Delete dreamvoice/src/.ipynb_checkpoints

dreamvoice/src/.ipynb_checkpoints/plugin_wrapper-checkpoint.py

@@ -1,76 +0,0 @@
-import yaml
-import torch
-from diffusers import DDIMScheduler
-from .model.p2e_cross import P2E_Cross
-from .utils import scale_shift, scale_shift_re, rescale_noise_cfg
-
-
-class DreamVG(object):
-    def __init__(self,
-                 config_path='configs/plugin_cross.yaml',
-                 ckpt_path='../ckpts/dreamvc_plugin.pt',
-                 device='cpu'):
-
-        with open(config_path, 'r') as fp:
-            config = yaml.safe_load(fp)
-
-        self.device = device
-        self.model = P2E_Cross(config['model']).to(device)
-        self.model.load_state_dict(torch.load(ckpt_path)['model'])
-        self.model.eval()
-
-        noise_scheduler = DDIMScheduler(num_train_timesteps=config['scheduler']['num_train_steps'],
-                                        beta_start=config['scheduler']['beta_start'],
-                                        beta_end=config['scheduler']['beta_end'],
-                                        rescale_betas_zero_snr=True,
-                                        timestep_spacing="trailing",
-                                        clip_sample=False,
-                                        prediction_type='v_prediction')
-        self.noise_scheduler = noise_scheduler
-        self.scale = config['scheduler']['scale']
-        self.shift = config['scheduler']['shift']
-        self.spk_shape = config['model']['unet']['in_channels']
-
-    @torch.no_grad()
-    def inference(self, text,
-                  guidance_scale=5, guidance_rescale=0.7,
-                  ddim_steps=50, eta=1, random_seed=2023,
-                 ):
-        text, text_mask = text
-        self.model.eval()
-
-        gen_shape = (1, self.spk_shape)
-        
-        if random_seed is not None:
-            generator = torch.Generator(device=self.device).manual_seed(random_seed)
-        else:
-            generator = torch.Generator(device=self.device)
-            generator.seed()
-
-        self.noise_scheduler.set_timesteps(ddim_steps)
-    
-        # init noise
-        noise = torch.randn(gen_shape, generator=generator, device=self.device)
-        latents = noise
-    
-        for t in self.noise_scheduler.timesteps:
-            latents = self.noise_scheduler.scale_model_input(latents, t)
-    
-            if guidance_scale:
-                output_text = self.model(latents, t, text, text_mask, train_cfg=False)
-                output_uncond = self.model(latents, t, text, text_mask, train_cfg=True, cfg_prob=1.0)
-    
-                output_pred = output_uncond + guidance_scale * (output_text - output_uncond)
-                if guidance_rescale > 0.0:
-                    output_pred = rescale_noise_cfg(output_pred, output_text,
-                                                    guidance_rescale=guidance_rescale)
-            else:
-                output_pred = self.model(latents, t, text, text_mask, train_cfg=False)
-    
-            latents = self.noise_scheduler.step(model_output=output_pred, timestep=t, sample=latents,
-                                                eta=eta, generator=generator).prev_sample
-    
-        # pred = reverse_minmax_norm_diff(latents, vmin=0.0, vmax=0.5)
-        pred = scale_shift_re(latents, 1/self.scale, self.shift)
-        # pred = torch.clip(pred, min=0.0, max=0.5)
-        return pred

