From 781c43c1fcbb6215a990fc9d696696d8248fe2e8 Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Wed, 15 Jun 2022 16:49:06 -0600
Subject: [PATCH] Clean up old TFD models

---
 .../audio/music/transformer_diffusion10.py    | 385 ----------------
 .../audio/music/transformer_diffusion11.py    | 413 ------------------
 .../audio/music/transformer_diffusion5.py     | 299 -------------
 .../audio/music/transformer_diffusion7.py     | 284 ------------
 .../audio/music/transformer_diffusion8.py     | 359 ---------------
 .../audio/music/transformer_diffusion8_mup.py | 388 ----------------
 .../audio/music/transformer_diffusion9.py     | 364 ---------------
 7 files changed, 2492 deletions(-)
 delete mode 100644 codes/models/audio/music/transformer_diffusion10.py
 delete mode 100644 codes/models/audio/music/transformer_diffusion11.py
 delete mode 100644 codes/models/audio/music/transformer_diffusion5.py
 delete mode 100644 codes/models/audio/music/transformer_diffusion7.py
 delete mode 100644 codes/models/audio/music/transformer_diffusion8.py
 delete mode 100644 codes/models/audio/music/transformer_diffusion8_mup.py
 delete mode 100644 codes/models/audio/music/transformer_diffusion9.py

diff --git a/codes/models/audio/music/transformer_diffusion10.py b/codes/models/audio/music/transformer_diffusion10.py
deleted file mode 100644
index 3ab15be2..00000000
--- a/codes/models/audio/music/transformer_diffusion10.py
+++ /dev/null
@@ -1,385 +0,0 @@
-import itertools
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from models.audio.music.music_quantizer2 import MusicQuantizer2
-from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
-from models.diffusion.unet_diffusion import TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
-    FeedForward
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class SubBlock(nn.Module):
-    def __init__(self, inp_dim, contraction_dim, heads, dropout):
-        super().__init__()
-        self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
-        self.attnorm = nn.LayerNorm(contraction_dim)
-        self.ff = FeedForward(inp_dim+contraction_dim, contraction_dim, mult=1, dropout=dropout)
-
-    def forward(self, x, rotary_emb):
-        ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
-        ah = F.gelu(self.attnorm(ah))
-        h = torch.cat([ah, x], dim=-1)
-        hf = checkpoint(self.ff, h)
-        h = torch.cat([h, hf], dim=-1)
-        return h
-
-class DietAttentionBlock(TimestepBlock):
-    def __init__(self, trunk_dim, heads, dropout):
-        super().__init__()
-        contraction_dim = trunk_dim // 4
-        self.prenorm = RMSScaleShiftNorm(trunk_dim, bias=False)
-        self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
-        self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
-        self.out = nn.Linear(trunk_dim+contraction_dim*4, trunk_dim, bias=False)
-        self.out.weight.data.zero_()
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
-        h = self.block1(h, rotary_emb)
-        h = self.block2(h, rotary_emb)
-        h = self.out(h)
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            prenet_layers=3,
-            model_channels=512,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            num_heads=16,
-            dropout=0,
-            use_fp16=False,
-            ar_prior=False,
-            # Parameters for regularization.
-            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.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, model_channels),
-        )
-
-        self.ar_prior = ar_prior
-        prenet_heads = prenet_channels//64
-        if ar_prior:
-            self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
-            self.ar_prior_intg = Encoder(
-                    dim=prenet_channels,
-                    depth=prenet_layers,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-        else:
-            self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-            self.code_converter = Encoder(
-                        dim=prenet_channels,
-                        depth=prenet_layers,
-                        heads=prenet_heads,
-                        ff_dropout=dropout,
-                        attn_dropout=dropout,
-                        use_rmsnorm=True,
-                        ff_glu=True,
-                        rotary_pos_emb=True,
-                        zero_init_branch_output=True,
-                        ff_mult=1,
-                    )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, num_heads, dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, prior, expected_seq_len):
-        code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
-        code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
-
-        # 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(prior.shape[0], 1, 1),
-                                   code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-            else:
-                code_emb = self.timestep_independent(codes, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        with torch.autocast(x.device.type, enabled=self.enable_fp16):
-            blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
-            x = self.inp_block(x).permute(0,2,1)
-
-            rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-            x = self.intg(torch.cat([x, code_emb], dim=-1))
-            for layer in self.layers:
-                x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, quantizer_dims=[1024], freeze_quantizer_until=20000, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        self.freeze_quantizer_until = freeze_quantizer_until
-        self.diff = TransformerDiffusion(**kwargs)
-        self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
-                                         codevector_dim=quantizer_dims[0], codebook_size=256,
-                                         codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
-        self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
-        del self.quantizer.up
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
-        self.quantizer.quantizer.temperature = max(
-            self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
-                    self.quantizer.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
-        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
-        with torch.set_grad_enabled(quant_grad_enabled):
-            proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
-            proj = proj.permute(0,2,1)
-
-        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
