# Reference: # 1. DiT https://github.com/facebookresearch/DiT # 2. TIMM https://github.com/rwightman/pytorch-image-models import torch import torch.nn as nn import numpy as np import math import time from .blocks import FinalLayer from .blocks import MMDoubleStreamBlock as DiTBlock2 from .blocks import MMSingleStreamBlock as DiTBlock from .blocks import CrossDiTBlock as DiTBlock3 from .blocks import MMfourStreamBlock as DiTBlock4 # from .positional_embedding import get_1d_sincos_pos_embed from .posemb_layers import apply_rotary_emb, get_1d_rotary_pos_embed from .embedders import TimestepEmbedder, MotionEmbedder, AudioEmbedder, ConditionAudioEmbedder, SimpleAudioEmbedder, LabelEmbedder from einops import rearrange, repeat audio_embedder_map = { "normal": AudioEmbedder, "cond": ConditionAudioEmbedder, "simple": SimpleAudioEmbedder } import matplotlib.pyplot as plt from sklearn.manifold import TSNE class TalkingHeadDiT(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_dim=265, output_dim =265, seq_len=80, audio_unit_len=5, audio_blocks=12, audio_dim=768, audio_tokens = 1, hidden_size=1152, depth=28, num_heads=16, mlp_ratio=4.0, audio_embedder_type="normal", audio_cond_dim = 63, norm_type="rms_norm", qk_norm="rms_norm", **kwargs ): super().__init__() self.num_emo_class = 8 self.emo_drop_prob = 0.1 self.num_heads = num_heads self.out_channels = output_dim self.motion_embedder = MotionEmbedder(input_dim, hidden_size) self.identity_embedder=MotionEmbedder(audio_cond_dim, hidden_size) self.time_embedder = TimestepEmbedder(hidden_size) self.audio_embedder = audio_embedder_map['normal']( seq_len = audio_unit_len, blocks = audio_blocks, channels = audio_dim, intermediate_dim = hidden_size, output_dim = hidden_size, context_tokens = audio_tokens, input_len = seq_len, condition_dim = audio_cond_dim, norm_type = norm_type, # qk_norm = qk_norm, # n_heads =num_heads ) self.dim=hidden_size//num_heads self.emo_embedder = LabelEmbedder(num_classes=self.num_emo_class, hidden_size=hidden_size, dropout_prob=self.emo_drop_prob) # Will use fixed sin-cos embedding: # self.pos_embed = nn.Parameter(torch.zeros(1, seq_len, hidden_size), requires_grad=False) self.blocks4 = nn.ModuleList([ DiTBlock4( hidden_size, num_heads, mlp_ratio=mlp_ratio, norm_type=norm_type, qk_norm=qk_norm ) for _ in range(3) ]) self.blocks2 = nn.ModuleList([ DiTBlock2( hidden_size, num_heads, mlp_ratio=mlp_ratio, norm_type=norm_type, qk_norm=qk_norm ) for _ in range(6) ]) self.blocks=nn.ModuleList([ DiTBlock( hidden_size, num_heads, mlp_ratio=mlp_ratio, norm_type=norm_type, qk_norm=qk_norm ) for _ in range(12) ]) self.final_layer = FinalLayer(hidden_size, self.out_channels, norm_type=norm_type) self.initialize_weights() self.bank=[] def initialize_weights(self): # Initialize (and freeze) pos_embed by sin-cos embedding: # pos_embed = get_1d_sincos_pos_embed(self.pos_embed.shape[-1], self.pos_embed.shape[-2]) # self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) # Initialize input layers nn.Linear self.motion_embedder.initialize_weights() self.identity_embedder.initialize_weights() # Initialize audio embedding self.audio_embedder.initialize_weights() # Initialize emotion embedding self.emo_embedder.initialize_weights() # Initialize timestep embedding MLP self.time_embedder.initialize_weights() # Initialize DiT blocks: for block in self.blocks: block.initialize_weights() for block in self.blocks2: block.initialize_weights() for block in self.blocks4: block.initialize_weights() # Initialize output layers: # self.final_layer.initialize_weights() def cal_sync_loss(self, audio_embedding, mouth_embedding, label): if isinstance(label, torch.Tensor): gt_d = label.float().view(-1,1).to(audio_embedding.device) else: gt_d = (torch.ones([audio_embedding.shape[0],1]) * label).float().to(audio_embedding.device) # int d = nn.functional.cosine_similarity(audio_embedding, mouth_embedding) loss = self.logloss(d.unsqueeze(1), gt_d) return loss, d def forward(self, motion, times, audio, emo, audio_cond,mask=None): """ Forward pass of Talking Head DiT. motion: (B, N, xD) tensor of moton features inputs (head motion, emotion, etc.) time: (B,) tensor of diffusion timesteps audio: (B, N, M, yD) tensor of audio features, (batch_size, video_length, blocks, channels). cond: (B, N, cD) tensor of conditional features audio_cond: (B, N, zD) or (B, zD) tensor of audio conditional features """ # bianma=time.time() # (B, D) motion_embeds = self.motion_embedder(motion) # (B, N, D), N: seq length _,seq_len,_=motion.shape time_embeds = self.time_embedder(times) cache=True if cache: # emotion embedding emo_embeds = self.emo_embedder(emo, self.training)# (B, D) audio_cond=audio_cond.mean(1) audio_cond_embeds = self.identity_embedder(audio_cond) # audio embedding freqs_cos, freqs_sin = get_1d_rotary_pos_embed(self.dim, seq_len,theta=256, use_real=True, theta_rescale_factor=1) freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None audio_embeds = self.audio_embedder(audio) # (B, N, M, D) # self.bank.append(audio_embeds) M=audio_embeds.shape[2] audio_embeds = rearrange(audio_embeds, "b n m d -> b (n m) d") # print(audio_embeds.shape) c = time_embeds+emo_embeds # motion embedding freqs_cos2=rearrange(freqs_cos.unsqueeze(0).repeat(M,1,1), "n m d -> (n m) d") freqs_sin2=rearrange(freqs_sin.unsqueeze(0).repeat(M,1,1),"n m d -> (n m) d") freqs_cis2 = (freqs_cos2, freqs_sin2) if freqs_cos2 is not None else None freqs_cos3=rearrange(freqs_cos.unsqueeze(0).repeat(3*M,1,1), "n m d -> (n m) d") freqs_sin3=rearrange(freqs_sin.unsqueeze(0).repeat(3*M,1,1),"n m d -> (n m) d") freqs_cis3 = (freqs_cos3, freqs_sin3) if freqs_cos2 is not None else None # self.bank.append(emo_embeds) # self.bank.append(audio_cond_embeds) emo_embeds=emo_embeds.unsqueeze(1).repeat(1,seq_len,1) audio_cond_embeds=audio_cond_embeds.unsqueeze(1).repeat(1,seq_len,1) for block in (self.blocks4): motion_embeds,audio_embeds,emo_embeds,audio_cond_embeds = block(motion_embeds, c, audio_embeds,emo_embeds,audio_cond_embeds,mask,freqs_cis,freqs_cis2,causal=False) audio_embeds=torch.cat((audio_embeds,emo_embeds,audio_cond_embeds), 1) for block in self.blocks2: motion_embeds,audio_embeds= block(seq_len,motion_embeds, c, audio_embeds,mask,freqs_cis,freqs_cis3,causal=False) motion_embeds=torch.cat((motion_embeds, audio_embeds), 1) for block in self.blocks: motion_embeds = block(seq_len,motion_embeds, c,mask,freqs_cis,freqs_cis3,causal=False) motion_embeds=motion_embeds[:,:seq_len,:] out = self.final_layer(motion_embeds, c) # (B, N, out_channels) # print("dit",time.time()-b) return out def forward_with_cfg(self, motion, time, audio, cfg_scale, emo=None, audio_cond=None): """ Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance. """ pass # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb # half = x[: len(x) // 2] # combined = torch.cat([half, half], dim=0) # model_out = self.forward(combined, t, y) # # For exact reproducibility reasons, we apply classifier-free guidance on only # # three channels by default. The standard approach to cfg applies it to all channels. # # This can be done by uncommenting the following line and commenting-out the line following that. # # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] # eps, rest = model_out[:, :3], model_out[:, 3:] # cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) # half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) # eps = torch.cat([half_eps, half_eps], dim=0) # return torch.cat([eps, rest], dim=1) def TalkingHeadDiT_XL(**kwargs): return TalkingHeadDiT(depth=28, hidden_size=1152, num_heads=16, **kwargs) def TalkingHeadDiT_L(**kwargs): return TalkingHeadDiT(depth=24, hidden_size=1024, num_heads=16, **kwargs) def TalkingHeadDiT_B(**kwargs): return TalkingHeadDiT(depth=12, hidden_size=768, num_heads=12, **kwargs) def TalkingHeadDiT_MM(**kwargs): return TalkingHeadDiT(depth=6, hidden_size=768, num_heads=12, **kwargs) def TalkingHeadDiT_S(**kwargs): return TalkingHeadDiT(depth=12, hidden_size=384, num_heads=6, **kwargs) def TalkingHeadDiT_T(**kwargs): return TalkingHeadDiT(depth=6, hidden_size=256, num_heads=4, **kwargs) TalkingHeadDiT_models = { 'TalkingHeadDiT-XL': TalkingHeadDiT_XL, 'TalkingHeadDiT-L': TalkingHeadDiT_L, 'TalkingHeadDiT-MM': TalkingHeadDiT_MM, 'TalkingHeadDiT-B': TalkingHeadDiT_B, 'TalkingHeadDiT-S': TalkingHeadDiT_S, 'TalkingHeadDiT-T': TalkingHeadDiT_T, }