RomanNum3ral
/
ComfyUI
mirror of https://github.com/Comfy-Org/ComfyUI.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge cc39c0f9b4 into 37ac9ff44f

This commit is contained in:
Jukka Seppänen 2026-08-15 15:02:29 -04:00 committed by GitHub
parent 37ac9ff44f cc39c0f9b4
commit 2241726db2
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 17 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
comfy/ldm/wan/model.py
View File

@ -758,12 +758,13 @@ class VaceWanModel(WanModel):
image_model=None,
vace_layers=None,
vace_in_dim=None,
vace_image_input=False,
device=None,
dtype=None,
operations=None,
):
super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
super().__init__(model_type='i2v' if vace_image_input else 't2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
operation_settings = {"operations": operations, "device": device, "dtype": dtype}
# Vace
@ -816,6 +817,12 @@ class VaceWanModel(WanModel):
context = torch.concat([context_clip, context], dim=1)
context_img_len = clip_fea.shape[-2]
# vace blocks are t2v pretrained, they attend over text tokens only
if context_img_len is None:
context_vace = context
else:
context_vace = context[:, context_img_len:]
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)
@ -850,7 +857,7 @@ class VaceWanModel(WanModel):
ii = self.vace_layers_mapping.get(i, None)
if ii is not None:
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, transformer_options=transformer_options)
c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context_vace, context_img_len=None, transformer_options=transformer_options)
x += c_skip * vace_strength[iii]
del c_skip
# head

2
comfy/model_detection.py
View File

@ -725,6 +725,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["model_type"] = "vace"
dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1]
dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.')
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: # ID-V2V, vace on an i2v model
dit_config["vace_image_input"] = True
elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys:
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "camera"

2
comfy/supported_models.py
View File

@ -1412,7 +1412,7 @@ class WAN21_Vace(WAN21_T2V):
self.memory_usage_factor = 1.2 * self.memory_usage_factor
def get_model(self, state_dict, prefix="", device=None):
out = model_base.WAN21_Vace(self, image_to_video=False, device=device)
out = model_base.WAN21_Vace(self, image_to_video=self.unet_config.get("vace_image_input", False), device=device)
return out
class WAN21_HuMo(WAN21_T2V):

6
comfy_extras/nodes_wan.py
View File

@ -31,6 +31,7 @@ class WanImageToVideo(io.ComfyNode):
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
io.Image.Input("start_image", optional=True),
io.Image.Input("ref_pad_image", optional=True, tooltip="Fills the padding frames of the image conditioning with this image instead of gray, anchoring identity without pinning frames (SVI-style anti-drift padding, used by models such as ID-V2V)."),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
@ -40,11 +41,14 @@ class WanImageToVideo(io.ComfyNode):
)
@classmethod
def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput:
def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, ref_pad_image=None) -> io.NodeOutput:
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
if start_image is not None:
start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5
if ref_pad_image is not None:
ref_pad_image = comfy.utils.common_upscale(ref_pad_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
image[:, :, :, :3] = ref_pad_image[:, :, :, :3].to(device=image.device, dtype=image.dtype)
image[:start_image.shape[0]] = start_image
concat_latent_image = vae.encode(image[:, :, :, :3])