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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import math

from typing import Any, Dict, Optional, Tuple, Union

import numpy as np

import torch

import torch.nn as nn

import torch.nn.functional as F

from ...configuration_utils import ConfigMixin, register_to_config

from ...loaders import FromOriginalModelMixin, PeftAdapterMixin

from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers

from ..attention import FeedForward

from ..attention_processor import Attention

from ..cache_utils import CacheMixin

from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed

from ..modeling_outputs import Transformer2DModelOutput

from ..modeling_utils import ModelMixin

from ..normalization import FP32LayerNorm

logger = logging.get_logger(__name__) # pylint: disable=invalid-name

class WanAttnProcessor2_0:

class SkyReelsV2AttnProcessor2_0:

def __init__(self):

if not hasattr(F, "scaled_dot_product_attention"):

raise ImportError("WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")

raise ImportError(

"SkyReelsV2AttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0."

)

self._flag_ar_attention = False

def __call__(

self,

attn: Attention,

hidden_states: torch.Tensor,

encoder_hidden_states: Optional[torch.Tensor] = None,

attention_mask: Optional[torch.Tensor] = None,

rotary_emb: Optional[torch.Tensor] = None,

) -> torch.Tensor:

encoder_hidden_states_img = None

if attn.add_k_proj is not None:

# 512 is the context length of the text encoder, hardcoded for now

image_context_length = encoder_hidden_states.shape[1] - 512

encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length]

encoder_hidden_states = encoder_hidden_states[:, image_context_length:]

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

query = attn.to_q(hidden_states)

key = attn.to_k(encoder_hidden_states)

value = attn.to_v(encoder_hidden_states)

if attn.norm_q is not None:

query = attn.norm_q(query)

if attn.norm_k is not None:

key = attn.norm_k(key)

query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)

key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)

value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)

if rotary_emb is not None:

def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):

x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2)))

x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4)

return x_out.type_as(hidden_states)

query = apply_rotary_emb(query, rotary_emb)

key = apply_rotary_emb(key, rotary_emb)

# I2V task

hidden_states_img = None

if encoder_hidden_states_img is not None:

key_img = attn.add_k_proj(encoder_hidden_states_img)

key_img = attn.norm_added_k(key_img)

value_img = attn.add_v_proj(encoder_hidden_states_img)

key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)

value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)

hidden_states_img = F.scaled_dot_product_attention(

query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False

)

hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3)

hidden_states_img = hidden_states_img.type_as(query)

hidden_states = F.scaled_dot_product_attention(

if self._flag_ar_attention:

query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False

is_self_attention = encoder_hidden_states == hidden_states

)

hidden_states = F.scaled_dot_product_attention(

query.to(torch.bfloat16) if is_self_attention else query,

key.to(torch.bfloat16) if is_self_attention else key,

value.to(torch.bfloat16) if is_self_attention else value,

attn_mask=attention_mask,

dropout_p=0.0,

is_causal=False,

)

else:

hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)

hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)

hidden_states = hidden_states.type_as(query)

if hidden_states_img is not None:

hidden_states = hidden_states + hidden_states_img

hidden_states = attn.to_out[0](hidden_states)

hidden_states = attn.to_out[1](hidden_states)

return hidden_states

def set_ar_attention(self):

self._flag_ar_attention = True

class WanImageEmbedding(torch.nn.Module):

class SkyReelsV2ImageEmbedding(torch.nn.Module):

def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):

super().__init__()

self.norm1 = FP32LayerNorm(in_features)

self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu")

self.norm2 = FP32LayerNorm(out_features)

if pos_embed_seq_len is not None:

self.pos_embed = nn.Parameter(torch.zeros(1, pos_embed_seq_len, in_features))

else:

self.pos_embed = None

def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:

if self.pos_embed is not None:

batch_size, seq_len, embed_dim = encoder_hidden_states_image.shape

encoder_hidden_states_image = encoder_hidden_states_image.view(-1, 2 * seq_len, embed_dim)

encoder_hidden_states_image = encoder_hidden_states_image + self.pos_embed

hidden_states = self.norm1(encoder_hidden_states_image)

hidden_states = self.ff(hidden_states)

hidden_states = self.norm2(hidden_states)

return hidden_states

class WanTimeTextImageEmbedding(nn.Module):

class SkyReelsV2TimeTextImageEmbedding(nn.Module):

def __init__(

self,

dim: int,

time_freq_dim: int,

time_proj_dim: int,

text_embed_dim: int,

image_embed_dim: Optional[int] = None,

pos_embed_seq_len: Optional[int] = None,

super().__init__()

self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)

self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim)

self.act_fn = nn.SiLU()

self.time_proj = nn.Linear(dim, time_proj_dim)

self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh")

self.image_embedder = None

if image_embed_dim is not None:

self.image_embedder = WanImageEmbedding(image_embed_dim, dim, pos_embed_seq_len=pos_embed_seq_len)

self.image_embedder = SkyReelsV2ImageEmbedding(image_embed_dim, dim, pos_embed_seq_len=pos_embed_seq_len)

def forward(

self,

timestep: torch.Tensor,

encoder_hidden_states: torch.Tensor,

encoder_hidden_states_image: Optional[torch.Tensor] = None,

timestep = self.timesteps_proj(timestep)

time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype

if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:

timestep = timestep.to(time_embedder_dtype)

temb = self.time_embedder(timestep).type_as(encoder_hidden_states)

timestep_proj = self.time_proj(self.act_fn(temb))

encoder_hidden_states = self.text_embedder(encoder_hidden_states)

if encoder_hidden_states_image is not None:

encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image)

return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image

class WanRotaryPosEmbed(nn.Module):

class SkyReelsV2RotaryPosEmbed(nn.Module):

def __init__(

self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0

super().__init__()

self.attention_head_dim = attention_head_dim

self.patch_size = patch_size

self.max_seq_len = max_seq_len

h_dim = w_dim = 2 * (attention_head_dim // 6)

t_dim = attention_head_dim - h_dim - w_dim

freqs = []

for dim in [t_dim, h_dim, w_dim]:

freq = get_1d_rotary_pos_embed(

dim, max_seq_len, theta, use_real=False, repeat_interleave_real=False, freqs_dtype=torch.float64

