# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math from einops import rearrange import torch import torch.cuda.amp as amp import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin import numpy as np from typing import Union,Optional from mmgp import offload from .attention import pay_attention from torch.backends.cuda import sdp_kernel __all__ = ['WanModel'] def sinusoidal_embedding_1d(dim, position): # preprocess assert dim % 2 == 0 half = dim // 2 position = position.type(torch.float32) # 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 def reshape_latent(latent, latent_frames): if latent_frames == latent.shape[0]: return latent return latent.reshape(latent_frames, -1, latent.shape[-1] ) def identify_k( b: float, d: int, N: int): """ This function identifies the index of the intrinsic frequency component in a RoPE-based pre-trained diffusion transformer. Args: b (`float`): The base frequency for RoPE. d (`int`): Dimension of the frequency tensor N (`int`): the first observed repetition frame in latent space Returns: k (`int`): the index of intrinsic frequency component N_k (`int`): the period of intrinsic frequency component in latent space Example: In HunyuanVideo, b=256 and d=16, the repetition occurs approximately 8s (N=48 in latent space). k, N_k = identify_k(b=256, d=16, N=48) In this case, the intrinsic frequency index k is 4, and the period N_k is 50. """ # Compute the period of each frequency in RoPE according to Eq.(4) periods = [] for j in range(1, d // 2 + 1): theta_j = 1.0 / (b ** (2 * (j - 1) / d)) N_j = round(2 * torch.pi / theta_j) periods.append(N_j) # Identify the intrinsic frequency whose period is closed to N（see Eq.(7)） diffs = [abs(N_j - N) for N_j in periods] k = diffs.index(min(diffs)) + 1 N_k = periods[k-1] return k, N_k def rope_params_riflex(max_seq_len, dim, theta=10000, L_test=30, k=6): assert dim % 2 == 0 exponents = torch.arange(0, dim, 2, dtype=torch.float64).div(dim) inv_theta_pow = 1.0 / torch.pow(theta, exponents) inv_theta_pow[k-1] = 0.9 * 2 * torch.pi / L_test freqs = torch.outer(torch.arange(max_seq_len), inv_theta_pow) if True: freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D] freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D] return (freqs_cos, freqs_sin) else: freqs = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] return freqs def relative_l1_distance(last_tensor, current_tensor): l1_distance = torch.abs(last_tensor - current_tensor).mean() norm = torch.abs(last_tensor).mean() relative_l1_distance = l1_distance / norm return relative_l1_distance.to(torch.float32) 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] """ y = x.float() y.pow_(2) y = y.mean(dim=-1, keepdim=True) y += self.eps y.rsqrt_() x *= y x *= self.weight return x # return self._norm(x).type_as(x) * self.weight def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) def my_LayerNorm(norm, x): y = x.float() y_m = y.mean(dim=-1, keepdim=True) y -= y_m del y_m y.pow_(2) y = y.mean(dim=-1, keepdim=True) y += norm.eps y.rsqrt_() x = x * y return x 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, x): r""" Args: x(Tensor): Shape [B, L, C] """ # return F.layer_norm( # input, self.normalized_shape, self.weight, self.bias, self.eps # ) y = super().forward(x) x = y.type_as(x) return x # return super().forward(x).type_as(x) from wan.modules.posemb_layers import apply_rotary_emb 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, xlist, grid_sizes, freqs, block_mask = None): r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ x = xlist[0] xlist.clear() b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim # query, key, value function q = self.q(x) self.norm_q(q) q = q.view(b, s, n, d) # !!! k = self.k(x) self.norm_k(k) k = k.view(b, s, n, d) v = self.v(x).view(b, s, n, d) del x qklist = [q,k] del q,k q,k = apply_rotary_emb(qklist, freqs, head_first=False) qkv_list = [q,k,v] del q,k,v if block_mask == None: x = pay_attention( qkv_list, window_size=self.window_size) else: with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): x = ( torch.nn.functional.scaled_dot_product_attention( q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=block_mask ) .transpose(1, 2) .contiguous() ) # if not self._flag_ar_attention: # q = rope_apply(q, grid_sizes, freqs) # k = rope_apply(k, grid_sizes, freqs) # x = flash_attention(q=q, k=k, v=v, window_size=self.window_size) # else: # q = rope_apply(q, grid_sizes, freqs) # k = rope_apply(k, grid_sizes, freqs) # q = q.to(torch.bfloat16) # k = k.to(torch.bfloat16) # v = v.to(torch.bfloat16) # with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): # x = ( # torch.nn.functional.scaled_dot_product_attention( # q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=block_mask # ) # .transpose(1, 2) # .contiguous() # ) # output x = x.flatten(2) x = self.o(x) return x class WanT2VCrossAttention(WanSelfAttention): def forward(self, xlist, context): r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] """ x = xlist[0] xlist.clear() b, n, d = x.size(0), self.num_heads, self.head_dim # compute query, key, value q = self.q(x) del x self.norm_q(q) q= q.view(b, -1, n, d) k = self.k(context) self.norm_k(k) k = k.view(b, -1, n, d) v = self.v(context).view(b, -1, n, d) # compute attention qvl_list=[q, k, v] del q, k, v x = pay_attention(qvl_list, cross_attn= True) # output x = x.flatten(2) x = self.o(x) return x 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.alpha = nn.Parameter(torch.zeros((1, ))) self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward(self, xlist, context): r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] """ ##### Enjoy this spagheti VRAM optimizations done by DeepBeepMeep ! # I am sure you are a nice person and as you copy this code, you will give me officially proper credits: # Please link to https://github.com/deepbeepmeep/Wan2GP and @deepbeepmeep on twitter x = xlist[0] xlist.clear() 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.q(x) del x self.norm_q(q) q= q.view(b, -1, n, d) k = self.k(context) self.norm_k(k) k = k.view(b, -1, n, d) v = self.v(context).view(b, -1, n, d) qkv_list = [q, k, v] del k,v x = pay_attention(qkv_list) k_img = self.k_img(context_img) self.norm_k_img(k_img) k_img = k_img.view(b, -1, n, d) v_img = self.v_img(context_img).view(b, -1, n, d) qkv_list = [q, k_img, v_img] del q, k_img, v_img img_x = pay_attention(qkv_list) # compute attention # output x = x.flatten(2) img_x = img_x.flatten(2) x += img_x del img_x x = self.o(x) return x WAN_CROSSATTENTION_CLASSES = { 't2v_cross_attn': WanT2VCrossAttention, 'i2v_cross_attn': WanI2VCrossAttention, } 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, block_id=None ): 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 = WAN_CROSSATTENTION_CLASSES[cross_attn_type](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) self.block_id = block_id def forward( self, x, e, grid_sizes, freqs, context, hints= None, context_scale=1.0, cam_emb= None, block_mask = None ): r""" Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, 6, C] grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] """ hint = None attention_dtype = self.self_attn.q.weight.dtype dtype = x.dtype if self.block_id is not None and hints is not None: kwargs = { "grid_sizes" : grid_sizes, "freqs" :freqs, "context" : context, "e" : e, } if self.block_id == 0: hint = self.vace(hints, x, **kwargs) else: hint = self.vace(hints, None, **kwargs) latent_frames = e.shape[0] e = (self.modulation + e).chunk(6, dim=1) # self-attention x_mod = self.norm1(x) x_mod = reshape_latent(x_mod , latent_frames) x_mod *= 1 + e[1] x_mod += e[0] x_mod = reshape_latent(x_mod , 1) if cam_emb != None: cam_emb = self.cam_encoder(cam_emb) cam_emb = cam_emb.repeat(1, 2, 1) cam_emb = cam_emb.unsqueeze(2).unsqueeze(3).repeat(1, 1, grid_sizes[0][1], grid_sizes[0][2], 1) cam_emb = rearrange(cam_emb, 'b f h w d -> b (f h w) d') x_mod += cam_emb xlist = [x_mod.to(attention_dtype)] del x_mod y = self.self_attn( xlist, grid_sizes, freqs, block_mask) y = y.to(dtype) if cam_emb != None: y = self.projector(y) x, y = reshape_latent(x , latent_frames), reshape_latent(y , latent_frames) x.addcmul_(y, e[2]) x, y = reshape_latent(x , 1), reshape_latent(y , 1) del y y = self.norm3(x) y = y.to(attention_dtype) ylist= [y] del y x += self.cross_attn(ylist, context).to(dtype) y = self.norm2(x) y = reshape_latent(y , latent_frames) y *= 1 + e[4] y += e[3] y = reshape_latent(y , 1) y = y.to(attention_dtype) ffn = self.ffn[0] gelu = self.ffn[1] ffn2= self.ffn[2] y_shape = y.shape y = y.view(-1, y_shape[-1]) chunk_size = int(y_shape[1]/2.7) chunks =torch.split(y, chunk_size) for y_chunk