[Core] Add AuraFlow (#8796)

* add lavender flow transformer

---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>

scripts/convert_aura_flow_to_diffusers.py

+import argparse
+
+import torch
+from huggingface_hub import hf_hub_download
+
+from diffusers.models.transformers.auraflow_transformer_2d import AuraFlowTransformer2DModel
+
+
+def load_original_state_dict(args):
+    model_pt = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename="aura_diffusion_pytorch_model.bin")
+    state_dict = torch.load(model_pt, map_location="cpu")
+    return state_dict
+
+
+def calculate_layers(state_dict_keys, key_prefix):
+    dit_layers = set()
+    for k in state_dict_keys:
+        if key_prefix in k:
+            dit_layers.add(int(k.split(".")[2]))
+    print(f"{key_prefix}: {len(dit_layers)}")
+    return len(dit_layers)
+
+
+# similar to SD3 but only for the last norm layer
+def swap_scale_shift(weight, dim):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
+
+def convert_transformer(state_dict):
+    converted_state_dict = {}
+    state_dict_keys = list(state_dict.keys())
+
+    converted_state_dict["register_tokens"] = state_dict.pop("model.register_tokens")
+    converted_state_dict["pos_embed.pos_embed"] = state_dict.pop("model.positional_encoding")
+    converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("model.init_x_linear.weight")
+    converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("model.init_x_linear.bias")
+
+    converted_state_dict["time_step_proj.linear_1.weight"] = state_dict.pop("model.t_embedder.mlp.0.weight")
+    converted_state_dict["time_step_proj.linear_1.bias"] = state_dict.pop("model.t_embedder.mlp.0.bias")
+    converted_state_dict["time_step_proj.linear_2.weight"] = state_dict.pop("model.t_embedder.mlp.2.weight")
+    converted_state_dict["time_step_proj.linear_2.bias"] = state_dict.pop("model.t_embedder.mlp.2.bias")
+
+    converted_state_dict["context_embedder.weight"] = state_dict.pop("model.cond_seq_linear.weight")
+
+    mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers")
+    single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers")
+
+    # MMDiT blocks 🎸.
+    for i in range(mmdit_layers):
+        # feed-forward
+        path_mapping = {"mlpX": "ff", "mlpC": "ff_context"}
+        weight_mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"}
+        for orig_k, diffuser_k in path_mapping.items():
+            for k, v in weight_mapping.items():
+                converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.{v}.weight"] = state_dict.pop(
+                    f"model.double_layers.{i}.{orig_k}.{k}.weight"
+                )
+
+        # norms
+        path_mapping = {"modX": "norm1", "modC": "norm1_context"}
+        for orig_k, diffuser_k in path_mapping.items():
+            converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.linear.weight"] = state_dict.pop(
+                f"model.double_layers.{i}.{orig_k}.1.weight"
+            )
+
+        # attns
+        x_attn_mapping = {"w2q": "to_q", "w2k": "to_k", "w2v": "to_v", "w2o": "to_out.0"}
+        context_attn_mapping = {"w1q": "add_q_proj", "w1k": "add_k_proj", "w1v": "add_v_proj", "w1o": "to_add_out"}
+        for attn_mapping in [x_attn_mapping, context_attn_mapping]:
+            for k, v in attn_mapping.items():
+                converted_state_dict[f"joint_transformer_blocks.{i}.attn.{v}.weight"] = state_dict.pop(
+                    f"model.double_layers.{i}.attn.{k}.weight"
+                )
+
+    # Single-DiT blocks.
+    for i in range(single_dit_layers):
+        # feed-forward
+        mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"}
+        for k, v in mapping.items():
+            converted_state_dict[f"single_transformer_blocks.{i}.ff.{v}.weight"] = state_dict.pop(
+                f"model.single_layers.{i}.mlp.{k}.weight"
+            )
+
+        # norms
+        converted_state_dict[f"single_transformer_blocks.{i}.norm1.linear.weight"] = state_dict.pop(
+            f"model.single_layers.{i}.modCX.1.weight"
+        )
+
+        # attns
+        x_attn_mapping = {"w1q": "to_q", "w1k": "to_k", "w1v": "to_v", "w1o": "to_out.0"}
+        for k, v in x_attn_mapping.items():
+            converted_state_dict[f"single_transformer_blocks.{i}.attn.{v}.weight"] = state_dict.pop(
+                f"model.single_layers.{i}.attn.{k}.weight"
+            )
+
+    # Final blocks.
+    converted_state_dict["proj_out.weight"] = state_dict.pop("model.final_linear.weight")
+    converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(state_dict.pop("model.modF.1.weight"), dim=None)
+
+    return converted_state_dict
+
+
+@torch.no_grad()
+def populate_state_dict(args):
+    original_state_dict = load_original_state_dict(args)
+    state_dict_keys = list(original_state_dict.keys())
+    mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers")
+    single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers")
+
+    converted_state_dict = convert_transformer(original_state_dict)
+    model_diffusers = AuraFlowTransformer2DModel(
+        num_mmdit_layers=mmdit_layers, num_single_dit_layers=single_dit_layers
+    )
+    model_diffusers.load_state_dict(converted_state_dict, strict=True)
+
+    return model_diffusers
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--original_state_dict_repo_id", default="AuraDiffusion/auradiffusion-v0.1a0", type=str)
+    parser.add_argument("--dump_path", default="aura-flow", type=str)
+    parser.add_argument("--hub_id", default=None, type=str)
+    args = parser.parse_args()
+
+    model_diffusers = populate_state_dict(args)
+    model_diffusers.save_pretrained(args.dump_path)
+    if args.hub_id is not None:
+        model_diffusers.push_to_hub(args.hub_id)