)

freqs.append(freq)

self.freqs = torch.cat(freqs, dim=1)

def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:

batch_size, num_channels, num_frames, height, width = hidden_states.shape

p_t, p_h, p_w = self.patch_size

ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w

freqs = self.freqs.to(hidden_states.device)

freqs = freqs.split_with_sizes(

[

self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6),

self.attention_head_dim // 6,

dim=1,

)

freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)

freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)

freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)

freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1).reshape(1, 1, ppf * pph * ppw, -1)

return freqs

class WanTransformerBlock(nn.Module):

class SkyReelsV2TransformerBlock(nn.Module):

def __init__(

self,

dim: int,

ffn_dim: int,

num_heads: int,

qk_norm: str = "rms_norm_across_heads",

qk_norm: str = "rms_norm",

cross_attn_norm: bool = False,

eps: float = 1e-6,

added_kv_proj_dim: Optional[int] = None,

super().__init__()

# 1. Self-attention

self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)

self.attn1 = Attention(

query_dim=dim,

heads=num_heads,

kv_heads=num_heads,

dim_head=dim // num_heads,

qk_norm=qk_norm,

eps=eps,

bias=True,

cross_attention_dim=None,

out_bias=True,

processor=WanAttnProcessor2_0(),

processor=SkyReelsV2AttnProcessor2_0(),

)

# 2. Cross-attention

self.attn2 = Attention(

query_dim=dim,

heads=num_heads,

kv_heads=num_heads,

dim_head=dim // num_heads,

qk_norm=qk_norm,

eps=eps,

bias=True,

cross_attention_dim=None,

out_bias=True,

added_kv_proj_dim=added_kv_proj_dim,

added_proj_bias=True,

processor=WanAttnProcessor2_0(),

processor=SkyReelsV2AttnProcessor2_0(),

)

self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()

# 3. Feed-forward

self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")

self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)

self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

def forward(

self,

hidden_states: torch.Tensor,

encoder_hidden_states: torch.Tensor,

temb: torch.Tensor,

rotary_emb: torch.Tensor,

attention_mask: torch.Tensor,

) -> torch.Tensor:

shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (

if temb.dim() == 3:

self.scale_shift_table + temb.float()

shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (

).chunk(6, dim=1)

self.scale_shift_table + temb.float()

).chunk(6, dim=1)

elif temb.dim() == 4:

e = (self.scale_shift_table.unsqueeze(2) + temb.float()).chunk(6, dim=1)

shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = [ei.squeeze(1) for ei in e]

# 1. Self-attention

norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)

attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)

attn_output = self.attn1(

hidden_states=norm_hidden_states, rotary_emb=rotary_emb, attention_mask=attention_mask

)

hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)

# 2. Cross-attention

norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)

attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)

hidden_states = hidden_states + attn_output

# 3. Feed-forward

norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(

hidden_states

)

ff_output = self.ffn(norm_hidden_states)

hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states)

return hidden_states

return hidden_states # TODO: check .to(torch.bfloat16)

def set_ar_attention(self):

self.attn1.processor.set_ar_attention()

class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):

class SkyReelsV2Transformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):

r"""

A Transformer model for video-like data used in the Wan model.

A Transformer model for video-like data used in the Wan-based SkyReels-V2 model.

Args:

patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):

3D patch dimensions for video embedding (t_patch, h_patch, w_patch).

num_attention_heads (`int`, defaults to `40`):

num_attention_heads (`int`, defaults to `16`):

Fixed length for text embeddings.

attention_head_dim (`int`, defaults to `128`):

The number of channels in each head.

in_channels (`int`, defaults to `16`):

The number of channels in the input.

out_channels (`int`, defaults to `16`):

The number of channels in the output.

text_dim (`int`, defaults to `512`):

text_dim (`int`, defaults to `4096`):

Input dimension for text embeddings.

freq_dim (`int`, defaults to `256`):

Dimension for sinusoidal time embeddings.

ffn_dim (`int`, defaults to `13824`):

ffn_dim (`int`, defaults to `8192`):

Intermediate dimension in feed-forward network.

num_layers (`int`, defaults to `40`):

num_layers (`int`, defaults to `32`):

The number of layers of transformer blocks to use.

window_size (`Tuple[int]`, defaults to `(-1, -1)`):

Window size for local attention (-1 indicates global attention).

cross_attn_norm (`bool`, defaults to `True`):

Enable cross-attention normalization.

qk_norm (`bool`, defaults to `True`):

qk_norm (`str`, *optional*, defaults to `"rms_norm"`):

Enable query/key normalization.

eps (`float`, defaults to `1e-6`):

Epsilon value for normalization layers.

add_img_emb (`bool`, defaults to `False`):

inject_sample_info (`bool`, defaults to `False`):

Whether to use img_emb.

Whether to inject sample information into the model.

added_kv_proj_dim (`int`, *optional*, defaults to `None`):

image_dim (`int`, *optional*):

The number of channels to use for the added key and value projections. If `None`, no projection is used.

The dimension of the image embeddings.

added_kv_proj_dim (`int`, *optional*):

The dimension of the added key/value projection.

rope_max_seq_len (`int`, defaults to `1024`):

The maximum sequence length for the rotary embeddings.

pos_embed_seq_len (`int`, *optional*):

The sequence length for the positional embeddings.