-        if not quant_grad_enabled:
-            unused = 0
-            for p in self.quantizer.parameters():
-                unused = unused + p.mean() * 0
-            proj = proj + unused
-            diversity_loss = diversity_loss * 0
-
-        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        return diff, diversity_loss
-
-    def get_debug_values(self, step, __):
-        if self.quantizer.total_codes > 0:
-            return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
-                    'gumbel_temperature': self.quantizer.quantizer.temperature}
-        else:
-            return {}
-
-    def get_grad_norm_parameter_groups(self):
-        attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
-        attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
-        ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
-        ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
-        groups = {
-            'blk1_attention_layers': attn1,
-            'blk2_attention_layers': attn2,
-            'attention_layers': attn1 + attn2,
-            'blk1_ff_layers': ff1,
-            'blk2_ff_layers': ff2,
-            'ff_layers': ff1 + ff2,
-            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
-            'quant_codebook': [self.quantizer.quantizer.codevectors],
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-
-class TransformerDiffusionWithARPrior(nn.Module):
-    def __init__(self, freeze_diff=False, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        from models.audio.music.gpt_music import GptMusicLower
-        self.ar = GptMusicLower(dim=512, layers=12)
-        for p in self.ar.parameters():
-            p.DO_NOT_TRAIN = True
-            p.requires_grad = False
-
-        self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
-        if freeze_diff:
-            for p in self.diff.parameters():
-                p.DO_NOT_TRAIN = True
-                p.requires_grad = False
-            for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
-                del p.DO_NOT_TRAIN
-                p.requires_grad = True
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-    def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
-        with torch.no_grad():
-            prior = self.ar(truth_mel, conditioning_input, return_latent=True)
-
-        diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
-        return diff
-
-
-@register_model
-def register_transformer_diffusion9(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion10_with_quantizer(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion10_with_ar_prior(opt_net, opt):
-    return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
-
-
-def test_quant_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024,
-                                              prenet_channels=1024, num_heads=8,
-                                              input_vec_dim=1024, num_layers=20, prenet_layers=6,
-                                              dropout=.1)
-
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
-    model.quantizer.load_state_dict(quant_weights, strict=False)
-
-    torch.save(model.state_dict(), 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, clip, cond)
-    model.get_grad_norm_parameter_groups()
-
-
-def test_ar_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithARPrior(model_channels=2048, prenet_channels=1536,
-                                            input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
-                                            unconditioned_percentage=.4)
-    model.get_grad_norm_parameter_groups()
-
-    ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
-    model.ar.load_state_dict(ar_weights, strict=True)
-    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
-    pruned_diff_weights = {}
-    for k,v in diff_weights.items():
-        if k.startswith('diff.'):
-            pruned_diff_weights[k.replace('diff.', '')] = v
-    model.diff.load_state_dict(pruned_diff_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-
-    model(clip, ts, cond, conditioning_input=cond)
-
-
-
-if __name__ == '__main__':
-    test_quant_model()
diff --git a/codes/models/audio/music/transformer_diffusion11.py b/codes/models/audio/music/transformer_diffusion11.py
deleted file mode 100644
index 3da9b6b9..00000000
--- a/codes/models/audio/music/transformer_diffusion11.py
+++ /dev/null
@@ -1,413 +0,0 @@
-import itertools
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from models.arch_util import ResBlock
-from models.audio.music.music_quantizer2 import MusicQuantizer2
-from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
-from models.diffusion.unet_diffusion import TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
-    FeedForward
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class SubBlock(nn.Module):
-    def __init__(self, inp_dim, contraction_dim, heads, dropout):
-        super().__init__()
-        self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
-        self.attnorm = nn.LayerNorm(contraction_dim)
-        self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
-        self.ffnorm = nn.LayerNorm(contraction_dim)
-
-    def forward(self, x, rotary_emb):
-        ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
-        ah = F.gelu(self.attnorm(ah))
-        h = torch.cat([ah, x], dim=-1)
-        hf = checkpoint(self.ff, h.permute(0,2,1)).permute(0,2,1)
-        hf = F.gelu(self.ffnorm(hf))
-        h = torch.cat([h, hf], dim=-1)
-        return h
-
-
-class ConcatAttentionBlock(TimestepBlock):
-    def __init__(self, trunk_dim, heads, dropout):
-        super().__init__()
-        contraction_dim = trunk_dim // 4
-        self.prenorm = RMSScaleShiftNorm(trunk_dim, bias=False)
-        self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
-        self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
-        self.block3 = SubBlock(trunk_dim+contraction_dim*4, contraction_dim, heads, dropout)
-        self.out = nn.Linear(contraction_dim*6, trunk_dim, bias=False)
-        self.out.weight.data.zero_()
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
-        h = self.block1(h, rotary_emb)
-        h = self.block2(h, rotary_emb)
-        h = self.block3(h, rotary_emb)
-        h = self.out(h[:,:,x.shape[-1]:])
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            prenet_layers=3,
-            model_channels=512,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            num_heads=16,
-            dropout=0,
-            use_fp16=False,
-            ar_prior=False,
-            # Parameters for regularization.
