Tencent Hunyuan Team: add HunyuanDiT related updates (#8240)

* Hunyuan Team: add HunyuanDiT related updates


---------
Co-authored-by: XCLiu <liuxc1996@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail.com>

src/diffusers/__init__.py

@@ -83,6 +83,7 @@ else:
             "ControlNetModel",
             "ControlNetXSAdapter",
             "DiTTransformer2DModel",
+            "HunyuanDiT2DModel",
             "I2VGenXLUNet",
             "Kandinsky3UNet",
             "ModelMixin",
@@ -229,6 +230,7 @@ else:
             "BlipDiffusionPipeline",
             "CLIPImageProjection",
             "CycleDiffusionPipeline",
+            "HunyuanDiTPipeline",
             "I2VGenXLPipeline",
             "IFImg2ImgPipeline",
             "IFImg2ImgSuperResolutionPipeline",
@@ -487,6 +489,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ControlNetModel,
             ControlNetXSAdapter,
             DiTTransformer2DModel,
+            HunyuanDiT2DModel,
             I2VGenXLUNet,
             Kandinsky3UNet,
             ModelMixin,
@@ -611,6 +614,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AudioLDMPipeline,
             CLIPImageProjection,
             CycleDiffusionPipeline,
+            HunyuanDiTPipeline,
             I2VGenXLPipeline,
             IFImg2ImgPipeline,
             IFImg2ImgSuperResolutionPipeline,

src/diffusers/models/__init__.py

@@ -38,6 +38,7 @@ if is_torch_available():
     _import_structure["embeddings"] = ["ImageProjection"]
     _import_structure["modeling_utils"] = ["ModelMixin"]
     _import_structure["transformers.dit_transformer_2d"] = ["DiTTransformer2DModel"]
+    _import_structure["transformers.hunyuan_transformer_2d"] = ["HunyuanDiT2DModel"]
     _import_structure["transformers.pixart_transformer_2d"] = ["PixArtTransformer2DModel"]
     _import_structure["transformers.prior_transformer"] = ["PriorTransformer"]
     _import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"]
@@ -78,6 +79,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .transformers import (
             DiTTransformer2DModel,
             DualTransformer2DModel,
+            HunyuanDiT2DModel,
             PixArtTransformer2DModel,
             PriorTransformer,
             T5FilmDecoder,

src/diffusers/models/activations.py

@@ -50,6 +50,18 @@ def get_activation(act_fn: str) -> nn.Module:
         raise ValueError(f"Unsupported activation function: {act_fn}")
 
 
+class FP32SiLU(nn.Module):
+    r"""
+    SiLU activation function with input upcasted to torch.float32.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        return F.silu(inputs.float(), inplace=False).to(inputs.dtype)
+
+
 class GELU(nn.Module):
     r"""
     GELU activation function with tanh approximation support with `approximate="tanh"`.

src/diffusers/models/attention_processor.py

@@ -103,6 +103,7 @@ class Attention(nn.Module):
         upcast_softmax: bool = False,
         cross_attention_norm: Optional[str] = None,
         cross_attention_norm_num_groups: int = 32,
+        qk_norm: Optional[str] = None,
         added_kv_proj_dim: Optional[int] = None,
         norm_num_groups: Optional[int] = None,
         spatial_norm_dim: Optional[int] = None,
@@ -161,6 +162,15 @@ class Attention(nn.Module):
         else:
             self.spatial_norm = None
 
+        if qk_norm is None:
+            self.norm_q = None
+            self.norm_k = None
+        elif qk_norm == "layer_norm":
+            self.norm_q = nn.LayerNorm(dim_head, eps=eps)
+            self.norm_k = nn.LayerNorm(dim_head, eps=eps)
+        else:
+            raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'")
+
         if cross_attention_norm is None:
             self.norm_cross = None
         elif cross_attention_norm == "layer_norm":
@@ -1426,6 +1436,104 @@ class AttnProcessor2_0:
         return hidden_states
 
 
+class HunyuanAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
+    used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        from .embeddings import apply_rotary_emb
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
 class FusedAttnProcessor2_0:
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses

src/diffusers/models/embeddings.py

@@ -16,10 +16,11 @@ from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
+import torch.nn.functional as F
 from torch import nn
 
 from ..utils import deprecate
-from .activations import get_activation
+from .activations import FP32SiLU, get_activation
 from .attention_processor import Attention
 
 
@@ -135,6 +136,7 @@ class PatchEmbed(nn.Module):
         flatten=True,
         bias=True,
         interpolation_scale=1,
+        pos_embed_type="sincos",
     ):
         super().__init__()
 
