forked from mrq/DL-Art-School
tfd13 for multi-resolution superscaling
This commit is contained in:
James Betker
parent
1b648abd7c
commit
1b4d9567f3
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@ -452,12 +452,14 @@ class AttentionBlock(nn.Module):
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channels,
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num_heads=1,
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num_head_channels=-1,
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out_channels=None,
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use_new_attention_order=False,
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do_checkpoint=True,
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do_activation=False,
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):
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super().__init__()
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self.channels = channels
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out_channels = channels if out_channels is None else out_channels
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self.do_checkpoint = do_checkpoint
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self.do_activation = do_activation
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if num_head_channels == -1:
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@ -468,7 +470,7 @@ class AttentionBlock(nn.Module):
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), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
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self.num_heads = channels // num_head_channels
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self.norm = normalization(channels)
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self.qkv = conv_nd(1, channels, channels * 3, 1)
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self.qkv = conv_nd(1, channels, out_channels * 3, 1)
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if use_new_attention_order:
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# split qkv before split heads
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self.attention = QKVAttention(self.num_heads)
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@ -476,7 +478,8 @@ class AttentionBlock(nn.Module):
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# split heads before split qkv
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
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self.x_proj = nn.Identity() if out_channels == channels else conv_nd(1, channels, out_channels, 1)
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self.proj_out = zero_module(conv_nd(1, out_channels, out_channels, 1))
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def forward(self, x, mask=None):
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if self.do_checkpoint:
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@ -496,7 +499,8 @@ class AttentionBlock(nn.Module):
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qkv = self.qkv(x)
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h = self.attention(qkv, mask)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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xp = self.x_proj(x)
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return (xp + h).reshape(b, xp.shape[1], *spatial)
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class QKVAttentionLegacy(nn.Module):
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@ -1,348 +0,0 @@
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import itertools
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import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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import torchvision
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
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FeedForward
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from trainer.networks import register_model
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from utils.util import checkpoint, print_network, load_audio
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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def forward(self, x, emb, rotary_emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, rotary_emb)
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else:
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x = layer(x, rotary_emb)
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return x
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class SubBlock(nn.Module):
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def __init__(self, inp_dim, contraction_dim, heads, dropout):
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super().__init__()
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self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
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self.attnorm = nn.LayerNorm(contraction_dim)
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self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
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self.ffnorm = nn.LayerNorm(contraction_dim)
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def forward(self, x, rotary_emb):
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ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
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ah = F.gelu(self.attnorm(ah))
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h = torch.cat([ah, x], dim=-1)
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hf = checkpoint(self.ff, h)
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hf = F.gelu(self.ffnorm(hf))
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h = torch.cat([h, hf], dim=-1)
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return h
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class ConcatAttentionBlock(TimestepBlock):
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def __init__(self, trunk_dim, contraction_dim, time_embed_dim, cond_dim_in, cond_dim_hidden, heads, dropout):
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super().__init__()
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self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
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self.cond_project = nn.Linear(cond_dim_in, cond_dim_hidden)
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self.block1 = SubBlock(trunk_dim+cond_dim_hidden, contraction_dim, heads, dropout)
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self.block2 = SubBlock(trunk_dim+cond_dim_hidden+contraction_dim*2, contraction_dim, heads, dropout)
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self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
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self.out.weight.data.zero_()
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def forward(self, x, cond, timestep_emb, rotary_emb):
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h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
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cond = self.cond_project(cond)
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h = torch.cat([h, cond], dim=-1)
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h = self.block1(h, rotary_emb)
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h = self.block2(h, rotary_emb)
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h = self.out(h[:,:,x.shape[-1]+cond.shape[-1]:])
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return h + x
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class TransformerDiffusionWithPointConditioning(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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def __init__(
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self,
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in_channels=256,
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out_channels=512, # mean and variance
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model_channels=1024,
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contraction_dim=256,
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time_embed_dim=256,
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num_layers=8,
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rotary_emb_dim=32,
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input_cond_dim=1024,
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num_heads=8,
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dropout=0,
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use_fp16=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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):
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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.time_embed_dim = time_embed_dim
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self.out_channels = out_channels
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self.dropout = dropout
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(time_embed_dim, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels,
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contraction_dim,
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time_embed_dim,
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cond_dim_in=input_cond_dim,
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cond_dim_hidden=input_cond_dim//2,
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heads=num_heads,
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dropout=dropout) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
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attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
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ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
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ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
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blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
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groups = {
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'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
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'blk1_attention_layers': attn1,
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'blk2_attention_layers': attn2,
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'attention_layers': attn1 + attn2,
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'blk1_ff_layers': ff1,
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'blk2_ff_layers': ff2,
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'ff_layers': ff1 + ff2,
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'block_out_layers': blkout_layers,
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'rotary_embeddings': list(self.rotary_embeddings.parameters()),
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'out': list(self.out.parameters()),
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'x_proj': list(self.inp_block.parameters()),
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'layers': list(self.layers.parameters()),
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'time_embed': list(self.time_embed.parameters()),
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}
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return groups
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def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
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unused_params = []
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if conditioning_free:
