nodes/billwuhao-ComfyUI_DiffRhythm
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

path to 🎤MW/, fix bug

Browse Source
This commit is contained in:
billwuhao
2025-03-25 18:48:41 +08:00
parent e6a3707c1f
commit 9ca7a7cbca
19 changed files with 10 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files
DiffRhythmNode.py
+2 -48
View File
@@ -122,7 +122,7 @@ class MultiLinePrompt:
},
}
CATEGORY = "MW/MW-DiffRhythm"
CATEGORY = "🎤MW/MW-DiffRhythm"
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("prompt",)
FUNCTION = "promptgen"
@@ -166,7 +166,7 @@ class DiffRhythmRun:
},
}
CATEGORY = "MW/MW-DiffRhythm"
CATEGORY = "🎤MW/MW-DiffRhythm"
RETURN_TYPES = ("AUDIO",)
RETURN_NAMES = ("audio",)
FUNCTION = "diffrhythmgen"
@@ -295,52 +295,6 @@ class DiffRhythmRun:
return text_emb
# @torch.no_grad()
# def get_style_prompt(self, model, audio=None, prompt=None):
# mulan = model
# if prompt is not None:
# return mulan(texts=prompt).half()
# if audio is None:
# raise ValueError("Audio data or style prompt must be provided")
# waveform = audio["waveform"]
# sample_rate = audio["sample_rate"]
# # Ensure waveform has correct shape
# if len(waveform.shape) == 3: # [1, channels, samples]
# waveform = waveform.squeeze(0)
# if waveform.shape[0] > 1: # If stereo, convert to mono
# waveform = waveform.mean(0, keepdim=True)
# # Calculate audio length (seconds)
# audio_len = waveform.shape[-1] / sample_rate
# if audio_len < 10:
# raise ValueError(f"Audio too short ({audio_len:.2f}s), minimum 10 seconds required.")
# # Extract middle 10-second segment
# mid_time = int((audio_len // 2) * sample_rate)
# start_sample = mid_time - int(5 * sample_rate)
# end_sample = start_sample + int(10 * sample_rate)
# wav_segment = waveform[..., start_sample:end_sample]
# # Resample to 24kHz
# if sample_rate != 24000:
# wav_segment = torchaudio.transforms.Resample(sample_rate, 24000)(wav_segment)
# # Ensure correct shape and device
# wav = wav_segment.to(model.device)
# if len(wav.shape) == 1:
# wav = wav.unsqueeze(0)
# with torch.no_grad():
# audio_emb = mulan(wavs=wav) # [1, 512]
# audio_emb = audio_emb.half()
# return audio_emb
def prepare_model(self, model, device, unload_model=False):
# prepare tokenizer