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DreamX-Creator 1.0 on ZeroGPU: vendored videox_fun + dreamx_inference from AMAP-ML upstream; generate(image, prompt)->(mp4, last-frame PNG, seed), neutral keyframe when image empty, DREAMX_CKPT_DIR for persistent checkpoints, diffusers 0.37.1 stack
982899c | # Modified from https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py | |
| # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. | |
| import glob | |
| import json | |
| import math | |
| import os | |
| import logging | |
| import torch | |
| import torch.cuda.amp as amp | |
| import torch.nn as nn | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.loaders.single_file_model import FromOriginalModelMixin | |
| from diffusers.models.modeling_utils import ModelMixin | |
| from .attention_utils import attention | |
| 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 | |
| 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 | |
| # Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid | |
| def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): | |
| tw = tgt_width | |
| th = tgt_height | |
| h, w = src | |
| r = h / w | |
| if r > (th / tw): | |
| resize_height = th | |
| resize_width = int(round(th / h * w)) | |
| else: | |
| resize_width = tw | |
| resize_height = int(round(tw / w * h)) | |
| crop_top = int(round((th - resize_height) / 2.0)) | |
| crop_left = int(round((tw - resize_width) / 2.0)) | |
| return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) | |
| def rope_apply(x, grid_sizes, freqs): | |
| n, c = x.size(2), x.size(3) // 2 | |
| # split freqs | |
| freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) | |
| # loop over samples | |
| output = [] | |
| for i, (f, h, w) in enumerate(grid_sizes.tolist()): | |
| seq_len = f * h * w | |
| # precompute multipliers | |
| x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float32).reshape( | |
| seq_len, 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) | |
| # apply rotary embedding | |
| x_i = torch.view_as_real(x_i * freqs_i).flatten(2) | |
| x_i = torch.cat([x_i, x[i, seq_len:]]) | |
| # append to collection | |
| output.append(x_i) | |
| return torch.stack(output).to(x.dtype) | |
| def rope_apply_qk(q, k, grid_sizes, freqs): | |
| q = rope_apply(q, grid_sizes, freqs) | |
| k = rope_apply(k, grid_sizes, freqs) | |
| return q, k | |
| 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, x): | |
| r""" | |
| Args: | |
| x(Tensor): Shape [B, L, C] | |
| """ | |
| return super().forward(x.float()).type_as(x) | |
| 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, dtype=torch.bfloat16, t=0, **kwargs): | |
| r""" | |
| 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.to(dtype))).view(b, s, n, d) | |
| k = self.norm_k(self.k(x.to(dtype))).view(b, s, n, d) | |
| v = self.v(x.to(dtype)).view(b, s, n, d) | |
| return q, k, v | |
| q, k, v = qkv_fn(x) | |
| q, k = rope_apply_qk(q, k, grid_sizes, freqs) | |
| x = attention( | |
| q.to(dtype), | |
| k.to(dtype), | |
| v=v.to(dtype), | |
| k_lens=seq_lens, | |
| window_size=self.window_size) | |
| x = x.to(dtype) | |
| # output | |
| x = x.flatten(2) | |
| x = self.o(x) | |
| return x | |
| class WanT2VCrossAttention(WanSelfAttention): | |
| def forward(self, x, context, context_lens, dtype=torch.bfloat16, t=0): | |
| r""" | |
| Args: | |
| x(Tensor): Shape [B, L1, C] | |
| context(Tensor): Shape [B, L2, C] | |
| context_lens(Tensor): Shape [B] | |
| """ | |
| b, n, d = x.size(0), self.num_heads, self.head_dim | |
| # compute query, key, value | |
| q = self.norm_q(self.q(x.to(dtype))).view(b, -1, n, d) | |
| k = self.norm_k(self.k(context.to(dtype))).view(b, -1, n, d) | |
| v = self.v(context.to(dtype)).view(b, -1, n, d) | |
| # compute attention | |
| x = attention( | |
| q.to(dtype), | |
| k.to(dtype), | |
| v.to(dtype), | |
| k_lens=context_lens | |
| ) | |
| x = x.to(dtype) | |
| # 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, x, context, context_lens, dtype=torch.bfloat16, t=0): | |
| r""" | |
| Args: | |
| x(Tensor): Shape [B, L1, C] | |
| context(Tensor): Shape [B, L2, C] | |
| context_lens(Tensor): Shape [B] | |
