diff --git a/codes/models/audio/music/unet_diffusion_waveform_gen_rrdb.py b/codes/models/audio/music/unet_diffusion_waveform_gen_rrdb.py
new file mode 100644
index 00000000..2214f297
--- /dev/null
+++ b/codes/models/audio/music/unet_diffusion_waveform_gen_rrdb.py
@@ -0,0 +1,415 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import TimestepEmbedSequential, \
+    Downsample, Upsample, TimestepBlock
+from scripts.audio.gen.use_diffuse_tts import ceil_multiple
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+
+def is_sequence(t):
+    return t.dtype == torch.long
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+        efficient_config=True,
+        use_scale_shift_norm=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_scale_shift_norm = use_scale_shift_norm
+        padding = {1: 0, 3: 1, 5: 2}[kernel_size]
+        eff_kernel = 1 if efficient_config else 3
+        eff_padding = 0 if efficient_config else 1
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else 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, eff_kernel, padding=eff_padding)
+
+    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]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class StackedResidualBlock(TimestepBlock):
+    def __init__(self, channels, emb_channels, dropout):
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * channels,
+            ),
+        )
+
+        gc = channels // 4
+        super().__init__()
+        self.initial_norm = nn.GroupNorm(num_groups=8, num_channels=channels)
+        for i in range(5):
+            out_channels = channels if i == 4 else gc
+            self.add_module(
+                f'conv{i + 1}',
+                nn.Conv2d(channels + i * gc, out_channels, 3, 1, 1))
+            self.add_module(f'gn{i+1}', nn.GroupNorm(num_groups=8, num_channels=channels))
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        zero_module(self.conv5)
+
+    def forawrd(self, x, emb):
+        return checkpoint(self.forward_, x, emb)
+
+    def forward_(self, x, emb):
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        scale, shift = torch.chunk(emb_out, 2, dim=1)
+        x0 = self.initial_norm(x) * (1 + scale) + shift
+        x1 = self.lrelu(self.gn1(self.conv1(x0)))
+        x2 = self.lrelu(self.gn2(self.conv2(torch.cat((x, x1), 1))))
+        x3 = self.lrelu(self.gn3(self.conv3(torch.cat((x, x1, x2), 1))))
+        x4 = self.lrelu(self.gn4(self.conv4(torch.cat((x, x1, x2, x3), 1))))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+
+        return x5 + x
+
+
+class DiffusionWaveformGen(nn.Module):
+    """
+    The full UNet model with residual blocks and timestep embedding.
+
+    Customized to be conditioned on an aligned prior derived from a autoregressive
+    GPT-style model.
+
+    :param in_channels: channels in the input Tensor.
+    :param in_latent_channels: channels from the input latent.
+    :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 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 use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    """
+
+    def __init__(
+            self,
+            model_channels,
+            in_channels=64,
+            in_mel_channels=256,
+            conditioning_dim_factor=8,
+            conditioning_expansion=4,
+            out_channels=128,  # mean and variance
+            dropout=0,
+            channel_mult=  (1,1.5,2),
+            num_res_blocks=(1,1,1),
+            token_conditioning_resolutions=(1,4),
+            mid_resnet_depth=10,
+            conv_resample=True,
+            dims=1,
+            use_fp16=False,
+            kernel_size=3,
+            scale_factor=2,
+            time_embed_dim_multiplier=4,
+            freeze_main_net=False,
+            efficient_convs=True,  # Uses kernels with width of 1 in several places rather than 3.
+            use_scale_shift_norm=True,
+            # Parameters for regularization.
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+            # Parameters for super-sampling.
+            super_sampling=False,
+            super_sampling_max_noising_factor=.1,
+    ):
+        super().__init__()
+
+        if super_sampling:
+            in_channels *= 2  # In super-sampling mode, the LR input is concatenated directly onto the input.
