# Copyright 2025 AuraFlow Authors, 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.
from typing import Any, Dict, Optional, 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 ...utils.torch_utils import maybe_allow_in_graph
from ..attention_processor import (
Attention,
AttentionProcessor,
AuraFlowAttnProcessor2_0,
FusedAuraFlowAttnProcessor2_0,
)
from ..embeddings import TimestepEmbedding, Timesteps
from ..modeling_outputs import Transformer2DModelOutput
from ..modeling_utils import ModelMixin
from ..normalization import AdaLayerNormZero, FP32LayerNorm
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Taken from the original aura flow inference code.
def find_multiple(n: int, k: int) -> int:
if n % k == 0:
return n
return n + k - (n % k)
# Aura Flow patch embed doesn't use convs for projections.
# Additionally, it uses learned positional embeddings.
class AuraFlowPatchEmbed(nn.Module):
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
pos_embed_max_size=None,
):
super().__init__()
self.num_patches = (height // patch_size) * (width // patch_size)
self.pos_embed_max_size = pos_embed_max_size
self.proj = nn.Linear(patch_size * patch_size * in_channels, embed_dim)
self.pos_embed = nn.Parameter(torch.randn(1, pos_embed_max_size, embed_dim) * 0.1)
self.patch_size = patch_size
self.height, self.width = height // patch_size, width // patch_size
self.base_size = height // patch_size
def pe_selection_index_based_on_dim(self, h, w):
# select subset of positional embedding based on H, W, where H, W is size of latent
# PE will be viewed as 2d-grid, and H/p x W/p of the PE will be selected
# because original input are in flattened format, we have to flatten this 2d grid as well.
h_p, w_p = h // self.patch_size, w // self.patch_size
h_max, w_max = int(self.pos_embed_max_size**0.5), int(self.pos_embed_max_size**0.5)
# Calculate the top-left corner indices for the centered patch grid
starth = h_max // 2 - h_p // 2
startw = w_max // 2 - w_p // 2
# Generate the row and column indices for the desired patch grid
rows = torch.arange(starth, starth + h_p, device=self.pos_embed.device)
cols = torch.arange(startw, startw + w_p, device=self.pos_embed.device)
# Create a 2D grid of indices
row_indices, col_indices = torch.meshgrid(rows, cols, indexing="ij")
# Convert the 2D grid indices to flattened 1D indices
selected_indices = (row_indices * w_max + col_indices).flatten()
return selected_indices
def forward(self, latent):
batch_size, num_channels, height, width = latent.size()
latent = latent.view(
batch_size,
num_channels,
height // self.patch_size,
self.patch_size,
width // self.patch_size,
self.patch_size,
)
latent = latent.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
latent = self.proj(latent)
pe_index = self.pe_selection_index_based_on_dim(height, width)
return latent + self.pos_embed[:, pe_index]
# Taken from the original Aura flow inference code.
# Our feedforward only has GELU but Aura uses SiLU.
class AuraFlowFeedForward(nn.Module):
def __init__(self, dim, hidden_dim=None) -> None:
super().__init__()
if hidden_dim is None:
hidden_dim = 4 * dim
final_hidden_dim = int(2 * hidden_dim / 3)
final_hidden_dim = find_multiple(final_hidden_dim, 256)
self.linear_1 = nn.Linear(dim, final_hidden_dim, bias=False)
self.linear_2 = nn.Linear(dim, final_hidden_dim, bias=False)
self.out_projection = nn.Linear(final_hidden_dim, dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.silu(self.linear_1(x)) * self.linear_2(x)
x = self.out_projection(x)
return x
class AuraFlowPreFinalBlock(nn.Module):
def __init__(self, embedding_dim: int, conditioning_embedding_dim: int):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=False)
def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
scale, shift = torch.chunk(emb, 2, dim=1)
x = x * (1 + scale)[:, None, :] + shift[:, None, :]
return x
@maybe_allow_in_graph
class AuraFlowSingleTransformerBlock(nn.Module):
"""Similar to `AuraFlowJointTransformerBlock` with a single DiT instead of an MMDiT."""