| """ | |
| 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.to(dtype))).view(b, -1, n, d) | |
| k = self.norm_k(self.k(context.to(dtype))).view(b, -1, n, d) | |
| v = self.v(context.to(dtype)).view(b, -1, n, d) | |
| k_img = self.norm_k_img(self.k_img(context_img.to(dtype))).view(b, -1, n, d) | |
| v_img = self.v_img(context_img.to(dtype)).view(b, -1, n, d) | |
| img_x = attention( | |
| q.to(dtype), | |
| k_img.to(dtype), | |
| v_img.to(dtype), | |
| k_lens=None | |
| ) | |
| img_x = img_x.to(dtype) | |
| # compute attention | |
| x = attention( | |
| q.to(dtype), | |
| k.to(dtype), | |
| v.to(dtype), | |
| k_lens=context_lens | |
| ) | |
| x = x.to(dtype) | |
| # output | |
| x = x.flatten(2) | |
| img_x = img_x.flatten(2) | |
| x = x + img_x | |
| x = self.o(x) | |
| return x | |
| class WanCrossAttention(WanSelfAttention): | |
| def forward(self, x, context, context_lens, dtype=torch.bfloat16, t=0): | |
| r""" | |
| Args: | |
| x(Tensor): Shape [B, L1, C] | |
| context(Tensor): Shape [B, L2, C] | |
| context_lens(Tensor): Shape [B] | |
| """ | |
| b, n, d = x.size(0), self.num_heads, self.head_dim | |
| # compute query, key, value | |
| q = self.norm_q(self.q(x.to(dtype))).view(b, -1, n, d) | |
| k = self.norm_k(self.k(context.to(dtype))).view(b, -1, n, d) | |
| v = self.v(context.to(dtype)).view(b, -1, n, d) | |
| # compute attention | |
| x = attention(q.to(dtype), k.to(dtype), v.to(dtype), k_lens=context_lens) | |
| # output | |
| x = x.flatten(2) | |
| x = self.o(x.to(dtype)) | |
| return x | |
| WAN_CROSSATTENTION_CLASSES = { | |
| 't2v_cross_attn': WanT2VCrossAttention, | |
| 'i2v_cross_attn': WanI2VCrossAttention, | |
| 'cross_attn': WanCrossAttention, | |
| } | |
| 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, | |
| **kwargs): | |
| 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) | |
| def forward( | |
| self, | |
| x, | |
| e, | |
| seq_lens, | |
| grid_sizes, | |
| freqs, | |
| context, | |
| context_lens, | |
| dtype=torch.bfloat16, | |
| t=0, | |
| group=None, | |
| ): | |
| r""" | |
| Args: | |
| x(Tensor): Shape [B, L, C] | |
| e(Tensor): Shape [B, 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] | |
| """ | |
| if e.dim() > 3: | |
| e = (self.modulation.unsqueeze(0) + e).chunk(6, dim=2) | |
| e = [e.squeeze(2) for e in e] | |
| else: | |
| e = (self.modulation + e).chunk(6, dim=1) | |
| # self-attention (RoPE branch) | |
| temp_x = self.norm1(x) * (1 + e[1]) + e[0] | |
| temp_x = temp_x.to(dtype) | |
| y = self.self_attn(temp_x, seq_lens, grid_sizes, freqs, dtype, group=group) | |
| x = x + y * e[2] | |
| # cross-attention & ffn function | |
| def cross_attn_ffn(x, context, context_lens, e): | |
| # cross-attention | |
| x = x + self.cross_attn(self.norm3(x), context, context_lens, dtype) | |
| # ffn function | |
| temp_x = self.norm2(x) * (1 + e[4]) + e[3] | |
| temp_x = temp_x.to(dtype) | |
| y = self.ffn(temp_x) | |
| x = x + y * e[5] | |
| return x | |
| x = cross_attn_ffn(x, context, context_lens, e) | |
| 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] | |
| """ | |
| if e.dim() > 2: | |
| e = (self.modulation.unsqueeze(0) + e.unsqueeze(2)).chunk(2, dim=2) | |
| e = [e.squeeze(2) for e in e] | |
| else: | |
| e = (self.modulation + 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 WanTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin): | |
| 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'] | |
| def __init__( | |
| self, | |
| 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, | |
| in_channels=16, | |
| hidden_size=2048, | |
| add_control_adapter=True, | |
| control_adapter_type="baseline", | |
| in_dim_control_adapter=24, | |
| downscale_factor_control_adapter=8, | |
| add_ref_conv=False, | |
| in_dim_ref_conv=16, | |
| cross_attn_type=None, | |
| cam_method='prope', | |
| attn_compress=2, | |
| cam_self_attn_layers=None, | |
| traning=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', 'ti2v'] | |
| 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 | |
| # 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 | |
| if cross_attn_type is None: | |
| if model_type == "t2v": | |