src/diffusers/__init__.py

@@ -76,6 +76,7 @@ else:
     _import_structure["models"].extend(
         [
             "AsymmetricAutoencoderKL",
+            "AuraFlowTransformer2DModel",
             "AutoencoderKL",
             "AutoencoderKLTemporalDecoder",
             "AutoencoderTiny",
@@ -235,6 +236,7 @@ else:
             "AudioLDM2ProjectionModel",
             "AudioLDM2UNet2DConditionModel",
             "AudioLDMPipeline",
+            "AuraFlowPipeline",
             "BlipDiffusionControlNetPipeline",
             "BlipDiffusionPipeline",
             "ChatGLMModel",
@@ -507,6 +509,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .models import (
             AsymmetricAutoencoderKL,
+            AuraFlowTransformer2DModel,
             AutoencoderKL,
             AutoencoderKLTemporalDecoder,
             AutoencoderTiny,
@@ -646,6 +649,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDM2ProjectionModel,
             AudioLDM2UNet2DConditionModel,
             AudioLDMPipeline,
+            AuraFlowPipeline,
             ChatGLMModel,
             ChatGLMTokenizer,
             CLIPImageProjection,

src/diffusers/models/__init__.py

@@ -38,6 +38,7 @@ if is_torch_available():
     _import_structure["controlnet_xs"] = ["ControlNetXSAdapter", "UNetControlNetXSModel"]
     _import_structure["embeddings"] = ["ImageProjection"]
     _import_structure["modeling_utils"] = ["ModelMixin"]
+    _import_structure["transformers.auraflow_transformer_2d"] = ["AuraFlowTransformer2DModel"]
     _import_structure["transformers.dit_transformer_2d"] = ["DiTTransformer2DModel"]
     _import_structure["transformers.dual_transformer_2d"] = ["DualTransformer2DModel"]
     _import_structure["transformers.hunyuan_transformer_2d"] = ["HunyuanDiT2DModel"]
@@ -84,6 +85,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .embeddings import ImageProjection
         from .modeling_utils import ModelMixin
         from .transformers import (
+            AuraFlowTransformer2DModel,
             DiTTransformer2DModel,
             DualTransformer2DModel,
             HunyuanDiT2DModel,