-            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.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, model_channels),
-        )
-
-        self.ar_prior = ar_prior
-        prenet_heads = prenet_channels//64
-        if ar_prior:
-            self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
-            self.ar_prior_intg = Encoder(
-                    dim=prenet_channels,
-                    depth=prenet_layers,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-        else:
-            self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-            self.code_converter = Encoder(
-                        dim=prenet_channels,
-                        depth=prenet_layers,
-                        heads=prenet_heads,
-                        ff_dropout=dropout,
-                        attn_dropout=dropout,
-                        use_rmsnorm=True,
-                        ff_glu=True,
-                        rotary_pos_emb=True,
-                        zero_init_branch_output=True,
-                        ff_mult=1,
-                    )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, num_heads, dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, prior, expected_seq_len):
-        code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
-        code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
-
-        # 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(prior.shape[0], 1, 1),
-                                   code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-            else:
-                code_emb = self.timestep_independent(codes, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        with torch.autocast(x.device.type, enabled=self.enable_fp16):
-            blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
-            x = self.inp_block(x).permute(0,2,1)
-
-            rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-            x = self.intg(torch.cat([x, code_emb], dim=-1))
-            for layer in self.layers:
-                x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, quantizer_dims=[1024], quantizer_codebook_size=256, quantizer_codebook_groups=2,
-                 freeze_quantizer_until=20000, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        self.freeze_quantizer_until = freeze_quantizer_until
-        self.diff = TransformerDiffusion(**kwargs)
-        self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
-                                         codevector_dim=quantizer_dims[0], codebook_size=quantizer_codebook_size,
-                                         codebook_groups=quantizer_codebook_groups, max_gumbel_temperature=4,
-                                         min_gumbel_temperature=.5)
-        self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
-        del self.quantizer.up
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
-        self.quantizer.quantizer.temperature = max(
-            self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
-                    self.quantizer.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
-        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
-        with torch.set_grad_enabled(quant_grad_enabled):
-            proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
-            proj = proj.permute(0,2,1)
-
-        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
-        if not quant_grad_enabled:
-            unused = 0
-            for p in self.quantizer.parameters():
-                unused = unused + p.mean() * 0
-            proj = proj + unused
-            diversity_loss = diversity_loss * 0
-
-        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        return diff, diversity_loss
-
-    def get_debug_values(self, step, __):
-        if self.quantizer.total_codes > 0:
-            return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
-                    'gumbel_temperature': self.quantizer.quantizer.temperature}
-        else:
-            return {}
-
-    def get_grad_norm_parameter_groups(self):
-        attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
-        attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
-        attn3 = list(itertools.chain.from_iterable([lyr.block3.attn.parameters() for lyr in self.diff.layers]))
-        ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
-        ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
-        ff3 = list(itertools.chain.from_iterable([lyr.block3.ff.parameters() for lyr in self.diff.layers]))
-        blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
-        groups = {
-            'blk1_attention_layers': attn1,
-            'blk2_attention_layers': attn2,
-            'blk3_attention_layers': attn3,
-            'attention_layers': attn1 + attn2 + attn3,
-            'blk1_ff_layers': ff1,
-            'blk2_ff_layers': ff2,
-            'blk3_ff_layers': ff2,
-            'ff_layers': ff1 + ff2 + ff3,
-            'block_out_layers': blkout_layers,
-            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
-            'quant_codebook': [self.quantizer.quantizer.codevectors],
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-
-class TransformerDiffusionWithARPrior(nn.Module):
-    def __init__(self, freeze_diff=False, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        from models.audio.music.gpt_music import GptMusicLower
-        self.ar = GptMusicLower(dim=512, layers=12)
-        for p in self.ar.parameters():
-            p.DO_NOT_TRAIN = True
-            p.requires_grad = False
-
-        self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
-        if freeze_diff:
-            for p in self.diff.parameters():
-                p.DO_NOT_TRAIN = True
-                p.requires_grad = False
-            for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
-                del p.DO_NOT_TRAIN
-                p.requires_grad = True
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-    def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
-        with torch.no_grad():
-            prior = self.ar(truth_mel, conditioning_input, return_latent=True)
-
-        diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
-        return diff
-
-
-@register_model
-def register_transformer_diffusion11(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion11_with_quantizer(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion11_with_ar_prior(opt_net, opt):
-    return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
-
-
-def test_quant_model():
-    clip = torch.randn(2, 100, 400)
-    ts = torch.LongTensor([600, 600])
-
-    """
-    # For music:
-    model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024,
-                                              prenet_channels=1024, num_heads=4,
-                                              input_vec_dim=1024, num_layers=20, prenet_layers=6,
-                                              dropout=.1)
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
-    model.quantizer.load_state_dict(quant_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-    """
-
-    # For TTS:
-    model = TransformerDiffusionWithQuantizer(in_channels=100, out_channels=200, model_channels=1024,
-                                              prenet_channels=1024, num_heads=4,
-                                              input_vec_dim=1024, num_layers=12, prenet_layers=10,
-                                              quantizer_dims=[1024,768,512], quantizer_codebook_size=64,
-                                              quantizer_codebook_groups=4,
-                                              dropout=.1)
-    quant_weights = torch.load('X:\\dlas\\experiments\\train_tts_quant_128\\models\\4000_generator.pth')
-    model.quantizer.load_state_dict(quant_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-
-
-    print_network(model)
-    o = model(clip, ts, clip)
-    model.get_grad_norm_parameter_groups()
-
-
-def test_ar_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithARPrior(model_channels=2048, prenet_channels=1536,
-                                            input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
-                                            unconditioned_percentage=.4)
-    model.get_grad_norm_parameter_groups()
-
-    ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
-    model.ar.load_state_dict(ar_weights, strict=True)
-    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
-    pruned_diff_weights = {}
-    for k,v in diff_weights.items():
-        if k.startswith('diff.'):
-            pruned_diff_weights[k.replace('diff.', '')] = v
-    model.diff.load_state_dict(pruned_diff_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-
-    model(clip, ts, cond, conditioning_input=cond)
-
-
-
-if __name__ == '__main__':
-    test_quant_model()
diff --git a/codes/models/audio/music/transformer_diffusion5.py b/codes/models/audio/music/transformer_diffusion5.py
deleted file mode 100644
index 1fc4053b..00000000
--- a/codes/models/audio/music/transformer_diffusion5.py
+++ /dev/null
@@ -1,299 +0,0 @@
-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 TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class DietAttentionBlock(TimestepBlock):
-    def __init__(self, in_dim, dim, heads, dropout):
-        super().__init__()
-        self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
-        self.proj = nn.Linear(in_dim, dim)
-        self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
-        self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
-        h = self.proj(h)
-        h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
-        h = checkpoint(self.ff, h)
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            model_channels=512,
-            block_channels=256,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            dropout=0,
-            use_fp16=False,
-            # Parameters for regularization.