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cond = self.unconditioned_embedding
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else:
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cond = conditioning_input
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((cond.shape[0], 1, 1),
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device=cond.device) < self.unconditioned_percentage
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cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond)
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unused_params.append(self.unconditioned_embedding)
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cond = cond.repeat(1,x.shape[-1],1)
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with torch.autocast(x.device.type, enabled=self.enable_fp16):
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
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x = self.inp_block(x).permute(0,2,1)
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rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
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for layer in self.layers:
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x = checkpoint(layer, x, cond, blk_emb, rotary_pos_emb)
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x = x.float().permute(0,2,1)
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out = self.out(x)
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# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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extraneous_addition = 0
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for p in unused_params:
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extraneous_addition = extraneous_addition + p.mean()
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out = out + extraneous_addition * 0
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return out
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class ConditioningEncoder(nn.Module):
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def __init__(self,
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cond_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=8,
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dropout=.1,
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do_checkpointing=False):
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super().__init__()
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attn = []
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self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
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self.attn = Encoder(
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dim=embedding_dim,
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depth=attn_blocks,
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heads=num_attn_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=2,
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)
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self.dim = embedding_dim
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self.do_checkpointing = do_checkpointing
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def forward(self, x):
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h = self.init(x).permute(0,2,1)
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h = self.attn(h).permute(0,2,1)
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return h.mean(dim=2).unsqueeze(1)
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class TransformerDiffusionWithConditioningEncoder(nn.Module):
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def __init__(self, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
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self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
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def forward(self, x, timesteps, conditioning_input=None, disable_diversity=False, conditioning_free=False):
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cond = self.conditioning_encoder(conditioning_input)
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diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
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return diff
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def get_debug_values(self, step, __):
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self.internal_step = step
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return {}
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def get_grad_norm_parameter_groups(self):
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groups = self.diff.get_grad_norm_parameter_groups()
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groups['conditioning_encoder'] = list(self.conditioning_encoder.parameters())
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return groups
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def before_step(self, step):
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
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list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
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# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
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# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
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# directly fiddling with the gradients.
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for p in scaled_grad_parameters:
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if hasattr(p, 'grad') and p.grad is not None:
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p.grad *= .2
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@register_model
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def register_tfdpc3(opt_net, opt):
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return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
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@register_model
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def register_tfdpc3_with_conditioning_encoder(opt_net, opt):
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return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs'])
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def test_cheater_model():
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clip = torch.randn(2, 256, 400)
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cl = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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# For music:
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model = TransformerDiffusionWithConditioningEncoder(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=24, dropout=0,
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unconditioned_percentage=.4)
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print_network(model)
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o = model(clip, ts, cl)
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pg = model.get_grad_norm_parameter_groups()
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def inference_tfdpc3_with_cheater():
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with torch.no_grad():
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os.makedirs('results/tfdpc_v3', exist_ok=True)
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#length = 40 * 22050 // 256 // 16
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samples = {'electronica1': load_audio('Y:\\split\\yt-music-eval\\00001.wav', 22050),
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'electronica2': load_audio('Y:\\split\\yt-music-eval\\00272.wav', 22050),
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'e_guitar': load_audio('Y:\\split\\yt-music-eval\\00227.wav', 22050),
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'creep': load_audio('Y:\\separated\\bt-music-3\\[2007] MTV Unplugged (Live) (Japan Edition)\\05 - Creep [Cover On Radiohead]\\00001\\no_vocals.wav', 22050),
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'rock1': load_audio('Y:\\separated\\bt-music-3\\2016 - Heal My Soul\\01 - Daze Of The Night\\00000\\no_vocals.wav', 22050),
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'kiss': load_audio('Y:\\separated\\bt-music-3\\KISS (2001) Box Set CD1\\02 Deuce (Demo Version)\\00000\\no_vocals.wav', 22050),
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'purp': load_audio('Y:\\separated\\bt-music-3\\Shades of Deep Purple\\11 Help (Alternate Take)\\00001\\no_vocals.wav', 22050),
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'western_stars': load_audio('Y:\\separated\\bt-music-3\\Western Stars\\01 Hitch Hikin\'\\00000\\no_vocals.wav', 22050),
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'silk': load_audio('Y:\\separated\\silk\\MonstercatSilkShowcase\\890\\00007\\no_vocals.wav', 22050),
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'long_electronica': load_audio('C:\\Users\\James\\Music\\longer_sample.wav', 22050),}
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for k, sample in samples.items():
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sample = sample.cuda()
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length = sample.shape[0]//256//16
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model = TransformerDiffusionWithConditioningEncoder(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=12, dropout=0,
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use_fp16=False, unconditioned_percentage=0).eval().cuda()
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model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v3/models/61000_generator_ema.pth'))
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from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
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spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000, 'true_normalization': True,
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'normalize': True, 'in': 'in', 'out': 'out'}, {}).cuda()
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ref_mel = spec_fn({'in': sample.unsqueeze(0)})['out']
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from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
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cheater_encoder = MusicCheaterLatentInjector({'in': 'in', 'out': 'out'}, {}).cuda()
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ref_cheater = cheater_encoder({'in': ref_mel})['out']
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from models.diffusion.respace import SpacedDiffusion
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from models.diffusion.respace import space_timesteps
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from models.diffusion.gaussian_diffusion import get_named_beta_schedule
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [128]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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# Conventional decoding method:
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gen_cheater = diffuser.ddim_sample_loop(model, (1,256,length), progress=True, model_kwargs={'true_cheater': ref_cheater})
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# Guidance decoding method:
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#mask = torch.ones_like(ref_cheater)
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#mask[:,:,15:-15] = 0
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#gen_cheater = diffuser.p_sample_loop_with_guidance(model, ref_cheater, mask, model_kwargs={'true_cheater': ref_cheater})
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# Just decode the ref.