src/diffusers/models/attention_processor.py

@@ -22,7 +22,7 @@ from torch import nn
 from ..image_processor import IPAdapterMaskProcessor
 from ..utils import deprecate, logging
 from ..utils.import_utils import is_torch_npu_available, is_xformers_available
-from ..utils.torch_utils import maybe_allow_in_graph
+from ..utils.torch_utils import is_torch_version, maybe_allow_in_graph
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -104,6 +104,7 @@ class Attention(nn.Module):
         cross_attention_norm_num_groups: int = 32,
         qk_norm: Optional[str] = None,
         added_kv_proj_dim: Optional[int] = None,
+        added_proj_bias: Optional[bool] = True,
         norm_num_groups: Optional[int] = None,
         spatial_norm_dim: Optional[int] = None,
         out_bias: bool = True,
@@ -118,6 +119,10 @@ class Attention(nn.Module):
         context_pre_only=None,
     ):
         super().__init__()
+
+        # To prevent circular import.
+        from .normalization import FP32LayerNorm
+
         self.inner_dim = out_dim if out_dim is not None else dim_head * heads
         self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
         self.query_dim = query_dim
@@ -170,6 +175,9 @@ class Attention(nn.Module):
         elif qk_norm == "layer_norm":
             self.norm_q = nn.LayerNorm(dim_head, eps=eps)
             self.norm_k = nn.LayerNorm(dim_head, eps=eps)
+        elif qk_norm == "fp32_layer_norm":
+            self.norm_q = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
+            self.norm_k = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
         elif qk_norm == "layer_norm_across_heads":
             # Lumina applys qk norm across all heads
             self.norm_q = nn.LayerNorm(dim_head * heads, eps=eps)
@@ -211,10 +219,10 @@ class Attention(nn.Module):
             self.to_v = None
 
         if self.added_kv_proj_dim is not None:
-            self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim)
-            self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim)
+            self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
+            self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
             if self.context_pre_only is not None:
-                self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim)
+                self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
 
         self.to_out = nn.ModuleList([])
         self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
@@ -223,6 +231,14 @@ class Attention(nn.Module):
         if self.context_pre_only is not None and not self.context_pre_only:
             self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)
 
+        if qk_norm is not None and added_kv_proj_dim is not None:
+            if qk_norm == "fp32_layer_norm":
+                self.norm_added_q = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
+                self.norm_added_k = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
+        else:
+            self.norm_added_q = None
+            self.norm_added_k = None
+
         # set attention processor
         # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
         # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
@@ -1137,6 +1153,100 @@ class FusedJointAttnProcessor2_0:
         return hidden_states, encoder_hidden_states
 
 
+class AuraFlowAttnProcessor2_0:
+    """Attention processor used typically in processing Aura Flow."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention") and is_torch_version("<", "2.1"):
+            raise ImportError(
+                "AuraFlowAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to at least 2.1 or above as we use `scale` in `F.scaled_dot_product_attention()`. "
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        i=0,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        batch_size = hidden_states.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        # `context` projections.
+        if encoder_hidden_states is not None:
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+        # Reshape.
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim)
+        key = key.view(batch_size, -1, attn.heads, head_dim)
+        value = value.view(batch_size, -1, attn.heads, head_dim)
+
+        # Apply QK norm.
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Concatenate the projections.
+        if encoder_hidden_states is not None:
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            )
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(batch_size, -1, attn.heads, head_dim)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            )
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_q(encoder_hidden_states_key_proj)
+
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
+
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        # Attention.
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, dropout_p=0.0, scale=attn.scale, is_causal=False
+        )
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # Split the attention outputs.
+        if encoder_hidden_states is not None:
+            hidden_states, encoder_hidden_states = (
+                hidden_states[:, encoder_hidden_states.shape[1] :],
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+            )
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+        if encoder_hidden_states is not None:
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        if encoder_hidden_states is not None:
+            return hidden_states, encoder_hidden_states
+        else:
+            return hidden_states
+
+
 class XFormersAttnAddedKVProcessor:
     r"""
     Processor for implementing memory efficient attention using xFormers.

src/diffusers/models/embeddings.py

@@ -473,11 +473,12 @@ class TimestepEmbedding(nn.Module):
 
 
 class Timesteps(nn.Module):
-    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
         super().__init__()
         self.num_channels = num_channels
         self.flip_sin_to_cos = flip_sin_to_cos
         self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
 
     def forward(self, timesteps):
         t_emb = get_timestep_embedding(
@@ -485,6 +486,7 @@ class Timesteps(nn.Module):
             self.num_channels,
             flip_sin_to_cos=self.flip_sin_to_cos,
             downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
         )
         return t_emb
 

src/diffusers/models/normalization.py

@@ -51,6 +51,18 @@ class AdaLayerNorm(nn.Module):
         return x
 
 
+class FP32LayerNorm(nn.LayerNorm):
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        origin_dtype = inputs.dtype
+        return F.layer_norm(
+            inputs.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        ).to(origin_dtype)
+
+
 class AdaLayerNormZero(nn.Module):
     r"""
     Norm layer adaptive layer norm zero (adaLN-Zero).
@@ -60,7 +72,7 @@ class AdaLayerNormZero(nn.Module):
         num_embeddings (`int`): The size of the embeddings dictionary.
     """
 