def __init__(self, dim, num_attention_heads, attention_head_dim):
super().__init__()
self.norm1 = AdaLayerNormZero(dim, bias=False, norm_type="fp32_layer_norm")
processor = AuraFlowAttnProcessor2_0()
self.attn = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=attention_head_dim,
heads=num_attention_heads,
qk_norm="fp32_layer_norm",
out_dim=dim,
bias=False,
out_bias=False,
processor=processor,
)
self.norm2 = FP32LayerNorm(dim, elementwise_affine=False, bias=False)
self.ff = AuraFlowFeedForward(dim, dim * 4)
def forward(
self,
hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
attention_kwargs: Optional[Dict[str, Any]] = None,
):
residual = hidden_states
attention_kwargs = attention_kwargs or {}
# Norm + Projection.
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
# Attention.
attn_output = self.attn(hidden_states=norm_hidden_states, **attention_kwargs)
# Process attention outputs for the `hidden_states`.
hidden_states = self.norm2(residual + gate_msa.unsqueeze(1) * attn_output)
hidden_states = hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
ff_output = self.ff(hidden_states)
hidden_states = gate_mlp.unsqueeze(1) * ff_output
hidden_states = residual + hidden_states
return hidden_states
@maybe_allow_in_graph
class AuraFlowJointTransformerBlock(nn.Module):
r"""
Transformer block for Aura Flow. Similar to SD3 MMDiT. Differences (non-exhaustive):
* QK Norm in the attention blocks
* No bias in the attention blocks
* Most LayerNorms are in FP32
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
is_last (`bool`): Boolean to determine if this is the last block in the model.
"""
def __init__(self, dim, num_attention_heads, attention_head_dim):
super().__init__()
self.norm1 = AdaLayerNormZero(dim, bias=False, norm_type="fp32_layer_norm")
self.norm1_context = AdaLayerNormZero(dim, bias=False, norm_type="fp32_layer_norm")
processor = AuraFlowAttnProcessor2_0()
self.attn = Attention(
query_dim=dim,
cross_attention_dim=None,
added_kv_proj_dim=dim,
added_proj_bias=False,
dim_head=attention_head_dim,
heads=num_attention_heads,
qk_norm="fp32_layer_norm",
out_dim=dim,
bias=False,
out_bias=False,
processor=processor,
context_pre_only=False,
)
self.norm2 = FP32LayerNorm(dim, elementwise_affine=False, bias=False)
self.ff = AuraFlowFeedForward(dim, dim * 4)
self.norm2_context = FP32LayerNorm(dim, elementwise_affine=False, bias=False)
self.ff_context = AuraFlowFeedForward(dim, dim * 4)
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
attention_kwargs: Optional[Dict[str, Any]] = None,
):
residual = hidden_states
residual_context = encoder_hidden_states
attention_kwargs = attention_kwargs or {}
# Norm + Projection.
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
# Attention.
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
**attention_kwargs,
)
# Process attention outputs for the `hidden_states`.
hidden_states = self.norm2(residual + gate_msa.unsqueeze(1) * attn_output)
hidden_states = hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
hidden_states = gate_mlp.unsqueeze(1) * self.ff(hidden_states)
hidden_states = residual + hidden_states
# Process attention outputs for the `encoder_hidden_states`.
encoder_hidden_states = self.norm2_context(residual_context + c_gate_msa.unsqueeze(1) * context_attn_output)
encoder_hidden_states = encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
encoder_hidden_states = c_gate_mlp.unsqueeze(1) * self.ff_context(encoder_hidden_states)
encoder_hidden_states = residual_context + encoder_hidden_states
return encoder_hidden_states, hidden_states
class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
r"""
A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input.
num_mmdit_layers (`int`, *optional*, defaults to 4): The number of layers of MMDiT Transformer blocks to use.
num_single_dit_layers (`int`, *optional*, defaults to 32):
The number of layers of Transformer blocks to use. These blocks use concatenated image and text
representations.
attention_head_dim (`int`, *optional*, defaults to 256): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 12): The number of heads to use for multi-head attention.
joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
out_channels (`int`, defaults to 4): Number of output channels.
pos_embed_max_size (`int`, defaults to 1024): Maximum positions to embed from the image latents.
