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# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // 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.
from dataclasses import dataclass
from typing import Optional, Tuple, Union, Callable
import torch
from torch import nn
from common.cache import Cache
from common.distributed.ops import slice_inputs
from . import na
from .embedding import TimeEmbedding
from .modulation import get_ada_layer
from .nablocks import get_nablock
from .normalization import get_norm_layer
from .patch import NaPatchIn, NaPatchOut
# Fake func, no checkpointing is required for inference
def gradient_checkpointing(module: Union[Callable, nn.Module], *args, enabled: bool, **kwargs):
return module(*args, **kwargs)
@dataclass
class NaDiTOutput:
vid_sample: torch.Tensor
class NaDiT(nn.Module):
"""
Native Resolution Diffusion Transformer (NaDiT)
"""
gradient_checkpointing = False
def __init__(
self,
vid_in_channels: int,
vid_out_channels: int,
vid_dim: int,
txt_in_dim: Optional[int],
txt_dim: Optional[int],
emb_dim: int,
heads: int,
head_dim: int,
expand_ratio: int,
norm: Optional[str],
norm_eps: float,
ada: str,
qk_bias: bool,
qk_rope: bool,
qk_norm: Optional[str],
patch_size: Union[int, Tuple[int, int, int]],
num_layers: int,
block_type: Union[str, Tuple[str]],
shared_qkv: bool = False,
shared_mlp: bool = False,
mlp_type: str = "normal",
window: Optional[Tuple] = None,
window_method: Optional[Tuple[str]] = None,
temporal_window_size: int = None,
temporal_shifted: bool = False,
**kwargs,
):
ada = get_ada_layer(ada)
norm = get_norm_layer(norm)
qk_norm = get_norm_layer(qk_norm)
if isinstance(block_type, str):
block_type = [block_type] * num_layers
elif len(block_type) != num_layers:
raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
super().__init__()
self.vid_in = NaPatchIn(
in_channels=vid_in_channels,
patch_size=patch_size,
dim=vid_dim,
)
self.txt_in = (
nn.Linear(txt_in_dim, txt_dim)
if txt_in_dim and txt_in_dim != txt_dim
else nn.Identity()
)
self.emb_in = TimeEmbedding(
sinusoidal_dim=256,
hidden_dim=max(vid_dim, txt_dim),
output_dim=emb_dim,
)
if window is None or isinstance(window[0], int):
window = [window] * num_layers
if window_method is None or isinstance(window_method, str):
window_method = [window_method] * num_layers
if temporal_window_size is None or isinstance(temporal_window_size, int):
temporal_window_size = [temporal_window_size] * num_layers
if temporal_shifted is None or isinstance(temporal_shifted, bool):
temporal_shifted = [temporal_shifted] * num_layers
self.blocks = nn.ModuleList(
[
get_nablock(block_type[i])(
vid_dim=vid_dim,
txt_dim=txt_dim,
emb_dim=emb_dim,
heads=heads,
head_dim=head_dim,
expand_ratio=expand_ratio,
norm=norm,
norm_eps=norm_eps,
ada=ada,
qk_bias=qk_bias,
qk_rope=qk_rope,
qk_norm=qk_norm,
shared_qkv=shared_qkv,
shared_mlp=shared_mlp,
mlp_type=mlp_type,
window=window[i],
window_method=window_method[i],
temporal_window_size=temporal_window_size[i],
temporal_shifted=temporal_shifted[i],
**kwargs,
)
for i in range(num_layers)
]
)
self.vid_out = NaPatchOut(
out_channels=vid_out_channels,
patch_size=patch_size,
dim=vid_dim,
)
self.need_txt_repeat = block_type[0] in [
"mmdit_stwin",
"mmdit_stwin_spatial",
"mmdit_stwin_3d_spatial",
]
def set_gradient_checkpointing(self, enable: bool):
self.gradient_checkpointing = enable
def forward(
self,
vid: torch.FloatTensor, # l c
txt: torch.FloatTensor, # l c
vid_shape: torch.LongTensor, # b 3
txt_shape: torch.LongTensor, # b 1
timestep: Union[int, float, torch.IntTensor, torch.FloatTensor], # b
disable_cache: bool = True, # for test
):
# Text input.
if txt_shape.size(-1) == 1 and self.need_txt_repeat:
txt, txt_shape = na.repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
# slice vid after patching in when using sequence parallelism
txt = slice_inputs(txt, dim=0)
txt = self.txt_in(txt)
