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# Modifications Copyright (c) 2025 Advanced Micro Devices, Inc. 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.
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers import PixArtTransformer2DModel as PixArtTransformer2DModelOriginal
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.embeddings import PatchEmbed, PixArtAlphaTextProjection
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormSingle
from diffusers.utils import is_torch_version
def random_masking(x, mask_ratio):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
ids_keep = torch.argsort(noise, dim=1)[:, :len_keep]
ids_keep, _ = torch.sort(ids_keep, dim=1)
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
return x_masked, ids_keep, len_keep
def build_projector(hidden_size, projector_dim, z_dim):
return nn.Sequential(
nn.Linear(hidden_size, projector_dim),
nn.SiLU(),
nn.Linear(projector_dim, projector_dim),
nn.SiLU(),
nn.Linear(projector_dim, z_dim),
)
# Source: https://github.com/NVlabs/Sana/blob/70459f414474c10c509e8b58f3f9442738f85577/diffusion/model/norms.py#L183
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, scale_factor=1.0, eps: float = 1e-6):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
super().__init__()
self.eps = eps
self.weight = torch.nn.Parameter(torch.ones(dim) * scale_factor)
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
return (self.weight * self._norm(x.float())).type_as(x)
# Modified from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/pixart_transformer_2d.py
class NitroDiTModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
_no_split_modules = ["BasicTransformerBlock", "PatchEmbed"]
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 72,
in_channels: int = 4,
out_channels: Optional[int] = 8,
num_layers: int = 28,
dropout: float = 0.0,
norm_num_groups: int = 32,
cross_attention_dim: Optional[int] = 1152,
attention_bias: bool = True,
sample_size: int = 128,
patch_size: int = 2,
activation_fn: str = "gelu-approximate",
num_embeds_ada_norm: Optional[int] = 1000,
upcast_attention: bool = False,
norm_type: str = "ada_norm_single",
norm_elementwise_affine: bool = False,
norm_eps: float = 1e-6,
interpolation_scale: Optional[int] = None,
caption_channels: Optional[int] = None,
attention_type: Optional[str] = "default",
repa_depth=-1,
projector_dim=2048,
z_dims=[768],
):
super().__init__()
# copy over some functions to avoid duplicating the code
_tmp_original_model = PixArtTransformer2DModelOriginal()
self.fuse_qkv_projections = _tmp_original_model.fuse_qkv_projections
self.unfuse_qkv_projections = _tmp_original_model.unfuse_qkv_projections
self.set_default_attn_processor = _tmp_original_model.set_default_attn_processor
self.set_attn_processor = _tmp_original_model.set_attn_processor
self.attn_processors = _tmp_original_model.attn_processors
del _tmp_original_model
self.patch_mixer_depth = None # initially no masking applied
self.mask_ratio = 0
if repa_depth != -1:
self.projectors = nn.ModuleList(
[build_projector(cross_attention_dim, projector_dim, z_dim) for z_dim in z_dims]
)
assert repa_depth >= 0 and repa_depth < num_layers
self.repa_depth = repa_depth
# Validate inputs.
if norm_type != "ada_norm_single":
raise NotImplementedError(
f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
)
elif norm_type == "ada_norm_single" and num_embeds_ada_norm is None:
raise ValueError(
f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None."
)
# Set some common variables used across the board.
self.attention_head_dim = attention_head_dim
self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
self.out_channels = in_channels if out_channels is None else out_channels
self.gradient_checkpointing = False
# 2. Initialize the position embedding and transformer blocks.
self.height = self.config.sample_size
self.width = self.config.sample_size
interpolation_scale = (
self.config.interpolation_scale
if self.config.interpolation_scale is not None
else max(self.config.sample_size // 64, 1)
)
self.pos_embed = PatchEmbed(
height=self.config.sample_size,
width=self.config.sample_size,
patch_size=self.config.patch_size,
in_channels=self.config.in_channels,
embed_dim=self.inner_dim,
interpolation_scale=interpolation_scale,
)
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
self.inner_dim,
self.config.num_attention_heads,
self.config.attention_head_dim,
dropout=self.config.dropout,
cross_attention_dim=self.config.cross_attention_dim,
activation_fn=self.config.activation_fn,
num_embeds_ada_norm=self.config.num_embeds_ada_norm,
attention_bias=self.config.attention_bias,
upcast_attention=self.config.upcast_attention,
norm_type=norm_type,
norm_elementwise_affine=self.config.norm_elementwise_affine,
norm_eps=self.config.norm_eps,
attention_type=self.config.attention_type,
)
for _ in range(self.config.num_layers)
]
)