-            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.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, prenet_channels),
-        )
-        prenet_heads = prenet_channels//64
-        self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2),
-                                                   nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2))
-        self.conditioning_encoder = Encoder(
-                    dim=prenet_channels,
-                    depth=4,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-
-        self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-        self.code_converter = Encoder(
-                    dim=prenet_channels,
-                    depth=3,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.cond_intg = nn.Linear(prenet_channels*2, model_channels)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, block_channels // 64, dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'contextual_embedder': list(self.conditioning_embedder.parameters()),
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, codes, conditioning_input, expected_seq_len):
-        cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
-        cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
-        code_emb = self.input_converter(codes)
-
-        # 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(codes.shape[0], 1, 1),
-                                   code_emb)
-        code_emb = self.code_converter(code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb, cond_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None,
-                precomputed_cond_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-            cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
-            cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
-            unused_params.extend(list(self.code_converter.parameters()))
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-                cond_emb = precomputed_cond_embeddings
-            else:
-                code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1)
-        blk_emb = self.cond_intg(blk_emb)
-        x = self.inp_block(x).permute(0,2,1)
-
-        rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-        x = self.intg(torch.cat([x, code_emb], dim=-1))
-        for layer in self.layers:
-            x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, **kwargs):
-        super().__init__()
-
-        self.diff = TransformerDiffusion(**kwargs)
-        from models.audio.mel2vec import ContrastiveTrainingWrapper
-        self.m2v = ContrastiveTrainingWrapper(mel_input_channels=256, inner_dim=1024, layers=24, dropout=0.1,
-                                           mask_time_prob=0, mask_time_length=6, num_negatives=100, codebook_size=16, codebook_groups=4,
-                                           disable_custom_linear_init=True, do_reconstruction_loss=True)
-        self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature
-
-        self.codes = torch.zeros((3000000,), dtype=torch.long)
-        self.internal_step = 0
-        self.code_ind = 0
-        self.total_codes = 0
-
-        del self.m2v.m2v.encoder
-        del self.m2v.reconstruction_net
-        del self.m2v.m2v.projector.projection
-        del self.m2v.project_hid
-        del self.m2v.project_q
-        del self.m2v.m2v.masked_spec_embed
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        self.m2v.quantizer.temperature = max(
-                    self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**step,
-                    self.m2v.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input, disable_diversity=False, conditioning_free=False):
-        proj = self.m2v.m2v.input_blocks(truth_mel).permute(0,2,1)
-        proj = self.m2v.m2v.projector.layer_norm(proj)
-        vectors, perplexity, probs = self.m2v.quantizer(proj, return_probs=True)
-        diversity = (self.m2v.quantizer.num_codevectors - perplexity) / self.m2v.quantizer.num_codevectors
-        self.log_codes(probs)
-        diff = self.diff(x, timesteps, codes=vectors, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        else:
-            return diff, diversity
-
-    def log_codes(self, codes):
-        if self.internal_step % 5 == 0:
-            codes = torch.argmax(codes, dim=-1)
-            codes = codes[:,:,0] + codes[:,:,1] * 16 + codes[:,:,2] * 16 ** 2 + codes[:,:,3] * 16 ** 3
-            codes = codes.flatten()
-            l = codes.shape[0]
-            i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
-            self.codes[i:i+l] = codes.cpu()
-            self.code_ind = self.code_ind + l
-            if self.code_ind >= self.codes.shape[0]:
-                self.code_ind = 0
-            self.total_codes += 1
-
-    def get_debug_values(self, step, __):
-        if self.total_codes > 0:
-            return {'histogram_codes': self.codes[:self.total_codes]}
-        else:
-            return {}
-
-
-@register_model
-def register_transformer_diffusion5(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion5_with_quantizer(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-"""
-# For TFD5
-if __name__ == '__main__':
-    clip = torch.randn(2, 256, 400)
-    aligned_sequence = torch.randn(2,100,512)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536)
-    torch.save(model, 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, aligned_sequence, cond)
-"""
-
-if __name__ == '__main__':
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, num_layers=16)
-
-    quant_weights = torch.load('../experiments/m2v_music.pth')
-    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
-    model.m2v.load_state_dict(quant_weights, strict=False)
-    model.diff.load_state_dict(diff_weights)
-
-    torch.save(model.state_dict(), 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, clip, cond)
-
diff --git a/codes/models/audio/music/transformer_diffusion7.py b/codes/models/audio/music/transformer_diffusion7.py
deleted file mode 100644
index 531a0868..00000000
--- a/codes/models/audio/music/transformer_diffusion7.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from models.audio.music.music_quantizer import MusicQuantizer
-from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
-from models.diffusion.unet_diffusion import TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class DietAttentionBlock(TimestepBlock):
-    def __init__(self, in_dim, dim, heads, dropout):
-        super().__init__()
-        self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
-        self.proj = nn.Linear(in_dim, dim)
-        self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
-        self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
-        h = self.proj(h)
-        h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
-        h = checkpoint(self.ff, h)
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            prenet_layers=3,
-            model_channels=512,
-            block_channels=256,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            dropout=0,
-            use_fp16=False,
-            # Parameters for regularization.
