File size: 15,086 Bytes
26ba8eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 | import torch, os, math
from einops import rearrange, repeat
from diffsynth.core import attention_forward, gradient_checkpoint_forward
from transformers.models.qwen3.modeling_qwen3 import Qwen3RotaryEmbedding
class TimestepEmbedding(torch.nn.Module):
def __init__(self, in_channels, time_embed_dim, scale=1):
super().__init__()
self.linear_1 = torch.nn.Linear(in_channels, time_embed_dim, bias=True)
self.act1 = torch.nn.SiLU()
self.linear_2 = torch.nn.Linear(time_embed_dim, time_embed_dim, bias=True)
self.in_channels = in_channels
self.act2 = torch.nn.SiLU()
self.time_proj = torch.nn.Linear(time_embed_dim, time_embed_dim * 6)
self.scale = scale
def timestep_embedding(self, t, dim, max_period=10000):
t = t * self.scale
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.in_channels)
temb = self.linear_1(t_freq.to(t.dtype))
temb = self.act1(temb)
temb = self.linear_2(temb)
timestep_proj = self.time_proj(self.act2(temb)).unflatten(1, (6, -1))
return temb, timestep_proj
class DiffSynthMusicTimestepEmbedding(torch.nn.Module):
def __init__(self, in_channels, time_embed_dim):
super().__init__()
self.time_embed = TimestepEmbedding(in_channels, time_embed_dim)
self.time_embed_r = TimestepEmbedding(in_channels, time_embed_dim)
def forward(self, timestep):
timestep_r = timestep
temb_t, timestep_proj_t = self.time_embed(timestep)
temb_r, timestep_proj_r = self.time_embed_r(timestep - timestep_r)
temb = temb_t + temb_r
timestep_proj = timestep_proj_t + timestep_proj_r
return temb, timestep_proj
class DiffSynthMusicAttention(torch.nn.Module):
def __init__(self, dim, num_heads_q, num_heads_kv, head_dim):
super().__init__()
self.head_dim = head_dim
self.q_proj = torch.nn.Linear(dim, num_heads_q * self.head_dim, bias=False)
self.k_proj = torch.nn.Linear(dim, num_heads_kv * self.head_dim, bias=False)
self.v_proj = torch.nn.Linear(dim, num_heads_kv * self.head_dim, bias=False)
self.o_proj = torch.nn.Linear(num_heads_q * self.head_dim, dim, bias=False)
self.q_norm = torch.nn.RMSNorm(self.head_dim, eps=1e-6)
self.k_norm = torch.nn.RMSNorm(self.head_dim, eps=1e-6)
def rotate_half(self, x):
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(self, q, k, cos, sin, unsqueeze_dim=2):
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (self.rotate_half(q) * sin)
k_embed = (k * cos) + (self.rotate_half(k) * sin)
return q_embed, k_embed
def forward(self, x, y=None, window_size=None, pos_emb=None, return_kv=False, kv_cache=None) -> torch.Tensor:
q = self.q_proj(x)
q = rearrange(q, "b s (n d) -> b s n d", d=self.head_dim)
q = self.q_norm(q)
if y is None: y = x
k, v = self.k_proj(y), self.v_proj(y)
k, v = rearrange(k, "b s (n d) -> b s n d", d=self.head_dim), rearrange(v, "b s (n d) -> b s n d", d=self.head_dim)
k = self.k_norm(k)
if pos_emb is not None: q, k = self.apply_rotary_pos_emb(q, k, *pos_emb)
if kv_cache is not None:
k = torch.concat([k, kv_cache[0]], dim=1)
v = torch.concat([v, kv_cache[1]], dim=1)
attn_output = attention_forward(
q, k, v,
q_pattern="b s n d", k_pattern="b s n d", v_pattern="b s n d", out_pattern="b s (n d)",
window_size=window_size,
)
attn_output = self.o_proj(attn_output)
if return_kv:
return attn_output, (k, v)
else:
return attn_output
class MLP(torch.nn.Module):
def __init__(self, dim, dim_hidden):
super().__init__()
self.gate_proj = torch.nn.Linear(dim, dim_hidden, bias=False)
self.up_proj = torch.nn.Linear(dim, dim_hidden, bias=False)
self.down_proj = torch.nn.Linear(dim_hidden, dim, bias=False)
self.act_fn = torch.nn.SiLU()
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class DiffSynthMusicDiTLayer(torch.nn.Module):
def __init__(self, dim=2560, num_heads_q=32, num_heads_kv=8, head_dim=128, dim_mlp=9728, window_size=None):
super().__init__()
self.self_attn_norm = torch.nn.RMSNorm(dim, eps=1e-6)
self.self_attn = DiffSynthMusicAttention(dim=dim, num_heads_q=num_heads_q, num_heads_kv=num_heads_kv, head_dim=head_dim)
self.cross_attn_norm = torch.nn.RMSNorm(dim, eps=1e-6)