# 3. Output blocks.
self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
self.proj_out = nn.Linear(
self.inner_dim,
self.config.patch_size * self.config.patch_size * self.out_channels,
)
self.adaln_single = AdaLayerNormSingle(self.inner_dim, use_additional_conditions=False)
self.caption_projection = None
if self.config.caption_channels is not None:
self.caption_projection = PixArtAlphaTextProjection(
in_features=self.config.caption_channels, hidden_size=self.inner_dim
)
self.text_embedding_norm = RMSNorm(
self.inner_dim if self.caption_projection else self.config.caption_channels,
scale_factor=0.01,
eps=1e-5,
)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
):
"""
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
timestep (`torch.LongTensor`, *optional*):
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
attention_mask ( `torch.Tensor`, *optional*):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
encoder_attention_mask ( `torch.Tensor`, *optional*):
Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
* Mask `(batch, sequence_length)` True = keep, False = discard.
* Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
above. This bias will be added to the cross-attention scores.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None and attention_mask.ndim == 2:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 1. Input
batch_size = hidden_states.shape[0]
height, width = (
hidden_states.shape[-2] // self.config.patch_size,
hidden_states.shape[-1] // self.config.patch_size,
)
hidden_states = self.pos_embed(hidden_states)
timestep, embedded_timestep = self.adaln_single(
timestep,
batch_size=batch_size,
hidden_dtype=hidden_states.dtype,
)
if self.caption_projection is not None:
encoder_hidden_states = self.caption_projection(encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
encoder_hidden_states = self.text_embedding_norm(encoder_hidden_states)
ids_keep = None
len_keep = hidden_states.shape[1]
zs = None
# 2. Blocks
for blk_ind, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing and block.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
timestep,
cross_attention_kwargs,
None,
**ckpt_kwargs,
)
else:
hidden_states = block(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
timestep=timestep,
cross_attention_kwargs=cross_attention_kwargs,
class_labels=None,
)
# patch masking
if self.training and (self.patch_mixer_depth != -1) and (self.patch_mixer_depth == blk_ind):
hidden_states, ids_keep, len_keep = random_masking(hidden_states, self.mask_ratio)
# REPA
if self.training and (self.repa_depth != -1) and (self.repa_depth == blk_ind):
N, T, D = hidden_states.shape
zs = [projector(hidden_states.reshape(-1, D)).reshape(N, len_keep, -1) for projector in self.projectors]
# 3. Output
shift, scale = (
self.scale_shift_table[None] + embedded_timestep[:, None].to(self.scale_shift_table.device)
).chunk(2, dim=1)
hidden_states = self.norm_out(hidden_states)
# Modulation
hidden_states = hidden_states * (1 + scale.to(hidden_states.device)) + shift.to(hidden_states.device)
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.squeeze(1)
# if inference, return the unpatchified output as usual
# if training, return the patch sequence
if not self.training:
hidden_states = hidden_states.reshape(
shape=(
-1,
height,
width,
self.config.patch_size,
self.config.patch_size,
self.out_channels,
)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(
-1,
self.out_channels,
height * self.config.patch_size,
width * self.config.patch_size,
)
)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
else:
return hidden_states, ids_keep, zs
def enable_masking(self, depth, mask_ratio):
# depth: apply masking after block_[depth]. should be [0, nblks-1]
assert depth >= 0 and depth < len(self.transformer_blocks)
self.patch_mixer_depth = depth
assert mask_ratio >= 0 and mask_ratio <= 1
self.mask_ratio = mask_ratio
def disable_masking(self):
self.patch_mixer_depth = None
def enable_gradient_checkpointing(self, nblocks_to_apply_grad_checkpointing):
N = len(self.transformer_blocks)
if nblocks_to_apply_grad_checkpointing == -1:
nblocks_to_apply_grad_checkpointing = N
nblocks_to_apply_grad_checkpointing = min(N, nblocks_to_apply_grad_checkpointing)
# Apply to blocks evenly spaced out
step = N / nblocks_to_apply_grad_checkpointing if nblocks_to_apply_grad_checkpointing > 0 else 0
indices = [int((i + 0.5) * step) for i in range(nblocks_to_apply_grad_checkpointing)]
self.gradient_checkpointing = True
for blk_ind, block in enumerate(self.transformer_blocks):
block.gradient_checkpointing = blk_ind in indices
print(f"Block {blk_ind} grad checkpointing set to {block.gradient_checkpointing}")
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