"""

_supports_gradient_checkpointing = True

_skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]

_no_split_modules = ["WanTransformerBlock"]

_keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]

_keys_to_ignore_on_load_unexpected = ["norm_added_q"]

@register_to_config

def __init__(

self,

patch_size: Tuple[int] = (1, 2, 2),

num_attention_heads: int = 40,

num_attention_heads: int = 16,

attention_head_dim: int = 128,

in_channels: int = 16,

out_channels: int = 16,

text_dim: int = 4096,

freq_dim: int = 256,

ffn_dim: int = 13824,

ffn_dim: int = 8192,

num_layers: int = 40,

num_layers: int = 32,

cross_attn_norm: bool = True,

qk_norm: Optional[str] = "rms_norm_across_heads",

qk_norm: Optional[str] = "rms_norm",

eps: float = 1e-6,

image_dim: Optional[int] = None,

added_kv_proj_dim: Optional[int] = None,

rope_max_seq_len: int = 1024,

pos_embed_seq_len: Optional[int] = None,

inject_sample_info: bool = False,

) -> None:

super().__init__()

inner_dim = num_attention_heads * attention_head_dim

out_channels = out_channels or in_channels

self.num_frame_per_block = 1

self.flag_causal_attention = False

self.enable_teacache = False

# 1. Patch & position embedding

self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)

self.rope = SkyReelsV2RotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)

self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)

# 2. Condition embeddings

# image_embedding_dim=1280 for I2V model

self.condition_embedder = WanTimeTextImageEmbedding(

self.condition_embedder = SkyReelsV2TimeTextImageEmbedding(

dim=inner_dim,

time_freq_dim=freq_dim,

time_proj_dim=inner_dim * 6,

text_embed_dim=text_dim,

image_embed_dim=image_dim,

pos_embed_seq_len=pos_embed_seq_len,

)

# 3. Transformer blocks

self.blocks = nn.ModuleList(

[

WanTransformerBlock(

SkyReelsV2TransformerBlock(

inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim

inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim=inner_dim

)

for _ in range(num_layers)

]

)

# 4. Output norm & projection

self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)

self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))

self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)

self.gradient_checkpointing = False

if inject_sample_info:

self.fps_embedding = nn.Embedding(2, inner_dim)

self.fps_projection = nn.Sequential(

nn.Linear(inner_dim, inner_dim), nn.SiLU(), nn.Linear(inner_dim, inner_dim * 6)

)

def forward(

self,

hidden_states: torch.Tensor,

timestep: torch.LongTensor,

encoder_hidden_states: torch.Tensor,

encoder_hidden_states_image: Optional[torch.Tensor] = None,

fps: Optional[torch.Tensor] = None,

return_dict: bool = True,

attention_kwargs: Optional[Dict[str, Any]] = None,

) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:

if attention_kwargs is not None:

attention_kwargs = attention_kwargs.copy()

lora_scale = attention_kwargs.pop("scale", 1.0)

else:

lora_scale = 1.0

if USE_PEFT_BACKEND:

# weight the lora layers by setting `lora_scale` for each PEFT layer

scale_lora_layers(self, lora_scale)

else:

if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:

logger.warning(

"Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."

)

batch_size, num_channels, num_frames, height, width = hidden_states.shape

p_t, p_h, p_w = self.config.patch_size

post_patch_num_frames = num_frames // p_t

post_patch_height = height // p_h

post_patch_width = width // p_w

rotary_emb = self.rope(hidden_states)

hidden_states = self.patch_embedding(hidden_states)

grid_sizes = torch.tensor(hidden_states.shape[2:], dtype=torch.long)

if self.flag_causal_attention:

frame_num, height, width = grid_sizes

block_num = frame_num // self.num_frame_per_block

range_tensor = torch.arange(block_num, device=hidden_states.device).view(-1, 1)

range_tensor = range_tensor.repeat(1, self.num_frame_per_block).flatten()

causal_mask = range_tensor.unsqueeze(0) <= range_tensor.unsqueeze(1) # f, f

causal_mask = causal_mask.view(frame_num, 1, 1, frame_num, 1, 1)

causal_mask = causal_mask.repeat(1, height, width, 1, height, width)

causal_mask = causal_mask.reshape(frame_num * height * width, frame_num * height * width)

causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)

hidden_states = hidden_states.flatten(2).transpose(1, 2)

# TODO: check here

if timestep.dim() == 2:

b, f = timestep.shape

_flag_df = True

else:

_flag_df = False

temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(

timestep, encoder_hidden_states, encoder_hidden_states_image

)

timestep_proj = timestep_proj.unflatten(1, (6, -1))

if encoder_hidden_states_image is not None:

encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)

# 4. Transformer blocks

if torch.is_grad_enabled() and self.gradient_checkpointing:

for block in self.blocks:

hidden_states = self._gradient_checkpointing_func(

block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb

block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, causal_mask

)

else:

if self.inject_sample_info:

for block in self.blocks:

fps = torch.tensor(fps, dtype=torch.long, device=hidden_states.device)

hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)

# 5. Output norm, projection & unpatchify

shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)

# Move the shift and scale tensors to the same device as hidden_states.

# When using multi-GPU inference via accelerate these will be on the

# first device rather than the last device, which hidden_states ends up

# on.

shift = shift.to(hidden_states.device)

scale = scale.to(hidden_states.device)

hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)

hidden_states = self.proj_out(hidden_states)

hidden_states = hidden_states.reshape(

fps_emb = self.fps_embedding(fps).float()

batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1

if _flag_df:

)

timestep_proj = timestep_proj + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)).repeat(

hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)

timestep.shape[1], 1, 1

output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

)

else:

timestep_proj = timestep_proj + self.fps_projection(fps_emb).unflatten(1, (6, self.dim))

if USE_PEFT_BACKEND:

if _flag_df:

# remove `lora_scale` from each PEFT layer

temb = temb.view(b, f, 1, 1, self.dim)

unscale_lora_layers(self, lora_scale)

timestep_proj = timestep_proj.view(b, f, 1, 1, 6, self.dim)

temb = temb.repeat(1, 1, grid_sizes[1], grid_sizes[2], 1).flatten(1, 3)

timestep_proj = timestep_proj.repeat(1, 1, grid_sizes[1], grid_sizes[2], 1, 1).flatten(1, 3)

timestep_proj = timestep_proj.transpose(1, 2).contiguous()

if not return_dict:

if self.enable_teacache:

return (output,)

modulated_inp = timestep_proj if self.use_ref_steps else temb

# teacache

if self.cnt % 2 == 0: # even -> condition

self.is_even = True

if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:

should_calc_even = True

self.accumulated_rel_l1_distance_even = 0

else:

rescale_func = np.poly1d(self.coefficients)

self.accumulated_rel_l1_distance_even += rescale_func(

((modulated_inp - self.previous_e0_even).abs().mean() / self.previous_e0_even.abs().mean())

.cpu()

.item()

)

if self.accumulated_rel_l1_distance_even < self.teacache_thresh:

should_calc_even = False

else:

should_calc_even = True

self.accumulated_rel_l1_distance_even = 0

self.previous_e0_even = modulated_inp.clone()

return Transformer2DModelOutput(sample=output)

else: # odd -> unconditon

self.is_even = False

if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:

should_calc_odd = True

self.accumulated_rel_l1_distance_odd = 0

else:

rescale_func = np.poly1d(self.coefficients)

self.accumulated_rel_l1_distance_odd += rescale_func(

((modulated_inp - self.previous_e0_odd).abs().mean() / self.previous_e0_odd.abs().mean())

.cpu()

.item()

)

if self.accumulated_rel_l1_distance_odd < self.teacache_thresh:

should_calc_odd = False

else:

should_calc_odd = True

self.accumulated_rel_l1_distance_odd = 0

self.previous_e0_odd = modulated_inp.clone()

if self.enable_teacache:

if self.is_even:

if not should_calc_even:

hidden_states += self.previous_residual_even

else:

ori_hidden_states = hidden_states.clone()

for block in self.blocks:

hidden_states = block(

hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, causal_ma

已保存差异

原始文本

打开文件

# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from typing import Any, Dict, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F

from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
from ..attention import FeedForward
from ..attention_processor import Attention
from ..cache_utils import CacheMixin
from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
from ..modeling_outputs import Transformer2DModelOutput
from ..modeling_utils import ModelMixin
from ..normalization import FP32LayerNorm

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

class WanAttnProcessor2_0:
    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")

def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        encoder_hidden_states_img = None
        if attn.add_k_proj is not None:
            # 512 is the context length of the text encoder, hardcoded for now
            image_context_length = encoder_hidden_states.shape[1] - 512
            encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length]
            encoder_hidden_states = encoder_hidden_states[:, image_context_length:]
        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states

query = attn.to_q(hidden_states)
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)

if rotary_emb is not None:

def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):
                x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2)))
                x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4)
                return x_out.type_as(hidden_states)

query = apply_rotary_emb(query, rotary_emb)
            key = apply_rotary_emb(key, rotary_emb)

# I2V task
        hidden_states_img = None
        if encoder_hidden_states_img is not None:
            key_img = attn.add_k_proj(encoder_hidden_states_img)
            key_img = attn.norm_added_k(key_img)
            value_img = attn.add_v_proj(encoder_hidden_states_img)

key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
            value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)

hidden_states_img = F.scaled_dot_product_attention(
                query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False
            )
            hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3)
            hidden_states_img = hidden_states_img.type_as(query)

hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
        hidden_states = hidden_states.type_as(query)

if hidden_states_img is not None:
            hidden_states = hidden_states + hidden_states_img

hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)
        return hidden_states

class WanImageEmbedding(torch.nn.Module):
    def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):
        super().__init__()

self.norm1 = FP32LayerNorm(in_features)
        self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu")
        self.norm2 = FP32LayerNorm(out_features)
        if pos_embed_seq_len is not None:
            self.pos_embed = nn.Parameter(torch.zeros(1, pos_embed_seq_len, in_features))
        else:
            self.pos_embed = None

def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
        if self.pos_embed is not None:
            batch_size, seq_len, embed_dim = encoder_hidden_states_image.shape
            encoder_hidden_states_image = encoder_hidden_states_image.view(-1, 2 * seq_len, embed_dim)
            encoder_hidden_states_image = encoder_hidden_states_image + self.pos_embed

hidden_states = self.norm1(encoder_hidden_states_image)
        hidden_states = self.ff(hidden_states)
        hidden_states = self.norm2(hidden_states)
        return hidden_states

class WanTimeTextImageEmbedding(nn.Module):
    def __init__(
        self,
        dim: int,
        time_freq_dim: int,
        time_proj_dim: int,
        text_embed_dim: int,
        image_embed_dim: Optional[int] = None,
        pos_embed_seq_len: Optional[int] = None,
    ):
        super().__init__()

self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim)
        self.act_fn = nn.SiLU()
        self.time_proj = nn.Linear(dim, time_proj_dim)
        self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh")

self.image_embedder = None
        if image_embed_dim is not None:
            self.image_embedder = WanImageEmbedding(image_embed_dim, dim, pos_embed_seq_len=pos_embed_seq_len)

def forward(
        self,
        timestep: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        encoder_hidden_states_image: Optional[torch.Tensor] = None,
    ):
        timestep = self.timesteps_proj(timestep)

time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
        if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:
            timestep = timestep.to(time_embedder_dtype)
        temb = self.time_embedder(timestep).type_as(encoder_hidden_states)
        timestep_proj = self.time_proj(self.act_fn(temb))

encoder_hidden_states = self.text_embedder(encoder_hidden_states)
        if encoder_hidden_states_image is not None:
            encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image)

return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image

class WanRotaryPosEmbed(nn.Module):
    def __init__(
        self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0
    ):
        super().__init__()

self.attention_head_dim = attention_head_dim
        self.patch_size = patch_size
        self.max_seq_len = max_seq_len

h_dim = w_dim = 2 * (attention_head_dim // 6)
        t_dim = attention_head_dim - h_dim - w_dim

