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https://git.datalinker.icu/comfyanonymous/ComfyUI
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Basic WIP support for the wan animate model. (#9939)
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comfyanonymous
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548
comfy/ldm/wan/model_animate.py
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548
comfy/ldm/wan/model_animate.py
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from torch import nn
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import torch
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from typing import Tuple, Optional
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from einops import rearrange
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import torch.nn.functional as F
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import math
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from .model import WanModel, sinusoidal_embedding_1d
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from comfy.ldm.modules.attention import optimized_attention
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import comfy.model_management
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class CausalConv1d(nn.Module):
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def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", operations=None, **kwargs):
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super().__init__()
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self.pad_mode = pad_mode
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padding = (kernel_size - 1, 0) # T
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self.time_causal_padding = padding
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self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
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def forward(self, x):
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x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
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return self.conv(x)
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class FaceEncoder(nn.Module):
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def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.num_heads = num_heads
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self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs)
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self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.act = nn.SiLU()
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self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
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self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
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self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs)
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self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs))
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def forward(self, x):
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x = rearrange(x, "b t c -> b c t")
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b, c, t = x.shape
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x = self.conv1_local(x)
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x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
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x = self.norm1(x)
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x = self.act(x)
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x = rearrange(x, "b t c -> b c t")
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x = self.conv2(x)
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x = rearrange(x, "b c t -> b t c")
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x = self.norm2(x)
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x = self.act(x)
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x = rearrange(x, "b t c -> b c t")
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x = self.conv3(x)
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x = rearrange(x, "b c t -> b t c")
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x = self.norm3(x)
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x = self.act(x)
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x = self.out_proj(x)
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x = rearrange(x, "(b n) t c -> b t n c", b=b)
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padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1)
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x = torch.cat([x, padding], dim=-2)
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x_local = x.clone()
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return x_local
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def get_norm_layer(norm_layer, operations=None):
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"""
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Get the normalization layer.
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Args:
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norm_layer (str): The type of normalization layer.
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Returns:
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norm_layer (nn.Module): The normalization layer.
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"""
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if norm_layer == "layer":
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return operations.LayerNorm
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elif norm_layer == "rms":
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return operations.RMSNorm
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else:
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raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
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class FaceAdapter(nn.Module):
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def __init__(
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self,
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hidden_dim: int,
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heads_num: int,
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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num_adapter_layers: int = 1,
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dtype=None, device=None, operations=None
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):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.hidden_size = hidden_dim
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self.heads_num = heads_num
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self.fuser_blocks = nn.ModuleList(
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[
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FaceBlock(
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self.hidden_size,
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self.heads_num,
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qk_norm=qk_norm,
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qk_norm_type=qk_norm_type,
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operations=operations,
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**factory_kwargs,
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)
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for _ in range(num_adapter_layers)
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]
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)
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def forward(
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self,
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x: torch.Tensor,
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motion_embed: torch.Tensor,
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idx: int,
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freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
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freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
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) -> torch.Tensor:
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return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)
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class FaceBlock(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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heads_num: int,
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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qk_scale: float = None,
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dtype: Optional[torch.dtype] = None,
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device: Optional[torch.device] = None,
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operations=None
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):
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.deterministic = False
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self.hidden_size = hidden_size
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self.heads_num = heads_num
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head_dim = hidden_size // heads_num
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self.scale = qk_scale or head_dim**-0.5
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self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
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self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
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self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
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qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations)
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self.q_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
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)
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self.k_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
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)
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self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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def forward(
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self,
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x: torch.Tensor,
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motion_vec: torch.Tensor,
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motion_mask: Optional[torch.Tensor] = None,
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# use_context_parallel=False,
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) -> torch.Tensor:
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B, T, N, C = motion_vec.shape
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T_comp = T
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x_motion = self.pre_norm_motion(motion_vec)
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x_feat = self.pre_norm_feat(x)
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kv = self.linear1_kv(x_motion)
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q = self.linear1_q(x_feat)
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k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
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q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)
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# Apply QK-Norm if needed.
