diff --git a/codes/models/audio/music/flat_diffusion_retest.py b/codes/models/audio/music/flat_diffusion_retest.py
new file mode 100644
index 00000000..6517f540
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+++ b/codes/models/audio/music/flat_diffusion_retest.py
@@ -0,0 +1,329 @@
+import random
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import autocast
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+
+def is_latent(t):
+    return t.dtype == torch.float
+
+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 DiffusionLayer(TimestepBlock):
+    def __init__(self, model_channels, dropout, num_heads):
+        super().__init__()
+        self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
+        self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
+
+    def forward(self, x, time_emb):
+        y = self.resblk(x, time_emb)
+        return self.attn(y)
+
+
+class DiffusionTtsFlat(nn.Module):
+    def __init__(
+            self,
+            model_channels=512,
+            num_layers=8,
+            in_channels=100,
+            in_latent_channels=512,
+            in_tokens=8193,
+            out_channels=200,  # mean and variance
+            dropout=0,
+            use_fp16=False,
+            num_heads=16,
+            freeze_everything_except_autoregressive_inputs=False,
+            # Parameters for regularization.
+            layer_drop=.1,
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.dropout = dropout
+        self.num_heads = num_heads
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        self.layer_drop = layer_drop
+
+        self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
+        self.time_embed = nn.Sequential(
+            linear(model_channels, model_channels),
+            nn.SiLU(),
+            linear(model_channels, model_channels),
+        )
+
+        # 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.code_embedding = nn.ModuleList([nn.Embedding(8, model_channels//8) for _ in range(8)])
+        self.code_converter = nn.Sequential(
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+        )
+        self.code_norm = normalization(model_channels)
+        self.latent_conditioner = nn.Sequential(
+            nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+        )
+        self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
+                                                 nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
+        self.conditioning_timestep_integrator = TimestepEmbedSequential(
+            DiffusionLayer(model_channels, dropout, num_heads),
+            DiffusionLayer(model_channels, dropout, num_heads),
+            DiffusionLayer(model_channels, dropout, num_heads),
+        )
+        self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
+        self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
+
+        self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
+                                    [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
+
+        self.out = nn.Sequential(
+            normalization(model_channels),
+            nn.SiLU(),
+            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
+        )
+
+        if freeze_everything_except_autoregressive_inputs:
+            for p in self.parameters():
+                p.requires_grad = False
+                p.DO_NOT_TRAIN = True
+            for ap in list(self.latent_conditioner.parameters()):
+                ap.requires_grad = True
+                del ap.DO_NOT_TRAIN
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'minicoder': list(self.contextual_embedder.parameters()),
+            'layers': list(self.layers.parameters()),
+            'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()),
+            'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
+            'time_embed': list(self.time_embed.parameters()),
+        }
+        return groups
+
+    def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred):
+        # Shuffle aligned_latent to BxCxS format
+        if is_latent(aligned_conditioning):
+            aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
+
+        # Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
+        speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
+            conditioning_input.shape) == 3 else conditioning_input
+        conds = []
+        for j in range(speech_conditioning_input.shape[1]):
+            conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
+        conds = torch.cat(conds, dim=-1)
+        cond_emb = conds.mean(dim=-1)
+        cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
+        if is_latent(aligned_conditioning):
+            code_emb = self.latent_conditioner(aligned_conditioning)
+        else:
+            code_emb = [embedding(aligned_conditioning[:, :, i]) for i, embedding in enumerate(self.code_embedding)]
+            code_emb = torch.cat(code_emb, dim=-1).permute(0,2,1)
+            code_emb = self.code_converter(code_emb)
+        code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
+
+        unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
+        # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
+        if self.training and self.unconditioned_percentage > 0:
+            unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
+                                               device=code_emb.device) < self.unconditioned_percentage
+            code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
+                                   code_emb)
+        expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
+
+        if not return_code_pred:
+            return expanded_code_emb
+        else:
+            mel_pred = self.mel_head(expanded_code_emb)
+            # Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
+            mel_pred = mel_pred * unconditioned_batches.logical_not()
+            return expanded_code_emb, mel_pred
+
+
+    def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=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_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
+        :param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
+        :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.
+        """
+        assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None)
+        assert not (return_code_pred and precomputed_aligned_embeddings is not None)  # These two are mutually exclusive.
+
+        unused_params = []
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
+            unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
+            unused_params.extend(list(self.latent_conditioner.parameters()))
+        else:
+            if precomputed_aligned_embeddings is not None:
+                code_emb = precomputed_aligned_embeddings
+            else:
+                code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True)
+                if is_latent(aligned_conditioning):
+                    unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
+                else:
+                    unused_params.extend(list(self.latent_conditioner.parameters()))
+
+            unused_params.append(self.unconditioned_embedding)
+
+        time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
+        x = self.inp_block(x)
+        x = torch.cat([x, code_emb], dim=1)
+        x = self.integrating_conv(x)
+        for i, lyr in enumerate(self.layers):
+            # Do layer drop where applicable. Do not drop first and last layers.
+            if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
+                unused_params.extend(list(lyr.parameters()))
+            else:
+                # First and last blocks will have autocast disabled for improved precision.
+                with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
+                    x = lyr(x, time_emb)
+
+        x = x.float()
+        out = self.out(x)
+
+        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        extraneous_addition = 0
+        for p in unused_params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        if return_code_pred:
+            return out, mel_pred
+        return out
+
+    def get_conditioning_latent(self, conditioning_input):
+        speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
+            conditioning_input.shape) == 3 else conditioning_input
+        conds = []
+        for j in range(speech_conditioning_input.shape[1]):
+            conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
+        conds = torch.cat(conds, dim=-1)
+        return conds.mean(dim=-1)
+
+@register_model
+def register_flat_diffusion_retest(opt_net, opt):
+    return DiffusionTtsFlat(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 100, 400)
+    aligned_latent = torch.randn(2,388,512)
+    aligned_sequence = torch.randint(0,8,(2,100,8))
+    cond = torch.randn(2, 100, 400)
+    ts = torch.LongTensor([600, 600])
+    model = DiffusionTtsFlat(512, layer_drop=.3, unconditioned_percentage=.5, freeze_everything_except_autoregressive_inputs=True)
+    # Test with latent aligned conditioning
+    #o = model(clip, ts, aligned_latent, cond)
+    # Test with sequence aligned conditioning
+    o = model(clip, ts, aligned_sequence, cond)
+