DungeonCrawler/comfyui-audio/comfyui_audiocraft/nodes.py

import torch
import torchaudio
import os
import time


class MusicGenNode:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "prompt":        ("STRING",  {"multiline": True, "default": "dark dungeon ambience, slow, mysterious"}),
                "duration":      ("FLOAT",   {"default": 10.0, "min": 1.0,  "max": 120.0, "step": 1.0}),
                "model":         (["facebook/musicgen-stereo-medium", "facebook/musicgen-stereo-large", "facebook/musicgen-medium", "facebook/musicgen-large", "facebook/musicgen-small"],),
                "seed":          ("INT",     {"default": 0,    "min": 0,    "max": 2**32 - 1}),
                "temperature":   ("FLOAT",   {"default": 1.0,  "min": 0.1,  "max": 2.0,   "step": 0.05}),
                "cfg_coef":      ("FLOAT",   {"default": 3.0,  "min": 0.5,  "max": 10.0,  "step": 0.5}),
                "top_k":         ("INT",     {"default": 250,  "min": 10,   "max": 500}),
                "extend_stride": ("FLOAT",   {"default": 18.0, "min": 5.0,  "max": 28.0,  "step": 1.0}),
            }
        }

    RETURN_TYPES = ("AUDIO_PATH",)
    RETURN_NAMES = ("audio_file",)
    FUNCTION = "generate"
    CATEGORY = "AudioCraft"
    OUTPUT_NODE = True

    def generate(self, prompt, duration, model, seed, temperature, cfg_coef, top_k, extend_stride):
        from audiocraft.models import MusicGen
        from audiocraft.data.audio import audio_write

        torch.manual_seed(seed)

        print(f"[MusicGen] Loading model: {model}")
        mg = MusicGen.get_pretrained(model)
        mg.set_generation_params(
            duration=duration,
            use_sampling=True,
            temperature=temperature,
            cfg_coef=cfg_coef,
            top_k=top_k,
            top_p=0.0,
            extend_stride=extend_stride,
        )

        print(f"[MusicGen] Generating: {prompt}")
        wav = mg.generate([prompt])

        output_dir = "/app/ComfyUI/output/audio"
        os.makedirs(output_dir, exist_ok=True)
        filename = f"musicgen_{int(time.time())}"
        out_path = os.path.join(output_dir, filename)
        audio_write(out_path, wav[0].cpu(), mg.sample_rate, strategy="loudness")

        return (out_path + ".wav",)


class MusicGenLongNode:
    """Generiert lange Musik in Segmenten mit generate_continuation für nahtlose Übergänge"""
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "prompt":           ("STRING", {"multiline": True, "default": "dark fantasy orchestral music, deep cello, french horn, slow war drums, mysterious ambient"}),
                "total_duration":   ("FLOAT",  {"default": 90.0,  "min": 10.0, "max": 300.0, "step": 5.0}),
                "segment_duration": ("FLOAT",  {"default": 30.0,  "min": 10.0, "max": 30.0,  "step": 5.0}),
                "context_seconds":  ("FLOAT",  {"default": 5.0,   "min": 1.0,  "max": 10.0,  "step": 0.5}),
                "model":            (["facebook/musicgen-stereo-medium", "facebook/musicgen-stereo-large", "facebook/musicgen-medium", "facebook/musicgen-large", "facebook/musicgen-small"],),
                "seed":             ("INT",    {"default": 0,     "min": 0,    "max": 2**32 - 1}),
                "temperature":      ("FLOAT",  {"default": 1.0,   "min": 0.1,  "max": 2.0,   "step": 0.05}),
                "cfg_coef":         ("FLOAT",  {"default": 3.0,   "min": 0.5,  "max": 10.0,  "step": 0.5}),
                "top_k":            ("INT",    {"default": 250,   "min": 10,   "max": 500}),
            }
        }

    RETURN_TYPES = ("AUDIO_PATH",)
    RETURN_NAMES = ("audio_file",)
    FUNCTION = "generate_long"
    CATEGORY = "AudioCraft"
    OUTPUT_NODE = True

    def generate_long(self, prompt, total_duration, segment_duration, context_seconds, model, seed, temperature, cfg_coef, top_k):
        from audiocraft.models import MusicGen
        from audiocraft.data.audio import audio_write

        torch.manual_seed(seed)
        import math
        total_segments = math.ceil(total_duration / segment_duration)
        print(f"[MusicGenLong] Model: {model}, Ziel: {total_duration}s in {total_segments} Segmenten à {segment_duration}s")

        mg = MusicGen.get_pretrained(model)
        sample_rate = mg.sample_rate
        context_samples = int(context_seconds * sample_rate)

        # Erstes Segment
        gen_params = dict(use_sampling=True, temperature=temperature, cfg_coef=cfg_coef, top_k=top_k, top_p=0.0)
        mg.set_generation_params(duration=segment_duration, **gen_params)
        print(f"[MusicGenLong] Segment 1 / {total_segments}")
        first = mg.generate([prompt])
        segments = [first[0].cpu()]
        generated = segment_duration

        # Weitere Segmente via Continuation
        seg_num = 2
        while generated < total_duration:
            remaining = total_duration - generated
            next_dur = min(segment_duration, remaining)
            mg.set_generation_params(duration=next_dur, **gen_params)