self.cross_attn = DiffSynthMusicAttention(dim=dim, num_heads_q=num_heads_q, num_heads_kv=num_heads_kv, head_dim=head_dim)
self.mlp_norm = torch.nn.RMSNorm(dim, eps=1e-6)
self.mlp = MLP(dim, dim_mlp)
self.scale_shift_table = torch.nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
self.window_size = window_size
def forward(self, x, y, pos_emb, temb, return_kv=False, kv_cache=None) -> torch.Tensor:
shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (self.scale_shift_table.to(dtype=x.dtype, device=x.device) + temb).chunk(6, dim=1)
x_hidden = self.self_attn_norm(x) * (1 + scale_msa) + shift_msa
x_hidden = self.self_attn(x=x_hidden, pos_emb=pos_emb, window_size=self.window_size, kv_cache=kv_cache, return_kv=return_kv)
if return_kv: x_hidden, kv = x_hidden
x = x + x_hidden * gate_msa
x_hidden = self.cross_attn_norm(x)
x_hidden = self.cross_attn(x=x_hidden, y=y)
x = x + x_hidden
x_hidden = self.mlp_norm(x) * (1 + c_scale_msa) + c_shift_msa
x_hidden = self.mlp(x_hidden)
x = x + x_hidden * c_gate_msa
if return_kv:
return x, kv
else:
return x
class DiffSynthMusicChannelProj(torch.nn.Module):
def __init__(self, in_channels, out_channels, patch_size, bias=False, transposed=False):
super().__init__()
if transposed:
self.conv = torch.nn.ConvTranspose1d(in_channels=in_channels, out_channels=out_channels, kernel_size=patch_size, stride=patch_size, bias=bias)
else:
self.conv = torch.nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=patch_size, stride=patch_size, bias=bias)
def forward(self, x):
x = x.transpose(1, 2)
x = self.conv(x)
x = x.transpose(1, 2)
return x
class DiffSynthMusicDiTModel(torch.nn.Module):
def __init__(self, dim=2560, dim_mlp=9728, dim_condition=2048, num_blocks=32, num_heads_q=32, num_heads_kv=8, head_dim=128, window_size=128, patch_size=2):
super().__init__()
self.rotary_emb = Qwen3RotaryEmbedding(type('RopeConfig', (), {'head_dim': head_dim, 'max_position_embeddings': 32768, 'rope_theta': 1000000, 'rope_parameters': {'rope_type': 'default', 'rope_theta': 1000000}})())
self.x_emb = DiffSynthMusicChannelProj(in_channels=64*3, out_channels=dim, patch_size=patch_size, bias=True)
self.timestep_emb = DiffSynthMusicTimestepEmbedding(in_channels=256, time_embed_dim=dim)
self.condition_emb = torch.nn.Linear(dim_condition, dim, bias=True)
self.layers = torch.nn.ModuleList([
DiffSynthMusicDiTLayer(dim=dim, num_heads_q=num_heads_q, num_heads_kv=num_heads_kv, dim_mlp=dim_mlp, window_size=window_size if block_id % 2 == 0 else None)
for block_id in range(num_blocks)
])
self.norm_out = torch.nn.RMSNorm(dim, eps=1e-6)
self.proj_out = DiffSynthMusicChannelProj(in_channels=dim, out_channels=64, patch_size=patch_size, bias=True, transposed=True)
self.scale_shift_table = torch.nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
self.placeholder_audio = torch.nn.Parameter(torch.zeros((1, 15000, 64)))
def forward_kv_cache(
self,
x: torch.Tensor,
y: torch.Tensor,
use_gradient_checkpointing: bool = False,
use_gradient_checkpointing_offload: bool = False,
**kwargs,
):
x = torch.concat([self.placeholder_audio[:, :x.shape[1]], torch.ones_like(x), x], dim=-1)
x = self.x_emb(x)
y = self.condition_emb(y)
timestep = torch.zeros((1,), dtype=x.dtype, device=x.device)
final_timestep_emb, timestep_emb = self.timestep_emb(timestep)
pos_emb = self.rotary_emb(x, torch.arange(0, x.shape[1], device=x.device).unsqueeze(0))
kv_cache = {}
for block_id, block in enumerate(self.layers):
return_kv = block.window_size is None
x = gradient_checkpoint_forward(
block,
use_gradient_checkpointing,
use_gradient_checkpointing_offload,
x, y, pos_emb, timestep_emb,
return_kv=return_kv,
kv_cache=None,
)
if return_kv:
x, kv = x
kv_cache[f"{block_id}"] = kv
return kv_cache
def forward(
self,
x: torch.Tensor,
y: torch.Tensor,
timestep: torch.Tensor,
residual: list[torch.Tensor] = None,
residual_x: torch.Tensor = None,
kv_cache = None,
use_gradient_checkpointing: bool = False,
use_gradient_checkpointing_offload: bool = False,
**kwargs,
):
x = torch.concat([self.placeholder_audio[:, :x.shape[1]], torch.ones_like(x), x], dim=-1)