-            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.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, prenet_channels),
-        )
-        prenet_heads = prenet_channels//64
-        self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2),
-                                                   nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2))
-        self.conditioning_encoder = Encoder(
-                    dim=prenet_channels,
-                    depth=4,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-
-        self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-        self.code_converter = Encoder(
-                    dim=prenet_channels,
-                    depth=prenet_layers,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.cond_intg = nn.Linear(prenet_channels*2, model_channels)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, block_channels // 64, dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'contextual_embedder': list(self.conditioning_embedder.parameters()),
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, codes, conditioning_input, expected_seq_len):
-        cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
-        cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
-        code_emb = self.input_converter(codes)
-
-        # 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(codes.shape[0], 1, 1),
-                                   code_emb)
-        code_emb = self.code_converter(code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb, cond_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None,
-                precomputed_cond_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-            cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
-            cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
-            unused_params.extend(list(self.code_converter.parameters()))
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-                cond_emb = precomputed_cond_embeddings
-            else:
-                code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1)
-        blk_emb = self.cond_intg(blk_emb)
-        x = self.inp_block(x).permute(0,2,1)
-
-        rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-        x = self.intg(torch.cat([x, code_emb], dim=-1))
-        for layer in self.layers:
-            x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, freeze_quantizer_until=20000, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        self.freeze_quantizer_until = freeze_quantizer_until
-        self.diff = TransformerDiffusion(**kwargs)
-        self.m2v = MusicQuantizer(inp_channels=256, inner_dim=[1024,1024,512], codevector_dim=1024, codebook_size=512, codebook_groups=2)
-        self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature
-        del self.m2v.up
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
-        self.m2v.quantizer.temperature = max(
-                    self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**qstep,
-                    self.m2v.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input, disable_diversity=False, conditioning_free=False):
-        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
-        with torch.set_grad_enabled(quant_grad_enabled):
-            proj, diversity_loss = self.m2v(truth_mel, return_decoder_latent=True)
-            proj = proj.permute(0,2,1)
-
-        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
-        if not quant_grad_enabled:
-            unused = 0
-            for p in self.m2v.parameters():
-                unused = unused + p.mean() * 0
-            proj = proj + unused
-            diversity_loss = diversity_loss * 0
-
-        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        return diff, diversity_loss
-
-    def get_debug_values(self, step, __):
-        if self.m2v.total_codes > 0:
-            return {'histogram_codes': self.m2v.codes[:self.m2v.total_codes]}
-        else:
-            return {}
-
-
-@register_model
-def register_transformer_diffusion7(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion7_with_quantizer(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-"""
-# For TFD5
-if __name__ == '__main__':
-    clip = torch.randn(2, 256, 400)
-    aligned_sequence = torch.randn(2,100,512)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536)
-    torch.save(model, 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, aligned_sequence, cond)
-"""
-
-if __name__ == '__main__':
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
-
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant\\models\\18000_generator_ema.pth')
-    #diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
-    model.m2v.load_state_dict(quant_weights, strict=False)
-    #model.diff.load_state_dict(diff_weights)
-
-    torch.save(model.state_dict(), 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, clip, cond)
-
diff --git a/codes/models/audio/music/transformer_diffusion8.py b/codes/models/audio/music/transformer_diffusion8.py
deleted file mode 100644
index 208f1091..00000000
--- a/codes/models/audio/music/transformer_diffusion8.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import itertools
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from models.arch_util import ResBlock
-from models.audio.music.music_quantizer2 import MusicQuantizer2
-from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
-from models.diffusion.unet_diffusion import TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class DietAttentionBlock(TimestepBlock):
-    def __init__(self, in_dim, dim, heads, dropout):
-        super().__init__()
-        self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
-        self.proj = nn.Linear(in_dim, dim)
-        self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
-        self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
-        h = self.proj(h)
-        h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
-        h = checkpoint(self.ff, h)
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            prenet_layers=3,
-            model_channels=512,
-            block_channels=256,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            num_heads=16,
-            dropout=0,
-            use_fp16=False,
-            ar_prior=False,
-            # Parameters for regularization.
-            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.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, model_channels),
-        )
-
-        self.ar_prior = ar_prior
-        if ar_prior:
-            self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
-            self.ar_prior_intg = Encoder(
-                    dim=prenet_channels,
-                    depth=prenet_layers,
-                    heads=num_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-        else:
-            self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-            self.code_converter = Encoder(
-                        dim=prenet_channels,
-                        depth=prenet_layers,
-                        heads=num_heads,
-                        ff_dropout=dropout,
-                        attn_dropout=dropout,
-                        use_rmsnorm=True,
-                        ff_glu=True,
-                        rotary_pos_emb=True,
-                        zero_init_branch_output=True,
-                        ff_mult=1,
-                    )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, num_heads, dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, prior, expected_seq_len):
-        code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
-        code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
-
-        # 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(prior.shape[0], 1, 1),
-                                   code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-            else:
-                code_emb = self.timestep_independent(codes, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        with torch.autocast(x.device.type, enabled=self.enable_fp16):
-            blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
-            x = self.inp_block(x).permute(0,2,1)
-
-            rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-            x = self.intg(torch.cat([x, code_emb], dim=-1))
-            for layer in self.layers:
-                x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, quantizer_dims=[1024], freeze_quantizer_until=20000, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        self.freeze_quantizer_until = freeze_quantizer_until
-        self.diff = TransformerDiffusion(**kwargs)
-        self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
-                                         codevector_dim=quantizer_dims[0], codebook_size=256,
-                                         codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
-        self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
-        del self.quantizer.up
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
-        self.quantizer.quantizer.temperature = max(
-            self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
-                    self.quantizer.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
-        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
-        with torch.set_grad_enabled(quant_grad_enabled):
-            proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
-            proj = proj.permute(0,2,1)
-
-        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
-        if not quant_grad_enabled:
-            unused = 0
-            for p in self.quantizer.parameters():
-                unused = unused + p.mean() * 0
-            proj = proj + unused
-            diversity_loss = diversity_loss * 0
-
-        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        return diff, diversity_loss
-
-    def get_debug_values(self, step, __):
-        if self.quantizer.total_codes > 0:
-            return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
-                    'gumbel_temperature': self.quantizer.quantizer.temperature}
-        else:
-            return {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
-            'quant_codebook': [self.quantizer.quantizer.codevectors],
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-
-class TransformerDiffusionWithARPrior(nn.Module):
-    def __init__(self, freeze_diff=False, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        from models.audio.music.gpt_music import GptMusicLower
-        self.ar = GptMusicLower(dim=512, layers=12)
-        for p in self.ar.parameters():
-            p.DO_NOT_TRAIN = True
-            p.requires_grad = False
-
-        self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
-        if freeze_diff:
-            for p in self.diff.parameters():
-                p.DO_NOT_TRAIN = True
-                p.requires_grad = False
-            for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
