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Make VACE conditionings stackable. (#8240)

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comfyanonymous 2025-05-22 16:22:26 -07:00 committed by GitHub
parent 4202e956a0
commit f85c08df06
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3 changed files with 23 additions and 13 deletions
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10
comfy/ldm/wan/model.py
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@ -635,7 +635,7 @@ class VaceWanModel(WanModel):
t,
context,
vace_context,
vace_strength=1.0,
vace_strength,
clip_fea=None,
freqs=None,
transformer_options={},
@ -661,8 +661,11 @@ class VaceWanModel(WanModel):
context = torch.concat([context_clip, context], dim=1)
context_img_len = clip_fea.shape[-2]
orig_shape = list(vace_context.shape)
vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:])
c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype)
c = c.flatten(2).transpose(1, 2)
c = list(c.split(orig_shape[0], dim=0))
# arguments
x_orig = x
@ -682,8 +685,9 @@ class VaceWanModel(WanModel):
ii = self.vace_layers_mapping.get(i, None)
if ii is not None:
c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
x += c_skip * vace_strength
for iii in range(len(c)):
c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
x += c_skip * vace_strength[iii]
del c_skip
# head
x = self.head(x, e)

21
comfy/model_base.py
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@ -1062,20 +1062,25 @@ class WAN21_Vace(WAN21):
vace_frames = kwargs.get("vace_frames", None)
if vace_frames is None:
noise_shape[1] = 32
vace_frames = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
for i in range(0, vace_frames.shape[1], 16):
vace_frames = vace_frames.clone()
vace_frames[:, i:i + 16] = self.process_latent_in(vace_frames[:, i:i + 16])
vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)]
mask = kwargs.get("vace_mask", None)
if mask is None:
noise_shape[1] = 64
mask = torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)
mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames)
out['vace_context'] = comfy.conds.CONDRegular(torch.cat([vace_frames.to(noise), mask.to(noise)], dim=1))
vace_frames_out = []
for j in range(len(vace_frames)):
vf = vace_frames[j].clone()
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)
vace_frames_out.append(vf)
vace_strength = kwargs.get("vace_strength", 1.0)
vace_frames = torch.stack(vace_frames_out, dim=1)
out['vace_context'] = comfy.conds.CONDRegular(vace_frames)
vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out))
out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
return out

5
comfy_extras/nodes_wan.py
View File

@ -268,8 +268,9 @@ class WanVaceToVideo:
trim_latent = reference_image.shape[2]
mask = mask.unsqueeze(0)
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device())
out_latent = {}