freqs = []
        for dim in [t_dim, h_dim, w_dim]:
            freq = get_1d_rotary_pos_embed(
                dim, max_seq_len, theta, use_real=False, repeat_interleave_real=False, freqs_dtype=torch.float64
            )
            freqs.append(freq)
        self.freqs = torch.cat(freqs, dim=1)

def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.patch_size
        ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w

freqs = self.freqs.to(hidden_states.device)
        freqs = freqs.split_with_sizes(
            [
                self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6),
                self.attention_head_dim // 6,
                self.attention_head_dim // 6,
            ],
            dim=1,
        )

freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
        freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
        freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
        freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1).reshape(1, 1, ppf * pph * ppw, -1)
        return freqs

class WanTransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        ffn_dim: int,
        num_heads: int,
        qk_norm: str = "rms_norm_across_heads",
        cross_attn_norm: bool = False,
        eps: float = 1e-6,
        added_kv_proj_dim: Optional[int] = None,
    ):
        super().__init__()

# 1. Self-attention
        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.attn1 = Attention(
            query_dim=dim,
            heads=num_heads,
            kv_heads=num_heads,
            dim_head=dim // num_heads,
            qk_norm=qk_norm,
            eps=eps,
            bias=True,
            cross_attention_dim=None,
            out_bias=True,
            processor=WanAttnProcessor2_0(),
        )

# 2. Cross-attention
        self.attn2 = Attention(
            query_dim=dim,
            heads=num_heads,
            kv_heads=num_heads,
            dim_head=dim // num_heads,
            qk_norm=qk_norm,
            eps=eps,
            bias=True,
            cross_attention_dim=None,
            out_bias=True,
            added_kv_proj_dim=added_kv_proj_dim,
            added_proj_bias=True,
            processor=WanAttnProcessor2_0(),
        )
        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()

# 3. Feed-forward
        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
        self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)

self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        temb: torch.Tensor,
        rotary_emb: torch.Tensor,
    ) -> torch.Tensor:
        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
            self.scale_shift_table + temb.float()
        ).chunk(6, dim=1)

# 1. Self-attention
        norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
        attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
        hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)

# 2. Cross-attention
        norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
        attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
        hidden_states = hidden_states + attn_output

# 3. Feed-forward
        norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
            hidden_states
        )
        ff_output = self.ffn(norm_hidden_states)
        hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states)

return hidden_states

class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
    r"""
    A Transformer model for video-like data used in the Wan model.

Args:
        patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
        num_attention_heads (`int`, defaults to `40`):
            Fixed length for text embeddings.
        attention_head_dim (`int`, defaults to `128`):
            The number of channels in each head.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        out_channels (`int`, defaults to `16`):
            The number of channels in the output.
        text_dim (`int`, defaults to `512`):
            Input dimension for text embeddings.
        freq_dim (`int`, defaults to `256`):
            Dimension for sinusoidal time embeddings.
        ffn_dim (`int`, defaults to `13824`):
            Intermediate dimension in feed-forward network.
        num_layers (`int`, defaults to `40`):
            The number of layers of transformer blocks to use.
        window_size (`Tuple[int]`, defaults to `(-1, -1)`):
            Window size for local attention (-1 indicates global attention).
        cross_attn_norm (`bool`, defaults to `True`):
            Enable cross-attention normalization.
        qk_norm (`bool`, defaults to `True`):
            Enable query/key normalization.
        eps (`float`, defaults to `1e-6`):
            Epsilon value for normalization layers.
        add_img_emb (`bool`, defaults to `False`):
            Whether to use img_emb.
        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the added key and value projections. If `None`, no projection is used.
    """

_supports_gradient_checkpointing = True
    _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
    _no_split_modules = ["WanTransformerBlock"]
    _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]

@register_to_config
    def __init__(
        self,
        patch_size: Tuple[int] = (1, 2, 2),
        num_attention_heads: int = 40,
        attention_head_dim: int = 128,
        in_channels: int = 16,
        out_channels: int = 16,
        text_dim: int = 4096,
        freq_dim: int = 256,
        ffn_dim: int = 13824,
        num_layers: int = 40,
        cross_attn_norm: bool = True,
        qk_norm: Optional[str] = "rms_norm_across_heads",
        eps: float = 1e-6,
        image_dim: Optional[int] = None,
        added_kv_proj_dim: Optional[int] = None,
        rope_max_seq_len: int = 1024,
        pos_embed_seq_len: Optional[int] = None,
    ) -> None:
        super().__init__()

inner_dim = num_attention_heads * attention_head_dim
        out_channels = out_channels or in_channels

# 1. Patch & position embedding
        self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)

# 2. Condition embeddings
        # image_embedding_dim=1280 for I2V model
        self.condition_embedder = WanTimeTextImageEmbedding(
            dim=inner_dim,
            time_freq_dim=freq_dim,
            time_proj_dim=inner_dim * 6,
            text_embed_dim=text_dim,
            image_embed_dim=image_dim,
            pos_embed_seq_len=pos_embed_seq_len,
        )

# 3. Transformer blocks
        self.blocks = nn.ModuleList(
            [
                WanTransformerBlock(
                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
                )
                for _ in range(num_layers)
            ]
        )

# 4. Output norm & projection
        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
        self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)

self.gradient_checkpointing = False

def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: torch.LongTensor,
        encoder_hidden_states: torch.Tensor,
        encoder_hidden_states_image: Optional[torch.Tensor] = None,
        return_dict: bool = True,
        attention_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
        if attention_kwargs is not None:
            attention_kwargs = attention_kwargs.copy()
            lora_scale = attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)
        else:
            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
                logger.warning(
                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
                )

batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.config.patch_size
        post_patch_num_frames = num_frames // p_t
        post_patch_height = height // p_h
        post_patch_width = width // p_w

rotary_emb = self.rope(hidden_states)

hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
            timestep, encoder_hidden_states, encoder_hidden_states_image
        )
        timestep_proj = timestep_proj.unflatten(1, (6, -1))

if encoder_hidden_states_image is not None:
            encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)

