -162,7 +162,7 @@ class BaseModel(torch.nn.Module):
xc = self.model_sampling.calculate_input(sigma, x)
if c_concat is not None:
xc = torch.cat([xc] + [c_concat], dim=1)
xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1)
context = c_crossattn
dtype = self.get_dtype()
@ -401,7 +401,7 @@ class SD21UNCLIP(BaseModel):
unclip_conditioning = kwargs.get("unclip_conditioning", None)
device = kwargs["device"]
if unclip_conditioning is None:
return torch.zeros((1, self.adm_channels))
return torch.zeros((1, self.adm_channels), device=device)
else:
return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
@ -615,9 +615,11 @@ class IP2P:
if image is None:
image = torch.zeros_like(noise)
else:
image = image.to(device=device)
if image.shape[1:] != noise.shape[1:]:
image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
image = utils.resize_to_batch_size(image, noise.shape[0])
return self.process_ip2p_image_in(image)
@ -696,7 +698,7 @@ class StableCascade_B(BaseModel):
#size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
out["effnet"] = comfy.conds.CONDRegular(prior)
out["effnet"] = comfy.conds.CONDRegular(prior.to(device=noise.device))
out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
return out
@ -1161,10 +1163,10 @@ class WAN21_Vace(WAN21):
vace_frames_out = []
for j in range(len(vace_frames)):
vf = vace_frames[j].clone()
vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True)
for i in range(0, vf.shape[1], 16):
vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16])
vf = torch.cat([vf, mask[j]], dim=1)
vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1)
vace_frames_out.append(vf)
vace_frames = torch.stack(vace_frames_out, dim=1)