from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16 from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \ Downsample, Upsample import torch import torch.nn as nn from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner from trainer.networks import register_model from utils.util import get_mask_from_lengths class DiscreteSpectrogramConditioningBlock(nn.Module): def __init__(self, dvae_channels, channels, level): super().__init__() self.intg = nn.Sequential(nn.Conv1d(dvae_channels, channels, kernel_size=1), normalization(channels), nn.SiLU(), nn.Conv1d(channels, channels, kernel_size=3)) self.level = level """ Embeds the given codes and concatenates them onto x. Return shape is the same as x.shape. :param x: bxcxS waveform latent :param codes: bxN discrete codes, N <= S """ def forward(self, x, dvae_in): b, c, S = x.shape _, q, N = dvae_in.shape emb = self.intg(dvae_in) emb = nn.functional.interpolate(emb, size=(S,), mode='nearest') return torch.cat([x, emb], dim=1) class DiffusionVocoderWithRef(nn.Module): """ The full UNet model with attention and timestep embedding. Customized to be conditioned on a spectrogram prior. :param in_channels: channels in the input Tensor. :param spectrogram_channels: channels in the conditioning spectrogram. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, model_channels, in_channels=1, out_channels=2, # mean and variance discrete_codes=512, dropout=0, # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48), num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2), # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0) # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 spectrogram_conditioning_resolutions=(512,), attention_resolutions=(512,1024,2048), conv_resample=True, dims=1, use_fp16=False, num_heads=1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, kernel_size=3, scale_factor=2, conditioning_inputs_provided=True, conditioning_input_dim=80, time_embed_dim_multiplier=4, freeze_layers_below=None, # powers of 2; ex: 1,2,4,8,16,32,etc.. ): super().__init__() if num_heads_upsample == -1: num_heads_upsample = num_heads self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.dtype = torch.float16 if use_fp16 else torch.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.dims = dims padding = 1 if kernel_size == 3 else 2 time_embed_dim = model_channels * time_embed_dim_multiplier self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.conditioning_enabled = conditioning_inputs_provided if conditioning_inputs_provided: self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1, attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5) seqlyr = TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) ) seqlyr.level = 0 self.input_blocks = nn.ModuleList([seqlyr]) spectrogram_blocks = [] self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): if ds in spectrogram_conditioning_resolutions: spec_cond_block = DiscreteSpectrogramConditioningBlock(discrete_codes, ch, 2 ** level) self.input_blocks.append(spec_cond_block) spectrogram_blocks.append(spec_cond_block) ch *= 2 for _ in range(num_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=int(mult * model_channels), dims=dims, use_scale_shift_norm=use_scale_shift_norm, kernel_size=kernel_size, ) ] ch = int(mult * model_channels) if ds in attention_resolutions: layers.append( AttentionBlock( ch, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ) ) layer = TimestepEmbedSequential(*layers) layer.level = 2 ** level self.input_blocks.append(layer) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch upblk = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_scale_shift_norm=use_scale_shift_norm, down=True, kernel_size=kernel_size, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor ) ) upblk.level = 2 ** level self.input_blocks.append(upblk) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_scale_shift_norm=use_scale_shift_norm, kernel_size=kernel_size, ), AttentionBlock( ch, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_scale_shift_norm=use_scale_shift_norm, kernel_size=kernel_size, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]: for i in range(num_blocks + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=int(model_channels * mult), dims=dims, use_scale_shift_norm=use_scale_shift_norm, kernel_size=kernel_size, ) ] ch = int(model_channels * mult) if ds in attention_resolutions: layers.append( AttentionBlock( ch, num_heads=num_heads_upsample, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ) ) if level and i == num_blocks: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_scale_shift_norm=use_scale_shift_norm, up=True, kernel_size=kernel_size, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor) ) ds //= 2 layer = TimestepEmbedSequential(*layers) layer.level = 2 ** level self.output_blocks.append(layer) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)), ) if freeze_layers_below is not None: # Freeze all parameters first. for p in self.parameters(): p.DO_NOT_TRAIN = True p.requires_grad = False # Now un-freeze the modules we actually want to train. unfrozen_modules = [self.out] for blk in self.input_blocks: if blk.level <= freeze_layers_below: unfrozen_modules.append(blk) last_frozen_output_block = None for blk in self.output_blocks: if blk.level <= freeze_layers_below: unfrozen_modules.append(blk) else: last_frozen_output_block = blk # And finally, the last upsample block in output blocks. unfrozen_modules.append(last_frozen_output_block[1]) unfrozen_params = 0 for m in unfrozen_modules: for p in m.parameters(): del p.DO_NOT_TRAIN p.requires_grad = True unfrozen_params += 1 print(f"freeze_layers_below specified. Training a total of {unfrozen_params} parameters.") def forward(self, x, timesteps, spectrogram, conditioning_input=None): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ assert x.shape[-1] % 2048 == 0 # This model operates at base//2048 at it's bottom levels, thus this requirement. if self.conditioning_enabled: assert conditioning_input is not None hs = [] emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) if self.conditioning_enabled: emb2 = self.contextual_embedder(conditioning_input) emb = emb1 + emb2 else: emb = emb1 h = x.type(self.dtype) for k, module in enumerate(self.input_blocks): if isinstance(module, DiscreteSpectrogramConditioningBlock): h = module(h, spectrogram) else: h = module(h, emb) hs.append(h) h = self.middle_block(h, emb) for module in self.output_blocks: h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb) h = h.type(x.dtype) return self.out(h) def move_all_layers_down(pretrained_path, output_path, layers_to_be_added=3): # layers_to_be_added should be=num_res_blocks+1+[1if spectrogram_conditioning_resolutions;else0] sd = torch.load(pretrained_path) out = sd.copy() replaced = [] for n, p in sd.items(): if n.startswith('input_blocks.') and not n.startswith('input_blocks.0.'): if n not in replaced: del out[n] components = n.split('.') components[1] = str(int(components[1]) + layers_to_be_added) new_name = '.'.join(components) out[new_name] = p replaced.append(new_name) torch.save(out, output_path) @register_model def register_unet_diffusion_vocoder_with_ref(opt_net, opt): return DiffusionVocoderWithRef(**opt_net['kwargs']) # Test for ~4 second audio clip at 22050Hz if __name__ == '__main__': path = 'X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth' move_all_layers_down(path, 'diffuse_new_lyr.pth', layers_to_be_added=2) clip = torch.randn(2, 1, 40960) spec = torch.randn(2,80,160) cond = torch.randn(2, 1, 40960) ts = torch.LongTensor([555, 556]) model = DiffusionVocoderWithRef(model_channels=128, channel_mult=[1,1,1.5,2, 3, 4, 6, 8, 8, 8, 8 ], num_res_blocks=[1,2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ], spectrogram_conditioning_resolutions=[2,512], dropout=.05, attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, conditioning_inputs_provided=True, conditioning_input_dim=80, time_embed_dim_multiplier=4, discrete_codes=80, freeze_layers_below=1) loading_errors = model.load_state_dict(torch.load('diffuse_new_lyr.pth'), strict=False) new_params = loading_errors.missing_keys new_params_trained = [] existing_params_trained = [] for n,p in model.named_parameters(): if not hasattr(p, 'DO_NOT_TRAIN'): if n in new_params: new_params_trained.append(n) else: existing_params_trained.append(n) for n in new_params: if n not in new_params_trained: print(f"{n} is a new parameter, but it is not marked as trainable.") print(model(clip, ts, spec, cond).shape)