-                del p.DO_NOT_TRAIN
-                p.requires_grad = True
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-    def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
-        with torch.no_grad():
-            prior = self.ar(truth_mel, conditioning_input, return_latent=True)
-
-        diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
-        return diff
-
-
-@register_model
-def register_transformer_diffusion8(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion8_with_quantizer(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
-    return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
-
-
-def test_quant_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=2048, block_channels=1024,
-                                              prenet_channels=1024, num_heads=8,
-                                              input_vec_dim=1024, num_layers=16, prenet_layers=6)
-    model.get_grad_norm_parameter_groups()
-
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
-    model.quantizer.load_state_dict(quant_weights, strict=False)
-
-    torch.save(model.state_dict(), 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, clip, cond)
-
-
-def test_ar_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithARPrior(model_channels=3072, block_channels=1536, prenet_channels=1536,
-                                            input_vec_dim=512, num_layers=24, prenet_layers=6, freeze_diff=True,
-                                            unconditioned_percentage=.4)
-    model.get_grad_norm_parameter_groups()
-
-    ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
-    model.ar.load_state_dict(ar_weights, strict=True)
-    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
-    pruned_diff_weights = {}
-    for k,v in diff_weights.items():
-        if k.startswith('diff.'):
-            pruned_diff_weights[k.replace('diff.', '')] = v
-    model.diff.load_state_dict(pruned_diff_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-
-    model(clip, ts, cond, conditioning_input=cond)
-
-
-
-if __name__ == '__main__':
-    test_quant_model()
diff --git a/codes/models/audio/music/transformer_diffusion8_mup.py b/codes/models/audio/music/transformer_diffusion8_mup.py
deleted file mode 100644
index d146c51f..00000000
--- a/codes/models/audio/music/transformer_diffusion8_mup.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import itertools
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-
-from models.arch_util import ResBlock
-from models.audio.music.music_quantizer2 import MusicQuantizer2
-from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
-from models.diffusion.unet_diffusion import TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class DietAttentionBlock(TimestepBlock):
-    def __init__(self, in_dim, dim, heads, dropout):
-        super().__init__()
-        self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
-        self.proj = nn.Linear(in_dim, dim)
-        self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
-        self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
-        h = self.proj(h)
-        h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
-        h = checkpoint(self.ff, h)
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            prenet_layers=3,
-            model_channels=512,
-            block_channels=256,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            dropout=0,
-            use_fp16=False,
-            ar_prior=False,
-            # Parameters for regularization.
-            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
-            # mUp base shapes.
-            mup_base_shapes=None,
-    ):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, model_channels),
-        )
-        prenet_heads = min(16, prenet_channels//64)
-
-        self.ar_prior = ar_prior
-        if ar_prior:
-            self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
-            self.ar_prior_intg = Encoder(
-                    dim=prenet_channels,
-                    depth=prenet_layers,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-        else:
-            self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-            self.code_converter = Encoder(
-                        dim=prenet_channels,
-                        depth=prenet_layers,
-                        heads=prenet_heads,
-                        ff_dropout=dropout,
-                        attn_dropout=dropout,
-                        use_rmsnorm=True,
-                        ff_glu=True,
-                        rotary_pos_emb=True,
-                        zero_init_branch_output=True,
-                        ff_mult=1,
-                    )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels,
-                                                                         min(16, block_channels//64), dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        if mup_base_shapes is not None:
-            from mup import set_base_shapes
-            set_base_shapes(self, mup_base_shapes, rescale_params=False)
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, prior, expected_seq_len):
-        code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
-
-        # 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(prior.shape[0], 1, 1),
-                                   code_emb)
-        code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-            else:
-                code_emb = self.timestep_independent(codes, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
-        x = self.inp_block(x).permute(0,2,1)
-
-        rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-        x = self.intg(torch.cat([x, code_emb], dim=-1))
-        for layer in self.layers:
-            x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, freeze_quantizer_until=20000, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        self.freeze_quantizer_until = freeze_quantizer_until
-        self.diff = TransformerDiffusion(**kwargs)
-        self.quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
-                                        codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
-        self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
-        del self.quantizer.up
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
-        self.quantizer.quantizer.temperature = max(
-            self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
-                    self.quantizer.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input, disable_diversity=False, conditioning_free=False):
-        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
-        with torch.set_grad_enabled(quant_grad_enabled):
-            proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
-            proj = proj.permute(0,2,1)
-
-        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
-        if not quant_grad_enabled:
-            unused = 0
-            for p in self.quantizer.parameters():
-                unused = unused + p.mean() * 0
-            proj = proj + unused
-            diversity_loss = diversity_loss * 0
-
-        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        return diff, diversity_loss
-
-    def get_debug_values(self, step, __):
-        if self.quantizer.total_codes > 0:
-            return {'histogram_codes': self.quantizer.codes[:self.quantizer.total_codes]}
-        else:
-            return {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
-            'quant_codebook': [self.quantizer.quantizer.codevectors],
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-
-class TransformerDiffusionWithARPrior(nn.Module):
-    def __init__(self, freeze_diff=False, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        from models.audio.music.gpt_music import GptMusicLower
-        self.ar = GptMusicLower(dim=512, layers=12)
-        for p in self.ar.parameters():
-            p.DO_NOT_TRAIN = True
-            p.requires_grad = False
-
-        self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
-        if freeze_diff:
-            for p in self.diff.parameters():
-                p.DO_NOT_TRAIN = True
-                p.requires_grad = False
-            for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
-                del p.DO_NOT_TRAIN
-                p.requires_grad = True
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-    def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
-        with torch.no_grad():
-            prior = self.ar(truth_mel, conditioning_input, return_latent=True)
-
-        diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
-        return diff
-
-
-@register_model
-def register_transformer_diffusion8_mup(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion8_with_quantizer_mup(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion8_with_ar_prior_mup(opt_net, opt):
-    return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
-
-
-def test_quant_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024,
-                                              input_vec_dim=1024, num_layers=16, prenet_layers=6)
-    model.get_grad_norm_parameter_groups()
-
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
-    #diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
-    model.quantizer.load_state_dict(quant_weights, strict=False)
-    #model.diff.load_state_dict(diff_weights)
-
-    torch.save(model.state_dict(), 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, clip, cond)
-
-
-def test_ar_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1024,
-                                              input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True)
-    model.get_grad_norm_parameter_groups()
-
-    ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
-    model.ar.load_state_dict(ar_weights, strict=True)
-    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
-    pruned_diff_weights = {}
-    for k,v in diff_weights.items():
-        if k.startswith('diff.'):
-            pruned_diff_weights[k.replace('diff.', '')] = v
-    model.diff.load_state_dict(pruned_diff_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-
-    model(clip, ts, cond, conditioning_input=cond)
-
-
-def init_mup():
-    base_model = TransformerDiffusion(model_channels=768, block_channels=768, prenet_channels=768,
-                                                   input_vec_dim=1024, num_layers=16, prenet_layers=4)
-    delta_model = TransformerDiffusion(model_channels=2048, block_channels=1024, prenet_channels=1024,
-                                                   input_vec_dim=1024, num_layers=16, prenet_layers=4)
-    target_model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536,
-                                                   input_vec_dim=1024, num_layers=16, prenet_layers=4)
-    from mup import set_base_shapes, save_base_shapes
-    set_base_shapes(target_model, base_model, delta=delta_model)
-    save_base_shapes(target_model, 'mup_base_shapes.bsh')
-
-    """
-    # Ah to have a simple loss..