| cross_attn_type = 't2v_cross_attn' | |
| else: | |
| cross_attn_type = 'i2v_cross_attn' | |
| # else: | |
| # cross_attn_type = "cross_attn" | |
| attn_class = WanAttentionBlock # by default use WanAttentionBlock | |
| self.control_adapter = None | |
| self.blocks = nn.ModuleList([ | |
| attn_class(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()) | |
| assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 | |
| d = dim // num_heads | |
| self.d = d | |
| self.dim = dim | |
| 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) | |
| if add_ref_conv: | |
| self.ref_conv = nn.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:]) | |
| else: | |
| self.ref_conv = None | |
| self.customize_initialize_weights() | |
| def customize_initialize_weights(self): | |
| self.init_weights() | |
| def forward( | |
| self, | |
| x, | |
| t, | |
| context, | |
| seq_len, | |
| clip_fea=None, | |
| y=None, | |
| y_camera=None, | |
| full_ref=None, | |
| subject_ref=None, | |
| cond_flag=True, | |
| dtype=torch.bfloat16, | |
| ): | |
| r""" | |
| Forward pass through the diffusion model | |
| Args: | |
| x (List[Tensor]): | |
| List of input video tensors, each with shape [C_in, F, H, W] | |
| t (Tensor): | |
| Diffusion timesteps tensor of shape [B] | |
| context (List[Tensor]): | |
| List of text embeddings each with shape [L, C] | |
| seq_len (`int`): | |
| Maximum sequence length for positional encoding | |
| clip_fea (Tensor, *optional*): | |
| CLIP image features for image-to-video mode | |
| y (List[Tensor], *optional*): | |
| Conditional video inputs for image-to-video mode, same shape as x | |
| cond_flag (`bool`, *optional*, defaults to True): | |
| Flag to indicate whether to forward the condition input | |
| Returns: | |
| List[Tensor]: | |
| List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] | |
| """ | |
| device = self.patch_embedding.weight.device | |
| dtype = x[0].dtype | |
| if self.freqs.device != device and torch.device(type="meta") != 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] | |
| grid_sizes = torch.stack( | |
| [torch.tensor(u.shape[2:], dtype=torch.long, device=device) 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, device=device) | |
| 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): | |
| if t.dim() != 1: | |
| if t.size(1) < seq_len: | |
| pad_size = seq_len - t.size(1) | |
| last_elements = t[:, -1].unsqueeze(1) | |
| padding = last_elements.repeat(1, pad_size) | |
| t = torch.cat([t, padding], dim=1) | |
| bt = t.size(0) | |
| ft = t.flatten() | |
| e = self.time_embedding( | |
| sinusoidal_embedding_1d(self.freq_dim, | |
| ft).unflatten(0, (bt, seq_len)).float()) | |
| e0 = self.time_projection(e).unflatten(2, (6, self.dim)) | |
| else: | |
| e = self.time_embedding( | |
| sinusoidal_embedding_1d(self.freq_dim, t).float()) | |
| e0 = self.time_projection(e).unflatten(1, (6, self.dim)) | |
| # 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 clip_fea is not None: | |
| # context_clip = self.img_emb(clip_fea) # bs x 257 x dim | |
| # context = torch.concat([context_clip, context], dim=1) | |
| for block in self.blocks: | |
| x = block( | |
| x, e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, | |
| freqs=self.freqs, context=context, context_lens=context_lens, | |
| dtype=dtype, t=t, group=None, | |
| ) | |
| x = self.head(x, e) | |
| return torch.stack(self.unpatchify(x, grid_sizes)) | |
| 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) | |
| def from_pretrained( | |
| cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs={}, | |
| low_cpu_mem_usage=False, torch_dtype=torch.bfloat16 | |
| ): | |
| if subfolder is not None: | |
| pretrained_model_path = os.path.join(pretrained_model_path, subfolder) | |
| config_file = os.path.join(pretrained_model_path, 'config.json') | |
| if not os.path.isfile(config_file): | |
| raise RuntimeError(f"{config_file} does not exist") | |
| with open(config_file, "r") as f: | |
| config = json.load(f) | |
| from diffusers.utils import WEIGHTS_NAME | |
| model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME) | |
