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Various fixes for broken things from earlier PR. (#9168)

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comfyanonymous 2025-08-04 01:02:40 -07:00 committed by GitHub
parent 140ffc7fdc
commit 7991341e89
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1 changed files with 8 additions and 6 deletions
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comfy/model_base.py
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@ -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)
@ -409,7 +409,7 @@ def sdxl_pooled(args, noise_augmentor):
if "unclip_conditioning" in args:
return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280]
else:
return args["pooled_output"]
return args["pooled_output"].to(device=args["device"])
class SDXLRefiner(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
@ -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)