@@ -156,10 +158,18 @@ class PatchEmbed(nn.Module):
         self.height, self.width = height // patch_size, width // patch_size
         self.base_size = height // patch_size
         self.interpolation_scale = interpolation_scale
+        if pos_embed_type is None:
+            self.pos_embed = None
+        elif pos_embed_type == "sincos":
             pos_embed = get_2d_sincos_pos_embed(
-            embed_dim, int(num_patches**0.5), base_size=self.base_size, interpolation_scale=self.interpolation_scale
+                embed_dim,
+                int(num_patches**0.5),
+                base_size=self.base_size,
+                interpolation_scale=self.interpolation_scale,
             )
             self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
+        else:
+            raise ValueError(f"Unsupported pos_embed_type: {pos_embed_type}")
 
     def forward(self, latent):
         height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size
@@ -169,6 +179,8 @@ class PatchEmbed(nn.Module):
             latent = latent.flatten(2).transpose(1, 2)  # BCHW -> BNC
         if self.layer_norm:
             latent = self.norm(latent)
+        if self.pos_embed is None:
+            return latent.to(latent.dtype)
 
         # Interpolate positional embeddings if needed.
         # (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160)
@@ -187,6 +199,113 @@ class PatchEmbed(nn.Module):
         return (latent + pos_embed).to(latent.dtype)
 
 
+def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
+    """
+    RoPE for image tokens with 2d structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size
+    crops_coords (`Tuple[int]`)
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the positional embedding.
+    use_real (`bool`):
+        If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+    Returns:
+        `torch.Tensor`: positional embdding with shape `( grid_size * grid_size, embed_dim/2)`.
+    """
+    start, stop = crops_coords
+    grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
+    grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)  # [2, W, H]
+
+    grid = grid.reshape([2, 1, *grid.shape[1:]])
+    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+    return pos_embed
+
+
+def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
+    assert embed_dim % 4 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
+    emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)
+
+    if use_real:
+        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)  # (H*W, D/2)
+        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)  # (H*W, D/2)
+        return cos, sin
+    else:
+        emb = torch.cat([emb_h, emb_w], dim=1)  # (H*W, D/2)
+        return emb
+
+
+def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
+    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
+    data type.
+
+    Args:
+        dim (`int`): Dimension of the frequency tensor.
+        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
+        theta (`float`, *optional*, defaults to 10000.0):
+            Scaling factor for frequency computation. Defaults to 10000.0.
+        use_real (`bool`, *optional*):
+            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+    Returns:
+        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
+    """
+    if isinstance(pos, int):
+        pos = np.arange(pos)
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))  # [D/2]
+    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
+    freqs = torch.outer(t, freqs).float()  # type: ignore   # [S, D/2]
+    if use_real:
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
+        return freqs_cos, freqs_sin
+    else:
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+        return freqs_cis
+
+
+def apply_rotary_emb(
+    x: torch.Tensor,
+    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
+    to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
+    reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
+    tensors contain rotary embeddings and are returned as real tensors.
+
+    Args:
+        x (`torch.Tensor`):
+            Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
+        freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+    """
+    cos, sin = freqs_cis  # [S, D]
+    cos = cos[None, None]
+    sin = sin[None, None]
+    cos, sin = cos.to(x.device), sin.to(x.device)
+
+    x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+    x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+    out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+
+    return out
+
+
 class TimestepEmbedding(nn.Module):
     def __init__(
         self,
@@ -507,6 +626,88 @@ class CombinedTimestepLabelEmbeddings(nn.Module):
         return conditioning
 
 
+class HunyuanDiTAttentionPool(nn.Module):
+    # Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6
+
+    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim**0.5)
+        self.k_proj = nn.Linear(embed_dim, embed_dim)
+        self.q_proj = nn.Linear(embed_dim, embed_dim)
+        self.v_proj = nn.Linear(embed_dim, embed_dim)
+        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
+        self.num_heads = num_heads
+
+    def forward(self, x):
+        x = x.permute(1, 0, 2)  # NLC -> LNC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
+        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (L+1)NC
+        x, _ = F.multi_head_attention_forward(
+            query=x[:1],
+            key=x,
+            value=x,
+            embed_dim_to_check=x.shape[-1],
+            num_heads=self.num_heads,
+            q_proj_weight=self.q_proj.weight,
+            k_proj_weight=self.k_proj.weight,
+            v_proj_weight=self.v_proj.weight,
+            in_proj_weight=None,
+            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
+            bias_k=None,
+            bias_v=None,
+            add_zero_attn=False,
+            dropout_p=0,
+            out_proj_weight=self.c_proj.weight,
+            out_proj_bias=self.c_proj.bias,
+            use_separate_proj_weight=True,
+            training=self.training,
+            need_weights=False,
+        )
+        return x.squeeze(0)
+
+
+class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
+    def __init__(self, embedding_dim, pooled_projection_dim=1024, seq_len=256, cross_attention_dim=2048):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+        self.pooler = HunyuanDiTAttentionPool(
+            seq_len, cross_attention_dim, num_heads=8, output_dim=pooled_projection_dim
+        )
+        # Here we use a default learned embedder layer for future extension.
+        self.style_embedder = nn.Embedding(1, embedding_dim)
+        extra_in_dim = 256 * 6 + embedding_dim + pooled_projection_dim
+        self.extra_embedder = PixArtAlphaTextProjection(
+            in_features=extra_in_dim,
+            hidden_size=embedding_dim * 4,
+            out_features=embedding_dim,
+            act_fn="silu_fp32",
+        )
+
+    def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidden_dtype=None):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, 256)
+
+        # extra condition1: text
+        pooled_projections = self.pooler(encoder_hidden_states)  # (N, 1024)
+
+        # extra condition2: image meta size embdding
+        image_meta_size = get_timestep_embedding(image_meta_size.view(-1), 256, True, 0)
+        image_meta_size = image_meta_size.to(dtype=hidden_dtype)
+        image_meta_size = image_meta_size.view(-1, 6 * 256)  # (N, 1536)
+
+        # extra condition3: style embedding
+        style_embedding = self.style_embedder(style)  # (N, embedding_dim)
+
+        # Concatenate all extra vectors
+        extra_cond = torch.cat([pooled_projections, image_meta_size, style_embedding], dim=1)
+        conditioning = timesteps_emb + self.extra_embedder(extra_cond)  # [B, D]
+
+        return conditioning
+
+
 class TextTimeEmbedding(nn.Module):
     def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
         super().__init__()
@@ -793,11 +994,18 @@ class PixArtAlphaTextProjection(nn.Module):
     Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
     """
 