| model_file_safetensors = model_file.replace(".bin", ".safetensors") | |
| if "dict_mapping" in transformer_additional_kwargs.keys(): | |
| for key in transformer_additional_kwargs["dict_mapping"]: | |
| transformer_additional_kwargs[transformer_additional_kwargs["dict_mapping"][key]] = config[key] | |
| if low_cpu_mem_usage: | |
| try: | |
| import re | |
| from diffusers import __version__ as diffusers_version | |
| if diffusers_version >= "0.33.0": | |
| from diffusers.models.model_loading_utils import \ | |
| load_model_dict_into_meta | |
| else: | |
| from diffusers.models.modeling_utils import \ | |
| load_model_dict_into_meta | |
| from diffusers.utils import is_accelerate_available | |
| if is_accelerate_available(): | |
| import accelerate | |
| # Instantiate model with empty weights | |
| with accelerate.init_empty_weights(): | |
| model = cls.from_config(config, **transformer_additional_kwargs) | |
| param_device = "cpu" | |
| if os.path.exists(model_file): | |
| state_dict = torch.load(model_file, map_location="cpu") | |
| elif os.path.exists(model_file_safetensors): | |
| from safetensors.torch import load_file, safe_open | |
| state_dict = load_file(model_file_safetensors) | |
| else: | |
| from safetensors.torch import load_file, safe_open | |
| model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors")) | |
| state_dict = {} | |
| for _model_file_safetensors in model_files_safetensors: | |
| _state_dict = load_file(_model_file_safetensors) | |
| for key in _state_dict: | |
| state_dict[key] = _state_dict[key] | |
| if model.state_dict()['patch_embedding.weight'].size() != state_dict['patch_embedding.weight'].size(): | |
| model.state_dict()['patch_embedding.weight'][:, :state_dict['patch_embedding.weight'].size()[1], :, :] = state_dict['patch_embedding.weight'][:, :model.state_dict()['patch_embedding.weight'].size()[1], :, :] | |
| model.state_dict()['patch_embedding.weight'][:, state_dict['patch_embedding.weight'].size()[1]:, :, :] = 0 | |
| state_dict['patch_embedding.weight'] = model.state_dict()['patch_embedding.weight'] | |
| filtered_state_dict = {} | |
| for key in state_dict: | |
| if key in model.state_dict() and model.state_dict()[key].size() == state_dict[key].size(): | |
| filtered_state_dict[key] = state_dict[key] | |
| model_keys = set(model.state_dict().keys()) | |
| loaded_keys = set(filtered_state_dict.keys()) | |
| missing_keys = model_keys - loaded_keys | |
| def initialize_missing_parameters(missing_keys, model_state_dict, torch_dtype=None): | |
| initialized_dict = {} | |
| with torch.no_grad(): | |
| for key in missing_keys: | |
| param_shape = model_state_dict[key].shape | |
| param_dtype = torch_dtype if torch_dtype is not None else model_state_dict[key].dtype | |
| if 'weight' in key: | |
| if any(norm_type in key for norm_type in ['norm', 'ln_', 'layer_norm', 'group_norm', 'batch_norm']): | |
| initialized_dict[key] = torch.ones(param_shape, dtype=param_dtype) | |
| elif 'embedding' in key or 'embed' in key: | |
| initialized_dict[key] = torch.randn(param_shape, dtype=param_dtype) * 0.02 | |
| elif 'head' in key or 'output' in key or 'proj_out' in key or 'project_out' in key or "out" in key or 'out_proj' in key: | |
| logging.info(f"Initialize {key} with zeros") | |
| initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype) | |
| elif len(param_shape) >= 2: | |
| initialized_dict[key] = torch.empty(param_shape, dtype=param_dtype) | |
| nn.init.xavier_uniform_(initialized_dict[key]) | |
| else: | |
| initialized_dict[key] = torch.randn(param_shape, dtype=param_dtype) * 0.02 | |
| elif 'bias' in key: | |
| initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype) | |
| elif 'running_mean' in key: | |
| initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype) | |
| elif 'running_var' in key: | |
| initialized_dict[key] = torch.ones(param_shape, dtype=param_dtype) | |
| elif 'num_batches_tracked' in key: | |
| initialized_dict[key] = torch.zeros(param_shape, dtype=torch.long) | |
| else: | |
| initialized_dict[key] = torch.zeros(param_shape, dtype=param_dtype) | |
| return initialized_dict | |
| if missing_keys: | |