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#gen_cheater = ref_cheater
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from models.audio.music.transformer_diffusion12 import TransformerDiffusionWithCheaterLatent
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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wrap = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, prenet_channels=1024, input_vec_dim=256,
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prenet_layers=6, num_heads=8, num_layers=16, new_code_expansion=True,
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dropout=0, unconditioned_percentage=0).eval().cuda()
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wrap.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd_cheater_from_scratch/models/56500_generator_ema.pth'))
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cheater_to_mel = wrap.diff
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gen_mel = diffuser.ddim_sample_loop(cheater_to_mel, (1,256,gen_cheater.shape[-1]*16), progress=True,
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model_kwargs={'codes': gen_cheater.permute(0,2,1)})
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torchvision.utils.save_image((gen_mel + 1)/2, f'results/tfdpc_v3/{k}.png')
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||||
|
||||
from utils.music_utils import get_mel2wav_v3_model
|
||||
m2w = get_mel2wav_v3_model().cuda()
|
||||
spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
|
||||
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
|
||||
conditioning_free=True, conditioning_free_k=1)
|
||||
from trainer.injectors.audio_injectors import denormalize_mel
|
||||
gen_mel_denorm = denormalize_mel(gen_mel)
|
||||
output_shape = (1,16,gen_mel_denorm.shape[-1]*256//16)
|
||||
gen_wav = spectral_diffuser.ddim_sample_loop(m2w, output_shape, model_kwargs={'codes': gen_mel_denorm})
|
||||
from trainer.injectors.audio_injectors import pixel_shuffle_1d
|
||||
gen_wav = pixel_shuffle_1d(gen_wav, 16)
|
||||
|
||||
torchaudio.save(f'results/tfdpc_v3/{k}.wav', gen_wav.squeeze(1).cpu(), 22050)
|
||||
torchaudio.save(f'results/tfdpc_v3/{k}_ref.wav', sample.unsqueeze(0).cpu(), 22050)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#test_cheater_model()
|
||||
inference_tfdpc3_with_cheater()
|
||||
|
|
@ -1,352 +0,0 @@
|
|||
import itertools
|
||||
import os
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import torchvision
|
||||
|
||||
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, load_audio
|
||||
|
||||
|
||||
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, use_conv):
|
||||
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.use_conv = use_conv
|
||||
if use_conv:
|
||||
self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
|
||||
else:
|
||||
self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
|
||||
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) if self.use_conv else h)
|
||||
hf = F.gelu(self.ffnorm(hf.permute(0,2,1) if self.use_conv else hf))
|
||||
h = torch.cat([h, hf], dim=-1)
|
||||
return h
|
||||
|
||||
|
||||
class ConcatAttentionBlock(TimestepBlock):
|
||||
def __init__(self, trunk_dim, contraction_dim, time_embed_dim, cond_dim_in, cond_dim_hidden, heads, dropout, cond_projection=True, use_conv=False):
|
||||
super().__init__()
|
||||
self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
|
||||
if cond_projection:
|
||||
self.tdim = trunk_dim+cond_dim_hidden
|
||||
self.cond_project = nn.Linear(cond_dim_in, cond_dim_hidden)
|
||||
else:
|
||||
self.tdim = trunk_dim
|
||||
self.block1 = SubBlock(self.tdim, contraction_dim, heads, dropout, use_conv)
|
||||
self.block2 = SubBlock(self.tdim+contraction_dim*2, contraction_dim, heads, dropout, use_conv)
|
||||
self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
|
||||
self.out.weight.data.zero_()
|
||||
|
||||
def forward(self, x, cond, timestep_emb, rotary_emb):
|
||||
h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
|
||||
if hasattr(self, 'cond_project'):
|
||||
cond = self.cond_project(cond)
|
||||
h = torch.cat([h, cond], dim=-1)
|
||||
h = self.block1(h, rotary_emb)
|
||||
h = self.block2(h, rotary_emb)
|
||||
h = self.out(h[:,:,self.tdim:])