-    def __init__(self, in_features, hidden_size, num_tokens=120):
+    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh"):
         super().__init__()
+        if out_features is None:
+            out_features = hidden_size
         self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
+        if act_fn == "gelu_tanh":
             self.act_1 = nn.GELU(approximate="tanh")
-        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=hidden_size, bias=True)
+        elif act_fn == "silu_fp32":
+            self.act_1 = FP32SiLU()
+        else:
+            raise ValueError(f"Unknown activation function: {act_fn}")
+        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)
 
     def forward(self, caption):
         hidden_states = self.linear_1(caption)

src/diffusers/models/normalization.py

@@ -176,7 +176,8 @@ class AdaLayerNormContinuous(nn.Module):
             raise ValueError(f"unknown norm_type {norm_type}")
 
     def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
-        emb = self.linear(self.silu(conditioning_embedding))
+        # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)
+        emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
         scale, shift = torch.chunk(emb, 2, dim=1)
         x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
         return x

src/diffusers/models/transformers/__init__.py

@@ -4,6 +4,7 @@ from ...utils import is_torch_available
 if is_torch_available():
     from .dit_transformer_2d import DiTTransformer2DModel
     from .dual_transformer_2d import DualTransformer2DModel
+    from .hunyuan_transformer_2d import HunyuanDiT2DModel
     from .pixart_transformer_2d import PixArtTransformer2DModel
     from .prior_transformer import PriorTransformer
     from .t5_film_transformer import T5FilmDecoder