            # Letzten context_seconds des vorherigen Segments als Kontext
            context = segments[-1][:, -context_samples:]
            print(f"[MusicGenLong] Segment {seg_num} ({next_dur}s)")
            cont = mg.generate_continuation(context, sample_rate, [prompt])
            segments.append(cont[0].cpu())
            generated += next_dur
            seg_num += 1

        # Crossfade und zusammenfügen (funktioniert für Mono und Stereo)
        crossfade_samples = context_samples
        result = segments[0]
        num_channels = result.shape[0]
        for seg in segments[1:]:
            fade_out = torch.linspace(1.0, 0.0, crossfade_samples).unsqueeze(0).expand(num_channels, -1)
            fade_in  = torch.linspace(0.0, 1.0, crossfade_samples).unsqueeze(0).expand(num_channels, -1)
            overlap  = result[:, -crossfade_samples:] * fade_out + seg[:, :crossfade_samples] * fade_in
            result   = torch.cat([result[:, :-crossfade_samples], overlap, seg[:, crossfade_samples:]], dim=1)

        output_dir = "/app/ComfyUI/output/audio"
        os.makedirs(output_dir, exist_ok=True)
        filename = f"musicgen_long_{int(time.time())}"
        out_path = os.path.join(output_dir, filename)
        audio_write(out_path, result, sample_rate, strategy="loudness")
        print(f"[MusicGenLong] Fertig: {out_path}.wav ({result.shape[1]/sample_rate:.1f}s)")
        return (out_path + ".wav",)


class AudioGenNode:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "prompt":      ("STRING", {"multiline": True, "default": "sword clash metal sound effect"}),
                "duration":    ("FLOAT",  {"default": 3.0,  "min": 0.5,  "max": 30.0, "step": 0.5}),
                "seed":        ("INT",    {"default": 0,    "min": 0,    "max": 2**32 - 1}),
                "temperature": ("FLOAT",  {"default": 1.0,  "min": 0.1,  "max": 2.0,  "step": 0.05}),
                "cfg_coef":    ("FLOAT",  {"default": 3.0,  "min": 0.5,  "max": 10.0, "step": 0.5}),
                "top_k":       ("INT",    {"default": 250,  "min": 1,    "max": 1000}),
            }
        }

    RETURN_TYPES = ("AUDIO_PATH",)
    RETURN_NAMES = ("audio_file",)
    FUNCTION = "generate"
    CATEGORY = "AudioCraft"
    OUTPUT_NODE = True

    def generate(self, prompt, duration, seed, temperature, cfg_coef, top_k):
        from audiocraft.models import AudioGen
        from audiocraft.data.audio import audio_write

        torch.manual_seed(seed)

        print(f"[AudioGen] Loading model...")
        ag = AudioGen.get_pretrained("facebook/audiogen-medium")
        ag.set_generation_params(
            duration=duration,
            use_sampling=True,
            temperature=temperature,
            cfg_coef=cfg_coef,
            top_k=top_k,
        )

        print(f"[AudioGen] Generating: {prompt}")
        wav = ag.generate([prompt])

        output_dir = "/app/ComfyUI/output/audio"
        os.makedirs(output_dir, exist_ok=True)
        filename = f"audiogen_{int(time.time())}"
        out_path = os.path.join(output_dir, filename)
        audio_write(out_path, wav[0].cpu(), ag.sample_rate, strategy="loudness")

        return (out_path + ".wav",)


class AudioUpsampleNode:
    """Upsampling via torchaudio - verbessert Qualität von 16kHz/32kHz auf 48kHz"""
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "audio_file":    ("AUDIO_PATH",),
                "target_sr":     ([48000, 44100], {"default": 48000}),
            }
        }

    RETURN_TYPES = ("AUDIO_PATH",)
    RETURN_NAMES = ("audio_file",)
    FUNCTION = "upsample"
    CATEGORY = "AudioCraft"
    OUTPUT_NODE = True

    def upsample(self, audio_file, target_sr):
        wav, sr = torchaudio.load(audio_file)
        if sr != target_sr:
            resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)
            wav = resampler(wav)
        out_path = audio_file.replace(".wav", f"_{target_sr}hz.wav")
        torchaudio.save(out_path, wav, target_sr)
        print(f"[Upsample] {sr}Hz → {target_sr}Hz: {out_path}")
        return (out_path,)


class AudioPreviewNode:
    """Zeigt Audiodatei in der ComfyUI-UI an"""
    @classmethod
    def INPUT_TYPES(cls):
        return {"required": {"audio_file": ("AUDIO_PATH",)}}

    RETURN_TYPES = ()
    FUNCTION = "preview"
    CATEGORY = "AudioCraft"
    OUTPUT_NODE = True

    def preview(self, audio_file):
        return {"ui": {"audio": [{"filename": os.path.basename(audio_file), "subfolder": "audio", "type": "output"}]}}


NODE_CLASS_MAPPINGS = {
    "MusicGen":        MusicGenNode,
    "MusicGenLong":    MusicGenLongNode,
    "AudioGen":        AudioGenNode,
    "AudioUpsample":   AudioUpsampleNode,
    "AudioPreview":    AudioPreviewNode,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "MusicGen":        "MusicGen (Musik)",
    "MusicGenLong":    "MusicGen Long (Lange Musik)",
    "AudioGen":        "AudioGen (Sound Effects)",
    "AudioUpsample":   "Audio Upsample (Qualität)",
    "AudioPreview":    "Audio Preview",
}