-    def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None):
+    def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None, norm_type="layer_norm", bias=True):
         super().__init__()
         if num_embeddings is not None:
             self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)
@@ -68,8 +80,15 @@ class AdaLayerNormZero(nn.Module):
             self.emb = None
 
         self.silu = nn.SiLU()
-        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
+        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=bias)
+        if norm_type == "layer_norm":
             self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
+        elif norm_type == "fp32_layer_norm":
+            self.norm = FP32LayerNorm(embedding_dim, elementwise_affine=False, bias=False)
+        else:
+            raise ValueError(
+                f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
+            )
 
     def forward(
         self,

src/diffusers/models/transformers/__init__.py

@@ -2,6 +2,7 @@ from ...utils import is_torch_available
 
 
 if is_torch_available():
+    from .auraflow_transformer_2d import AuraFlowTransformer2DModel
     from .dit_transformer_2d import DiTTransformer2DModel
     from .dual_transformer_2d import DualTransformer2DModel
     from .hunyuan_transformer_2d import HunyuanDiT2DModel

src/diffusers/models/transformers/auraflow_transformer_2d.py

+# Copyright 2024 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, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils import is_torch_version, logging
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention_processor import Attention, AuraFlowAttnProcessor2_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 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)
+        return latent + self.pos_embed
+
+
+# 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, i=9999):
+        residual = hidden_states
+
+        # 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, i=i)
+
+        # 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, i=0
+    ):
+        residual = hidden_states
+        residual_context = encoder_hidden_states
+
+        # 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, i=i
+        )
+
+        # 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):
+    _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://arxiv.org/abs/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
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        timestep: torch.LongTensor = None,
+        return_dict: bool = True,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        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 self.training and self.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 {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb, i=index_block
+                )
+
+        # 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 self.training and self.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 {}
+                    combined_hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        combined_hidden_states,
+                        temb,
+                        **ckpt_kwargs,
+                    )
+
+                else:
+                    combined_hidden_states = block(hidden_states=combined_hidden_states, temb=temb)
+
+            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 not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

src/diffusers/models/transformers/hunyuan_transformer_2d.py

@@ -14,7 +14,6 @@
 from typing import Dict, Optional, Union
 
 import torch
-import torch.nn.functional as F
 from torch import nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
@@ -29,20 +28,12 @@ from ..embeddings import (
 )
 from ..modeling_outputs import Transformer2DModelOutput
 from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNormContinuous
+from ..normalization import AdaLayerNormContinuous, FP32LayerNorm
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class FP32LayerNorm(nn.LayerNorm):
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        origin_dtype = inputs.dtype
-        return F.layer_norm(
-            inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps
-        ).to(origin_dtype)
-
-
 class AdaLayerNormShift(nn.Module):
     r"""
     Norm layer modified to incorporate timestep embeddings.

src/diffusers/pipelines/__init__.py

@@ -250,6 +250,7 @@ else:
             "StableDiffusionLDM3DPipeline",
         ]
     )
+    _import_structure["aura_flow"] = ["AuraFlowPipeline"]
     _import_structure["stable_diffusion_3"] = [
         "StableDiffusion3Pipeline",
         "StableDiffusion3Img2ImgPipeline",
@@ -418,6 +419,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDM2ProjectionModel,
             AudioLDM2UNet2DConditionModel,
         )
+        from .aura_flow import AuraFlowPipeline
         from .blip_diffusion import BlipDiffusionPipeline
         from .controlnet import (
             BlipDiffusionControlNetPipeline,

src/diffusers/pipelines/aura_flow/__init__.py

+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_aura_flow"] = ["AuraFlowPipeline"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_aura_flow import AuraFlowPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py

