ComfyUI/comfy_extras/nodes_hunyuan_foley.py
2025-10-01 00:06:01 +03:00

61 lines
2.3 KiB
Python

import torch
import comfy.model_management
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class EmptyLatentHunyuanFoley(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="EmptyLatentHunyuanFoley",
display_name="EmptyLatentHunyuanFoley",
category="audio/latent",
inputs = [
io.Int.Input("length", min = 1, max = 15, default = 12),
io.Int.Input("batch_size", min = 1, max = 48_000, default = 1),
io.Video.Input("video", optional=True),
],
outputs=[io.Latent.Output(display_name="latent")]
)
@classmethod
def execute(cls, length, batch_size, video = None):
if video is not None:
length = video.size(0)
length /= 25
shape = (batch_size, 128, int(50 * length))
latent = torch.randn(shape, device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples": latent, "type": "hunyuan_foley"}, )
class HunyuanFoleyConditioning(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="HunyuanFoleyConditioning",
display_name="HunyuanFoleyConditioning",
category="conditioning/video_models",
inputs = [
io.Conditioning.Input("siglip_encoding_1"),
io.Conditioning.Input("synchformer_encoding_2"),
io.Conditioning.Input("text_encoding"),
],
outputs=[io.Conditioning.Output(display_name= "positive"), io.Conditioning.Output(display_name="negative")]
)
@classmethod
def execute(cls, video_encoding_1, video_encoding_2, text_encoding):
embeds = torch.cat([video_encoding_1, video_encoding_2, text_encoding], dim = 0)
positive = [[embeds, {}]]
negative = [[torch.zeros_like(embeds), {}]]
return io.NodeOutput(positive, negative)
class FoleyExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
HunyuanFoleyConditioning,
EmptyLatentHunyuanFoley
]
async def comfy_entrypoint() -> FoleyExtension:
return FoleyExtension()