-    def lazy_model(width):
-        return lambda: set_base_shapes(TransformerDiffusion(model_channels=width*2, block_channels=width,
-                                                            prenet_channels=width, num_layers=16, prenet_layers=4,
-                                                            input_vec_dim=1024),
-                                       'mup_base_shapes.bsh')
-    from mup.coord_check import get_coord_data, plot_coord_data
-    models = {256: lazy_model(256), 512: lazy_model(512), 1024: lazy_model(1024), 1536: lazy_model(1536)}
-    dataloader = DataLoader(MupSampleDataset())
-    df = get_coord_data(models, dataloader, dict_in_out=True)
-    plot_coord_data(df, 'coord_check')
-    """
-
-if __name__ == '__main__':
-    init_mup()
diff --git a/codes/models/audio/music/transformer_diffusion9.py b/codes/models/audio/music/transformer_diffusion9.py
deleted file mode 100644
index 9dc0226e..00000000
--- a/codes/models/audio/music/transformer_diffusion9.py
+++ /dev/null
@@ -1,364 +0,0 @@
-import itertools
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from models.audio.music.music_quantizer2 import MusicQuantizer2
-from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
-from models.diffusion.unet_diffusion import TimestepBlock
-from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
-    FeedForward
-from trainer.networks import register_model
-from utils.util import checkpoint, print_network
-
-
-def is_latent(t):
-    return t.dtype == torch.float
-
-def is_sequence(t):
-    return t.dtype == torch.long
-
-
-class MultiGroupEmbedding(nn.Module):
-    def __init__(self, tokens, groups, dim):
-        super().__init__()
-        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
-
-    def forward(self, x):
-        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
-        return torch.cat(h, dim=-1)
-
-
-class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
-    def forward(self, x, emb, rotary_emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb, rotary_emb)
-            else:
-                x = layer(x, rotary_emb)
-        return x
-
-
-class DietAttentionBlock(TimestepBlock):
-    def __init__(self, in_dim, dim, heads, dropout):
-        super().__init__()
-        self.proj = nn.Linear(in_dim, dim, bias=False)
-        self.attn = Attention(dim, heads=heads, dim_head=dim//heads, causal=False, dropout=dropout)
-        self.attnorm = nn.LayerNorm(dim)
-        self.prenorm = RMSScaleShiftNorm(dim, bias=False)
-        self.ff = FeedForward(dim*2, in_dim, mult=1, dropout=dropout, zero_init_output=True)
-
-    def forward(self, x, timestep_emb, rotary_emb):
-        h = self.proj(x)
-        ah, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
-        ah = F.gelu(self.attnorm(ah))
-        h = self.prenorm(h, norm_scale_shift_inp=timestep_emb)
-        h = torch.cat([ah, h], dim=-1)
-        h = checkpoint(self.ff, h)
-        return h + x
-
-
-class TransformerDiffusion(nn.Module):
-    """
-    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
-    """
-    def __init__(
-            self,
-            prenet_channels=256,
-            prenet_layers=3,
-            model_channels=512,
-            block_channels=256,
-            num_layers=8,
-            in_channels=256,
-            rotary_emb_dim=32,
-            input_vec_dim=512,
-            out_channels=512,  # mean and variance
-            num_heads=16,
-            dropout=0,
-            use_fp16=False,
-            ar_prior=False,
-            # Parameters for regularization.