src/diffusers/models/transformers/hunyuan_transformer_2d.py

+# Copyright 2024 HunyuanDiT 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.
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils import logging
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention import FeedForward
+from ..attention_processor import Attention, HunyuanAttnProcessor2_0
+from ..embeddings import (
+    HunyuanCombinedTimestepTextSizeStyleEmbedding,
+    PatchEmbed,
+    PixArtAlphaTextProjection,
+)
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import AdaLayerNormContinuous
+
+
+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.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`): The size of the embeddings dictionary.
+    """
+
+    def __init__(self, embedding_dim: int, elementwise_affine=True, eps=1e-6):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, embedding_dim)
+        self.norm = FP32LayerNorm(embedding_dim, elementwise_affine=elementwise_affine, eps=eps)
+
+    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
+        shift = self.linear(self.silu(emb.to(torch.float32)).to(emb.dtype))
+        x = self.norm(x) + shift.unsqueeze(dim=1)
+        return x
+
+
+@maybe_allow_in_graph
+class HunyuanDiTBlock(nn.Module):
+    r"""
+    Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and
+    QKNorm
+
+    Parameters:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of headsto use for multi-head attention.
+        cross_attention_dim (`int`,*optional*):
+            The size of the encoder_hidden_states vector for cross attention.
+        dropout(`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        activation_fn (`str`,*optional*, defaults to `"geglu"`):
+            Activation function to be used in feed-forward. .
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_eps (`float`, *optional*, defaults to 1e-6):
+            A small constant added to the denominator in normalization layers to prevent division by zero.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*):
+            The size of the hidden layer in the feed-forward block. Defaults to `None`.
+        ff_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the feed-forward block.
+        skip (`bool`, *optional*, defaults to `False`):
+            Whether to use skip connection. Defaults to `False` for down-blocks and mid-blocks.
+        qk_norm (`bool`, *optional*, defaults to `True`):
+            Whether to use normalization in QK calculation. Defaults to `True`.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        cross_attention_dim: int = 1024,
+        dropout=0.0,
+        activation_fn: str = "geglu",
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-6,
+        final_dropout: bool = False,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = True,
+        skip: bool = False,
+        qk_norm: bool = True,
+    ):
+        super().__init__()
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # NOTE: when new version comes, check norm2 and norm 3
+        # 1. Self-Attn
+        self.norm1 = AdaLayerNormShift(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=dim // num_attention_heads,
+            heads=num_attention_heads,
+            qk_norm="layer_norm" if qk_norm else None,
+            eps=1e-6,
+            bias=True,
+            processor=HunyuanAttnProcessor2_0(),
+        )
+
+        # 2. Cross-Attn
+        self.norm2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+        self.attn2 = Attention(
+            query_dim=dim,
+            cross_attention_dim=cross_attention_dim,
+            dim_head=dim // num_attention_heads,
+            heads=num_attention_heads,
+            qk_norm="layer_norm" if qk_norm else None,
+            eps=1e-6,
+            bias=True,
+            processor=HunyuanAttnProcessor2_0(),
+        )
+        # 3. Feed-forward
+        self.norm3 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,  ### 0.0
+            activation_fn=activation_fn,  ### approx GeLU
+            final_dropout=final_dropout,  ### 0.0
+            inner_dim=ff_inner_dim,  ### int(dim * mlp_ratio)
+            bias=ff_bias,
+        )
+
+        # 4. Skip Connection
+        if skip:
+            self.skip_norm = FP32LayerNorm(2 * dim, norm_eps, elementwise_affine=True)
+            self.skip_linear = nn.Linear(2 * dim, dim)
+        else:
+            self.skip_linear = None
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb=None,
+        skip=None,
+    ) -> torch.Tensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Long Skip Connection
+        if self.skip_linear is not None:
+            cat = torch.cat([hidden_states, skip], dim=-1)
+            cat = self.skip_norm(cat)
+            hidden_states = self.skip_linear(cat)
+
+        # 1. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states, temb)  ### checked: self.norm1 is correct
+        attn_output = self.attn1(
+            norm_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+        hidden_states = hidden_states + attn_output
+
+        # 2. Cross-Attention
+        hidden_states = hidden_states + self.attn2(
+            self.norm2(hidden_states),
+            encoder_hidden_states=encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        # FFN Layer ### TODO: switch norm2 and norm3 in the state dict
+        mlp_inputs = self.norm3(hidden_states)
+        hidden_states = hidden_states + self.ff(mlp_inputs)
+
+        return hidden_states
+
+
+class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
+    """
+    HunYuanDiT: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88):
+            The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        patch_size (`int`, *optional*):
+            The size of the patch to use for the input.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`):
+            Activation function to use in feed-forward.
+        sample_size (`int`, *optional*):
+            The width of the latent images. This is fixed during training since it is used to learn a number of
+            position embeddings.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        cross_attention_dim (`int`, *optional*):
+            The number of dimension in the clip text embedding.
+        hidden_size (`int`, *optional*):
+            The size of hidden layer in the conditioning embedding layers.
+        num_layers (`int`, *optional*, defaults to 1):
+            The number of layers of Transformer blocks to use.
+        mlp_ratio (`float`, *optional*, defaults to 4.0):
+            The ratio of the hidden layer size to the input size.
+        learn_sigma (`bool`, *optional*, defaults to `True`):