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q = self.q_norm(q).to(v)
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k = self.k_norm(k).to(v)
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k = rearrange(k, "B L N H D -> (B L) N H D")
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v = rearrange(v, "B L N H D -> (B L) N H D")
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q = rearrange(q, "B (L S) H D -> (B L) S (H D)", L=T_comp)
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attn = optimized_attention(q, k, v, heads=self.heads_num)
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attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp)
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output = self.linear2(attn)
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if motion_mask is not None:
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output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)
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return output
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# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162
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def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
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_, minor, in_h, in_w = input.shape
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kernel_h, kernel_w = kernel.shape
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out = input.view(-1, minor, in_h, 1, in_w, 1)
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out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
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out = out.view(-1, minor, in_h * up_y, in_w * up_x)
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out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
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out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)]
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out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
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w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
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out = F.conv2d(out, w)
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out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
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return out[:, :, ::down_y, ::down_x]
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
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# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81
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class FusedLeakyReLU(torch.nn.Module):
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def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None):
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super().__init__()
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self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device))
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale)
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def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
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return F.leaky_relu(input + bias, negative_slope) * scale
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class Blur(torch.nn.Module):
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def __init__(self, kernel, pad, dtype=None, device=None):
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super().__init__()
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kernel = torch.tensor(kernel, dtype=dtype, device=device)
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kernel = kernel[None, :] * kernel[:, None]
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kernel = kernel / kernel.sum()
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self.register_buffer('kernel', kernel)
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self.pad = pad
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def forward(self, input):
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return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad)
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#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590
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class ScaledLeakyReLU(torch.nn.Module):
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def __init__(self, negative_slope=0.2):
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super().__init__()
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self.negative_slope = negative_slope
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def forward(self, input):
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return F.leaky_relu(input, negative_slope=self.negative_slope)
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# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605
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class EqualConv2d(torch.nn.Module):
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def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype))
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self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
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self.stride = stride
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self.padding = padding
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self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None
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def forward(self, input):
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if self.bias is None:
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bias = None
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else:
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bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype)