-            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.prenet_channels = prenet_channels
-        self.out_channels = out_channels
-        self.dropout = dropout
-        self.unconditioned_percentage = unconditioned_percentage
-        self.enable_fp16 = use_fp16
-
-        self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
-
-        self.time_embed = nn.Sequential(
-            linear(prenet_channels, prenet_channels),
-            nn.SiLU(),
-            linear(prenet_channels, block_channels),
-        )
-
-        self.ar_prior = ar_prior
-        prenet_heads = prenet_channels//64
-        if ar_prior:
-            self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
-            self.ar_prior_intg = Encoder(
-                    dim=prenet_channels,
-                    depth=prenet_layers,
-                    heads=prenet_heads,
-                    ff_dropout=dropout,
-                    attn_dropout=dropout,
-                    use_rmsnorm=True,
-                    ff_glu=True,
-                    rotary_pos_emb=True,
-                    zero_init_branch_output=True,
-                    ff_mult=1,
-                )
-        else:
-            self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
-            self.code_converter = Encoder(
-                        dim=prenet_channels,
-                        depth=prenet_layers,
-                        heads=prenet_heads,
-                        ff_dropout=dropout,
-                        attn_dropout=dropout,
-                        use_rmsnorm=True,
-                        ff_glu=True,
-                        rotary_pos_emb=True,
-                        zero_init_branch_output=True,
-                        ff_mult=1,
-                    )
-
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
-        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
-        self.intg = nn.Linear(prenet_channels*2, model_channels)
-        self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, num_heads, dropout) for _ in range(num_layers)])
-
-        self.out = nn.Sequential(
-            normalization(model_channels),
-            nn.SiLU(),
-            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
-        )
-
-        self.debug_codes = {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
-            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
-            'time_embed': list(self.time_embed.parameters()),
-        }
-        return groups
-
-    def timestep_independent(self, prior, expected_seq_len):
-        code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
-        code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
-
-        # 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(prior.shape[0], 1, 1),
-                                   code_emb)
-
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
-        return expanded_code_emb
-
-    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
-        if precomputed_code_embeddings is not None:
-            assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
-
-        unused_params = []
-        if conditioning_free:
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
-        else:
-            if precomputed_code_embeddings is not None:
-                code_emb = precomputed_code_embeddings
-            else:
-                code_emb = self.timestep_independent(codes, x.shape[-1])
-            unused_params.append(self.unconditioned_embedding)
-
-        with torch.autocast(x.device.type, enabled=self.enable_fp16):
-            blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
-            x = self.inp_block(x).permute(0,2,1)
-
-            rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
-            x = self.intg(torch.cat([x, code_emb], dim=-1))
-            for layer in self.layers:
-                x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
-
-        x = x.float().permute(0,2,1)
-        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
-
-        return out
-
-
-class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, quantizer_dims=[1024], freeze_quantizer_until=20000, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        self.freeze_quantizer_until = freeze_quantizer_until
-        self.diff = TransformerDiffusion(**kwargs)
-        self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
-                                         codevector_dim=quantizer_dims[0], codebook_size=256,
-                                         codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
-        self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
-        del self.quantizer.up
-
-    def update_for_step(self, step, *args):
-        self.internal_step = step
-        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
-        self.quantizer.quantizer.temperature = max(
-            self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
-                    self.quantizer.min_gumbel_temperature,
-                )
-
-    def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
-        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
-        with torch.set_grad_enabled(quant_grad_enabled):
-            proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
-            proj = proj.permute(0,2,1)
-
-        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
-        if not quant_grad_enabled:
-            unused = 0
-            for p in self.quantizer.parameters():
-                unused = unused + p.mean() * 0
-            proj = proj + unused
-            diversity_loss = diversity_loss * 0
-
-        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
-        if disable_diversity:
-            return diff
-        return diff, diversity_loss
-
-    def get_debug_values(self, step, __):
-        if self.quantizer.total_codes > 0:
-            return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
-                    'gumbel_temperature': self.quantizer.quantizer.temperature}
-        else:
-            return {}
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
-            'quant_codebook': [self.quantizer.quantizer.codevectors],
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-
-class TransformerDiffusionWithARPrior(nn.Module):
-    def __init__(self, freeze_diff=False, **kwargs):
-        super().__init__()
-
-        self.internal_step = 0
-        from models.audio.music.gpt_music import GptMusicLower
-        self.ar = GptMusicLower(dim=512, layers=12)
-        for p in self.ar.parameters():
-            p.DO_NOT_TRAIN = True
-            p.requires_grad = False
-
-        self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
-        if freeze_diff:
-            for p in self.diff.parameters():
-                p.DO_NOT_TRAIN = True
-                p.requires_grad = False
-            for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
-                del p.DO_NOT_TRAIN
-                p.requires_grad = True
-
-    def get_grad_norm_parameter_groups(self):
-        groups = {
-            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
-            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
-            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
-            'out': list(self.diff.out.parameters()),
-            'x_proj': list(self.diff.inp_block.parameters()),
-            'layers': list(self.diff.layers.parameters()),
-            'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
-            'time_embed': list(self.diff.time_embed.parameters()),
-        }
-        return groups
-
-    def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
-        with torch.no_grad():
-            prior = self.ar(truth_mel, conditioning_input, return_latent=True)
-
-        diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
-        return diff
-
-
-@register_model
-def register_transformer_diffusion9(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion9_with_quantizer(opt_net, opt):
-    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
-
-
-@register_model
-def register_transformer_diffusion9_with_ar_prior(opt_net, opt):
-    return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
-
-
-def test_quant_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024, block_channels=1024,
-                                              prenet_channels=1024, num_heads=8,
-                                              input_vec_dim=1024, num_layers=20, prenet_layers=6,
-                                              dropout=.1)
-    model.get_grad_norm_parameter_groups()
-
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
-    model.quantizer.load_state_dict(quant_weights, strict=False)
-
-    torch.save(model.state_dict(), 'sample.pth')
-    print_network(model)
-    o = model(clip, ts, clip, cond)
-
-
-def test_ar_model():
-    clip = torch.randn(2, 256, 400)
-    cond = torch.randn(2, 256, 400)
-    ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1536,
-                                            input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
-                                            unconditioned_percentage=.4)
-    model.get_grad_norm_parameter_groups()
-
-    ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
-    model.ar.load_state_dict(ar_weights, strict=True)
-    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
-    pruned_diff_weights = {}
-    for k,v in diff_weights.items():
-        if k.startswith('diff.'):
-            pruned_diff_weights[k.replace('diff.', '')] = v
-    model.diff.load_state_dict(pruned_diff_weights, strict=False)
-    torch.save(model.state_dict(), 'sample.pth')
-
-    model(clip, ts, cond, conditioning_input=cond)
-
-
-
-if __name__ == '__main__':
-    test_quant_model()