+             Whether to predict variance.
+        cross_attention_dim_t5 (`int`, *optional*):
+            The number dimensions in t5 text embedding.
+        pooled_projection_dim (`int`, *optional*):
+            The size of the pooled projection.
+        text_len (`int`, *optional*):
+            The length of the clip text embedding.
+        text_len_t5 (`int`, *optional*):
+            The length of the T5 text embedding.
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        patch_size: Optional[int] = None,
+        activation_fn: str = "gelu-approximate",
+        sample_size=32,
+        hidden_size=1152,
+        num_layers: int = 28,
+        mlp_ratio: float = 4.0,
+        learn_sigma: bool = True,
+        cross_attention_dim: int = 1024,
+        norm_type: str = "layer_norm",
+        cross_attention_dim_t5: int = 2048,
+        pooled_projection_dim: int = 1024,
+        text_len: int = 77,
+        text_len_t5: int = 256,
+    ):
+        super().__init__()
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.num_heads = num_attention_heads
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.text_embedder = PixArtAlphaTextProjection(
+            in_features=cross_attention_dim_t5,
+            hidden_size=cross_attention_dim_t5 * 4,
+            out_features=cross_attention_dim,
+            act_fn="silu_fp32",
+        )
+
+        self.text_embedding_padding = nn.Parameter(
+            torch.randn(text_len + text_len_t5, cross_attention_dim, dtype=torch.float32)
+        )
+
+        self.pos_embed = PatchEmbed(
+            height=sample_size,
+            width=sample_size,
+            in_channels=in_channels,
+            embed_dim=hidden_size,
+            patch_size=patch_size,
+            pos_embed_type=None,
+        )
+
+        self.time_extra_emb = HunyuanCombinedTimestepTextSizeStyleEmbedding(
+            hidden_size,
+            pooled_projection_dim=pooled_projection_dim,
+            seq_len=text_len_t5,
+            cross_attention_dim=cross_attention_dim_t5,
+        )
+
+        # HunyuanDiT Blocks
+        self.blocks = nn.ModuleList(
+            [
+                HunyuanDiTBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    activation_fn=activation_fn,
+                    ff_inner_dim=int(self.inner_dim * mlp_ratio),
+                    cross_attention_dim=cross_attention_dim,
+                    qk_norm=True,  # See http://arxiv.org/abs/2302.05442 for details.
+                    skip=layer > num_layers // 2,
+                )
+                for layer in range(num_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
+
+    def forward(
+        self,
+        hidden_states,
+        timestep,
+        encoder_hidden_states=None,
+        text_embedding_mask=None,
+        encoder_hidden_states_t5=None,
+        text_embedding_mask_t5=None,
+        image_meta_size=None,
+        style=None,
+        image_rotary_emb=None,
+        return_dict=True,
+    ):
+        """
+        The [`HunyuanDiT2DModel`] forward method.
+
+        Args:
+        hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
+            The input tensor.
+        timestep ( `torch.LongTensor`, *optional*):
+            Used to indicate denoising step.
+        encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+            Conditional embeddings for cross attention layer. This is the output of `BertModel`.
+        text_embedding_mask: torch.Tensor
+            An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
+            of `BertModel`.
+        encoder_hidden_states_t5 ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+            Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.
+        text_embedding_mask_t5: torch.Tensor
+            An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
+            of T5 Text Encoder.
+        image_meta_size (torch.Tensor):
+            Conditional embedding indicate the image sizes
+        style: torch.Tensor:
+            Conditional embedding indicate the style
+        image_rotary_emb (`torch.Tensor`):
+            The image rotary embeddings to apply on query and key tensors during attention calculation.
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+
+        height, width = hidden_states.shape[-2:]
+
+        hidden_states = self.pos_embed(hidden_states)
+
+        temb = self.time_extra_emb(
+            timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype
+        )  # [B, D]
+
+        # text projection
+        batch_size, sequence_length, _ = encoder_hidden_states_t5.shape
+        encoder_hidden_states_t5 = self.text_embedder(
+            encoder_hidden_states_t5.view(-1, encoder_hidden_states_t5.shape[-1])
+        )
+        encoder_hidden_states_t5 = encoder_hidden_states_t5.view(batch_size, sequence_length, -1)
+
+        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states_t5], dim=1)
+        text_embedding_mask = torch.cat([text_embedding_mask, text_embedding_mask_t5], dim=-1)
+        text_embedding_mask = text_embedding_mask.unsqueeze(2).bool()
+
+        encoder_hidden_states = torch.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)
+
+        skips = []
+        for layer, block in enumerate(self.blocks):
+            if layer > self.config.num_layers // 2:
+                skip = skips.pop()
+                hidden_states = block(
+                    hidden_states,
+                    temb=temb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_rotary_emb=image_rotary_emb,
+                    skip=skip,
+                )  # (N, L, D)
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    temb=temb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_rotary_emb=image_rotary_emb,
+                )  # (N, L, D)
+
+            if layer < (self.config.num_layers // 2 - 1):
+                skips.append(hidden_states)
+
+        # final layer
+        hidden_states = self.norm_out(hidden_states, temb.to(torch.float32))
+        hidden_states = self.proj_out(hidden_states)
+        # (N, L, patch_size ** 2 * out_channels)
+
+        # unpatchify: (N, out_channels, H, W)
+        patch_size = self.pos_embed.patch_size
+        height = height // patch_size
+        width = width // patch_size
+
+        hidden_states = hidden_states.reshape(
+            shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
+        )
+        hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
+        output = hidden_states.reshape(
+            shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
+        )
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)