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return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding)
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# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134
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class EqualLinear(torch.nn.Module):
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def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype))
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self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None
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self.activation = activation
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul
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self.lr_mul = lr_mul
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def forward(self, input):
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if self.bias is None:
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bias = None
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else:
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bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul
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if self.activation:
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out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale)
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return fused_leaky_relu(out, bias)
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return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias)
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# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654
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class ConvLayer(torch.nn.Sequential):
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def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None):
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layers = []
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if downsample:
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factor = 2
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p = (len(blur_kernel) - factor) + (kernel_size - 1)
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layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2)))
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stride, padding = 2, 0
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else:
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stride, padding = 1, kernel_size // 2
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layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations))
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if activate:
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layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2))
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super().__init__(*layers)
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# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704
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class ResBlock(torch.nn.Module):
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def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None):
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super().__init__()
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self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations)
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self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations)
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self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations)
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def forward(self, input):
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out = self.conv2(self.conv1(input))
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skip = self.skip(input)
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return (out + skip) / math.sqrt(2)
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class EncoderApp(torch.nn.Module):
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def __init__(self, w_dim=512, dtype=None, device=None, operations=None):
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super().__init__()
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kwargs = {"device": device, "dtype": dtype, "operations": operations}
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self.convs = torch.nn.ModuleList([
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ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs),
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ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs),
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ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs),
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ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs),
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EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs)
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])
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def forward(self, x):
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h = x
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for conv in self.convs:
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h = conv(h)
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return h.squeeze(-1).squeeze(-1)
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class Encoder(torch.nn.Module):
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def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None):
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super().__init__()
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self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations)
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self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)])
