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70ceb8b | 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 | import torch.nn as nn
import torch.nn.functional as F
import torch
import math
from einops import rearrange
from ..wanvideo.modules.model import WanRMSNorm, attention
from ..multitalk.multitalk import RotaryPositionalEmbedding1D, normalize_and_scale
class FeedForwardSwiGLU(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int = 256,
):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.dim = dim
self.hidden_dim = hidden_dim
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, t_embed_dim, frequency_embedding_size=256):
super().__init__()
self.t_embed_dim = t_embed_dim
self.frequency_embedding_size = frequency_embedding_size
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, t_embed_dim, bias=True),
nn.SiLU(),
nn.Linear(t_embed_dim, t_embed_dim, bias=True),
)
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half)
freqs = freqs.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, dtype):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
if t_freq.dtype != dtype:
t_freq = t_freq.to(dtype)
t_emb = self.mlp(t_freq)
return t_emb
class SingleStreamAttention(nn.Module):
def __init__(
self,
dim: int,
encoder_hidden_states_dim: int,
num_heads: int,
qkv_bias: bool,
qk_norm: bool,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
eps: float = 1e-6,
class_range: int = 24,
class_interval: int = 4,
attention_mode: str = "sdpa",
) -> None:
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.dim = dim
self.encoder_hidden_states_dim = encoder_hidden_states_dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.q_linear = nn.Linear(dim, dim, bias=qkv_bias)
self.q_norm = WanRMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.kv_linear = nn.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias)
self.k_norm = WanRMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
self.attention_mode = attention_mode
# multitalk related params
self.class_interval = class_interval
self.class_range = class_range
self.rope_h1 = (0, self.class_interval)
self.rope_h2 = (self.class_range - self.class_interval, self.class_range)
self.rope_bak = int(self.class_range // 2)
self.rope_1d = RotaryPositionalEmbedding1D(self.head_dim)
def _process_cross_attn(self, x, cond, frames_num=None, x_ref_attn_map=None):
N_t = frames_num
out_dtype = x.dtype
x = rearrange(x, "B (N_t S) C -> (B N_t) S C", N_t=N_t)
# get q for hidden_state
B, N, C = x.shape
q = self.q_linear(x)
q_shape = (B, N, self.num_heads, self.head_dim)
q = q.view(q_shape).permute((0, 2, 1, 3)) # [B, H, N, D]
q = self.q_norm(q.to(self.q_norm.weight.dtype)).to(q.dtype)
# multitalk with rope1d pe
if x_ref_attn_map is not None:
max_values = x_ref_attn_map.max(1).values[:, None, None]
min_values = x_ref_attn_map.min(1).values[:, None, None]
max_min_values = torch.cat([max_values, min_values], dim=2)
human1_max_value, human1_min_value = max_min_values[0, :, 0].max(), max_min_values[0, :, 1].min()
human2_max_value, human2_min_value = max_min_values[1, :, 0].max(), max_min_values[1, :, 1].min()
human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), (self.rope_h1[0], self.rope_h1[1]))
human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), (self.rope_h2[0], self.rope_h2[1]))
back = torch.full((x_ref_attn_map.size(1),), self.rope_bak, dtype=human1.dtype).to(human1.device)
max_indices = x_ref_attn_map.argmax(dim=0)
normalized_map = torch.stack([human1, human2, back], dim=1)
normalized_pos = normalized_map[range(x_ref_attn_map.size(1)), max_indices]
q = rearrange(q, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t)
q = self.rope_1d(q, normalized_pos)
q = rearrange(q, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t)
# get kv from encoder_hidden_states
_, N_a, _ = cond.shape
encoder_kv = self.kv_linear(cond)
encoder_kv_shape = (B, N_a, 2, self.num_heads, self.head_dim)
encoder_kv = encoder_kv.view(encoder_kv_shape).permute((2, 0, 3, 1, 4))
encoder_k, encoder_v = encoder_kv.unbind(0)
encoder_k = self.k_norm(encoder_k.to(self.k_norm.weight.dtype)).to(encoder_k.dtype)
# multitalk with rope1d pe
if x_ref_attn_map is not None:
per_frame = torch.zeros(N_a, dtype=encoder_k.dtype).to(encoder_k.device)
per_frame[:per_frame.size(0)//2] = (self.rope_h1[0] + self.rope_h1[1]) / 2
per_frame[per_frame.size(0)//2:] = (self.rope_h2[0] + self.rope_h2[1]) / 2
encoder_pos = torch.concat([per_frame]*N_t, dim=0)
encoder_k = rearrange(encoder_k, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t)
encoder_k = self.rope_1d(encoder_k, encoder_pos)
encoder_k = rearrange(encoder_k, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t)
# Input tensors must be in format ``[B, M, H, K]``, where B is the batch size, M \
# the sequence length, H the number of heads, and K the embeding size per head
q = rearrange(q, "B H M K -> B M H K")
encoder_k = rearrange(encoder_k, "B H M K -> B M H K")
encoder_v = rearrange(encoder_v, "B H M K -> B M H K")
x = attention(q, encoder_k, encoder_v, attention_mode=self.attention_mode)
x = rearrange(x, "B M H K -> B H M K")
# linear transform
x_output_shape = (B, N, C)
x = x.transpose(1, 2)
x = x.reshape(x_output_shape)
x = self.proj(x)
x = self.proj_drop(x)
# reshape x to origin shape
x = rearrange(x, "(B N_t) S C -> B (N_t S) C", N_t=N_t)
return x.type(out_dtype)
def forward(self, x, cond, num_latent_frames=None, num_cond_latents=None, x_ref_attn_map=None, human_num=None):
B, N, C = x.shape
if (num_cond_latents is None or num_cond_latents == 0):
# text to video
output = self._process_cross_attn(x, cond, num_latent_frames, x_ref_attn_map)
return None, output
elif num_cond_latents is not None and num_cond_latents > 0:
# image to video or video continuation
num_cond_latents_thw = num_cond_latents * (N // num_latent_frames)
x_noise = x[:, num_cond_latents_thw:]
cond = rearrange(cond, "(B N_t) M C -> B N_t M C", B=B)
cond = cond[:, num_cond_latents:]
cond = rearrange(cond, "B N_t M C -> (B N_t) M C")
frames_num = num_latent_frames - num_cond_latents
if human_num is not None and human_num == 2:
# multitalk mode
output_noise = self._process_cross_attn(x_noise, cond, frames_num, x_ref_attn_map)
else:
# singletalk mode
output_noise = self._process_cross_attn(x_noise, cond, frames_num)
output_cond = torch.zeros((B, num_cond_latents_thw, C), dtype=output_noise.dtype, device=output_noise.device)
return output_cond, output_noise
else:
raise NotImplementedError
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