src/diffusers/pipelines/__init__.py

@@ -150,6 +150,7 @@ else:
         "IFPipeline",
         "IFSuperResolutionPipeline",
     ]
+    _import_structure["hunyuandit"] = ["HunyuanDiTPipeline"]
     _import_structure["kandinsky"] = [
         "KandinskyCombinedPipeline",
         "KandinskyImg2ImgCombinedPipeline",
@@ -418,6 +419,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             VersatileDiffusionTextToImagePipeline,
             VQDiffusionPipeline,
         )
+        from .hunyuandit import HunyuanDiTPipeline
         from .i2vgen_xl import I2VGenXLPipeline
         from .kandinsky import (
             KandinskyCombinedPipeline,

src/diffusers/pipelines/hunyuandit/__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_hunyuandit"] = ["HunyuanDiTPipeline"]
+
+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_hunyuandit import HunyuanDiTPipeline
+
+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/hunyuandit/pipeline_hunyuandit.py

+# Copyright 2024 HunyuanDiT 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 Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import BertModel, BertTokenizer, CLIPImageProcessor, MT5Tokenizer, T5EncoderModel
+
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import VaeImageProcessor
+from ...models import AutoencoderKL, HunyuanDiT2DModel
+from ...models.embeddings import get_2d_rotary_pos_embed
+from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from ...schedulers import DDPMScheduler
+from ...utils import (
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+)
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import HunyuanDiTPipeline
+
+        >>> pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT", torch_dtype=torch.float16)
+        >>> pipe.to("cuda")
+
+        >>> # You may also use English prompt as HunyuanDiT supports both English and Chinese
+        >>> # prompt = "An astronaut riding a horse"
+        >>> prompt = "一个宇航员在骑马"
+        >>> image = pipe(prompt).images[0]
+        ```
+"""
+
+STANDARD_RATIO = np.array(
+    [
+        1.0,  # 1:1
+        4.0 / 3.0,  # 4:3
+        3.0 / 4.0,  # 3:4
+        16.0 / 9.0,  # 16:9
+        9.0 / 16.0,  # 9:16
+    ]
+)
+STANDARD_SHAPE = [
+    [(1024, 1024), (1280, 1280)],  # 1:1
+    [(1024, 768), (1152, 864), (1280, 960)],  # 4:3
+    [(768, 1024), (864, 1152), (960, 1280)],  # 3:4
+    [(1280, 768)],  # 16:9
+    [(768, 1280)],  # 9:16
+]
+STANDARD_AREA = [np.array([w * h for w, h in shapes]) for shapes in STANDARD_SHAPE]
+SUPPORTED_SHAPE = [
+    (1024, 1024),
+    (1280, 1280),  # 1:1
+    (1024, 768),
+    (1152, 864),
+    (1280, 960),  # 4:3
+    (768, 1024),
+    (864, 1152),
+    (960, 1280),  # 3:4
+    (1280, 768),  # 16:9
+    (768, 1280),  # 9:16
+]
+
+
+def map_to_standard_shapes(target_width, target_height):
+    target_ratio = target_width / target_height
+    closest_ratio_idx = np.argmin(np.abs(STANDARD_RATIO - target_ratio))
+    closest_area_idx = np.argmin(np.abs(STANDARD_AREA[closest_ratio_idx] - target_width * target_height))
+    width, height = STANDARD_SHAPE[closest_ratio_idx][closest_area_idx]
+    return width, height
+
+
+def get_resize_crop_region_for_grid(src, tgt_size):
+    th = tw = tgt_size
+    h, w = src
+
+    r = h / w
+
+    # resize
+    if r > 1:
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+class HunyuanDiTPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for English/Chinese-to-image generation using HunyuanDiT.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    HunyuanDiT uses two text encoders: [mT5](https://huggingface.co/google/mt5-base) and [bilingual CLIP](fine-tuned by
+    ourselves)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. We use
+            `sdxl-vae-fp16-fix`.
+        text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+            HunyuanDiT uses a fine-tuned [bilingual CLIP].
+        tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.CLIPTokenizer`]):
+            A `BertTokenizer` or `CLIPTokenizer` to tokenize text.
+        transformer ([`HunyuanDiT2DModel`]):
+            The HunyuanDiT model designed by Tencent Hunyuan.
+        text_encoder_2 (`T5EncoderModel`):
+            The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
+        tokenizer_2 (`MT5Tokenizer`):
+            The tokenizer for the mT5 embedder.
+        scheduler ([`DDPMScheduler`]):
+            A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _optional_components = [
+        "safety_checker",
+        "feature_extractor",
+        "text_encoder_2",
+        "tokenizer_2",
+        "text_encoder",
+        "tokenizer",
+    ]
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "prompt_embeds_2",
+        "negative_prompt_embeds_2",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: BertModel,
+        tokenizer: BertTokenizer,
+        transformer: HunyuanDiT2DModel,
+        scheduler: DDPMScheduler,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+        text_encoder_2=T5EncoderModel,
+        tokenizer_2=MT5Tokenizer,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            text_encoder_2=text_encoder_2,
+        )
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+        self.default_sample_size = self.transformer.config.sample_size
+
+    def encode_prompt(
+        self,
+        prompt: str,
+        device: torch.device,
+        dtype: torch.dtype,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = 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: Optional[int] = None,
+        text_encoder_index: int = 0,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            dtype (`torch.dtype`):
+                torch dtype
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts 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`).
+            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. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
+            negative_prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
+            max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt.
+            text_encoder_index (`int`, *optional*):
+                Index of the text encoder to use. `0` for clip and `1` for T5.
+        """
+        tokenizers = [self.tokenizer, self.tokenizer_2]
+        text_encoders = [self.text_encoder, self.text_encoder_2]
+
+        tokenizer = tokenizers[text_encoder_index]
+        text_encoder = text_encoders[text_encoder_index]
+
+        if max_sequence_length is None:
+            if text_encoder_index == 0:
+                max_length = 77
+            if text_encoder_index == 1:
+                max_length = 256
+        else:
+            max_length = max_sequence_length
+
+        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]
+