|
||||
return h + x
|
||||
|
||||
|
||||
class ConditioningEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
cond_dim,
|
||||
embedding_dim,
|
||||
time_embed_dim,
|
||||
attn_blocks=6,
|
||||
num_attn_heads=8,
|
||||
dropout=.1,
|
||||
do_checkpointing=False):
|
||||
super().__init__()
|
||||
attn = []
|
||||
self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
|
||||
self.time_proj = nn.Linear(time_embed_dim, embedding_dim)
|
||||
self.attn = Encoder(
|
||||
dim=embedding_dim,
|
||||
depth=attn_blocks,
|
||||
heads=num_attn_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
zero_init_branch_output=True,
|
||||
ff_mult=2,
|
||||
)
|
||||
self.dim = embedding_dim
|
||||
self.do_checkpointing = do_checkpointing
|
||||
|
||||
def forward(self, x, time_emb):
|
||||
h = self.init(x).permute(0,2,1)
|
||||
time_enc = self.time_proj(time_emb)
|
||||
h = torch.cat([time_enc.unsqueeze(1), h], dim=1)
|
||||
h = self.attn(h).permute(0,2,1)
|
||||
return h.mean(dim=2).unsqueeze(1)
|
||||
|
||||
|
||||
class TransformerDiffusionWithPointConditioning(nn.Module):
|
||||
"""
|
||||
A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=256,
|
||||
out_channels=512, # mean and variance
|
||||
model_channels=1024,
|
||||
contraction_dim=256,
|
||||
time_embed_dim=256,
|
||||
num_layers=8,
|
||||
rotary_emb_dim=32,
|
||||
input_cond_dim=1024,
|
||||
num_heads=8,
|
||||
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.time_embed_dim = time_embed_dim
|
||||
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, model_channels, 3, 1, 1)
|
||||
self.conditioning_encoder = ConditioningEncoder(256, model_channels, time_embed_dim)
|
||||
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
|
||||
self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
|
||||
self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels,
|
||||
contraction_dim,
|
||||
time_embed_dim,
|
||||
cond_dim_in=input_cond_dim,
|
||||
cond_dim_hidden=input_cond_dim//2,
|
||||
heads=num_heads,
|
||||
dropout=dropout,
|
||||
cond_projection=(k % 3 == 0),
|
||||
use_conv=(k % 3 != 0),
|
||||
) for k 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):
|
||||
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
|
||||
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
|
||||
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
|
||||
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
|
||||
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
|
||||
groups = {
|
||||
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
|
||||
'blk1_attention_layers': attn1,
|
||||
'blk2_attention_layers': attn2,
|
||||
'attention_layers': attn1 + attn2,
|
||||
'blk1_ff_layers': ff1,
|
||||
'blk2_ff_layers': ff2,
|
||||
'ff_layers': ff1 + ff2,
|
||||
'block_out_layers': blkout_layers,
|
||||
'rotary_embeddings': list(self.rotary_embeddings.parameters()),
|
||||
'out': list(self.out.parameters()),
|
||||
'x_proj': list(self.inp_block.parameters()),
|
||||
'layers': list(self.layers.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
'conditioning_encoder': list(self.conditioning_encoder.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
|
||||
unused_params = []
|
||||
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
|
||||
cond_enc = self.conditioning_encoder(conditioning_input, time_emb)
|
||||
|
||||
if conditioning_free:
|
||||
cond = self.unconditioned_embedding
|
||||
else:
|
||||
cond = cond_enc
|
||||
# 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((cond.shape[0], 1, 1),
|
||||
device=cond.device) < self.unconditioned_percentage
|
||||
cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond)
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
cond = cond.repeat(1,x.shape[-1],1)
|
||||
|
||||
with torch.autocast(x.device.type, enabled=self.enable_fp16):
|
||||
x = self.inp_block(x).permute(0,2,1)
|
||||
|
||||
rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
|
||||
for layer in self.layers:
|
||||
x = checkpoint(layer, x, cond, time_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
|
||||
|
||||
def before_step(self, step):
|
||||
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers])) + \
|
||||
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers]))
