diff --git a/codes/models/audio/music/flat_diffusion_retest.py b/codes/models/audio/music/flat_diffusion_retest.py
deleted file mode 100644
index 6517f540..00000000
--- a/codes/models/audio/music/flat_diffusion_retest.py
+++ /dev/null
@@ -1,329 +0,0 @@
-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)
-