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import torch
from typing_extensions import override
import comfy.model_management
import node_helpers
from comfy_api.latest import ComfyExtension, io
class TextEncodeAceStepAudio(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="TextEncodeAceStepAudio",
category="conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("tags", multiline=True, dynamic_prompts=True),
io.String.Input("lyrics", multiline=True, dynamic_prompts=True),
io.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01),
],
outputs=[io.Conditioning.Output()],
)
@classmethod
def execute(cls, clip, tags, lyrics, lyrics_strength) -> io.NodeOutput:
tokens = clip.tokenize(tags, lyrics=lyrics)
conditioning = clip.encode_from_tokens_scheduled(tokens)
conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength})
return io.NodeOutput(conditioning)
class EmptyAceStepLatentAudio(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="EmptyAceStepLatentAudio",
category="latent/audio",
inputs=[
io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
io.Int.Input(
"batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
),
],
outputs=[io.Latent.Output()],
)
@classmethod
def execute(cls, seconds, batch_size) -> io.NodeOutput:
length = int(seconds * 44100 / 512 / 8)
latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples": latent, "type": "audio"})
class AceExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
TextEncodeAceStepAudio,
EmptyAceStepLatentAudio,
]
async def comfy_entrypoint() -> AceExtension:
return AceExtension()
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