+        if prompt_embeds is None:
+            text_inputs = tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_attention_mask=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            prompt_attention_mask = text_inputs.attention_mask.to(device)
+            prompt_embeds = text_encoder(
+                text_input_ids.to(device),
+                attention_mask=prompt_attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+            prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings 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)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            negative_prompt_attention_mask = uncond_input.attention_mask.to(device)
+            negative_prompt_embeds = text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=negative_prompt_attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
+
+        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)
+
+        return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_attention_mask=None,
+        negative_prompt_attention_mask=None,
+        prompt_embeds_2=None,
+        negative_prompt_embeds_2=None,
+        prompt_attention_mask_2=None,
+        negative_prompt_attention_mask_2=None,
+        callback_on_step_end_tensor_inputs=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 callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        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 None and prompt_embeds_2 is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` 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_embeds is not None and prompt_attention_mask is None:
+            raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
+
+        if prompt_embeds_2 is not None and prompt_attention_mask_2 is None:
+            raise ValueError("Must provide `prompt_attention_mask_2` when specifying `prompt_embeds_2`.")
+
+        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 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 negative_prompt_embeds_2 is not None and negative_prompt_attention_mask_2 is None:
+            raise ValueError(
+                "Must provide `negative_prompt_attention_mask_2` when specifying `negative_prompt_embeds_2`."
+            )
+        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_embeds_2 is not None and negative_prompt_embeds_2 is not None:
+            if prompt_embeds_2.shape != negative_prompt_embeds_2.shape:
+                raise ValueError(
+                    "`prompt_embeds_2` and `negative_prompt_embeds_2` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds_2` {prompt_embeds_2.shape} != `negative_prompt_embeds_2`"
+                    f" {negative_prompt_embeds_2.shape}."
+                )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        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."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    # 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.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_2: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_2: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        prompt_attention_mask_2: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask_2: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = (1024, 1024),
+        target_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        use_resolution_binning: bool = True,
+    ):
+        r"""
+        The call function to the pipeline for generation with HunyuanDiT.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`):
+                The height in pixels of the generated image.
+            width (`int`):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter is modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            negative_prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
+            prompt_attention_mask_2 (`torch.Tensor`, *optional*):
+                Attention mask for the prompt. Required when `prompt_embeds_2` is passed directly.
+            negative_prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
+            negative_prompt_attention_mask_2 (`torch.Tensor`, *optional*):
+                Attention mask for the negative prompt. Required when `negative_prompt_embeds_2` is passed directly.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A callback function or a list of callback functions to be called at the end of each denoising step.
+            callback_on_step_end_tensor_inputs (`List[str]`, *optional*):
+                A list of tensor inputs that should be passed to the callback function. If not defined, all tensor
+                inputs will be passed.
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Rescale the noise_cfg according to `guidance_rescale`. Based on findings of [Common Diffusion Noise
+                Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+            original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`):
+                The original size of the image. Used to calculate the time ids.
+            target_size (`Tuple[int, int]`, *optional*):
+                The target size of the image. Used to calculate the time ids.
+            crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to `(0, 0)`):
+                The top left coordinates of the crop. Used to calculate the time ids.
+            use_resolution_binning (`bool`, *optional*, defaults to `True`):
+                Whether to use resolution binning or not. If `True`, the input resolution will be mapped to the closest
+                standard resolution. Supported resolutions are 1024x1024, 1280x1280, 1024x768, 1152x864, 1280x960,
+                768x1024, 864x1152, 960x1280, 1280x768, and 768x1280. It is recommended to set this to `True`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. default height and width
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+        height = int((height // 16) * 16)
+        width = int((width // 16) * 16)
+
+        if use_resolution_binning and (height, width) not in SUPPORTED_SHAPE:
+            width, height = map_to_standard_shapes(width, height)
+            height = int(height)
+            width = int(width)
+            logger.warning(f"Reshaped to (height, width)=({height}, {width}), Supported shapes are {SUPPORTED_SHAPE}")
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+            prompt_embeds_2,
+            negative_prompt_embeds_2,
+            prompt_attention_mask_2,
+            negative_prompt_attention_mask_2,
+            callback_on_step_end_tensor_inputs,
+        )
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._interrupt = False
+
+        # 2. Define call parameters
+        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
+