|
||||
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
|
||||
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
|
||||
# directly fiddling with the gradients.
|
||||
for p in scaled_grad_parameters:
|
||||
if hasattr(p, 'grad') and p.grad is not None:
|
||||
p.grad *= .2
|
||||
|
||||
|
||||
@register_model
|
||||
def register_tfdpc4(opt_net, opt):
|
||||
return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
|
||||
|
||||
|
||||
def test_cheater_model():
|
||||
clip = torch.randn(2, 256, 400)
|
||||
cl = torch.randn(2, 256, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
|
||||
# For music:
|
||||
model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
|
||||
contraction_dim=512, num_heads=8, num_layers=15, dropout=0,
|
||||
unconditioned_percentage=.4)
|
||||
print_network(model)
|
||||
o = model(clip, ts, cl)
|
||||
pg = model.get_grad_norm_parameter_groups()
|
||||
def prmsz(lp):
|
||||
sz = 0
|
||||
for p in lp:
|
||||
q = 1
|
||||
for s in p.shape:
|
||||
q *= s
|
||||
sz += q
|
||||
return sz
|
||||
for k, v in pg.items():
|
||||
print(f'{k}: {prmsz(v)/1000000}')
|
||||
|
||||
|
||||
def inference_tfdpc4_with_cheater():
|
||||
with torch.no_grad():
|
||||
os.makedirs('results/tfdpc_v4', exist_ok=True)
|
||||
|
||||
#length = 40 * 22050 // 256 // 16
|
||||
samples = {'electronica1': load_audio('Y:\\split\\yt-music-eval\\00001.wav', 22050),
|
||||
'electronica2': load_audio('Y:\\split\\yt-music-eval\\00272.wav', 22050),
|
||||
'e_guitar': load_audio('Y:\\split\\yt-music-eval\\00227.wav', 22050),
|
||||
'creep': load_audio('Y:\\separated\\bt-music-3\\[2007] MTV Unplugged (Live) (Japan Edition)\\05 - Creep [Cover On Radiohead]\\00001\\no_vocals.wav', 22050),
|
||||
'rock1': load_audio('Y:\\separated\\bt-music-3\\2016 - Heal My Soul\\01 - Daze Of The Night\\00000\\no_vocals.wav', 22050),
|
||||
'kiss': load_audio('Y:\\separated\\bt-music-3\\KISS (2001) Box Set CD1\\02 Deuce (Demo Version)\\00000\\no_vocals.wav', 22050),
|
||||
'purp': load_audio('Y:\\separated\\bt-music-3\\Shades of Deep Purple\\11 Help (Alternate Take)\\00001\\no_vocals.wav', 22050),
|
||||
'western_stars': load_audio('Y:\\separated\\bt-music-3\\Western Stars\\01 Hitch Hikin\'\\00000\\no_vocals.wav', 22050),
|
||||
'silk': load_audio('Y:\\separated\\silk\\MonstercatSilkShowcase\\890\\00007\\no_vocals.wav', 22050),
|
||||
'long_electronica': load_audio('C:\\Users\\James\\Music\\longer_sample.wav', 22050),}
|
||||
for k, sample in samples.items():
|
||||
sample = sample.cuda()
|
||||
length = sample.shape[0]//256//16
|
||||
|
||||
model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
|
||||
contraction_dim=512, num_heads=8, num_layers=12, dropout=0,
|
||||
use_fp16=False, unconditioned_percentage=0).eval().cuda()
|
||||
model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v4/models/28000_generator_ema.pth'))
|
||||
|
||||
from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
|
||||
spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000, 'true_normalization': True,
|
||||
'normalize': True, 'in': 'in', 'out': 'out'}, {}).cuda()
|
||||
ref_mel = spec_fn({'in': sample.unsqueeze(0)})['out']
|
||||
from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
|
||||
cheater_encoder = MusicCheaterLatentInjector({'in': 'in', 'out': 'out'}, {}).cuda()
|
||||
ref_cheater = cheater_encoder({'in': ref_mel})['out']
|
||||
|
||||
from models.diffusion.respace import SpacedDiffusion
|
||||
from models.diffusion.respace import space_timesteps
|
||||
from models.diffusion.gaussian_diffusion import get_named_beta_schedule
|
||||
diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [128]), model_mean_type='epsilon',
|
||||
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
|
||||
conditioning_free=True, conditioning_free_k=1)
|
||||
|
||||
# Conventional decoding method:
|
||||
gen_cheater = diffuser.ddim_sample_loop(model, (1,256,length), progress=True, model_kwargs={'conditioning_input': ref_cheater})
|
||||
|
||||
# Guidance decoding method:
|
||||
#mask = torch.ones_like(ref_cheater)
|
||||
#mask[:,:,15:-15] = 0
|
||||
#gen_cheater = diffuser.p_sample_loop_with_guidance(model, ref_cheater, mask, model_kwargs={'true_cheater': ref_cheater})
