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tfd13 for multi-resolution superscaling

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This commit is contained in:
James Betker 2022-07-18 16:36:22 -06:00
parent 1b648abd7c
commit 1b4d9567f3
6 changed files with 282 additions and 716 deletions
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10
codes/models/arch_util.py
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@ -452,12 +452,14 @@ class AttentionBlock(nn.Module):
channels, channels,
num_heads=1, num_heads=1,
num_head_channels=-1, num_head_channels=-1,
out_channels=None,
use_new_attention_order=False, use_new_attention_order=False,
do_checkpoint=True, do_checkpoint=True,
do_activation=False, do_activation=False,
): ):
super().__init__() super().__init__()
self.channels = channels self.channels = channels
out_channels = channels if out_channels is None else out_channels
self.do_checkpoint = do_checkpoint self.do_checkpoint = do_checkpoint
self.do_activation = do_activation self.do_activation = do_activation
if num_head_channels == -1: if num_head_channels == -1:
@ -468,7 +470,7 @@ class AttentionBlock(nn.Module):
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels self.num_heads = channels // num_head_channels
self.norm = normalization(channels) self.norm = normalization(channels)
self.qkv = conv_nd(1, channels, channels * 3, 1) self.qkv = conv_nd(1, channels, out_channels * 3, 1)
if use_new_attention_order: if use_new_attention_order:
# split qkv before split heads # split qkv before split heads
self.attention = QKVAttention(self.num_heads) self.attention = QKVAttention(self.num_heads)
@ -476,7 +478,8 @@ class AttentionBlock(nn.Module):
# split heads before split qkv # split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads) self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) self.x_proj = nn.Identity() if out_channels == channels else conv_nd(1, channels, out_channels, 1)
self.proj_out = zero_module(conv_nd(1, out_channels, out_channels, 1))
def forward(self, x, mask=None): def forward(self, x, mask=None):
if self.do_checkpoint: if self.do_checkpoint:
@ -496,7 +499,8 @@ class AttentionBlock(nn.Module):
qkv = self.qkv(x) qkv = self.qkv(x)
h = self.attention(qkv, mask) h = self.attention(qkv, mask)
h = self.proj_out(h) h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial) xp = self.x_proj(x)
return (xp + h).reshape(b, xp.shape[1], *spatial)
class QKVAttentionLegacy(nn.Module): class QKVAttentionLegacy(nn.Module):

348
codes/models/audio/music/tfdpc_v3.py
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@ -1,348 +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):
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, 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)
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, time_embed_dim, cond_dim_in, cond_dim_hidden, heads, dropout):
super().__init__()
self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
self.cond_project = nn.Linear(cond_dim_in, cond_dim_hidden)
self.block1 = SubBlock(trunk_dim+cond_dim_hidden, contraction_dim, heads, dropout)
self.block2 = SubBlock(trunk_dim+cond_dim_hidden+contraction_dim*2, contraction_dim, heads, dropout)
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)
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[:,:,x.shape[-1]+cond.shape[-1]:])
return h + x
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.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) 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,
'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()),
}
return groups
def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
unused_params = []
if conditioning_free:
cond = self.unconditioned_embedding
else:
cond = conditioning_input
# 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):
blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
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, 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 ConditioningEncoder(nn.Module):
def __init__(self,
cond_dim,
embedding_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.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):
h = self.init(x).permute(0,2,1)
h = self.attn(h).permute(0,2,1)
return h.mean(dim=2).unsqueeze(1)
class TransformerDiffusionWithConditioningEncoder(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.internal_step = 0
self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
def forward(self, x, timesteps, conditioning_input=None, disable_diversity=False, conditioning_free=False):
cond = self.conditioning_encoder(conditioning_input)
diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
return diff
def get_debug_values(self, step, __):
self.internal_step = step
return {}
def get_grad_norm_parameter_groups(self):
groups = self.diff.get_grad_norm_parameter_groups()
groups['conditioning_encoder'] = list(self.conditioning_encoder.parameters())
return groups
def before_step(self, step):
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.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_tfdpc3(opt_net, opt):
return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
@register_model
def register_tfdpc3_with_conditioning_encoder(opt_net, opt):
return TransformerDiffusionWithConditioningEncoder(**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 = TransformerDiffusionWithConditioningEncoder(in_channels=256, out_channels=512, model_channels=1024,
contraction_dim=512, num_heads=8, num_layers=24, dropout=0,
unconditioned_percentage=.4)
print_network(model)
o = model(clip, ts, cl)
pg = model.get_grad_norm_parameter_groups()
def inference_tfdpc3_with_cheater():
with torch.no_grad():
os.makedirs('results/tfdpc_v3', 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 = TransformerDiffusionWithConditioningEncoder(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_v3/models/61000_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={'true_cheater': 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_v3/{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_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()

352
codes/models/audio/music/tfdpc_v4.py
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@ -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
View 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()

17
codes/models/lucidrains/x_transformers.py
View File

@ -352,23 +352,30 @@ class RMSNorm(nn.Module):
class RMSScaleShiftNorm(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__() super().__init__()
embed_dim = default(embed_dim, dim) embed_dim = default(embed_dim, dim)
self.scale = dim ** -0.5 self.scale = dim ** -0.5
self.eps = eps self.eps = eps
self.g = nn.Parameter(torch.ones(dim)) self.g = nn.Parameter(torch.ones(dim))
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.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): 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 norm = x / norm.clamp(min=self.eps) * self.g
ss_emb = self.scale_shift_process(norm_scale_shift_inp) 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: if len(scale.shape) == 2 and len(x.shape) == 3:
scale = scale.unsqueeze(1) scale = scale.unsqueeze(self.pdim)
shift = shift.unsqueeze(1) shift = shift.unsqueeze(self.pdim)
h = norm * (1 + scale) + shift h = norm * (1 + scale) + shift
return h return h

13
codes/trainer/eval/music_diffusion_fid.py
View File

@ -436,20 +436,19 @@ class MusicDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__': 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, 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() ).cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :) 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. #'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'diffusion_steps': 256, # basis: 192 'diffusion_steps': 256, # basis: 192
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': False, 'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': True,
'diffusion_schedule': 'cosine', 'diffusion_type': 'cheater_gen', 'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes_quant',
# Slope 1: 1.03x, 2: 1.06, 4: 1.135, 8: 1.27, 16: 1.54 #'causal': True, 'causal_slope': 4,
#'causal': True, 'causal_slope': 4, # DONT FORGET TO INCREMENT THE STEP!
#'partial_low': 128, 'partial_high': 192 #'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) eval = MusicDiffusionFid(diffusion, opt_eval, env)
fds = [] fds = []
for i in range(2): for i in range(2):