# 4. Transformer blocks
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            for block in self.blocks:
                hidden_states = self._gradient_checkpointing_func(
                    block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb
                )
        else:
            for block in self.blocks:
                hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)

# 5. Output norm, projection & unpatchify
        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)

# Move the shift and scale tensors to the same device as hidden_states.
        # When using multi-GPU inference via accelerate these will be on the
        # first device rather than the last device, which hidden_states ends up
        # on.
        shift = shift.to(hidden_states.device)
        scale = scale.to(hidden_states.device)

hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
        hidden_states = self.proj_out(hidden_states)

hidden_states = hidden_states.reshape(
            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
        )
        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

if not return_dict:
            return (output,)

return Transformer2DModelOutput(sample=output)

更改后文本

打开文件

# Copyright 2025 The SkyReels-V2 Team, The Wan Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from typing import Any, Dict, Optional, Tuple, Union

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

class SkyReelsV2AttnProcessor2_0:
    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "SkyReelsV2AttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0."
            )

self._flag_ar_attention = False

query = attn.to_q(hidden_states)
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)

if rotary_emb is not None:

query = apply_rotary_emb(query, rotary_emb)
            key = apply_rotary_emb(key, rotary_emb)

key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
            value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)

if self._flag_ar_attention:
            is_self_attention = encoder_hidden_states == hidden_states
            hidden_states = F.scaled_dot_product_attention(
                query.to(torch.bfloat16) if is_self_attention else query,
                key.to(torch.bfloat16) if is_self_attention else key,
                value.to(torch.bfloat16) if is_self_attention else value,
                attn_mask=attention_mask,
                dropout_p=0.0,
                is_causal=False,
            )
        else:
            hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)

hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
        hidden_states = hidden_states.type_as(query)

if hidden_states_img is not None:
            hidden_states = hidden_states + hidden_states_img

hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)
        return hidden_states

def set_ar_attention(self):
        self._flag_ar_attention = True

class SkyReelsV2ImageEmbedding(torch.nn.Module):
    def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):
        super().__init__()

hidden_states = self.norm1(encoder_hidden_states_image)
        hidden_states = self.ff(hidden_states)
        hidden_states = self.norm2(hidden_states)
        return hidden_states

class SkyReelsV2TimeTextImageEmbedding(nn.Module):
    def __init__(
        self,
        dim: int,
        time_freq_dim: int,
        time_proj_dim: int,
        text_embed_dim: int,
        image_embed_dim: Optional[int] = None,
        pos_embed_seq_len: Optional[int] = None,
    ):
        super().__init__()

self.image_embedder = None
        if image_embed_dim is not None:
            self.image_embedder = SkyReelsV2ImageEmbedding(image_embed_dim, dim, pos_embed_seq_len=pos_embed_seq_len)

return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image

class SkyReelsV2RotaryPosEmbed(nn.Module):
    def __init__(
        self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0
    ):
        super().__init__()

self.attention_head_dim = attention_head_dim
        self.patch_size = patch_size
        self.max_seq_len = max_seq_len

h_dim = w_dim = 2 * (attention_head_dim // 6)
        t_dim = attention_head_dim - h_dim - w_dim

class SkyReelsV2TransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        ffn_dim: int,
        num_heads: int,
        qk_norm: str = "rms_norm",
        cross_attn_norm: bool = False,
        eps: float = 1e-6,
        added_kv_proj_dim: Optional[int] = None,
    ):
        super().__init__()

# 2. Cross-attention
        self.attn2 = Attention(
            query_dim=dim,
            heads=num_heads,
            kv_heads=num_heads,
            dim_head=dim // num_heads,
            qk_norm=qk_norm,
            eps=eps,
            bias=True,
            cross_attention_dim=None,
            out_bias=True,
            added_kv_proj_dim=added_kv_proj_dim,
            added_proj_bias=True,
            processor=SkyReelsV2AttnProcessor2_0(),
        )
        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()

# 3. Feed-forward
        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
        self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)

self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        temb: torch.Tensor,
        rotary_emb: torch.Tensor,
        attention_mask: torch.Tensor,
    ) -> torch.Tensor:
        if temb.dim() == 3:
            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
                self.scale_shift_table + temb.float()
            ).chunk(6, dim=1)
        elif temb.dim() == 4:
            e = (self.scale_shift_table.unsqueeze(2) + temb.float()).chunk(6, dim=1)
            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = [ei.squeeze(1) for ei in e]

# 1. Self-attention
        norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
        attn_output = self.attn1(
            hidden_states=norm_hidden_states, rotary_emb=rotary_emb, attention_mask=attention_mask
        )
        hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)

return hidden_states  # TODO: check .to(torch.bfloat16)

def set_ar_attention(self):
        self.attn1.processor.set_ar_attention()

class SkyReelsV2Transformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
    r"""
    A Transformer model for video-like data used in the Wan-based SkyReels-V2 model.