dreamvoice/src/.ipynb_checkpoints/vc_wrapper-checkpoint.py

@@ -1,144 +0,0 @@
-import yaml
-import torch
-from diffusers import DDIMScheduler
-from .model.model import DiffVC
-from .model.model_cross import DiffVC_Cross
-from .utils import scale_shift, scale_shift_re, rescale_noise_cfg
-
-
-class ReDiffVC(object):
-    def __init__(self,
-                 config_path='configs/diffvc_base.yaml',
-                 ckpt_path='../ckpts/dreamvc_base.pt',
-                 device='cpu'):
-
-        with open(config_path, 'r') as fp:
-            config = yaml.safe_load(fp)
-
-        self.device = device
-        self.model = DiffVC(config['model']).to(device)
-        self.model.load_state_dict(torch.load(ckpt_path)['model'])
-        self.model.eval()
-
-        noise_scheduler = DDIMScheduler(num_train_timesteps=config['scheduler']['num_train_steps'],
-                                        beta_start=config['scheduler']['beta_start'],
-                                        beta_end=config['scheduler']['beta_end'],
-                                        rescale_betas_zero_snr=True,
-                                        timestep_spacing="trailing",
-                                        clip_sample=False,
-                                        prediction_type='v_prediction')
-        self.noise_scheduler = noise_scheduler
-        self.scale = config['scheduler']['scale']
-        self.shift = config['scheduler']['shift']
-        self.melshape = config['model']['unet']['sample_size'][0]
-
-    @torch.no_grad()
-    def inference(self,
-                  spk_embed, content_clip, f0_clip=None,
-                  guidance_scale=3, guidance_rescale=0.7,
-                  ddim_steps=50, eta=1, random_seed=2023):
-
-        self.model.eval()
-        if random_seed is not None:
-            generator = torch.Generator(device=self.device).manual_seed(random_seed)
-        else:
-            generator = torch.Generator(device=self.device)
-            generator.seed()
-
-        self.noise_scheduler.set_timesteps(ddim_steps)
-
-        # init noise
-        gen_shape = (1, 1, self.melshape, content_clip.shape[-2])
-        noise = torch.randn(gen_shape, generator=generator, device=self.device)
-        latents = noise
-
-        for t in self.noise_scheduler.timesteps:
-            latents = self.noise_scheduler.scale_model_input(latents, t)
-
-            if guidance_scale:
-                output_text = self.model(latents, t, content_clip, spk_embed, f0_clip, train_cfg=False)
-                output_uncond = self.model(latents, t, content_clip, spk_embed, f0_clip, train_cfg=True,
-                                           speaker_cfg=1.0, pitch_cfg=0.0)
-
-                output_pred = output_uncond + guidance_scale * (output_text - output_uncond)
-                if guidance_rescale > 0.0:
-                    output_pred = rescale_noise_cfg(output_pred, output_text,
-                                                    guidance_rescale=guidance_rescale)
-            else:
-                output_pred = self.model(latents, t, content_clip, spk_embed, f0_clip, train_cfg=False)
-
-            latents = self.noise_scheduler.step(model_output=output_pred, timestep=t, sample=latents,
-                                                eta=eta, generator=generator).prev_sample
-
-        pred = scale_shift_re(latents, scale=1/self.scale, shift=self.shift)
-        return pred
-
-
-class DreamVC(object):
-    def __init__(self,
-                 config_path='configs/diffvc_cross.yaml',
-                 ckpt_path='../ckpts/dreamvc_cross.pt',
-                 device='cpu'):
-
-        with open(config_path, 'r') as fp:
-            config = yaml.safe_load(fp)
-
-        self.device = device
-        self.model = DiffVC_Cross(config['model']).to(device)
-        self.model.load_state_dict(torch.load(ckpt_path)['model'])
-        self.model.eval()
-
-        noise_scheduler = DDIMScheduler(num_train_timesteps=config['scheduler']['num_train_steps'],
-                                        beta_start=config['scheduler']['beta_start'],
-                                        beta_end=config['scheduler']['beta_end'],
-                                        rescale_betas_zero_snr=True,
-                                        timestep_spacing="trailing",
-                                        clip_sample=False,
-                                        prediction_type='v_prediction')
-        self.noise_scheduler = noise_scheduler
-        self.scale = config['scheduler']['scale']
-        self.shift = config['scheduler']['shift']
-        self.melshape = config['model']['unet']['sample_size'][0]
-
-    @torch.no_grad()
-    def inference(self,
-                  text, content_clip, f0_clip=None,
-                  guidance_scale=3, guidance_rescale=0.7,
-                  ddim_steps=50, eta=1, random_seed=2023):
-
-        text, text_mask = text
-        self.model.eval()
-        if random_seed is not None:
-            generator = torch.Generator(device=self.device).manual_seed(random_seed)
-        else:
-            generator = torch.Generator(device=self.device)
-            generator.seed()
-
-        self.noise_scheduler.set_timesteps(ddim_steps)
-
-        # init noise
-        gen_shape = (1, 1, self.melshape, content_clip.shape[-2])
-        noise = torch.randn(gen_shape, generator=generator, device=self.device)
-        latents = noise
-
-        for t in self.noise_scheduler.timesteps:
-            latents = self.noise_scheduler.scale_model_input(latents, t)
-
-            if guidance_scale:
-                output_text = self.model(latents, t, content_clip, text, text_mask, f0_clip, train_cfg=False)
-                output_uncond = self.model(latents, t, content_clip, text, text_mask, f0_clip, train_cfg=True,
-                                           speaker_cfg=1.0, pitch_cfg=0.0)
-
-                output_pred = output_uncond + guidance_scale * (output_text - output_uncond)
-                if guidance_rescale > 0.0:
-                    output_pred = rescale_noise_cfg(output_pred, output_text,
-                                                    guidance_rescale=guidance_rescale)
-            else:
-                output_pred = self.model(latents, t, content_clip, text, text_mask, f0_clip, train_cfg=False)
-
-            latents = self.noise_scheduler.step(model_output=output_pred, timestep=t, sample=latents,
-                                                eta=eta, generator=generator).prev_sample
-
-        pred = scale_shift_re(latents, scale=1/self.scale, shift=self.shift)
-        return pred
-