rename unet diffusion tts and add 3

codes/models/gpt_voice/unet_diffusion_tts2.py

@@ -390,7 +390,7 @@ class DiffusionTts(nn.Module):
 
 
 @register_model
-def register_diffusion_tts_experimental(opt_net, opt):
+def register_diffusion_tts2(opt_net, opt):
     return DiffusionTts(**opt_net['kwargs'])
 
 

codes/models/gpt_voice/unet_diffusion_tts3.py

@@ -0,0 +1,374 @@
+import operator
+from collections import OrderedDict
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, TimestepEmbedSequential, \
+    Downsample, Upsample, TimestepBlock
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
+from scripts.audio.gen.use_diffuse_tts import ceil_multiple
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        padding = 1 if kernel_size == 3 else 2
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 1, padding=0),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, x, emb
+        )
+
+    def _forward(self, x, emb):
+        h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        h = h + emb_out
+        h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class DiffusionTts(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    Customized to be conditioned on an aligned token prior.
+
+    :param in_channels: channels in the input Tensor.
+    :param num_tokens: number of tokens (e.g. characters) which can be provided.
+    :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,
+            num_tokens=32,
+            out_channels=2,  # mean and variance
+            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
+            token_conditioning_resolutions=(1,16,),
+            attention_resolutions=(512,1024,2048),
+            conv_resample=True,
+            dims=1,
+            use_fp16=False,
+            num_heads=1,
+            num_head_channels=-1,
+            num_heads_upsample=-1,
+            kernel_size=3,
+            scale_factor=2,
+            conditioning_inputs_provided=True,
+            time_embed_dim_multiplier=4,
+            nil_guidance_fwd_proportion=.3,
+    ):
+        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
+        self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
+        self.mask_token_id = num_tokens
+
+        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),
+        )
+
+        embedding_dim = model_channels * 4
+        self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
+        self.conditioning_enabled = conditioning_inputs_provided
+        if conditioning_inputs_provided:
+            self.contextual_embedder = AudioMiniEncoder(in_channels, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
+                             attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
+                )
+            ]
+        )
+        token_conditioning_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 token_conditioning_resolutions:
+                token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
+                token_conditioning_block.weight.data *= .02
+                self.input_blocks.append(token_conditioning_block)
+                token_conditioning_blocks.append(token_conditioning_block)
+
+            for _ in range(num_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        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,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
+                        )
+                    )
+                )
+                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,
+                kernel_size=kernel_size,
+            ),
+            AttentionBlock(
+                ch,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                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,
+                        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,
+                        )
+                    )
+                if level and i == num_blocks:
+                    out_ch = ch
+                    layers.append(
+                        Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                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)),
+        )
+
+    def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
+                        strict: bool = True):
+        # Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
+        lsd = self.state_dict()
+        revised = 0
+        for i, blk in enumerate(self.input_blocks):
+            if isinstance(blk, nn.Embedding):
+                key = f'input_blocks.{i}.weight'
+                if state_dict[key].shape[0] != lsd[key].shape[0]:
+                    t = torch.randn_like(lsd[key]) * .02
+                    t[:state_dict[key].shape[0]] = state_dict[key]
+                    state_dict[key] = t
+                    revised += 1
+        print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
+        return super().load_state_dict(state_dict, strict)
+
+
+
+    def forward(self, x, timesteps, tokens, 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 tokens: an aligned text input.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        orig_x_shape = x.shape[-1]
+        cm = ceil_multiple(x.shape[-1], 2048)
+        if cm != 0:
+            pc = (cm-x.shape[-1])/x.shape[-1]
+            x = F.pad(x, (0,cm-x.shape[-1]))
+            tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
+        if self.conditioning_enabled:
+            assert conditioning_input is not None
+
+        hs = []
+        time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        # Mask out guidance tokens for un-guided diffusion.
+        if self.training and self.nil_guidance_fwd_proportion > 0:
+            token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
+            tokens = torch.where(token_mask, self.mask_token_id, tokens)
+        code_emb = self.code_embedding(tokens).permute(0,2,1)
+        if self.conditioning_enabled:
+            cond_emb = self.contextual_embedder(conditioning_input)
+            code_emb = cond_emb.unsqueeze(-1) * code_emb
+
+        h = x.type(self.dtype)
+        for k, module in enumerate(self.input_blocks):
+            if isinstance(module, nn.Conv1d):
+                h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
+                h = h + h_tok
+            else:
+                h = module(h, time_emb)
+                hs.append(h)
+        h = self.middle_block(h, time_emb)
+        for module in self.output_blocks:
+            h = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, time_emb)
+        h = h.type(x.dtype)
+        out = self.out(h)
+        return out[:, :, :orig_x_shape]
+
+
+@register_model
+def register_diffusion_tts3(opt_net, opt):
+    return DiffusionTts(**opt_net['kwargs'])
+
+
+# Test for ~4 second audio clip at 22050Hz
+if __name__ == '__main__':
+    clip = torch.randn(4, 1, 86016)
+    tok = torch.randint(0,30, (4,388))
+    cond = torch.randn(4, 1, 44000)
+    ts = torch.LongTensor([555, 556, 600, 600])
+    model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
+                         token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
+                         scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
+    model(clip, ts, tok, cond)
+