Args:
        patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
        num_attention_heads (`int`, defaults to `16`):
            Fixed length for text embeddings.
        attention_head_dim (`int`, defaults to `128`):
            The number of channels in each head.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        out_channels (`int`, defaults to `16`):
            The number of channels in the output.
        text_dim (`int`, defaults to `4096`):
            Input dimension for text embeddings.
        freq_dim (`int`, defaults to `256`):
            Dimension for sinusoidal time embeddings.
        ffn_dim (`int`, defaults to `8192`):
            Intermediate dimension in feed-forward network.
        num_layers (`int`, defaults to `32`):
            The number of layers of transformer blocks to use.
        window_size (`Tuple[int]`, defaults to `(-1, -1)`):
            Window size for local attention (-1 indicates global attention).
        cross_attn_norm (`bool`, defaults to `True`):
            Enable cross-attention normalization.
        qk_norm (`str`, *optional*, defaults to `"rms_norm"`):
            Enable query/key normalization.
        eps (`float`, defaults to `1e-6`):
            Epsilon value for normalization layers.
        inject_sample_info (`bool`, defaults to `False`):
            Whether to inject sample information into the model.
        image_dim (`int`, *optional*):
            The dimension of the image embeddings.
        added_kv_proj_dim (`int`, *optional*):
            The dimension of the added key/value projection.
        rope_max_seq_len (`int`, defaults to `1024`):
            The maximum sequence length for the rotary embeddings.
        pos_embed_seq_len (`int`, *optional*):
            The sequence length for the positional embeddings.
    """

@register_to_config
    def __init__(
        self,
        patch_size: Tuple[int] = (1, 2, 2),
        num_attention_heads: int = 16,
        attention_head_dim: int = 128,
        in_channels: int = 16,
        out_channels: int = 16,
        text_dim: int = 4096,
        freq_dim: int = 256,
        ffn_dim: int = 8192,
        num_layers: int = 32,
        cross_attn_norm: bool = True,
        qk_norm: Optional[str] = "rms_norm",
        eps: float = 1e-6,
        image_dim: Optional[int] = None,
        added_kv_proj_dim: Optional[int] = None,
        rope_max_seq_len: int = 1024,
        pos_embed_seq_len: Optional[int] = None,
        inject_sample_info: bool = False,
    ) -> None:
        super().__init__()

inner_dim = num_attention_heads * attention_head_dim
        out_channels = out_channels or in_channels

self.num_frame_per_block = 1
        self.flag_causal_attention = False
        self.enable_teacache = False

# 1. Patch & position embedding
        self.rope = SkyReelsV2RotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)

# 2. Condition embeddings
        # image_embedding_dim=1280 for I2V model
        self.condition_embedder = SkyReelsV2TimeTextImageEmbedding(
            dim=inner_dim,
            time_freq_dim=freq_dim,
            time_proj_dim=inner_dim * 6,
            text_embed_dim=text_dim,
            image_embed_dim=image_dim,
            pos_embed_seq_len=pos_embed_seq_len,
        )

# 3. Transformer blocks
        self.blocks = nn.ModuleList(
            [
                SkyReelsV2TransformerBlock(
                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim=inner_dim
                )
                for _ in range(num_layers)
            ]
        )

self.gradient_checkpointing = False

if inject_sample_info:
            self.fps_embedding = nn.Embedding(2, inner_dim)
            self.fps_projection = nn.Sequential(
                nn.Linear(inner_dim, inner_dim), nn.SiLU(), nn.Linear(inner_dim, inner_dim * 6)
            )

def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: torch.LongTensor,
        encoder_hidden_states: torch.Tensor,
        encoder_hidden_states_image: Optional[torch.Tensor] = None,
        fps: Optional[torch.Tensor] = None,
        return_dict: bool = True,
        attention_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
        if attention_kwargs is not None:
            attention_kwargs = attention_kwargs.copy()
            lora_scale = attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

rotary_emb = self.rope(hidden_states)

hidden_states = self.patch_embedding(hidden_states)
        grid_sizes = torch.tensor(hidden_states.shape[2:], dtype=torch.long)

if self.flag_causal_attention:
            frame_num, height, width = grid_sizes
            block_num = frame_num // self.num_frame_per_block
            range_tensor = torch.arange(block_num, device=hidden_states.device).view(-1, 1)
            range_tensor = range_tensor.repeat(1, self.num_frame_per_block).flatten()
            causal_mask = range_tensor.unsqueeze(0) <= range_tensor.unsqueeze(1)  # f, f
            causal_mask = causal_mask.view(frame_num, 1, 1, frame_num, 1, 1)
            causal_mask = causal_mask.repeat(1, height, width, 1, height, width)
            causal_mask = causal_mask.reshape(frame_num * height * width, frame_num * height * width)
            causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)

hidden_states = hidden_states.flatten(2).transpose(1, 2)

# TODO: check here
        if timestep.dim() == 2:
            b, f = timestep.shape
            _flag_df = True
        else:
            _flag_df = False

if encoder_hidden_states_image is not None:
            encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)

# 4. Transformer blocks
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            for block in self.blocks:
                hidden_states = self._gradient_checkpointing_func(
                    block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, causal_mask
                )
        if self.inject_sample_info:
            fps = torch.tensor(fps, dtype=torch.long, device=hidden_states.device)

fps_emb = self.fps_embedding(fps).float()
            if _flag_df:
                timestep_proj = timestep_proj + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)).repeat(
                    timestep.shape[1], 1, 1
                )
            else:
                timestep_proj = timestep_proj + self.fps_projection(fps_emb).unflatten(1, (6, self.dim))

if _flag_df:
            temb = temb.view(b, f, 1, 1, self.dim)
            timestep_proj = timestep_proj.view(b, f, 1, 1, 6, self.dim)
            temb = temb.repeat(1, 1, grid_sizes[1], grid_sizes[2], 1).flatten(1, 3)
            timestep_proj = timestep_proj.repeat(1, 1, grid_sizes[1], grid_sizes[2], 1, 1).flatten(1, 3)
            timestep_proj = timestep_proj.transpose(1, 2).contiguous()

if self.enable_teacache:
            modulated_inp = timestep_proj if self.use_ref_steps else temb
            # teacache
            if self.cnt % 2 == 0:  # even -> condition
                self.is_even = True
                if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
                    should_calc_even = True
                    self.accumulated_rel_l1_distance_even = 0
                else:
                    rescale_func = np.poly1d(self.coefficients)
                    self.accumulated_rel_l1_distance_even += rescale_func(
                        ((modulated_inp - self.previous_e0_even).abs().mean() / self.previous_e0_even.abs().mean())
                        .cpu()
                        .item()
                    )
                    if self.accumulated_rel_l1_distance_even < self.teacache_thresh:
                        should_calc_even = False
                    else:
                        should_calc_even = True
                        self.accumulated_rel_l1_distance_even = 0
                self.previous_e0_even = modulated_inp.clone()