+# Copyright 2024 AuraFlow Authors 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 inspect
+from typing import List, Optional, Tuple, Union
+
+import torch
+from transformers import T5Tokenizer, UMT5EncoderModel
+
+from ...image_processor import VaeImageProcessor
+from ...models import AuraFlowTransformer2DModel, AutoencoderKL
+from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import logging
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class AuraFlowPipeline(DiffusionPipeline):
+    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: UMT5EncoderModel,
+        vae: AutoencoderKL,
+        transformer: AuraFlowTransformer2DModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
+        )
+
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_attention_mask=None,
+        negative_prompt_attention_mask=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_attention_mask is None:
+            raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
+
+        if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
+            raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+            if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
+                raise ValueError(
+                    "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
+                    f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
+                    f" {negative_prompt_attention_mask.shape}."
+                )
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Union[str, List[str]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 256,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                whether to use classifier free guidance or not
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                number of images that should be generated per prompt
+            device: (`torch.device`, *optional*):
+                torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings.
+            max_sequence_length (`int`, defaults to 256): Maximum sequence length to use for the prompt.
+        """
+        if device is None:
+            device = self._execution_device
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        max_length = max_sequence_length
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                truncation=True,
+                max_length=max_length,
+                padding="max_length",
+                return_tensors="pt",
+            )
+            text_inputs = {k: v.to(device) for k, v in text_inputs.items()}
+            text_input_ids = text_inputs["input_ids"]
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
+                logger.warning(
+                    "The following part of your input was truncated because T5 can only handle sequences up to"
+                    f" {max_length} tokens: {removed_text}"
+                )
+
+            prompt_embeds = self.text_encoder(**text_inputs)[0]
+            prompt_attention_mask = text_inputs["attention_mask"].unsqueeze(-1).expand(prompt_embeds.shape)
+            prompt_embeds = prompt_embeds * prompt_attention_mask
+
+        if self.text_encoder is not None:
+            dtype = self.text_encoder.dtype
+        elif self.transformer is not None:
+            dtype = self.transformer.dtype
+        else:
+            dtype = None
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+        prompt_attention_mask = prompt_attention_mask.reshape(bs_embed, -1)
+        prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            uncond_tokens = [negative_prompt] * batch_size if isinstance(negative_prompt, str) else negative_prompt
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                truncation=True,
+                max_length=max_length,
+                padding="max_length",
+                return_tensors="pt",
+            )
+            uncond_input = {k: v.to(device) for k, v in uncond_input.items()}
+            negative_prompt_embeds = self.text_encoder(**uncond_input)[0]
+            negative_prompt_attention_mask = (
+                uncond_input["attention_mask"].unsqueeze(-1).expand(negative_prompt_embeds.shape)
+            )
+            negative_prompt_embeds = negative_prompt_embeds * negative_prompt_attention_mask
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            negative_prompt_attention_mask = negative_prompt_attention_mask.reshape(bs_embed, -1)
+            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
+        else:
+            negative_prompt_embeds = None
+            negative_prompt_attention_mask = None
+
+        return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
+
+    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_latents
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
+    def upcast_vae(self):
+        dtype = self.vae.dtype
+        self.vae.to(dtype=torch.float32)
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                FusedAttnProcessor2_0,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 256,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        # 1. Check inputs. Raise error if not correct
+        height = height or self.transformer.config.sample_size * self.vae_scale_factor
+        width = width or self.transformer.config.sample_size * self.vae_scale_factor
+
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+        )
+
+        # 2. Determine batch size.
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            max_sequence_length=max_sequence_length,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # 4. Prepare timesteps
+
+        # sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+
+        # 5. Prepare latents.
+        latent_channels = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            latent_channels,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                # aura use timestep value between 0 and 1, with t=1 as noise and t=0 as the image
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = torch.tensor([t / 1000]).expand(latent_model_input.shape[0])
+                timestep = timestep.to(latents.device, dtype=latents.dtype)
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+        else:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -158,7 +158,12 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
     def _sigma_to_t(self, sigma):
         return sigma * self.config.num_train_timesteps
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+    ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -168,17 +173,19 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             device (`str` or `torch.device`, *optional*):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
         """
-        self.num_inference_steps = num_inference_steps
 