|
||||
|
||||
# Just decode the ref.
|
||||
#gen_cheater = ref_cheater
|
||||
|
||||
from models.audio.music.transformer_diffusion12 import TransformerDiffusionWithCheaterLatent
|
||||
diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
|
||||
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
|
||||
conditioning_free=True, conditioning_free_k=1)
|
||||
wrap = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512, model_channels=1024,
|
||||
contraction_dim=512, prenet_channels=1024, input_vec_dim=256,
|
||||
prenet_layers=6, num_heads=8, num_layers=16, new_code_expansion=True,
|
||||
dropout=0, unconditioned_percentage=0).eval().cuda()
|
||||
wrap.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd_cheater_from_scratch/models/56500_generator_ema.pth'))
|
||||
cheater_to_mel = wrap.diff
|
||||
gen_mel = diffuser.ddim_sample_loop(cheater_to_mel, (1,256,gen_cheater.shape[-1]*16), progress=True,
|
||||
model_kwargs={'codes': gen_cheater.permute(0,2,1)})
|
||||
torchvision.utils.save_image((gen_mel + 1)/2, f'results/tfdpc_v4/{k}.png')
|
||||
|
||||
from utils.music_utils import get_mel2wav_v3_model
|
||||
m2w = get_mel2wav_v3_model().cuda()
|
||||
spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
|
||||
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
|
||||
conditioning_free=True, conditioning_free_k=1)
|
||||
from trainer.injectors.audio_injectors import denormalize_mel
|
||||
gen_mel_denorm = denormalize_mel(gen_mel)
|
||||
output_shape = (1,16,gen_mel_denorm.shape[-1]*256//16)
|
||||
gen_wav = spectral_diffuser.ddim_sample_loop(m2w, output_shape, model_kwargs={'codes': gen_mel_denorm})
|
||||
from trainer.injectors.audio_injectors import pixel_shuffle_1d
|
||||
gen_wav = pixel_shuffle_1d(gen_wav, 16)
|
||||
|
||||
torchaudio.save(f'results/tfdpc_v4/{k}.wav', gen_wav.squeeze(1).cpu(), 22050)
|
||||
torchaudio.save(f'results/tfdpc_v4/{k}_ref.wav', sample.unsqueeze(0).cpu(), 22050)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#test_cheater_model()
|
||||
inference_tfdpc4_with_cheater()
|
||||
256
codes/models/audio/music/transformer_diffusion13.py
Normal file
256
codes/models/audio/music/transformer_diffusion13.py
Normal file
|
|
@ -0,0 +1,256 @@
|
|||
import itertools
|
||||
from random import randrange
|
||||
from time import time
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from models.arch_util import ResBlock, TimestepEmbedSequential, AttentionBlock
|
||||
from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
|
||||
from models.audio.music.music_quantizer2 import MusicQuantizer2
|
||||
from models.audio.tts.lucidrains_dvae import DiscreteVAE
|
||||
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, \
|
||||
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 SubBlock(nn.Module):
|
||||
def __init__(self, inp_dim, contraction_dim, blk_dim, heads, dropout):
|
||||
super().__init__()
|
||||
self.dropout = nn.Dropout(p=dropout, inplace=True)
|
||||
self.blk_emb_proj = nn.Conv1d(blk_dim, inp_dim, 1)
|
||||
self.attn = AttentionBlock(inp_dim, out_channels=contraction_dim, num_heads=heads)
|
||||
self.attnorm = nn.GroupNorm(8, contraction_dim)
|
||||
self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
|
||||
self.ffnorm = nn.GroupNorm(8, contraction_dim)
|
||||
|
||||
def forward(self, x, blk_emb):
|
||||
blk_enc = self.blk_emb_proj(blk_emb)
|
||||
ah = self.dropout(self.attn(torch.cat([blk_enc, x], dim=-1)))
|
||||
ah = ah[:,:,blk_emb.shape[-1]:] # Strip off the blk_emb and re-align with x.
|
||||
ah = F.gelu(self.attnorm(ah))
|
||||
h = torch.cat([ah, x], dim=1)
|
||||
hf = self.dropout(checkpoint(self.ff, h))
|
||||
hf = F.gelu(self.ffnorm(hf))
|
||||
h = torch.cat([h, hf], dim=1)
|
||||
return h
|
||||
|
||||
|
||||
class ConcatAttentionBlock(TimestepBlock):
|
||||
def __init__(self, trunk_dim, contraction_dim, heads, dropout):
|
||||
super().__init__()
|
||||
self.prenorm = nn.GroupNorm(8, trunk_dim)
|
||||
self.block1 = SubBlock(trunk_dim, contraction_dim, trunk_dim, heads, dropout)
|
||||
self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, trunk_dim, heads, dropout)
|
||||
self.out = nn.Conv1d(contraction_dim*4, trunk_dim, kernel_size=1, bias=False)
|
||||
self.out.weight.data.zero_()
|
||||
|
||||
def forward(self, x, blk_emb):
|
||||
h = self.prenorm(x)
|
||||
h = self.block1(h, blk_emb)
|
||||
h = self.block2(h, blk_emb)
|
||||
h = self.out(h[:,x.shape[1]:])
|
||||
return h + x
|
||||
|
||||
|
||||
class ConditioningEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
spec_dim,
|
||||
embedding_dim,
|
||||
num_resolutions,
|
||||
attn_blocks=6,
|
||||
num_attn_heads=4,
|
||||
do_checkpointing=False):
|
||||
super().__init__()
|
||||
attn = []
|
||||
self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=5, stride=2)
|
||||
self.resolution_embedding = nn.Embedding(num_resolutions, embedding_dim)
|
||||
self.resolution_embedding.weight.data.mul(.1) # Reduces the relative influence of this embedding from the start.