else:  # odd -> unconditon
                self.is_even = False
                if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
                    should_calc_odd = True
                    self.accumulated_rel_l1_distance_odd = 0
                else:
                    rescale_func = np.poly1d(self.coefficients)
                    self.accumulated_rel_l1_distance_odd += rescale_func(
                        ((modulated_inp - self.previous_e0_odd).abs().mean() / self.previous_e0_odd.abs().mean())
                        .cpu()
                        .item()
                    )
                    if self.accumulated_rel_l1_distance_odd < self.teacache_thresh:
                        should_calc_odd = False
                    else:
                        should_calc_odd = True
                        self.accumulated_rel_l1_distance_odd = 0
                self.previous_e0_odd = modulated_inp.clone()

if self.enable_teacache:
            if self.is_even:
                if not should_calc_even:
                    hidden_states += self.previous_residual_even
                else:
                    ori_hidden_states = hidden_states.clone()
                    for block in self.blocks:
                        hidden_states = block(
                            hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, causal_mask
                        )
                    self.previous_residual_even = hidden_states - ori_hidden_states
            else:
                if not should_calc_odd:
                    hidden_states += self.previous_residual_odd
                else:
                    ori_hidden_states = hidden_states.clone()
                    for block in self.blocks:
                        hidden_states = block(
                            hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, causal_mask
                        )
                    self.previous_residual_odd = hidden_states - ori_hidden_states

self.cnt += 1
            if self.cnt >= self.num_steps:
                self.cnt = 0
        else:
            for block in self.blocks:
                hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, causal_mask)

# 5. Output norm, projection & unpatchify
        if temb.dim() == 2:
            shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
        elif temb.dim() == 3:
            shift, scale = (self.scale_shift_table.unsqueeze(2) + temb.unsqueeze(1)).chunk(2, dim=1)
            shift, scale = shift.squeeze(1), scale.squeeze(1)

hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
        hidden_states = self.proj_out(hidden_states)

if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

if not return_dict:
            return (output,)

return Transformer2DModelOutput(sample=output)

def set_ar_attention(self, causal_block_size):
        self.num_frame_per_block = causal_block_size
        self.flag_causal_attention = True
        for block in self.blocks:
            block.set_ar_attention()

@staticmethod
    def _prepare_blockwise_causal_attn_mask(
        device: torch.device | str, num_frames: int = 21, frame_seqlen: int = 1560, num_frame_per_block=1
    ) -> BlockMask:
        """
        we will divide the token sequence into the following format [1 latent frame] [1 latent frame] ... [1 latent
        frame] We use flexattention to construct the attention mask
        """
        total_length = num_frames * frame_seqlen

# we do right padding to get to a multiple of 128
        padded_length = math.ceil(total_length / 128) * 128 - total_length

ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)

# Block-wise causal mask will attend to all elements that are before the end of the current chunk
        frame_indices = torch.arange(start=0, end=total_length, step=frame_seqlen * num_frame_per_block, device=device)

for tmp in frame_indices:
            ends[tmp : tmp + frame_seqlen * num_frame_per_block] = tmp + frame_seqlen * num_frame_per_block

def attention_mask(b, h, q_idx, kv_idx):
            return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask

block_mask = create_block_mask(
            attention_mask,
            B=None,
            H=None,
            Q_LEN=total_length + padded_length,
            KV_LEN=total_length + padded_length,
            _compile=False,
            device=device,
        )

return block_mask

def initialize_teacache(
        self, enable_teacache=True, num_steps=25, teacache_thresh=0.15, use_ret_steps=False, ckpt_dir=""
    ):
        self.enable_teacache = enable_teacache
        print("using teacache")
        self.cnt = 0
        self.num_steps = num_steps
        self.teacache_thresh = teacache_thresh
        self.accumulated_rel_l1_distance_even = 0
        self.accumulated_rel_l1_distance_odd = 0
        self.previous_e0_even = None
        self.previous_e0_odd = None
        self.previous_residual_even = None
        self.previous_residual_odd = None
        self.use_ref_steps = use_ret_steps
        if "I2V" in ckpt_dir:
            if use_ret_steps:
                if "540P" in ckpt_dir:
                    self.coefficients = [2.57151496e05, -3.54229917e04, 1.40286849e03, -1.35890334e01, 1.32517977e-01]
                if "720P" in ckpt_dir:
                    self.coefficients = [8.10705460e03, 2.13393892e03, -3.72934672e02, 1.66203073e01, -4.17769401e-02]
                self.ret_steps = 5 * 2
                self.cutoff_steps = num_steps * 2
            else:
                if "540P" in ckpt_dir:
                    self.coefficients = [-3.02331670e02, 2.23948934e02, -5.25463970e01, 5.87348440e00, -2.01973289e-01]
                if "720P" in ckpt_dir:
                    self.coefficients = [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683]
                self.ret_steps = 1 * 2
                self.cutoff_steps = num_steps * 2 - 2
        else:
            if use_ret_steps:
                if "1.3B" in ckpt_dir:
                    self.coefficients = [-5.21862437e04, 9.23041404e03, -5.28275948e02, 1.36987616e01, -4.99875664e-02]
                if "14B" in ckpt_dir:
                    self.coefficients = [-3.03318725e05, 4.90537029e04, -2.65530556e03, 5.87365115e01, -3.15583525e-01]
                self.ret_steps = 5 * 2
                self.cutoff_steps = num_steps * 2
            else:
                if "1.3B" in ckpt_dir:
                    self.coefficients = [2.39676752e03, -1.31110545e03, 2.01331979e02, -8.29855975e00, 1.37887774e-01]
                if "14B" in ckpt_dir:
                    self.coefficients = [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404]
                self.ret_steps = 1 * 2
                self.cutoff_steps = num_steps * 2 - 2