in chunks: mlp_chunk = ffn(y_chunk) mlp_chunk = gelu(mlp_chunk) y_chunk[...] = ffn2(mlp_chunk) del mlp_chunk y = y.view(y_shape) y = y.to(dtype) x, y = reshape_latent(x , latent_frames), reshape_latent(y , latent_frames) x.addcmul_(y, e[5]) x, y = reshape_latent(x , 1), reshape_latent(y , 1) if hint is not None: if context_scale == 1: x.add_(hint) else: x.add_(hint, alpha= context_scale) return x class VaceWanAttentionBlock(WanAttentionBlock): 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, block_id=0 ): super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps) self.block_id = block_id if block_id == 0: self.before_proj = nn.Linear(self.dim, self.dim) nn.init.zeros_(self.before_proj.weight) nn.init.zeros_(self.before_proj.bias) self.after_proj = nn.Linear(self.dim, self.dim) nn.init.zeros_(self.after_proj.weight) nn.init.zeros_(self.after_proj.bias) def forward(self, hints, x, **kwargs): # behold dbm magic ! c = hints[0] hints[0] = None if self.block_id == 0: c = self.before_proj(c) c += x c = super().forward(c, **kwargs) c_skip = self.after_proj(c) hints[0] = c return c_skip 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 dtype = x.dtype latent_frames = e.shape[0] e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1) x = self.norm(x).to(dtype) x = reshape_latent(x , latent_frames) x *= (1 + e[1]) x += e[0] x = reshape_latent(x , 1) x= x.to(self.head.weight.dtype) x = self.head(x) 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 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, vace_layers=None, vace_in_dim=None, model_type='t2v', 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, recammaster = False, inject_sample_info = False, ): r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) text_len (`int`, *optional*, defaults to 512): Fixed length for text embeddings in_dim (`int`, *optional*, defaults to 16): Input video channels (C_in) dim (`int`, *optional*, defaults to 2048): Hidden dimension of the transformer ffn_dim (`int`, *optional*, defaults to 8192): Intermediate dimension in feed-forward network freq_dim (`int`, *optional*, defaults to 256): Dimension for sinusoidal time embeddings text_dim (`int`, *optional*, defaults to 4096): Input dimension for text embeddings out_dim (`int`, *optional*, defaults to 16): Output video channels (C_out) num_heads (`int`, *optional*, defaults to 16): Number of attention heads num_layers (`int`, *optional*, defaults to 32): Number of transformer blocks window_size (`tuple`, *optional*, defaults to (-1, -1)): Window size for local attention (-1 indicates global attention) qk_norm (`bool`, *optional*, defaults to True): Enable query/key normalization cross_attn_norm (`bool`, *optional*, defaults to False): Enable cross-attention normalization eps (`float`, *optional*, defaults to 1e-6): Epsilon value for normalization layers """ super().__init__() assert model_type in ['t2v', '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.num_frame_per_block = 1 self.flag_causal_attention = False self.block_mask = None self.inject_sample_info = inject_sample_info # 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)) if inject_sample_info: self.fps_embedding = nn.Embedding(2, dim) self.fps_projection = nn.Sequential(nn.Linear(dim, dim), nn.SiLU(), nn.Linear(dim, dim * 6)) 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 if vace_layers == None: cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' self.blocks = nn.ModuleList([ WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps) for _ in range(num_layers) ]) # head self.head = Head(dim, out_dim, patch_size, eps) # buffers (don't use register_buffer otherwise dtype will be changed in to()) if model_type == 'i2v': self.img_emb = MLPProj(1280, dim) # initialize weights self.init_weights() if vace_layers != None: self.vace_layers = [i for i in range(0, self.num_layers, 2)] if vace_layers is None else vace_layers self.vace_in_dim = self.in_dim if vace_in_dim is None else vace_in_dim assert 0 in self.vace_layers self.vace_layers_mapping = {i: n for n, i in enumerate(self.vace_layers)} # blocks self.blocks = nn.ModuleList([ WanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=self.vace_layers_mapping[i] if i in self.vace_layers else None) for i in range(self.num_layers) ]) # vace blocks self.vace_blocks = nn.ModuleList([ VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=i) for i in self.vace_layers ]) # vace patch embeddings self.vace_patch_embedding = nn.Conv3d( self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size ) if recammaster : dim=self.blocks[0].self_attn.q.weight.shape[0] for block in self.blocks: block.cam_encoder = nn.Linear(12, dim) block.projector = nn.Linear(dim, dim) block.cam_encoder.weight.data.zero_() block.cam_encoder.bias.data.zero_() block.projector.weight = nn.Parameter(torch.eye(dim)) block.projector.bias = nn.Parameter(torch.zeros(dim)) def lock_layers_dtypes(self, dtype = torch.float32, force = False): count = 0 layer_list = [self.head, self.head.head, self.patch_embedding, self.time_embedding, self.time_embedding[0], self.time_embedding[2], self.time_projection, self.time_projection[1]] #, self.text_embedding, self.text_embedding[0], self.text_embedding[2] ] if hasattr(self, "fps_embedding"): layer_list += [self.fps_embedding, self.fps_projection, self.fps_projection[0], self.fps_projection[2]] if hasattr(self, "vace_patch_embedding"): layer_list += [self.vace_patch_embedding] layer_list += [self.vace_blocks[0].before_proj] for block in self.vace_blocks: layer_list += [block.after_proj, block.norm3] # cam master if hasattr(self.blocks[0], "projector"): for block in self.blocks: layer_list += [block.projector] for block in self.blocks: layer_list += [block.norm3] for layer in layer_list: if hasattr(layer, "weight"): if layer.weight.dtype == dtype : count += 1 elif force: if hasattr(layer, "weight"): layer.weight.data = layer.weight.data.to(dtype) if hasattr(layer, "bias"): layer.bias.data = layer.bias.data.to(dtype) count += 1 layer._lock_dtype = dtype if count > 0: self._lock_dtype = dtype def compute_teacache_threshold(self, start_step, timesteps = None, speed_factor =0): rescale_func = np.poly1d(self.coefficients) e_list = [] for t in timesteps: t = torch.stack([t]) time_emb = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(self.patch_embedding.weight.dtype) ) # b, dim e_list.append(time_emb) best_threshold = 0.01 best_diff = 1000 best_signed_diff = 1000 target_nb_steps= int(len(timesteps) / speed_factor) threshold = 0.01 while threshold <= 0.6: accumulated_rel_l1_distance =0 nb_steps = 0 diff = 1000 for i, t in enumerate(timesteps): skip = False if not (i<=start_step or i== len(timesteps)): accumulated_rel_l1_distance += abs(rescale_func(((e_list[i]-e_list[i-1]).abs().mean() / e_list[i-1].abs().mean()).cpu().item())) if accumulated_rel_l1_distance < threshold: skip = True else: accumulated_rel_l1_distance = 0 if not skip: nb_steps += 1 signed_diff = target_nb_steps - nb_steps diff = abs(signed_diff) if diff < best_diff: best_threshold = threshold best_diff = diff best_signed_diff = signed_diff elif diff > best_diff: break threshold += 0.01 self.rel_l1_thresh = best_threshold print(f"Tea Cache, best threshold found:{best_threshold:0.2f} with gain x{len(timesteps)/(target_nb_steps - best_signed_diff):0.2f} for a target of x{speed_factor}") return best_threshold def forward( self, x, t, context, vace_context = None, vace_context_scale=1.0, clip_fea=None, y=None, freqs = None, pipeline = None, current_step = 0, is_uncond=False, max_steps = 0, slg_layers=None, callback = None, cam_emb: torch.Tensor = None, fps = None, causal_block_size = 1, causal_attention = False, x_neg = None ): # dtype = self.blocks[0].self_attn.q.weight.dtype dtype = self.patch_embedding.weight.dtype if self.model_type == 'i2v': assert clip_fea is not None and y is not None # params device = self.patch_embedding.weight.device if torch.is_tensor(freqs) and freqs.device != device: freqs = freqs.to(device) if y is not None: x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] # embeddings x = [self.patch_embedding(u.unsqueeze(0)).to(dtype) for u in x] if x_neg !