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convert WAN nodes to V3 schema (#9201)

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Alexander Piskun
2025-08-15 04:48:41 +03:00
committed by GitHub
parent deebee4ff6
commit 5d65d6753b
1 changed files with 298 additions and 251 deletions
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549
comfy_extras/nodes_wan.py
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@@ -9,29 +9,35 @@ import comfy.clip_vision
import json import json
import numpy as np import numpy as np
from typing import Tuple from typing import Tuple
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class WanImageToVideo: class WanImageToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanImageToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"start_image": ("IMAGE", ), 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("start_image", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) 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: 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) start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
@@ -51,32 +57,36 @@ class WanImageToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent) return io.NodeOutput(positive, negative, out_latent)
class WanFunControlToVideo: class WanFunControlToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanFunControlToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"start_image": ("IMAGE", ), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
"control_video": ("IMAGE", ), 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("control_video", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, control_video=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, control_video=None):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
@@ -101,31 +111,34 @@ class WanFunControlToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent) return io.NodeOutput(positive, negative, out_latent)
class Wan22FunControlToVideo: class Wan22FunControlToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="Wan22FunControlToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"ref_image": ("IMAGE", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"control_video": ("IMAGE", ), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
# "start_image": ("IMAGE", ), io.Int.Input("batch_size", default=1, min=1, max=4096),
}} io.Image.Input("ref_image", optional=True),
io.Image.Input("control_video", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, ref_image=None, start_image=None, control_video=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, ref_image=None, start_image=None, control_video=None):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
@@ -158,32 +171,36 @@ class Wan22FunControlToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent) return io.NodeOutput(positive, negative, out_latent)
class WanFirstLastFrameToVideo: class WanFirstLastFrameToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanFirstLastFrameToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"clip_vision_start_image": ("CLIP_VISION_OUTPUT", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"clip_vision_end_image": ("CLIP_VISION_OUTPUT", ), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
"start_image": ("IMAGE", ), io.Int.Input("batch_size", default=1, min=1, max=4096),
"end_image": ("IMAGE", ), io.ClipVisionOutput.Input("clip_vision_start_image", optional=True),
}} io.ClipVisionOutput.Input("clip_vision_end_image", optional=True),
io.Image.Input("start_image", optional=True),
io.Image.Input("end_image", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_start_image=None, clip_vision_end_image=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_start_image=None, clip_vision_end_image=None):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) 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: 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) start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
@@ -224,62 +241,70 @@ class WanFirstLastFrameToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent) return io.NodeOutput(positive, negative, out_latent)
class WanFunInpaintToVideo: class WanFunInpaintToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanFunInpaintToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"start_image": ("IMAGE", ), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
"end_image": ("IMAGE", ), 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("end_image", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_output=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_output=None):
flfv = WanFirstLastFrameToVideo() flfv = WanFirstLastFrameToVideo()
return flfv.encode(positive, negative, vae, width, height, length, batch_size, start_image=start_image, end_image=end_image, clip_vision_start_image=clip_vision_output) return flfv.execute(positive, negative, vae, width, height, length, batch_size, start_image=start_image, end_image=end_image, clip_vision_start_image=clip_vision_output)
class WanVaceToVideo: class WanVaceToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanVaceToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), is_experimental=True,
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("positive"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Conditioning.Input("negative"),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1000.0, "step": 0.01}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"control_video": ("IMAGE", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"control_masks": ("MASK", ), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
"reference_image": ("IMAGE", ), io.Int.Input("batch_size", default=1, min=1, max=4096),
}} io.Float.Input("strength", default=1.0, min=0.0, max=1000.0, step=0.01),
io.Image.Input("control_video", optional=True),
io.Mask.Input("control_masks", optional=True),
io.Image.Input("reference_image", optional=True),
],
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"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT", "INT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent", "trim_latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
EXPERIMENTAL = True
def encode(self, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None):
latent_length = ((length - 1) // 4) + 1 latent_length = ((length - 1) // 4) + 1
if control_video is not None: if control_video is not None:
control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
@@ -336,52 +361,59 @@ class WanVaceToVideo:
latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device())
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent, trim_latent) return io.NodeOutput(positive, negative, out_latent, trim_latent)
class TrimVideoLatent: class TrimVideoLatent(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": { "samples": ("LATENT",), return io.Schema(
"trim_amount": ("INT", {"default": 0, "min": 0, "max": 99999}), node_id="TrimVideoLatent",
}} category="latent/video",
is_experimental=True,
inputs=[
io.Latent.Input("samples"),
io.Int.Input("trim_amount", default=0, min=0, max=99999),
],
outputs=[
io.Latent.Output(),
],
)
RETURN_TYPES = ("LATENT",) @classmethod
FUNCTION = "op" def execute(cls, samples, trim_amount) -> io.NodeOutput:
CATEGORY = "latent/video"
EXPERIMENTAL = True
def op(self, samples, trim_amount):
samples_out = samples.copy() samples_out = samples.copy()
s1 = samples["samples"] s1 = samples["samples"]
samples_out["samples"] = s1[:, :, trim_amount:] samples_out["samples"] = s1[:, :, trim_amount:]
