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feat: Support SCAIL WanVideo model (#12614)

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This commit is contained in:
Jukka Seppänen
2026-02-28 23:49:12 +02:00
committed by GitHub
parent 95e1059661
commit 1f6744162f
6 changed files with 255 additions and 1 deletions
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58
comfy_extras/nodes_wan.py
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@@ -1456,6 +1456,63 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image)
class WanSCAILToVideo(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="WanSCAILToVideo",
category="conditioning/video_models",
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Vae.Input("vae"),
io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
io.Image.Input("reference_image", optional=True),
io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step to use pose conditioning."),
io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step to use pose conditioning."),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
],
is_experimental=True,
)
@classmethod
def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, reference_image=None, clip_vision_output=None, pose_video=None) -> io.NodeOutput:
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
ref_latent = None
if reference_image is not None:
reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
ref_latent = vae.encode(reference_image[:, :, :, :3])
if ref_latent is not None:
positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True)
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 pose_video is not None:
pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
out_latent = {}
out_latent["samples"] = latent
return io.NodeOutput(positive, negative, out_latent)
class WanExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -1476,6 +1533,7 @@ class WanExtension(ComfyExtension):
WanAnimateToVideo,
Wan22ImageToVideoLatent,
WanInfiniteTalkToVideo,
WanSCAILToVideo,
]
async def comfy_entrypoint() -> WanExtension: