# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math import numpy as np import os import torch import torch.cuda.amp as amp import torch.nn as nn import torch.nn.functional as F from einops import rearrange from diffusers import ModelMixin from diffusers.configuration_utils import ConfigMixin, register_to_config from .attention import flash_attention, SingleStreamMutiAttention from ..utils.multitalk_utils import get_attn_map_with_target __all__ = ['WanModel'] def sinusoidal_embedding_1d(dim, position): # preprocess assert dim % 2 == 0 half = dim // 2 position = position.type(torch.float64) # calculation sinusoid = torch.outer( position, torch.pow(10000, -torch.arange(half).to(position).div(half))) x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) return x @amp.autocast(enabled=False) def rope_params(max_seq_len, dim, theta=10000): assert dim % 2 == 0 freqs = torch.outer( torch.arange(max_seq_len), 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim))) freqs = torch.polar(torch.ones_like(freqs), freqs) return freqs @amp.autocast(enabled=False) def rope_apply(x, grid_sizes, freqs): s, n, c = x.size(1), x.size(2), x.size(3) // 2 freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) output = [] for i, (f, h, w) in enumerate(grid_sizes.tolist()): seq_len = f * h * w x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape( s, n, -1, 2)) freqs_i = torch.cat([ freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(seq_len, 1, -1) freqs_i = freqs_i.to(device=x_i.device) x_i = torch.view_as_real(x_i * freqs_i).flatten(2) x_i = torch.cat([x_i, x[i, seq_len:]]) output.append(x_i) return torch.stack(output).float() class WanRMSNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.dim = dim self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): r""" Args: x(Tensor): Shape [B, L, C] """ return self._norm(x.float()).type_as(x) * self.weight def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) class WanLayerNorm(nn.LayerNorm): def __init__(self, dim, eps=1e-6, elementwise_affine=False): super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps) def forward(self, inputs: torch.Tensor) -> torch.Tensor: origin_dtype = inputs.dtype out = F.layer_norm( inputs.float(), self.normalized_shape, None if self.weight is None else self.weight.float(), None if self.bias is None else self.bias.float() , self.eps ).to(origin_dtype) return out class WanSelfAttention(nn.Module): def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.window_size = window_size self.qk_norm = qk_norm self.eps = eps # layers self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward(self, x, seq_lens, grid_sizes, freqs, ref_target_masks=None): b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim # query, key, value function def qkv_fn(x): q = self.norm_q(self.q(x)).view(b, s, n, d) k = self.norm_k(self.k(x)).view(b, s, n, d) v = self.v(x).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) q = rope_apply(q, grid_sizes, freqs) k = rope_apply(k, grid_sizes, freqs) x = flash_attention( q=q, k=k, v=v, k_lens=seq_lens, window_size=self.window_size ).type_as(x) # output x = x.flatten(2) x = self.o(x) with torch.no_grad(): x_ref_attn_map = get_attn_map_with_target(q.type_as(x), k.type_as(x), grid_sizes[0], ref_target_masks=ref_target_masks) return x, x_ref_attn_map class WanI2VCrossAttention(WanSelfAttention): def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6): super().__init__(dim, num_heads, window_size, qk_norm, eps) self.k_img = nn.Linear(dim, dim) self.v_img = nn.Linear(dim, dim) self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward(self, x, context, context_lens): context_img = context[:, :257] context = context[:, 257:] b, n, d = x.size(0), self.num_heads, self.head_dim # compute query, key, value q = self.norm_q(self.q(x)).view(b, -1, n, d) k = self.norm_k(self.k(context)).view(b, -1, n, d) v = self.v(context).view(b, -1, n, d) k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d) v_img = self.v_img(context_img).view(b, -1, n, d) img_x = flash_attention(q, k_img, v_img, k_lens=None) # compute attention x = flash_attention(q, k, v, k_lens=context_lens) # output x = x.flatten(2) img_x = img_x.flatten(2) x = x + img_x x = self.o(x) return x class WanAttentionBlock(nn.Module): def __init__(self, cross_attn_type, dim, ffn_dim, num_heads, window_size=(-1, -1), qk_norm=True, cross_attn_norm=False, eps=1e-6, output_dim=768, norm_input_visual=True, class_range=24, class_interval=4): super().__init__() self.dim = dim self.ffn_dim = ffn_dim self.num_heads = num_heads self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # layers self.norm1 = WanLayerNorm(dim, eps) self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps) self.norm3 = WanLayerNorm( dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity() self.cross_attn = WanI2VCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps) self.norm2 = WanLayerNorm(dim, eps) self.ffn = nn.Sequential( nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim)) # modulation self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) # init audio module self.audio_cross_attn = SingleStreamMutiAttention( dim=dim, encoder_hidden_states_dim=output_dim, num_heads=num_heads, qk_norm=False, qkv_bias=True, eps=eps, norm_layer=WanRMSNorm, class_range=class_range, class_interval=class_interval ) self.norm_x = WanLayerNorm(dim, eps, elementwise_affine=True) if norm_input_visual else nn.Identity() def forward( self, x, e, seq_lens, grid_sizes, freqs, context, context_lens, audio_embedding=None, ref_target_masks=None, human_num=None, ): dtype = x.dtype assert e.dtype == torch.float32 with amp.autocast(dtype=torch.float32): e = (self.modulation.to(e.device) + e).chunk(6, dim=1) assert e[0].dtype == torch.float32 # self-attention y, x_ref_attn_map = self.self_attn( (self.norm1(x).float() * (1 + e[1]) + e[0]).type_as(x), seq_lens, grid_sizes, freqs, ref_target_masks=ref_target_masks) with amp.autocast(dtype=torch.float32): x = x + y * e[2] x = x.to(dtype) # cross-attention of text x = x + self.cross_attn(self.norm3(x), context, context_lens) # cross attn of audio x_a = self.audio_cross_attn(self.norm_x(x), encoder_hidden_states=audio_embedding, shape=grid_sizes[0], x_ref_attn_map=x_ref_attn_map, human_num=human_num) x = x + x_a y = self.ffn((self.norm2(x).float() * (1 + e[4]) + e[3]).to(dtype)) with amp.autocast(dtype=torch.float32): x = x + y * e[5] x = x.to(dtype) return x class Head(nn.Module): def __init__(self, dim, out_dim, patch_size, eps=1e-6): super().__init__() self.dim = dim self.out_dim = out_dim self.patch_size = patch_size self.eps = eps # layers out_dim = math.prod(patch_size) * out_dim self.norm = WanLayerNorm(dim, eps) self.head = nn.Linear(dim, out_dim) # modulation self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) def forward(self, x, e): r""" Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, C] """ assert e.dtype == torch.float32 with amp.autocast(dtype=torch.float32): e = (self.modulation.to(e.device) + e.unsqueeze(1)).chunk(2, dim=1) x = (self.head(self.norm(x) * (1 + e[1]) + e[0])) return x class MLPProj(torch.nn.Module): def __init__(self, in_dim, out_dim): super().__init__() self.proj = torch.nn.Sequential( torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim), torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim), torch.nn.LayerNorm(out_dim)) def forward(self, image_embeds): clip_extra_context_tokens = self.proj(image_embeds) return clip_extra_context_tokens class AudioProjModel(ModelMixin, ConfigMixin): def __init__( self, seq_len=5, seq_len_vf=12, blocks=12, channels=768, intermediate_dim=512, output_dim=768, context_tokens=32, norm_output_audio=False, ): super().__init__() self.seq_len = seq_len self.blocks = blocks self.channels = channels self.input_dim = seq_len * blocks * channels self.input_dim_vf = seq_len_vf * blocks * channels self.intermediate_dim = intermediate_dim self.context_tokens = context_tokens self.output_dim = output_dim # define multiple linear layers self.proj1 = nn.Linear(self.input_dim, intermediate_dim) self.proj1_vf = nn.Linear(self.input_dim_vf, intermediate_dim) self.proj2 = nn.Linear(intermediate_dim, intermediate_dim) self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim) self.norm = nn.LayerNorm(output_dim) if norm_output_audio else nn.Identity() def forward(self, audio_embeds, audio_embeds_vf): video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1] B, _, _, S, C = audio_embeds.shape # process audio of first frame audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") batch_size, window_size, blocks, channels = audio_embeds.shape audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) # process audio of latter frame audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c") batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf) # first projection audio_embeds = torch.relu(self.proj1(audio_embeds)) audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf)) audio_embeds = rearrange(audio_embeds, "(bz f) c -> bz f c", bz=B) audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B) audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1) batch_size_c, N_t, C_a = audio_embeds_c.shape audio_embeds_c = audio_embeds_c.view(batch_size_c*N_t, C_a) # second projection audio_embeds_c = torch.relu(self.proj2(audio_embeds_c)) context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c*N_t, self.context_tokens, self.output_dim) # normalization and reshape context_tokens = self.norm(context_tokens) context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length) return context_tokens class WanModel(ModelMixin, ConfigMixin): r""" Wan diffusion backbone supporting both text-to-video and image-to-video. """ ignore_for_config = [ 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size' ] _no_split_modules = ['WanAttentionBlock'] @register_to_config def __init__(self, model_type='i2v', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, window_size=(-1, -1), qk_norm=True, cross_attn_norm=True, eps=1e-6, # audio params audio_window=5, intermediate_dim=512, output_dim=768, context_tokens=32, vae_scale=4, # vae timedownsample scale norm_input_visual=True, norm_output_audio=True): super().__init__() assert model_type == 'i2v', 'MultiTalk model requires your model_type is i2v.' self.model_type = model_type self.patch_size = patch_size self.text_len = text_len self.in_dim = in_dim self.dim = dim self.ffn_dim = ffn_dim self.freq_dim = freq_dim self.text_dim = text_dim self.out_dim = out_dim self.num_heads = num_heads self.num_layers = num_layers self.window_size = window_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps self.norm_output_audio = norm_output_audio self.audio_window = audio_window self.intermediate_dim = intermediate_dim self.vae_scale = vae_scale # embeddings self.patch_embedding = nn.Conv3d( in_dim, dim, kernel_size=patch_size, stride=patch_size) self.text_embedding = nn.Sequential( nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim)) self.time_embedding = nn.Sequential( nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6)) # blocks cross_attn_type = 'i2v_cross_attn' self.blocks = nn.ModuleList([ WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, output_dim=output_dim, norm_input_visual=norm_input_visual) for _ in range(num_layers) ]) # head self.head = Head(dim, out_dim, patch_size, eps) assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 d = dim // num_heads self.freqs = torch.cat([ rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6)) ], dim=1) if model_type == 'i2v': self.img_emb = MLPProj(1280, dim) else: raise NotImplementedError('Not supported model type.') # init audio adapter self.audio_proj = AudioProjModel( seq_len=audio_window, seq_len_vf=audio_window+vae_scale-1, intermediate_dim=intermediate_dim, output_dim=output_dim, context_tokens=context_tokens, norm_output_audio=norm_output_audio, ) # initialize weights self.init_weights() def teacache_init( self, use_ret_steps=True, teacache_thresh=0.2, sample_steps=40, model_scale='multitalk-480', ): print("teacache_init") self.enable_teacache = True self.__class__.cnt = 0 self.__class__.num_steps = sample_steps*3 self.__class__.teacache_thresh = teacache_thresh self.__class__.accumulated_rel_l1_distance_even = 0 self.__class__.accumulated_rel_l1_distance_odd = 0 self.__class__.previous_e0_even = None self.__class__.previous_e0_odd = None self.__class__.previous_residual_even = None self.__class__.previous_residual_odd = None self.__class__.use_ret_steps = use_ret_steps if use_ret_steps: if model_scale == 'multitalk-480': self.__class__.coefficients = [ 2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01] if model_scale == 'multitalk-720': self.__class__.coefficients = [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02] self.__class__.ret_steps = 5*3 self.__class__.cutoff_steps = sample_steps*3 else: if model_scale == 'multitalk-480': self.__class__.coefficients = [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01] if model_scale == 'multitalk-720': self.__class__.coefficients = [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683] self.__class__.ret_steps = 1*3 self.__class__.cutoff_steps = sample_steps*3 - 3 print("teacache_init done") def disable_teacache(self): self.enable_teacache = False def forward( self, x, t, context, seq_len, clip_fea=None, y=None, audio=None, ref_target_masks=None, ): assert clip_fea is not None and y is not None _, T, H, W = x[0].shape N_t = T // self.patch_size[0] N_h = H // self.patch_size[1] N_w = W // self.patch_size[2] if y is not None: x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] x[0] = x[0].to(context[0].dtype) # embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] grid_sizes = torch.stack( [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) x = [u.flatten(2).transpose(1, 2) for u in x] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) assert seq_lens.max() <= seq_len x = torch.cat([ torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in x ]) # time embeddings with amp.autocast(dtype=torch.float32): e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t).float()) e0 = self.time_projection(e).unflatten(1, (6, self.dim)) assert e.dtype == torch.float32 and e0.dtype == torch.float32 # text embedding context_lens = None context = self.text_embedding( torch.stack([ torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ])) # clip embedding if clip_fea is not None: context_clip = self.img_emb(clip_fea) context = torch.concat([context_clip, context], dim=1).to(x.dtype) audio_cond = audio.to(device=x.device, dtype=x.dtype) first_frame_audio_emb_s = audio_cond[:, :1, ...] latter_frame_audio_emb = audio_cond[:, 1:, ...] latter_frame_audio_emb = rearrange(latter_frame_audio_emb, "b (n_t n) w s c -> b n_t n w s c", n=self.vae_scale) middle_index = self.audio_window // 2 latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index+1, ...] latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c") latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...] latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c") latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index+1, ...] latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c") latter_frame_audio_emb_s = torch.concat([latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2) audio_embedding = self.audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s) human_num = len(audio_embedding) audio_embedding = torch.concat(audio_embedding.split(1), dim=2).to(x.dtype) # convert ref_target_masks to token_ref_target_masks if ref_target_masks is not None: ref_target_masks = ref_target_masks.unsqueeze(0).to(torch.float32) token_ref_target_masks = nn.functional.interpolate(ref_target_masks, size=(N_h, N_w), mode='nearest') token_ref_target_masks = token_ref_target_masks.squeeze(0) token_ref_target_masks = (token_ref_target_masks > 0) token_ref_target_masks = token_ref_target_masks.view(token_ref_target_masks.shape[0], -1) token_ref_target_masks = token_ref_target_masks.to(x.dtype) # teacache if self.enable_teacache: modulated_inp = e0 if self.use_ret_steps else e if self.cnt%3==0: # cond if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps: should_calc_cond = True self.accumulated_rel_l1_distance_cond = 0 else: rescale_func = np.poly1d(self.coefficients) self.accumulated_rel_l1_distance_cond += rescale_func(((modulated_inp-self.previous_e0_cond).abs().mean() / self.previous_e0_cond.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance_cond < self.teacache_thresh: should_calc_cond = False else: should_calc_cond = True self.accumulated_rel_l1_distance_cond = 0 self.previous_e0_cond = modulated_inp.clone() elif self.cnt%3==1: # drop_text if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps: should_calc_drop_text = True self.accumulated_rel_l1_distance_drop_text = 0 else: rescale_func = np.poly1d(self.coefficients) self.accumulated_rel_l1_distance_drop_text += rescale_func(((modulated_inp-self.previous_e0_drop_text).abs().mean() / self.previous_e0_drop_text.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance_drop_text < self.teacache_thresh: should_calc_drop_text = False else: should_calc_drop_text = True self.accumulated_rel_l1_distance_drop_text = 0 self.previous_e0_drop_text = modulated_inp.clone() else: # uncond if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps: should_calc_uncond = True self.accumulated_rel_l1_distance_uncond = 0 else: rescale_func = np.poly1d(self.coefficients) self.accumulated_rel_l1_distance_uncond += rescale_func(((modulated_inp-self.previous_e0_uncond).abs().mean() / self.previous_e0_uncond.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance_uncond < self.teacache_thresh: should_calc_uncond = False else: should_calc_uncond = True self.accumulated_rel_l1_distance_uncond = 0 self.previous_e0_uncond = modulated_inp.clone() # arguments kwargs = dict( e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs, context=context, context_lens=context_lens, audio_embedding=audio_embedding, ref_target_masks=token_ref_target_masks, human_num=human_num, ) if self.enable_teacache: if self.cnt%3==0: if not should_calc_cond: x += self.previous_residual_cond else: ori_x = x.clone() for block in self.blocks: x = block(x, **kwargs) self.previous_residual_cond = x - ori_x elif self.cnt%3==1: if not should_calc_drop_text: x += self.previous_residual_drop_text else: ori_x = x.clone() for block in self.blocks: x = block(x, **kwargs) self.previous_residual_drop_text = x - ori_x else: if not should_calc_uncond: x += self.previous_residual_uncond else: ori_x = x.clone() for block in self.blocks: x = block(x, **kwargs) self.previous_residual_uncond = x - ori_x else: for block in self.blocks: x = block(x, **kwargs) # head x = self.head(x, e) # unpatchify x = self.unpatchify(x, grid_sizes) if self.enable_teacache: self.cnt += 1 if self.cnt >= self.num_steps: self.cnt = 0 return torch.stack(x).float() def unpatchify(self, x, grid_sizes): r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features, each with shape [L, C_out * prod(patch_size)] grid_sizes (Tensor): Original spatial-temporal grid dimensions before patching, shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) Returns: List[Tensor]: Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] """ c = self.out_dim out = [] for u, v in zip(x, grid_sizes.tolist()): u = u[:math.prod(v)].view(*v, *self.patch_size, c) u = torch.einsum('fhwpqrc->cfphqwr', u) u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) out.append(u) return out def init_weights(self): r""" Initialize model parameters using Xavier initialization. """ # basic init for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.zeros_(m.bias) # init embeddings nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) for m in self.text_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) for m in self.time_embedding.modules(): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, std=.02) # init output layer nn.init.zeros_(self.head.head.weight)