return (samples_out,) return io.NodeOutput(samples_out)
class WanCameraImageToVideo: class WanCameraImageToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanCameraImageToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"start_image": ("IMAGE", ), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
"camera_conditions": ("WAN_CAMERA_EMBEDDING", ), 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.WanCameraEmbedding.Input("camera_conditions", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
@@ -404,29 +436,34 @@ class WanCameraImageToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent) return io.NodeOutput(positive, negative, out_latent)
class WanPhantomSubjectToVideo: class WanPhantomSubjectToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"positive": ("CONDITIONING", ), return io.Schema(
"negative": ("CONDITIONING", ), node_id="WanPhantomSubjectToVideo",
"vae": ("VAE", ), category="conditioning/video_models",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), inputs=[
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("positive"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.Conditioning.Input("negative"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"optional": {"images": ("IMAGE", ), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
}} 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.Image.Input("images", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative_text"),
io.Conditioning.Output(display_name="negative_img_text"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent") def execute(cls, positive, negative, vae, width, height, length, batch_size, images) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, images):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
cond2 = negative cond2 = negative
if images is not None: if images is not None:
@@ -442,7 +479,7 @@ class WanPhantomSubjectToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, cond2, negative, out_latent) return io.NodeOutput(positive, cond2, negative, out_latent)
def parse_json_tracks(tracks): def parse_json_tracks(tracks):
"""Parse JSON track data into a standardized format""" """Parse JSON track data into a standardized format"""
@@ -655,39 +692,41 @@ def patch_motion(
return out_mask_full, out_feature_full return out_mask_full, out_feature_full
class WanTrackToVideo: class WanTrackToVideo(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": { return io.Schema(
"positive": ("CONDITIONING", ), node_id="WanPhantomSubjectToVideo",
"negative": ("CONDITIONING", ), category="conditioning/video_models",
"vae": ("VAE", ), inputs=[
"tracks": ("STRING", {"multiline": True, "default": "[]"}), io.Conditioning.Input("positive"),
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Conditioning.Input("negative"),
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), io.Vae.Input("vae"),
"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), io.String.Input("tracks", multiline=True, default="[]"),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
"temperature": ("FLOAT", {"default": 220.0, "min": 1.0, "max": 1000.0, "step": 0.1}), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
"topk": ("INT", {"default": 2, "min": 1, "max": 10}), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
"start_image": ("IMAGE", ), io.Int.Input("batch_size", default=1, min=1, max=4096),
}, io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1),
"optional": { io.Int.Input("topk", default=2, min=1, max=10),
"clip_vision_output": ("CLIP_VISION_OUTPUT", ), io.Image.Input("start_image"),
}} io.ClipVisionOutput.Input("clip_vision_output", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @classmethod
RETURN_NAMES = ("positive", "negative", "latent") def execute(cls, positive, negative, vae, tracks, width, height, length, batch_size,
FUNCTION = "encode" temperature, topk, start_image=None, clip_vision_output=None) -> io.NodeOutput:
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, tracks, width, height, length, batch_size,
temperature, topk, start_image=None, clip_vision_output=None):
tracks_data = parse_json_tracks(tracks) tracks_data = parse_json_tracks(tracks)
if not tracks_data: if not tracks_data:
return WanImageToVideo().encode(positive, negative, vae, width, height, length, batch_size, start_image=start_image, clip_vision_output=clip_vision_output) return WanImageToVideo().execute(positive, negative, vae, width, height, length, batch_size, start_image=start_image, clip_vision_output=clip_vision_output)
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
device=comfy.model_management.intermediate_device()) device=comfy.model_management.intermediate_device())
@@ -741,34 +780,36 @@ class WanTrackToVideo:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (positive, negative, out_latent) return io.NodeOutput(positive, negative, out_latent)
class Wan22ImageToVideoLatent: class Wan22ImageToVideoLatent(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": {"vae": ("VAE", ), return io.Schema(
"width": ("INT", {"default": 1280, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}), node_id="Wan22ImageToVideoLatent",
"height": ("INT", {"default": 704, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}), category="conditioning/inpaint",
"length": ("INT", {"default": 49, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), inputs=[
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), io.Vae.Input("vae"),
}, io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32),
"optional": {"start_image": ("IMAGE", ), io.Int.Input("height", default=704, min=32, max=nodes.MAX_RESOLUTION, step=32),
}} io.Int.Input("length", default=49, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Image.Input("start_image", optional=True),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
RETURN_TYPES = ("LATENT",) def execute(cls, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput:
FUNCTION = "encode"
CATEGORY = "conditioning/inpaint"
def encode(self, vae, width, height, length, batch_size, start_image=None):
latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device()) latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
if start_image is None: if start_image is None:
out_latent = {} out_latent = {}
out_latent["samples"] = latent out_latent["samples"] = latent
return (out_latent,) return io.NodeOutput(out_latent)
mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
@@ -783,19 +824,25 @@ class Wan22ImageToVideoLatent:
latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
return (out_latent,) return io.NodeOutput(out_latent)
NODE_CLASS_MAPPINGS = { class WanExtension(ComfyExtension):
"WanTrackToVideo": WanTrackToVideo, @override
"WanImageToVideo": WanImageToVideo, async def get_node_list(self) -> list[type[io.ComfyNode]]:
"WanFunControlToVideo": WanFunControlToVideo, return [
"Wan22FunControlToVideo": Wan22FunControlToVideo, WanTrackToVideo,
"WanFunInpaintToVideo": WanFunInpaintToVideo, WanImageToVideo,
"WanFirstLastFrameToVideo": WanFirstLastFrameToVideo, WanFunControlToVideo,
"WanVaceToVideo": WanVaceToVideo, Wan22FunControlToVideo,
"TrimVideoLatent": TrimVideoLatent, WanFunInpaintToVideo,
"WanCameraImageToVideo": WanCameraImageToVideo, WanFirstLastFrameToVideo,
"WanPhantomSubjectToVideo": WanPhantomSubjectToVideo, WanVaceToVideo,
"Wan22ImageToVideoLatent": Wan22ImageToVideoLatent, TrimVideoLatent,
} WanCameraImageToVideo,
WanPhantomSubjectToVideo,
Wan22ImageToVideoLatent,
]
async def comfy_entrypoint() -> WanExtension:
return WanExtension()