x = self.x_emb(x)
if residual_x is not None: x = x + residual_x
y = self.condition_emb(y)
final_timestep_emb, timestep_emb = self.timestep_emb(timestep)
pos_emb = self.rotary_emb(x, torch.arange(0, x.shape[1], device=x.device).unsqueeze(0))
for block_id, block in enumerate(self.layers):
x = gradient_checkpoint_forward(
block,
use_gradient_checkpointing,
use_gradient_checkpointing_offload,
x, y, pos_emb, timestep_emb,
return_kv=False,
kv_cache=None if kv_cache is None else kv_cache.get(f"{block_id}"),
)
if residual is not None: x = x + residual[block_id]
shift, scale = (self.scale_shift_table.to(dtype=x.dtype, device=x.device) + final_timestep_emb.unsqueeze(1)).chunk(2, dim=1)
x = self.proj_out(self.norm_out(x) * (1 + scale) + shift)
return x
class MusicKVCacheModel(DiffSynthMusicDiTModel):
def __init__(self):
super().__init__()
self.y = torch.nn.Parameter(torch.zeros((1, 70, 2560)))
def cut_audio(self, audio, length=1920*256):
x = audio.abs().mean(axis=0)
value = []
for i in range(0, len(x) - length + 1, 1*1920):
value.append(x[i: i + length].mean().item())
idx = value.index(max(value))
audio = audio[:, idx * 1 * 1920: idx * 1 * 1920 + length]
return audio
@torch.no_grad()
def process_inputs(self, pipe, audio=None, **kwargs):
if audio.shape[-1] == 64:
x = audio
else:
pipe.load_models_to_device(["vae"])
audio = self.cut_audio(audio)
audio = audio.unsqueeze(0)
audio = pipe.vae.encode(audio.to(dtype=pipe.torch_dtype, device=pipe.device)).transpose(1, 2)
x = audio
x = x.to(dtype=pipe.torch_dtype, device=pipe.device)
return {"x": x}
def forward(
self,
x: torch.Tensor,
use_gradient_checkpointing: bool = False,
use_gradient_checkpointing_offload: bool = False,
**kwargs,
):
x = torch.concat([self.placeholder_audio[:, :x.shape[1]], torch.ones_like(x), x], dim=-1)
x = self.x_emb(x)
y = self.y
timestep = torch.zeros((1,), dtype=x.dtype, device=x.device)
final_timestep_emb, timestep_emb = self.timestep_emb(timestep)
pos_emb = self.rotary_emb(x, torch.arange(0, x.shape[1], device=x.device).unsqueeze(0))
kv_cache = {}
for block_id, block in enumerate(self.layers):
return_kv = block.window_size is None
x = gradient_checkpoint_forward(
block,
use_gradient_checkpointing,
use_gradient_checkpointing_offload,
x, y, pos_emb, timestep_emb,
return_kv=return_kv,
kv_cache=None,
)
if return_kv:
x, kv = x
kv_cache[f"{block_id}"] = kv
return {"kv_cache": kv_cache}
class DataAnnotator:
def __init__(self):
self.target_sample_rate = 48000
self.max_audio_duration = 480
import torchaudio
self.audio_loader = torchaudio.load
self.audio_resampler = torchaudio.functional.resample
def load_audio(self, path):
waveform, sample_rate = self.audio_loader(path)
if len(waveform.shape) == 2 and waveform.shape[0] == 1:
waveform = repeat(waveform, "c l -> (n c) l", n=2)
if self.target_sample_rate is not None and sample_rate != self.target_sample_rate:
waveform = self.audio_resampler(waveform, sample_rate, self.target_sample_rate)
sample_rate = self.target_sample_rate
if self.max_audio_duration is not None and waveform.shape[1] > sample_rate * self.max_audio_duration:
waveform = waveform[:, :int(sample_rate * self.max_audio_duration)]
return waveform
def load_latents(self, path):
latents = torch.load(path, weights_only=True, map_location="cpu")
return latents
def __call__(self, audio, **kwargs):
if audio.endswith(".pth"):
return {"audio": self.load_latents(audio)}
else:
return {"audio": self.load_audio(audio)}
def initialize_model_weights():
from diffsynth import load_state_dict
from safetensors.torch import save_file
sd_ = MusicKVCacheModel().state_dict()
sd = load_state_dict("models/DiffSynth-Music/dit_base_v4.safetensors")
for i in sd_:
if i in sd:
sd_[i] = sd[i]
elif i == "y":
sd_[i] = torch.load(os.path.join(os.path.dirname(__file__), "y.pth"))
else:
print(i, sd_[i].shape)
save_file(sd_, os.path.join(os.path.dirname(__file__), "model.safetensors"))
TEMPLATE_MODEL = MusicKVCacheModel
TEMPLATE_MODEL_PATH = "model.safetensors"
TEMPLATE_DATA_PROCESSOR = DataAnnotator
|