|
||||
for a in range(attn_blocks):
|
||||
attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
|
||||
attn.append(ResBlock(embedding_dim, dims=1, checkpointing_enabled=do_checkpointing))
|
||||
self.attn = nn.Sequential(*attn)
|
||||
self.dim = embedding_dim
|
||||
self.do_checkpointing = do_checkpointing
|
||||
|
||||
def forward(self, x, resolution):
|
||||
h = self.init(x) + self.resolution_embedding(resolution).unsqueeze(-1)
|
||||
h = self.attn(h)
|
||||
return h[:, :, :6]
|
||||
|
||||
|
||||
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,
|
||||
time_embed_dim=256,
|
||||
resolution_steps=8,
|
||||
max_window=384,
|
||||
model_channels=1024,
|
||||
contraction_dim=256,
|
||||
num_layers=8,
|
||||
in_channels=256,
|
||||
input_vec_dim=1024,
|
||||
out_channels=512, # mean and variance
|
||||
num_heads=4,
|
||||
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.time_embed_dim = time_embed_dim
|
||||
self.out_channels = out_channels
|
||||
self.dropout = dropout
|
||||
self.unconditioned_percentage = unconditioned_percentage
|
||||
self.enable_fp16 = use_fp16
|
||||
self.resolution_steps = resolution_steps
|
||||
self.max_window = max_window
|
||||
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, model_channels),
|
||||
)
|
||||
self.resolution_embed = nn.Embedding(resolution_steps, model_channels)
|
||||
self.conditioning_encoder = ConditioningEncoder(in_channels, model_channels, resolution_steps, num_attn_heads=model_channels//64)
|
||||
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
|
||||
|
||||
self.inp_block = conv_nd(1, in_channels+input_vec_dim, model_channels, 3, 1, 1)
|
||||
self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, 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):
|
||||
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
|
||||
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
|
||||
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
|
||||
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
|
||||
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
|
||||
groups = {
|
||||
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
|
||||
'blk1_attention_layers': attn1,
|
||||
'blk2_attention_layers': attn2,
|
||||
'attention_layers': attn1 + attn2,
|
||||
'blk1_ff_layers': ff1,
|
||||
'blk2_ff_layers': ff2,
|
||||
'ff_layers': ff1 + ff2,
|
||||
'block_out_layers': blkout_layers,
|
||||
'out': list(self.out.parameters()),
|
||||
'x_proj': list(self.inp_block.parameters()),
|
||||
'layers': list(self.layers.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
'resolution_embed': list(self.resolution_embed.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def input_to_random_resolution_and_window(self, x, x_prior):
|
||||
assert x.shape == x_prior.shape, f'{x.shape} {x_prior.shape}'
|
||||
resolution = randrange(0, self.resolution_steps)
|
||||
resolution_scale = 2 ** resolution
|
||||
s = F.interpolate(x, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
|
||||
s_prior = F.interpolate(x_prior, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
|
||||
s_diff = s.shape[-1] - self.max_window
|
||||
if s_diff > 1:
|
||||
start = randrange(0, s_diff)
|
||||
s = s[:,:,start:start+self.max_window]
|
||||
s_prior = x_prior[:,:,start:start+self.max_window]
|
||||
s_prior = F.interpolate(s_prior, scale_factor=.25, mode='linear', align_corners=True)
|
||||
s_prior = F.interpolate(s_prior, size=(s.shape[-1],), mode='linear', align_corners=True)
|
||||
return s, s_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device)
|
||||
|
||||
def forward(self, x, timesteps, x_prior=None, resolution=None, conditioning_input=None, conditioning_free=False):
|
||||
unused_params = []
|
||||
|
||||
if resolution is None:
|
||||
h, h_sub, resolution = self.input_to_random_resolution_and_window(x, x_prior)
|
||||
else:
|
||||
h = x
|
||||
h_sub = F.interpolate(x_prior, scale_factor=4, mode='linear', align_corners=True)
|
||||
assert h.shape == h_sub.shape, f'{h.shape} {h_sub.shape}'
|
||||
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
|
||||
else:
|
||||
MIN_COND_LEN = 200
|
||||
MAX_COND_LEN = 1200
|
||||
if self.training and conditioning_input.shape[-1] > MAX_COND_LEN:
|
||||
clen = randrange(MIN_COND_LEN, MAX_COND_LEN)
|
||||
gap = conditioning_input.shape[-1] - clen
|
||||
cstart = randrange(0, gap)
