fixed regression where the auto_conds do not move to the GPU and causes a problem during CVVP compare pass
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3d69274a46
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@ -483,8 +483,11 @@ class TextToSpeech:
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auto_conditioning, diffusion_conditioning, auto_conds, _ = conditioning_latents
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auto_conditioning, diffusion_conditioning, auto_conds, _ = conditioning_latents
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else:
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else:
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auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
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auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
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auto_conditioning = auto_conditioning.to(self.device)
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auto_conditioning = auto_conditioning.to(self.device)
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diffusion_conditioning = diffusion_conditioning.to(self.device)
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diffusion_conditioning = diffusion_conditioning.to(self.device)
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if auto_conds is not None:
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auto_conds = auto_conds.to(self.device)
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
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@ -539,8 +542,10 @@ class TextToSpeech:
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for batch in tqdm_override(samples, verbose=verbose, progress=progress, desc=desc):
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for batch in tqdm_override(samples, verbose=verbose, progress=progress, desc=desc):
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for i in range(batch.shape[0]):
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for i in range(batch.shape[0]):
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batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
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batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
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if cvvp_amount != 1:
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if cvvp_amount != 1:
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clvp = self.clvp(text_tokens.repeat(batch.shape[0], 1), batch, return_loss=False)
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clvp = self.clvp(text_tokens.repeat(batch.shape[0], 1), batch, return_loss=False)
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if auto_conds is not None and cvvp_amount > 0:
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if auto_conds is not None and cvvp_amount > 0:
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cvvp_accumulator = 0
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cvvp_accumulator = 0
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for cl in range(auto_conds.shape[1]):
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for cl in range(auto_conds.shape[1]):
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2
webui.py
2
webui.py
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@ -265,7 +265,7 @@ def generate(
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with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
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with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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if voicefixer:
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if args.voice_fixer and voicefixer:
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# we could do this on the pieces before they get stiched up anyways to save some compute
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# we could do this on the pieces before they get stiched up anyways to save some compute
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# but the stitching would need to read back from disk, defeating the point of caching the waveform
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# but the stitching would need to read back from disk, defeating the point of caching the waveform
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for path in progress.tqdm(audio_cache, desc="Running voicefix..."):
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for path in progress.tqdm(audio_cache, desc="Running voicefix..."):
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