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.dims = dims
+        self.super_sampling_enabled = super_sampling
+        self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        self.alignment_size = 2 ** (len(channel_mult)+1)
+        self.freeze_main_net = freeze_main_net
+        self.in_mel_channels = in_mel_channels
+        padding = 1 if kernel_size == 3 else 2
+        down_kernel = 1 if efficient_convs else 3
+
+        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),
+        )
+
+        conditioning_dim = model_channels * conditioning_dim_factor
+        # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
+        # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
+        # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
+        # transformer network.
+        self.mel_converter = nn.Conv1d(in_mel_channels, conditioning_dim, 3, padding=1)
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
+        self.conditioning_timestep_integrator = TimestepEmbedSequential(
+                    ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
+                    ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
+                    ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
+        )
+        self.conditioning_expansion = conditioning_expansion
+
+        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(conditioning_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,
+                        efficient_config=efficient_convs,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_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=down_kernel, pad=0 if down_kernel == 1 else 1
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(*[StackedResidualBlock(ch, time_embed_dim, dropout) for _ in range(mid_resnet_depth)])
+        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,
+                        efficient_config=efficient_convs,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(model_channels * mult)
+                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)),
+        )
+
+        if self.freeze_main_net:
+            mains = [self.time_embed, self.contextual_embedder, self.unconditioned_embedding, self.conditioning_timestep_integrator,
+                     self.input_blocks, self.middle_block, self.output_blocks, self.out]
+            for m in mains:
+                for p in m.parameters():
+                    p.requires_grad = False
+                    p.DO_NOT_TRAIN = True
+
+    def get_grad_norm_parameter_groups(self):
+        if self.freeze_main_net:
+            return {}
+        groups = {
+            'input_blocks': list(self.input_blocks.parameters()),
+            'output_blocks': list(self.output_blocks.parameters()),
+            'middle_transformer': list(self.middle_block.parameters()),
+        }
+        return groups
+
+    def fix_alignment(self, x, aligned_conditioning):
+        """
+        The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
+        padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
+        """
+        cm = ceil_multiple(x.shape[-1], self.alignment_size)
+        if cm != 0:
+            pc = (cm-x.shape[-1])/x.shape[-1]
+            x = F.pad(x, (0,cm-x.shape[-1]))
+            aligned_conditioning = F.pad(aligned_conditioning, (0,int(pc*aligned_conditioning.shape[-1])))
+        return x, aligned_conditioning
+
+    def forward(self, x, timesteps, aligned_conditioning, conditioning_free=False):
+        """
+        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 aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
+        :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        # Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
+        orig_x_shape = x.shape[-1]
+        x, aligned_conditioning = self.fix_alignment(x, aligned_conditioning)
+
+
+        hs = []
+        time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        # Note: this block does not need to repeated on inference, since it is not timestep-dependent.
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
+        else:
+            code_emb = self.mel_converter(aligned_conditioning)
+
+        # Everything after this comment is timestep dependent.
+        code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1)
+        code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
+
+        first = True
+        time_emb = time_emb.float()
+        h = x
+        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)
+            first = False
+        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)
+
+        out = self.out(h)
+
+        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        extraneous_addition = 0
+        params = [self.unconditioned_embedding]
+        for p in params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        return out[:, :, :orig_x_shape]
+
+
+@register_model
+def register_unet_diffusion_waveform_gen3(opt_net, opt):
+    return DiffusionWaveformGen(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 1, 32868)
+    aligned_sequence = torch.randn(2,120,220)
+    ts = torch.LongTensor([600, 600])
+    model = DiffusionWaveformGen(128,
+                                 channel_mult=[1,1.5,2, 3, 4, 6, 8],
+                                 num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
+                                 token_conditioning_resolutions=[1,4,16,64],
+                                 kernel_size=3,
+                                 scale_factor=2,
+                                 time_embed_dim_multiplier=4,
+                                 super_sampling=False,
+                                 efficient_convs=False)
+    # Test with sequence aligned conditioning
+    o = model(clip, ts, aligned_sequence)
+