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||||
def encode_motion(self, x):
|
||||
return self.fc(self.net_app(x))
|
||||
|
||||
class Direction(torch.nn.Module):
|
||||
def __init__(self, motion_dim, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype))
|
||||
self.motion_dim = motion_dim
|
||||
|
||||
def forward(self, input):
|
||||
stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype)
|
||||
Q, _ = torch.linalg.qr(stabilized_weight.float())
|
||||
if input is None:
|
||||
return Q
|
||||
return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1)
|
||||
|
||||
class Synthesis(torch.nn.Module):
|
||||
def __init__(self, motion_dim, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations)
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations)
|
||||
self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations)
|
||||
|
||||
def get_motion(self, img):
|
||||
motion_feat = self.enc.encode_motion(img)
|
||||
return self.dec.direction(motion_feat)
|
||||
|
||||
class AnimateWanModel(WanModel):
|
||||
r"""
|
||||
Wan diffusion backbone supporting both text-to-video and image-to-video.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
model_type='animate',
|
||||
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,
|
||||
flf_pos_embed_token_number=None,
|
||||
motion_encoder_dim=512,
|
||||
image_model=None,
|
||||
device=None,
|
||||
dtype=None,
|
||||
operations=None,
|
||||
):
|
||||
|
||||
super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
|
||||
|
||||
self.pose_patch_embedding = operations.Conv3d(
|
||||
16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype
|
||||
)
|
||||
|
||||
self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations)
|
||||
|
||||
self.face_adapter = FaceAdapter(
|
||||
heads_num=self.num_heads,
|
||||
hidden_dim=self.dim,
|
||||
num_adapter_layers=self.num_layers // 5,
|
||||
device=device, dtype=dtype, operations=operations
|
||||
)
|
||||
|
||||
self.face_encoder = FaceEncoder(
|
||||
in_dim=motion_encoder_dim,
|
||||
hidden_dim=self.dim,
|
||||
num_heads=4,
|
||||
device=device, dtype=dtype, operations=operations
|
||||
)
|
||||
|
||||
def after_patch_embedding(self, x, pose_latents, face_pixel_values):
|
||||
if pose_latents is not None:
|
||||
pose_latents = self.pose_patch_embedding(pose_latents)
|
||||
x[:, :, 1:] += pose_latents
|
||||
|
||||
if face_pixel_values is None:
|
||||
return x, None
|
||||
|
||||
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)
|
||||
|
||||
if motion_vec.shape[1] < x.shape[2]:
|
||||
B, L, H, C = motion_vec.shape
|
||||
pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec)
|
||||
motion_vec = torch.cat([motion_vec, pad], dim=1)
|
||||
else:
|
||||
motion_vec = motion_vec[:, :x.shape[2]]
|
||||
return x, motion_vec
|
||||
|
||||
def forward_orig(
|
||||
self,
|
||||
x,
|
||||
t,
|
||||
context,
|
||||
clip_fea=None,
|
||||
pose_latents=None,
|
||||
face_pixel_values=None,
|
||||
freqs=None,
|
||||
transformer_options={},
|
||||
**kwargs,
|
||||
):
|
||||
# embeddings
|
||||
x = self.patch_embedding(x.float()).to(x.dtype)
|
||||
x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
|
||||
grid_sizes = x.shape[2:]
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
|
||||
# time embeddings
|
||||
e = self.time_embedding(
|
||||
sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
|
||||
e = e.reshape(t.shape[0], -1, e.shape[-1])
|
||||
e0 = self.time_projection(e).unflatten(2, (6, self.dim))
|
||||
|
||||
full_ref = None
|
||||
if self.ref_conv is not None:
|
||||
full_ref = kwargs.get("reference_latent", None)
|
||||
if full_ref is not None:
|
||||
full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
|
||||
x = torch.concat((full_ref, x), dim=1)
|
||||
|
||||
# context
|
||||
context = self.text_embedding(context)
|
||||
|
||||
context_img_len = None
|
||||
if clip_fea is not None:
|
||||
if self.img_emb is not None:
|
||||
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
|
||||
context = torch.concat([context_clip, context], dim=1)
|
||||
context_img_len = clip_fea.shape[-2]
|
||||
|
||||
patches_replace = transformer_options.get("patches_replace", {})
|
||||
blocks_replace = patches_replace.get("dit", {})
|
||||
for i, block in enumerate(self.blocks):
|
||||
if ("double_block", i) in blocks_replace:
|
||||
def block_wrap(args):
|
||||
out = {}
|
||||
out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
|
||||
return out
|
||||
out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
|
||||
x = out["img"]
|
||||
else:
|
||||
x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
|
||||
|
||||
if i % 5 == 0 and motion_vec is not None:
|
||||
x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec)
|
||||
|
||||
# head
|
||||
x = self.head(x, e)
|
||||
|
||||
if full_ref is not None:
|
||||
x = x[:, full_ref.shape[1]:]
|
||||
|
||||
# unpatchify
|
||||
x = self.unpatchify(x, grid_sizes)
|
||||
return x
|
||||
@ -39,6 +39,7 @@ import comfy.ldm.cosmos.model
|
||||
import comfy.ldm.cosmos.predict2
|
||||
import comfy.ldm.lumina.model
|
||||
import comfy.ldm.wan.model
|
||||
import comfy.ldm.wan.model_animate
|
||||
import comfy.ldm.hunyuan3d.model
|
||||
import comfy.ldm.hidream.model
|
||||
import comfy.ldm.chroma.model
|
||||
@ -1253,6 +1254,23 @@ class WAN21_HuMo(WAN21):
|
||||
|
||||
return out
|
||||
|
||||
class WAN22_Animate(WAN21):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
|
||||
super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel)
|
||||
self.image_to_video = image_to_video
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
|
||||
face_video_pixels = kwargs.get("face_video_pixels", None)
|
||||
if face_video_pixels is not None:
|
||||
out['face_pixel_values'] = comfy.conds.CONDRegular(face_video_pixels)
|
||||
|
||||
pose_latents = kwargs.get("pose_video_latent", None)
|
||||
if pose_latents is not None:
|
||||
out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents))