+        if sigmas is None:
+            self.num_inference_steps = num_inference_steps
             timesteps = np.linspace(
                 self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps
             )
 
             sigmas = timesteps / self.config.num_train_timesteps
             sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
-        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
 
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
         timesteps = sigmas * self.config.num_train_timesteps
+
         self.timesteps = timesteps.to(device=device)
         self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
 

src/diffusers/utils/dummy_pt_objects.py

@@ -17,6 +17,21 @@ class AsymmetricAutoencoderKL(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class AuraFlowTransformer2DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class AutoencoderKL(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -182,6 +182,21 @@ class AudioLDMPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class AuraFlowPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class ChatGLMModel(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/models/transformers/test_models_transformer_aura_flow.py

+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# 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 unittest
+
+import torch
+
+from diffusers import AuraFlowTransformer2DModel
+from diffusers.utils.testing_utils import enable_full_determinism, torch_device
+
+from ..test_modeling_common import ModelTesterMixin
+
+
+enable_full_determinism()
+
+
+class SD3TransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = AuraFlowTransformer2DModel
+    main_input_name = "hidden_states"
+
+    @property
+    def dummy_input(self):
+        batch_size = 2
+        num_channels = 4
+        height = width = embedding_dim = 32
+        sequence_length = 256
+
+        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "timestep": timestep,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (4, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "sample_size": 32,
+            "patch_size": 2,
+            "in_channels": 4,
+            "num_mmdit_layers": 1,
+            "num_single_dit_layers": 1,
+            "attention_head_dim": 8,
+            "num_attention_heads": 4,
+            "caption_projection_dim": 32,
+            "joint_attention_dim": 32,
+            "out_channels": 4,
+            "pos_embed_max_size": 256,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict

tests/pipelines/aura_flow/test_pipeline_aura_dlow.py

+import unittest
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer, UMT5EncoderModel
+
+from diffusers import AuraFlowPipeline, AuraFlowTransformer2DModel, AutoencoderKL, FlowMatchEulerDiscreteScheduler
+from diffusers.utils.testing_utils import (
+    torch_device,
+)
+
+from ..test_pipelines_common import PipelineTesterMixin
+
+
+class AuraFlowPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
+    pipeline_class = AuraFlowPipeline
+    params = frozenset(
+        [
+            "prompt",
+            "height",
+            "width",
+            "guidance_scale",
+            "negative_prompt",
+            "prompt_embeds",
+            "negative_prompt_embeds",
+        ]
+    )
+    batch_params = frozenset(["prompt", "negative_prompt"])
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = AuraFlowTransformer2DModel(
+            sample_size=32,
+            patch_size=2,
+            in_channels=4,
+            num_mmdit_layers=1,
+            num_single_dit_layers=1,
+            attention_head_dim=8,
+            num_attention_heads=4,
+            caption_projection_dim=32,
+            joint_attention_dim=32,
+            out_channels=4,
+            pos_embed_max_size=256,
+        )
+
+        text_encoder = UMT5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-umt5")
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+            sample_size=32,
+        )
+
+        scheduler = FlowMatchEulerDiscreteScheduler()
+
+        return {
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "transformer": transformer,
+            "vae": vae,
+        }
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device="cpu").manual_seed(seed)
+
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "output_type": "np",
+            "height": None,
+            "width": None,
+        }
+        return inputs
+
+    def test_aura_flow_prompt_embeds(self):
+        pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
+        inputs = self.get_dummy_inputs(torch_device)
+
+        output_with_prompt = pipe(**inputs).images[0]
+
+        inputs = self.get_dummy_inputs(torch_device)
+        prompt = inputs.pop("prompt")
+
+        do_classifier_free_guidance = inputs["guidance_scale"] > 1
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        ) = pipe.encode_prompt(
+            prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            device=torch_device,
+        )
+        output_with_embeds = pipe(
+            prompt_embeds=prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            **inputs,
+        ).images[0]
+
+        max_diff = np.abs(output_with_prompt - output_with_embeds).max()
+        assert max_diff < 1e-4
+
+    def test_attention_slicing_forward_pass(self):
+        # Attention slicing needs to implemented differently for this because how single DiT and MMDiT
+        # blocks interfere with each other.
+        return