=None: x_neg = [self.patch_embedding(u.unsqueeze(0)).to(dtype) for u in x_neg] grid_sizes = [ list(u.shape[2:]) for u in x] embed_sizes = grid_sizes[0] if causal_attention : #causal_block_size > 0: frame_num = embed_sizes[0] height = embed_sizes[1] width = embed_sizes[2] block_num = frame_num // causal_block_size range_tensor = torch.arange(block_num).view(-1, 1) range_tensor = range_tensor.repeat(1, causal_block_size).flatten() causal_mask = range_tensor.unsqueeze(0) <= range_tensor.unsqueeze(1) # f, f causal_mask = causal_mask.view(frame_num, 1, 1, frame_num, 1, 1).to(x[0].device) causal_mask = causal_mask.repeat(1, height, width, 1, height, width) causal_mask = causal_mask.reshape(frame_num * height * width, frame_num * height * width) block_mask = causal_mask.unsqueeze(0).unsqueeze(0) del causal_mask offload.shared_state["embed_sizes"] = embed_sizes offload.shared_state["step_no"] = current_step offload.shared_state["max_steps"] = max_steps x = [u.flatten(2).transpose(1, 2) for u in x] x = x[0] if x_neg !=None: x_neg = [u.flatten(2).transpose(1, 2) for u in x_neg] x_neg = x_neg[0] if t.dim() == 2: b, f = t.shape _flag_df = True else: _flag_df = False e = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype) # self.patch_embedding.weight.dtype) ) # b, dim e0 = self.time_projection(e).unflatten(1, (6, self.dim)).to(e.dtype) if self.inject_sample_info: fps = torch.tensor(fps, dtype=torch.long, device=device) fps_emb = self.fps_embedding(fps).to(dtype) # float() if _flag_df: e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)).repeat(t.shape[1], 1, 1) else: e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)) # context context = [self.text_embedding( torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))] ).unsqueeze(0) ) for u in context ] if clip_fea is not None: context_clip = self.img_emb(clip_fea) # bs x 257 x dim context = [ torch.cat( [context_clip, u ], dim=1 ) for u in context ] joint_pass = len(context) > 0 x_list = [x] if joint_pass: if x_neg == None: x_list += [x.clone() for i in range(len(context) - 1) ] else: x_list += [x.clone() for i in range(len(context) - 2) ] + [x_neg] is_uncond = False del x context_list = context # arguments kwargs = dict( grid_sizes=grid_sizes, freqs=freqs, cam_emb = cam_emb ) if vace_context == None: hints_list = [None ] *len(x_list) else: # embeddings c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context] c = [u.flatten(2).transpose(1, 2) for u in c] c = c[0] kwargs['context_scale'] = vace_context_scale hints_list = [ [c] for _ in range(len(x_list)) ] del c should_calc = True if self.enable_teacache: if is_uncond: should_calc = self.should_calc else: if current_step <= self.teacache_start_step or current_step == self.num_steps-1: should_calc = True self.accumulated_rel_l1_distance = 0 else: rescale_func = np.poly1d(self.coefficients) self.accumulated_rel_l1_distance += abs(rescale_func(((e-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False self.teacache_skipped_steps += 1 # print(f"Teacache Skipped Step:{self.teacache_skipped_steps}/{current_step}" ) else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = e self.should_calc = should_calc if not should_calc: for i, x in enumerate(x_list): x += self.previous_residual_uncond if i==1 or is_uncond else self.previous_residual_cond else: if self.enable_teacache: if joint_pass or is_uncond: self.previous_residual_uncond = None if joint_pass or not is_uncond: self.previous_residual_cond = None ori_hidden_states = x_list[0].clone() for block_idx, block in enumerate(self.blocks): offload.shared_state["layer"] = block_idx if callback != None: callback(-1, None, False, True) if pipeline._interrupt: return [None] * len(x_list) if slg_layers is not None and block_idx in slg_layers: if is_uncond and not joint_pass: continue x_list[0] = block(x_list[0], context = context_list[0], e= e0, **kwargs) else: for i, (x, context, hints) in enumerate(zip(x_list, context_list, hints_list)): x_list[i] = block(x, context = context, hints= hints, e= e0, **kwargs) del x del context, hints if self.enable_teacache: if joint_pass: self.previous_residual_cond = torch.sub(x_list[0], ori_hidden_states) self.previous_residual_uncond = ori_hidden_states torch.sub(x_list[1], ori_hidden_states, out=self.previous_residual_uncond) else: residual = ori_hidden_states # just to have a readable code torch.sub(x_list[0], ori_hidden_states, out=residual) if i==1 or is_uncond: self.previous_residual_uncond = residual else: self.previous_residual_cond = residual residual, ori_hidden_states = None, None for i, x in enumerate(x_list): # head x = self.head(x, e) # unpatchify x_list[i] = self.unpatchify(x, grid_sizes) del x return [x[0].float() for x in x_list] 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): 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)