Wan2.2/wan/modules/animate/model_animate.py

# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
import types
from copy import deepcopy
from einops import  rearrange
from typing import List
import numpy as np
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
from diffusers.loaders import PeftAdapterMixin

from ...distributed.sequence_parallel import (
    distributed_attention,
    gather_forward,
    get_rank,
    get_world_size,
)


from ..model import (
    Head,
    WanAttentionBlock,
    WanLayerNorm,
    WanRMSNorm,
    WanModel,
    WanSelfAttention,
    flash_attention,
    rope_params,
    sinusoidal_embedding_1d,
    rope_apply
)

from .face_blocks import FaceEncoder, FaceAdapter
from .motion_encoder import Generator

class HeadAnimate(Head):

    def forward(self, x, e):
        """
        Args:
            x(Tensor): Shape [B, L1, C]
            e(Tensor): Shape [B, L1, C]
        """
        assert e.dtype == torch.float32
        with amp.autocast(dtype=torch.float32):
            e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
            x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
        return x


class WanAnimateSelfAttention(WanSelfAttention):

    def forward(self, x, seq_lens, grid_sizes, freqs):
        """
        Args:
            x(Tensor): Shape [B, L, num_heads, C / num_heads]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        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)

        x = flash_attention(
            q=rope_apply(q, grid_sizes, freqs),
            k=rope_apply(k, grid_sizes, freqs),
            v=v,
            k_lens=seq_lens,
            window_size=self.window_size)

        # output
        x = x.flatten(2)
        x = self.o(x)
        return x


class WanAnimateCrossAttention(WanSelfAttention):
    def __init__(
        self,
        dim,
        num_heads,
        window_size=(-1, -1),
        qk_norm=True,
        eps=1e-6,
        use_img_emb=True
    ):
        super().__init__(
            dim,
            num_heads,
            window_size,
            qk_norm,
            eps
        )
        self.use_img_emb = use_img_emb

        if use_img_emb:
            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):
        """
        x:              [B, L1, C].
        context:        [B, L2, C].
        context_lens:   [B].
        """
        if self.use_img_emb:
            context_img = context[:, :257]
            context = context[:, 257:]
        else:
            context = context

        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)

        if self.use_img_emb:
            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)

        if self.use_img_emb:
            img_x = img_x.flatten(2)
            x = x + img_x

        x = self.o(x)
        return x


class WanAnimateAttentionBlock(nn.Module):
    def __init__(self,
                 dim,
                 ffn_dim,
                 num_heads,
                 window_size=(-1, -1),
                 qk_norm=True,
                 cross_attn_norm=True,
                 eps=1e-6,
                 use_img_emb=True):

        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 = WanAnimateSelfAttention(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 = WanAnimateCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps, use_img_emb=use_img_emb)
        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)

    def forward(
        self,
        x,
        e,
        seq_lens,
        grid_sizes,
        freqs,
        context,
        context_lens,
    ):
        """
        Args:
            x(Tensor): Shape [B, L, C]
            e(Tensor): Shape [B, L1, 6, C]
            seq_lens(Tensor): Shape [B], length of each sequence in batch
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        assert e.dtype == torch.float32
        with amp.autocast(dtype=torch.float32):
            e = (self.modulation + e).chunk(6, dim=1)
        assert e[0].dtype == torch.float32

        # self-attention
        y = self.self_attn(
            self.norm1(x).float() * (1 + e[1]) + e[0], seq_lens, grid_sizes, freqs
        )
        with amp.autocast(dtype=torch.float32):
            x = x + y * e[2]

        # cross-attention & ffn function
        def cross_attn_ffn(x, context, context_lens, e):
            x = x + self.cross_attn(self.norm3(x), context, context_lens)
            y = self.ffn(self.norm2(x).float() * (1 + e[4]) + e[3])
            with amp.autocast(dtype=torch.float32):
                x = x + y * e[5]
            return x

        x = cross_attn_ffn(x, context, context_lens, e)
        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 WanAnimateModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    _no_split_modules = ['WanAttentionBlock']

