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Update WanAnimateToVideo to more easily extend videos. #9959

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Merged
merged 1 commit into from
Sep 19, 2025
Merged

Update WanAnimateToVideo to more easily extend videos. #9959

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2 changes: 1 addition & 1 deletion comfy/ldm/wan/model_animate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -451,7 +451,7 @@ def __init__(self,
def after_patch_embedding(self, x, pose_latents, face_pixel_values):
if pose_latents is not None:
pose_latents = self.pose_patch_embedding(pose_latents)
x[:, :, 1:] += pose_latents
x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1]

if face_pixel_values is None:
return x, None
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59 changes: 44 additions & 15 deletions comfy_extras/nodes_wan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1128,18 +1128,22 @@ def define_schema(cls):
io.Image.Input("pose_video", optional=True),
io.Int.Input("continue_motion_max_frames", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Image.Input("continue_motion", optional=True),
io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="The amount of frames to seek in all the input videos. Used for generating longer videos by chunk. Connect to the video_frame_offset output of the previous node for extending a video."),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
io.Int.Output(display_name="trim_latent"),
io.Int.Output(display_name="trim_image"),
io.Int.Output(display_name="video_frame_offset"),
],
is_experimental=True,
)

@classmethod
def execute(cls, positive, negative, vae, width, height, length, batch_size, continue_motion_max_frames, reference_image=None, clip_vision_output=None, face_video=None, pose_video=None, continue_motion=None) -> io.NodeOutput:
def execute(cls, positive, negative, vae, width, height, length, batch_size, continue_motion_max_frames, video_frame_offset, reference_image=None, clip_vision_output=None, face_video=None, pose_video=None, continue_motion=None) -> io.NodeOutput:
trim_to_pose_video = False
latent_length = ((length - 1) // 4) + 1
latent_width = width // 8
latent_height = height // 8
Expand All @@ -1152,35 +1156,60 @@ def execute(cls, positive, negative, vae, width, height, length, batch_size, con
concat_latent_image = vae.encode(image[:, :, :, :3])
mask = torch.zeros((1, 1, concat_latent_image.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=concat_latent_image.device, dtype=concat_latent_image.dtype)
trim_latent += concat_latent_image.shape[2]
ref_motion_latent_length = 0

if continue_motion is None:
image = torch.ones((length, height, width, 3)) * 0.5
else:
continue_motion = continue_motion[-continue_motion_max_frames:]
video_frame_offset -= continue_motion.shape[0]
video_frame_offset = max(0, video_frame_offset)
continue_motion = comfy.utils.common_upscale(continue_motion[-length:].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
image = torch.ones((length, height, width, continue_motion.shape[-1]), device=continue_motion.device, dtype=continue_motion.dtype) * 0.5
image[:continue_motion.shape[0]] = continue_motion
ref_motion_latent_length += ((continue_motion.shape[0] - 1) // 4) + 1

if clip_vision_output is not None:
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})

if face_video is not None:
face_video = comfy.utils.common_upscale(face_video[:length].movedim(-1, 1), 512, 512, "area", "center") * 2.0 - 1.0
face_video = face_video.movedim(0, 1).unsqueeze(0)
positive = node_helpers.conditioning_set_values(positive, {"face_video_pixels": face_video})
negative = node_helpers.conditioning_set_values(negative, {"face_video_pixels": face_video * 0.0 - 1.0})
if pose_video is not None:
if pose_video.shape[0] <= video_frame_offset:
pose_video = None
else:
pose_video = pose_video[video_frame_offset:]

if pose_video is not None:
pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
if not trim_to_pose_video:
if pose_video.shape[0] < length:
pose_video = torch.cat((pose_video,) + (pose_video[-1:],) * (length - pose_video.shape[0]), dim=0)

pose_video_latent = vae.encode(pose_video[:, :, :, :3])
positive = node_helpers.conditioning_set_values(positive, {"pose_video_latent": pose_video_latent})
negative = node_helpers.conditioning_set_values(negative, {"pose_video_latent": pose_video_latent})

if continue_motion is None:
image = torch.ones((length, height, width, 3)) * 0.5
else:
continue_motion = continue_motion[-continue_motion_max_frames:]
continue_motion = comfy.utils.common_upscale(continue_motion[-length:].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
image = torch.ones((length, height, width, continue_motion.shape[-1]), device=continue_motion.device, dtype=continue_motion.dtype) * 0.5
image[:continue_motion.shape[0]] = continue_motion
if trim_to_pose_video:
latent_length = pose_video_latent.shape[2]
length = latent_length * 4 - 3
image = image[:length]

if face_video is not None:
if face_video.shape[0] <= video_frame_offset:
face_video = None
else:
face_video = face_video[video_frame_offset:]

if face_video is not None:
face_video = comfy.utils.common_upscale(face_video[:length].movedim(-1, 1), 512, 512, "area", "center") * 2.0 - 1.0
face_video = face_video.movedim(0, 1).unsqueeze(0)
positive = node_helpers.conditioning_set_values(positive, {"face_video_pixels": face_video})
negative = node_helpers.conditioning_set_values(negative, {"face_video_pixels": face_video * 0.0 - 1.0})

concat_latent_image = torch.cat((concat_latent_image, vae.encode(image[:, :, :, :3])), dim=2)
mask_refmotion = torch.ones((1, 1, latent_length, concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=mask.device, dtype=mask.dtype)
if continue_motion is not None:
mask_refmotion[:, :, :((continue_motion.shape[0] - 1) // 4) + 1] = 0.0
mask_refmotion[:, :, :ref_motion_latent_length] = 0.0

mask = torch.cat((mask, mask_refmotion), dim=2)
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
Expand All @@ -1189,7 +1218,7 @@ def execute(cls, positive, negative, vae, width, height, length, batch_size, con
latent = torch.zeros([batch_size, 16, latent_length + trim_latent, latent_height, latent_width], device=comfy.model_management.intermediate_device())
out_latent = {}
out_latent["samples"] = latent
return io.NodeOutput(positive, negative, out_latent, trim_latent)
return io.NodeOutput(positive, negative, out_latent, trim_latent, max(0, ref_motion_latent_length * 4 - 3), video_frame_offset + length)

class Wan22ImageToVideoLatent(io.ComfyNode):
@classmethod
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