"""
_no_split_modules = ["AuraFlowJointTransformerBlock", "AuraFlowSingleTransformerBlock", "AuraFlowPatchEmbed"]
_skip_layerwise_casting_patterns = ["pos_embed", "norm"]
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 64,
patch_size: int = 2,
in_channels: int = 4,
num_mmdit_layers: int = 4,
num_single_dit_layers: int = 32,
attention_head_dim: int = 256,
num_attention_heads: int = 12,
joint_attention_dim: int = 2048,
caption_projection_dim: int = 3072,
out_channels: int = 4,
pos_embed_max_size: int = 1024,
):
super().__init__()
default_out_channels = in_channels
self.out_channels = out_channels if out_channels is not None else default_out_channels
self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
self.pos_embed = AuraFlowPatchEmbed(
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,
pos_embed_max_size=pos_embed_max_size,
)
self.context_embedder = nn.Linear(
self.config.joint_attention_dim, self.config.caption_projection_dim, bias=False
)
self.time_step_embed = Timesteps(num_channels=256, downscale_freq_shift=0, scale=1000, flip_sin_to_cos=True)
self.time_step_proj = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim)
self.joint_transformer_blocks = nn.ModuleList(
[
AuraFlowJointTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
)
for i in range(self.config.num_mmdit_layers)
]
)
self.single_transformer_blocks = nn.ModuleList(
[
AuraFlowSingleTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
)
for _ in range(self.config.num_single_dit_layers)
]
)
self.norm_out = AuraFlowPreFinalBlock(self.inner_dim, self.inner_dim)
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=False)
# https://huggingface.co/papers/2309.16588
# prevents artifacts in the attention maps
self.register_tokens = nn.Parameter(torch.randn(1, 8, self.inner_dim) * 0.02)
self.gradient_checkpointing = False
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedAuraFlowAttnProcessor2_0
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
This API is 🧪 experimental.
"""
self.original_attn_processors = None
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module in self.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
self.set_attn_processor(FusedAuraFlowAttnProcessor2_0())
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(self):
"""Disables the fused QKV projection if enabled.
This API is 🧪 experimental.
"""
if self.original_attn_processors is not None:
self.set_attn_processor(self.original_attn_processors)
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
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."
)
height, width = hidden_states.shape[-2:]
# Apply patch embedding, timestep embedding, and project the caption embeddings.
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
temb = self.time_step_embed(timestep).to(dtype=next(self.parameters()).dtype)
temb = self.time_step_proj(temb)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
encoder_hidden_states = torch.cat(
[self.register_tokens.repeat(encoder_hidden_states.size(0), 1, 1), encoder_hidden_states], dim=1
)
# MMDiT blocks.
for index_block, block in enumerate(self.joint_transformer_blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
block,
hidden_states,
encoder_hidden_states,
temb,
)
else:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
temb=temb,
attention_kwargs=attention_kwargs,
)
# Single DiT blocks that combine the `hidden_states` (image) and `encoder_hidden_states` (text)
if len(self.single_transformer_blocks) > 0:
encoder_seq_len = encoder_hidden_states.size(1)
combined_hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
for index_block, block in enumerate(self.single_transformer_blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
combined_hidden_states = self._gradient_checkpointing_func(
block,
combined_hidden_states,
temb,
)
else:
combined_hidden_states = block(
hidden_states=combined_hidden_states, temb=temb, attention_kwargs=attention_kwargs
)
hidden_states = combined_hidden_states[:, encoder_seq_len:]
hidden_states = self.norm_out(hidden_states, temb)
hidden_states = self.proj_out(hidden_states)
# unpatchify
patch_size = self.config.patch_size
out_channels = self.config.out_channels
height = height // patch_size
width = width // patch_size
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], out_channels, height * patch_size, width * patch_size)
)
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)