    @register_to_config
    def __init__(self,
                 patch_size=(1, 2, 2),
                 text_len=512,
                 in_dim=36,
                 dim=5120,
                 ffn_dim=13824,
                 freq_dim=256,
                 text_dim=4096,
                 out_dim=16,
                 num_heads=40,
                 num_layers=40,
                 window_size=(-1, -1),
                 qk_norm=True,
                 cross_attn_norm=True,
                 eps=1e-6,
                 motion_encoder_dim=512,
                 use_context_parallel=False,
                 use_img_emb=True):

        super().__init__()
        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.motion_encoder_dim = motion_encoder_dim
        self.use_context_parallel = use_context_parallel
        self.use_img_emb = use_img_emb

        # embeddings
        self.patch_embedding = nn.Conv3d(
            in_dim, dim, kernel_size=patch_size, stride=patch_size)

        self.pose_patch_embedding = nn.Conv3d(
            16, 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
        self.blocks = nn.ModuleList([
            WanAnimateAttentionBlock(dim, ffn_dim, num_heads, window_size, qk_norm,
                              cross_attn_norm, eps, use_img_emb) for _ in range(num_layers)
        ])

        # head
        self.head = HeadAnimate(dim, out_dim, patch_size, eps)

        # buffers (don't use register_buffer otherwise dtype will be changed in to())
        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)

        self.img_emb = MLPProj(1280, dim)
        
        # initialize weights
        self.init_weights()

        self.motion_encoder = Generator(size=512, style_dim=512, motion_dim=20)
        self.face_adapter = FaceAdapter(
            heads_num=self.num_heads,
            hidden_dim=self.dim,
            num_adapter_layers=self.num_layers // 5,
        )

        self.face_encoder = FaceEncoder(
            in_dim=motion_encoder_dim,
            hidden_dim=self.dim,
            num_heads=4,
        )

    def after_patch_embedding(self, x: List[torch.Tensor], pose_latents, face_pixel_values):
        pose_latents = [self.pose_patch_embedding(u.unsqueeze(0)) for u in pose_latents]
        for x_, pose_latents_ in zip(x, pose_latents):
            x_[:, :, 1:] += pose_latents_
        
        b,c,T,h,w = face_pixel_values.shape
        face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")

        encode_bs = 8
        face_pixel_values_tmp = []
        for i in range(math.ceil(face_pixel_values.shape[0]/encode_bs)):
            face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i*encode_bs:(i+1)*encode_bs]))

        motion_vec = torch.cat(face_pixel_values_tmp)
        
        motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
        motion_vec = self.face_encoder(motion_vec)

        B, L, H, C = motion_vec.shape
        pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
        motion_vec = torch.cat([pad_face, motion_vec], dim=1)
        return x, motion_vec


    def after_transformer_block(self, block_idx, x, motion_vec, motion_masks=None):
        if block_idx % 5 == 0:
            adapter_args = [x, motion_vec, motion_masks, self.use_context_parallel]
            residual_out = self.face_adapter.fuser_blocks[block_idx // 5](*adapter_args)
            x = residual_out + x
        return x


    def forward(
        self,
        x,
        t,
        clip_fea,
        context,
        seq_len,
        y=None,
        pose_latents=None, 
        face_pixel_values=None
    ):
        # params
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.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)) for u in x]
        x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)

        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

        # context
        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
            ]))

        if self.use_img_emb:
            context_clip = self.img_emb(clip_fea) # bs x 257 x dim
            context = torch.concat([context_clip, context], dim=1)

        # arguments
        kwargs = dict(
            e=e0,
            seq_lens=seq_lens,
            grid_sizes=grid_sizes,
            freqs=self.freqs,
            context=context,
            context_lens=context_lens)

        if self.use_context_parallel:
            x = torch.chunk(x, get_world_size(), dim=1)[get_rank()]

        for idx, block in enumerate(self.blocks):
            x = block(x, **kwargs)
            x = self.after_transformer_block(idx, x, motion_vec)

        # head
        x = self.head(x, e)

        if self.use_context_parallel:
            x = gather_forward(x, dim=1)

        # unpatchify
        x = self.unpatchify(x, grid_sizes)
        return [u.float() for u in x]


    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)