|
||||
return out
|
||||
|
||||
class WAN22_S2V(WAN21):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)
|
||||
|
||||
@ -404,6 +404,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
dit_config["model_type"] = "s2v"
|
||||
elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys:
|
||||
dit_config["model_type"] = "humo"
|
||||
elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config["model_type"] = "animate"
|
||||
else:
|
||||
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
|
||||
dit_config["model_type"] = "i2v"
|
||||
|
||||
@ -1096,6 +1096,19 @@ class WAN22_S2V(WAN21_T2V):
|
||||
out = model_base.WAN22_S2V(self, device=device)
|
||||
return out
|
||||
|
||||
class WAN22_Animate(WAN21_T2V):
|
||||
unet_config = {
|
||||
"image_model": "wan2.1",
|
||||
"model_type": "animate",
|
||||
}
|
||||
|
||||
def __init__(self, unet_config):
|
||||
super().__init__(unet_config)
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.WAN22_Animate(self, device=device)
|
||||
return out
|
||||
|
||||
class WAN22_T2V(WAN21_T2V):
|
||||
unet_config = {
|
||||
"image_model": "wan2.1",
|
||||
@ -1361,6 +1374,6 @@ class HunyuanImage21Refiner(HunyuanVideo):
|
||||
out = model_base.HunyuanImage21Refiner(self, device=device)
|
||||
return out
|
||||
|
||||
models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage]
|
||||
models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
||||
@ -1108,6 +1108,89 @@ class WanHuMoImageToVideo(io.ComfyNode):
|
||||
out_latent["samples"] = latent
|
||||
return io.NodeOutput(positive, negative, out_latent)
|
||||
|
||||
class WanAnimateToVideo(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanAnimateToVideo",
|
||||
category="conditioning/video_models",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("length", default=77, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
|
||||
io.Image.Input("reference_image", optional=True),
|
||||
io.Image.Input("face_video", optional=True),
|
||||
io.Image.Input("pose_video", optional=True),
|
||||
io.Int.Input("continue_motion_max_frames", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
io.Image.Input("continue_motion", optional=True),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(display_name="positive"),
|
||||
io.Conditioning.Output(display_name="negative"),
|
||||
io.Latent.Output(display_name="latent"),
|
||||
io.Int.Output(display_name="trim_latent"),
|
||||
],
|
||||
is_experimental=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, positive, negative, vae, width, height, length, batch_size, continue_motion_max_frames, reference_image=None, clip_vision_output=None, face_video=None, pose_video=None, continue_motion=None) -> io.NodeOutput:
|
||||
latent_length = ((length - 1) // 4) + 1
|
||||
latent_width = width // 8
|
||||
latent_height = height // 8
|
||||
trim_latent = 0
|
||||
|
||||
if reference_image is None:
|
||||
reference_image = torch.zeros((1, height, width, 3))
|
||||
|
||||
image = comfy.utils.common_upscale(reference_image[:length].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
|
||||
concat_latent_image = vae.encode(image[:, :, :, :3])
|
||||
mask = torch.zeros((1, 1, concat_latent_image.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=concat_latent_image.device, dtype=concat_latent_image.dtype)
|
||||
trim_latent += concat_latent_image.shape[2]
|
||||
|
||||
if clip_vision_output is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
|
||||
|
||||
if face_video is not None:
|
||||
face_video = comfy.utils.common_upscale(face_video[:length].movedim(-1, 1), 512, 512, "area", "center") * 2.0 - 1.0
|
||||
face_video = face_video.movedim(0, 1).unsqueeze(0)
|
||||
positive = node_helpers.conditioning_set_values(positive, {"face_video_pixels": face_video})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"face_video_pixels": face_video * 0.0 - 1.0})
|
||||
|
||||
if pose_video is not None:
|
||||
pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
|
||||
pose_video_latent = vae.encode(pose_video[:, :, :, :3])
|
||||
positive = node_helpers.conditioning_set_values(positive, {"pose_video_latent": pose_video_latent})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"pose_video_latent": pose_video_latent})
|
||||
|
||||
if continue_motion is None:
|
||||
image = torch.ones((length, height, width, 3)) * 0.5
|
||||
else:
|
||||
continue_motion = continue_motion[-continue_motion_max_frames:]
|
||||
continue_motion = comfy.utils.common_upscale(continue_motion[-length:].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
|
||||
image = torch.ones((length, height, width, continue_motion.shape[-1]), device=continue_motion.device, dtype=continue_motion.dtype) * 0.5
|
||||
image[:continue_motion.shape[0]] = continue_motion
|
||||
|
||||
concat_latent_image = torch.cat((concat_latent_image, vae.encode(image[:, :, :, :3])), dim=2)
|
||||
mask_refmotion = torch.ones((1, 1, latent_length, concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=mask.device, dtype=mask.dtype)
|
||||
if continue_motion is not None:
|
||||
mask_refmotion[:, :, :((continue_motion.shape[0] - 1) // 4) + 1] = 0.0
|
||||
|
||||
mask = torch.cat((mask, mask_refmotion), dim=2)
|
||||
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
|
||||
|
||||
latent = torch.zeros([batch_size, 16, latent_length + trim_latent, latent_height, latent_width], device=comfy.model_management.intermediate_device())
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent
|
||||
return io.NodeOutput(positive, negative, out_latent, trim_latent)
|
||||
|
||||
class Wan22ImageToVideoLatent(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
@ -1169,6 +1252,7 @@ class WanExtension(ComfyExtension):
|
||||
WanSoundImageToVideo,
|
||||
WanSoundImageToVideoExtend,
|
||||
WanHuMoImageToVideo,
|
||||
WanAnimateToVideo,
|
||||
Wan22ImageToVideoLatent,
|
||||
]
|
||||
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user