|
||||
conditioning_input = conditioning_input[:,:,cstart:cstart+clen]
|
||||
|
||||
code_emb = self.conditioning_encoder(conditioning_input, resolution)
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
|
||||
with torch.autocast(x.device.type, enabled=self.enable_fp16):
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
|
||||
res_emb = self.resolution_embed(resolution)
|
||||
blk_emb = torch.cat([time_emb.unsqueeze(-1), res_emb.unsqueeze(-1), code_emb], dim=-1)
|
||||
h = torch.cat([h, h_sub], dim=1)
|
||||
|
||||
h = self.inp_block(h)
|
||||
for layer in self.layers:
|
||||
h = checkpoint(layer, h, blk_emb)
|
||||
|
||||
h = h.float()
|
||||
out = self.out(h)
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
@register_model
|
||||
def register_transformer_diffusion13(opt_net, opt):
|
||||
return TransformerDiffusion(**opt_net['kwargs'])
|
||||
|
||||
|
||||
def test_tfd():
|
||||
clip = torch.randn(2,256,2583)
|
||||
cond = torch.randn(2,256,2583)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512,
|
||||
num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1)
|
||||
for k in range(100):
|
||||
model(clip, ts, clip, conditioning_input=cond)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_tfd()
|
||||
|
|
@ -352,23 +352,30 @@ class RMSNorm(nn.Module):
|
|||
|
||||
|
||||
class RMSScaleShiftNorm(nn.Module):
|
||||
def __init__(self, dim, embed_dim=None, eps=1e-8, bias=True):
|
||||
def __init__(self, dim, embed_dim=None, eps=1e-8, bias=True, conv_ch_order=False):
|
||||
super().__init__()
|
||||
embed_dim = default(embed_dim, dim)
|
||||
self.scale = dim ** -0.5
|
||||
self.eps = eps
|
||||
self.g = nn.Parameter(torch.ones(dim))
|
||||
self.scale_shift_process = nn.Linear(embed_dim, dim * 2, bias=bias)
|
||||
if conv_ch_order:
|
||||
self.scale_shift_process = nn.Conv1d(embed_dim, dim*2, kernel_size=1, bias=bias)
|
||||
self.cdim = 1
|
||||
self.pdim = -1
|
||||
else:
|
||||
self.scale_shift_process = nn.Linear(embed_dim, dim * 2, bias=bias)
|
||||
self.cdim = -1
|
||||
self.pdim = 1
|
||||
|
||||
def forward(self, x, norm_scale_shift_inp):
|
||||
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
|
||||
norm = torch.norm(x, dim=self.cdim, keepdim=True) * self.scale
|
||||
norm = x / norm.clamp(min=self.eps) * self.g
|
||||
|
||||
ss_emb = self.scale_shift_process(norm_scale_shift_inp)
|
||||
scale, shift = torch.chunk(ss_emb, 2, dim=-1)
|
||||
scale, shift = torch.chunk(ss_emb, 2, dim=self.cdim)
|
||||
if len(scale.shape) == 2 and len(x.shape) == 3:
|
||||
scale = scale.unsqueeze(1)
|
||||
shift = shift.unsqueeze(1)
|
||||
scale = scale.unsqueeze(self.pdim)
|
||||
shift = shift.unsqueeze(self.pdim)
|
||||
h = norm * (1 + scale) + shift
|
||||
return h
|
||||
|
||||
|
|
|
|||
|
|
@ -436,20 +436,19 @@ class MusicDiffusionFid(evaluator.Evaluator):
|
|||
|
||||
|
||||
if __name__ == '__main__':
|
||||
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen.yml', 'generator',
|
||||
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_and_cheater.yml', 'generator',
|
||||
also_load_savepoint=False,
|
||||
load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5_cosine_40_lyr\\models\\40000_generator_ema.pth'
|
||||
load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd_and_cheater\\models\\93500_generator_ema.pth'
|
||||
).cuda()
|
||||
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
|
||||
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
|
||||
'diffusion_steps': 256, # basis: 192
|
||||
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': False,
|
||||
'diffusion_schedule': 'cosine', 'diffusion_type': 'cheater_gen',
|
||||
# Slope 1: 1.03x, 2: 1.06, 4: 1.135, 8: 1.27, 16: 1.54
|
||||
#'causal': True, 'causal_slope': 4, # DONT FORGET TO INCREMENT THE STEP!
|
||||
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': True,
|
||||
'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes_quant',
|
||||
#'causal': True, 'causal_slope': 4,
|
||||
#'partial_low': 128, 'partial_high': 192
|
||||
}
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 5, 'device': 'cuda', 'opt': {}}
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 7, 'device': 'cuda', 'opt': {}}
|
||||
eval = MusicDiffusionFid(diffusion, opt_eval, env)
|
||||
fds = []
|
||||
for i in range(2):
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user