| initialized_params = initialize_missing_parameters( | |
| missing_keys, | |
| model.state_dict(), | |
| torch_dtype | |
| ) | |
| filtered_state_dict.update(initialized_params) | |
| # logging.info(f"Weights of Camera_adapter: {initialized_params}") | |
| if diffusers_version >= "0.33.0": | |
| # Diffusers has refactored `load_model_dict_into_meta` since version 0.33.0 in this commit: | |
| # https://github.com/huggingface/diffusers/commit/f5929e03060d56063ff34b25a8308833bec7c785. | |
| load_model_dict_into_meta( | |
| model, | |
| filtered_state_dict, | |
| dtype=torch_dtype, | |
| model_name_or_path=pretrained_model_path, | |
| ) | |
| else: | |
| model._convert_deprecated_attention_blocks(filtered_state_dict) | |
| unexpected_keys = load_model_dict_into_meta( | |
| model, | |
| filtered_state_dict, | |
| device=param_device, | |
| dtype=torch_dtype, | |
| model_name_or_path=pretrained_model_path, | |
| ) | |
| if cls._keys_to_ignore_on_load_unexpected is not None: | |
| for pat in cls._keys_to_ignore_on_load_unexpected: | |
| unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] | |
| if len(unexpected_keys) > 0: | |
| logging.warning("Unused checkpoint keys: %d", len(unexpected_keys)) | |
| return model | |
| except Exception: | |
| logging.warning("Falling back to regular transformer loading") | |
| model = cls.from_config(config, **transformer_additional_kwargs) | |
| if os.path.exists(model_file): | |
| state_dict = torch.load(model_file, map_location="cpu") | |
| elif os.path.exists(model_file_safetensors): | |
| from safetensors.torch import load_file, safe_open | |
| state_dict = load_file(model_file_safetensors) | |
| else: | |
| from safetensors.torch import load_file, safe_open | |
| model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors")) | |
| state_dict = {} | |
| for _model_file_safetensors in model_files_safetensors: | |
| _state_dict = load_file(_model_file_safetensors) | |
| for key in _state_dict: | |
| state_dict[key] = _state_dict[key] | |
| if model.state_dict()['patch_embedding.weight'].size() != state_dict['patch_embedding.weight'].size(): | |
| model.state_dict()['patch_embedding.weight'][:, :state_dict['patch_embedding.weight'].size()[1], :, :] = state_dict['patch_embedding.weight'][:, :model.state_dict()['patch_embedding.weight'].size()[1], :, :] | |
| model.state_dict()['patch_embedding.weight'][:, state_dict['patch_embedding.weight'].size()[1]:, :, :] = 0 | |
| state_dict['patch_embedding.weight'] = model.state_dict()['patch_embedding.weight'] | |
| tmp_state_dict = {} | |
| for key in state_dict: | |
| if key in model.state_dict().keys() and model.state_dict()[key].size() == state_dict[key].size(): | |
| tmp_state_dict[key] = state_dict[key] | |
| state_dict = tmp_state_dict | |
| m, u = model.load_state_dict(state_dict, strict=False) | |
| model = model.to(torch_dtype) | |
| return model | |
| class Wan2_2Transformer3DModel(WanTransformer3DModel): | |
| 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'] | |
| def __init__( | |
| self, | |
| 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, | |
| in_channels=16, | |
| hidden_size=2048, | |
| add_control_adapter=False, | |
| in_dim_control_adapter=24, | |
| downscale_factor_control_adapter=8, | |
| add_ref_conv=False, | |
| control_adapter_type="baseline", | |
| in_dim_ref_conv=16, | |
| cam_method='prope', | |
| attn_compress=2, | |
| cam_self_attn_layers=None, | |
| ): | |
| 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__( | |
| model_type=model_type, | |
| 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, | |
| in_channels=in_channels, | |
| hidden_size=hidden_size, | |
| add_control_adapter=add_control_adapter, | |
| control_adapter_type=control_adapter_type, | |
| in_dim_control_adapter=in_dim_control_adapter, | |
| downscale_factor_control_adapter=downscale_factor_control_adapter, | |
| add_ref_conv=add_ref_conv, | |
| in_dim_ref_conv=in_dim_ref_conv, | |
| cross_attn_type="cross_attn", | |
| cam_method=cam_method, | |
| attn_compress=attn_compress, | |
| cam_self_attn_layers=cam_self_attn_layers, | |
| ) | |
| if hasattr(self, "img_emb"): | |
| del self.img_emb | |