# Video input.
# Sequence parallel slicing is done inside patching class.
vid, vid_shape = self.vid_in(vid, vid_shape)
# Embedding input.
emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
# Body
cache = Cache(disable=disable_cache)
for i, block in enumerate(self.blocks):
vid, txt, vid_shape, txt_shape = gradient_checkpointing(
enabled=(self.gradient_checkpointing and self.training),
module=block,
vid=vid,
txt=txt,
vid_shape=vid_shape,
txt_shape=txt_shape,
emb=emb,
cache=cache,
)
vid, vid_shape = self.vid_out(vid, vid_shape, cache)
return NaDiTOutput(vid_sample=vid)
class NaDiTUpscaler(nn.Module):
"""
Native Resolution Diffusion Transformer (NaDiT)
"""
gradient_checkpointing = False
def __init__(
self,
vid_in_channels: int,
vid_out_channels: int,
vid_dim: int,
txt_in_dim: Optional[int],
txt_dim: Optional[int],
emb_dim: int,
heads: int,
head_dim: int,
expand_ratio: int,
norm: Optional[str],
norm_eps: float,
ada: str,
qk_bias: bool,
qk_rope: bool,
qk_norm: Optional[str],
patch_size: Union[int, Tuple[int, int, int]],
num_layers: int,
block_type: Union[str, Tuple[str]],
shared_qkv: bool = False,
shared_mlp: bool = False,
mlp_type: str = "normal",
window: Optional[Tuple] = None,
window_method: Optional[Tuple[str]] = None,
temporal_window_size: int = None,
temporal_shifted: bool = False,
**kwargs,
):
ada = get_ada_layer(ada)
norm = get_norm_layer(norm)
qk_norm = get_norm_layer(qk_norm)
if isinstance(block_type, str):
block_type = [block_type] * num_layers
elif len(block_type) != num_layers:
raise ValueError("The ``block_type`` list should equal to ``num_layers``.")
super().__init__()
self.vid_in = NaPatchIn(
in_channels=vid_in_channels,
patch_size=patch_size,
dim=vid_dim,
)
self.txt_in = (
nn.Linear(txt_in_dim, txt_dim)
if txt_in_dim and txt_in_dim != txt_dim
else nn.Identity()
)
self.emb_in = TimeEmbedding(
sinusoidal_dim=256,
hidden_dim=max(vid_dim, txt_dim),
output_dim=emb_dim,
)
self.emb_scale = TimeEmbedding(
sinusoidal_dim=256,
hidden_dim=max(vid_dim, txt_dim),
output_dim=emb_dim,
)
if window is None or isinstance(window[0], int):
window = [window] * num_layers
if window_method is None or isinstance(window_method, str):
window_method = [window_method] * num_layers
if temporal_window_size is None or isinstance(temporal_window_size, int):
temporal_window_size = [temporal_window_size] * num_layers
if temporal_shifted is None or isinstance(temporal_shifted, bool):
temporal_shifted = [temporal_shifted] * num_layers
self.blocks = nn.ModuleList(
[
get_nablock(block_type[i])(
vid_dim=vid_dim,
txt_dim=txt_dim,
emb_dim=emb_dim,
heads=heads,
head_dim=head_dim,
expand_ratio=expand_ratio,
norm=norm,
norm_eps=norm_eps,
ada=ada,
qk_bias=qk_bias,
qk_rope=qk_rope,
qk_norm=qk_norm,
shared_qkv=shared_qkv,
shared_mlp=shared_mlp,
mlp_type=mlp_type,
window=window[i],
window_method=window_method[i],
temporal_window_size=temporal_window_size[i],
temporal_shifted=temporal_shifted[i],
**kwargs,
)
for i in range(num_layers)
]
)
self.vid_out = NaPatchOut(
out_channels=vid_out_channels,
patch_size=patch_size,
dim=vid_dim,
)
self.need_txt_repeat = block_type[0] in [
"mmdit_stwin",
"mmdit_stwin_spatial",
"mmdit_stwin_3d_spatial",
]
def set_gradient_checkpointing(self, enable: bool):
self.gradient_checkpointing = enable
def forward(
self,
vid: torch.FloatTensor, # l c
txt: torch.FloatTensor, # l c
vid_shape: torch.LongTensor, # b 3
txt_shape: torch.LongTensor, # b 1
timestep: Union[int, float, torch.IntTensor, torch.FloatTensor], # b
downscale: Union[int, float, torch.IntTensor, torch.FloatTensor], # b
disable_cache: bool = False, # for test
):
# Text input.
if txt_shape.size(-1) == 1 and self.need_txt_repeat:
txt, txt_shape = na.repeat(txt, txt_shape, "l c -> t l c", t=vid_shape[:, 0])
# slice vid after patching in when using sequence parallelism
txt = slice_inputs(txt, dim=0)
txt = self.txt_in(txt)
# Video input.
# Sequence parallel slicing is done inside patching class.
vid, vid_shape = self.vid_in(vid, vid_shape)
# Embedding input.
emb = self.emb_in(timestep, device=vid.device, dtype=vid.dtype)
emb_scale = self.emb_scale(downscale, device=vid.device, dtype=vid.dtype)
emb = emb + emb_scale
# Body
cache = Cache(disable=disable_cache)
for i, block in enumerate(self.blocks):
vid, txt, vid_shape, txt_shape = gradient_checkpointing(
enabled=(self.gradient_checkpointing and self.training),
module=block,
vid=vid,
txt=txt,
vid_shape=vid_shape,
txt_shape=txt_shape,
emb=emb,
cache=cache,
)
vid, vid_shape = self.vid_out(vid, vid_shape, cache)
return NaDiTOutput(vid_sample=vid)
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