+        # 3. Encode input prompt
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            dtype=self.transformer.dtype,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            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=77,
+            text_encoder_index=0,
+        )
+        (
+            prompt_embeds_2,
+            negative_prompt_embeds_2,
+            prompt_attention_mask_2,
+            negative_prompt_attention_mask_2,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            dtype=self.transformer.dtype,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds_2,
+            negative_prompt_embeds=negative_prompt_embeds_2,
+            prompt_attention_mask=prompt_attention_mask_2,
+            negative_prompt_attention_mask=negative_prompt_attention_mask_2,
+            max_sequence_length=256,
+            text_encoder_index=1,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7 create image_rotary_emb, style embedding & time ids
+        grid_height = height // 8 // self.transformer.config.patch_size
+        grid_width = width // 8 // self.transformer.config.patch_size
+        base_size = 512 // 8 // self.transformer.config.patch_size
+        grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)
+        image_rotary_emb = get_2d_rotary_pos_embed(
+            self.transformer.inner_dim // self.transformer.num_heads, grid_crops_coords, (grid_height, grid_width)
+        )
+
+        style = torch.tensor([0], device=device)
+
+        target_size = target_size or (height, width)
+        add_time_ids = list(original_size + target_size + crops_coords_top_left)
+        add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype)
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask])
+            prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2])
+            prompt_attention_mask_2 = torch.cat([negative_prompt_attention_mask_2, prompt_attention_mask_2])
+            add_time_ids = torch.cat([add_time_ids] * 2, dim=0)
+            style = torch.cat([style] * 2, dim=0)
+
+        prompt_embeds = prompt_embeds.to(device=device)
+        prompt_attention_mask = prompt_attention_mask.to(device=device)
+        prompt_embeds_2 = prompt_embeds_2.to(device=device)
+        prompt_attention_mask_2 = prompt_attention_mask_2.to(device=device)
+        add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=device).repeat(
+            batch_size * num_images_per_prompt, 1
+        )
+        style = style.to(device=device).repeat(batch_size * num_images_per_prompt)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # expand scalar t to 1-D tensor to match the 1st dim of latent_model_input
+                t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to(
+                    dtype=latent_model_input.dtype
+                )
+
+                # predict the noise residual
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    t_expand,
+                    encoder_hidden_states=prompt_embeds,
+                    text_embedding_mask=prompt_attention_mask,
+                    encoder_hidden_states_t5=prompt_embeds_2,
+                    text_embedding_mask_t5=prompt_attention_mask_2,
+                    image_meta_size=add_time_ids,
+                    style=style,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                )[0]
+
+                noise_pred, _ = noise_pred.chunk(2, dim=1)
+
+                # perform guidance
+                if self.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)
+
+                if self.do_classifier_free_guidance and guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    prompt_embeds_2 = callback_outputs.pop("prompt_embeds_2", prompt_embeds_2)
+                    negative_prompt_embeds_2 = callback_outputs.pop(
+                        "negative_prompt_embeds_2", negative_prompt_embeds_2
+                    )
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

src/diffusers/utils/dummy_pt_objects.py

@@ -122,6 +122,21 @@ class DiTTransformer2DModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class HunyuanDiT2DModel(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 I2VGenXLUNet(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -212,6 +212,21 @@ class CycleDiffusionPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class HunyuanDiTPipeline(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 I2VGenXLPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/hunyuan_dit/test_hunyuan_dit.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 gc
+import tempfile
+import unittest
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer, BertModel, T5EncoderModel
+
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    HunyuanDiT2DModel,
+    HunyuanDiTPipeline,
+)
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    numpy_cosine_similarity_distance,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+enable_full_determinism()
+
+
+class HunyuanDiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = HunyuanDiTPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+
+    required_optional_params = PipelineTesterMixin.required_optional_params
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = HunyuanDiT2DModel(
+            sample_size=16,
+            num_layers=2,
+            patch_size=2,
+            attention_head_dim=8,
+            num_attention_heads=3,
+            in_channels=4,
+            cross_attention_dim=32,
+            cross_attention_dim_t5=32,
+            pooled_projection_dim=16,
+            hidden_size=24,
+            activation_fn="gelu-approximate",
+        )
+        torch.manual_seed(0)
+        vae = AutoencoderKL()
+
+        scheduler = DDPMScheduler()
+        text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
+        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        components = {
+            "transformer": transformer.eval(),
+            "vae": vae.eval(),
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer_2": tokenizer_2,
+            "safety_checker": None,
+            "feature_extractor": None,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).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",
+            "use_resolution_binning": False,
+        }
+        return inputs
+
+    def test_inference(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        self.assertEqual(image.shape, (1, 16, 16, 3))
+        expected_slice = np.array(
+            [0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364]
+        )
+        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
+        self.assertLessEqual(max_diff, 1e-3)
+
+    def test_sequential_cpu_offload_forward_pass(self):
+        # TODO(YiYi) need to fix later
+        pass
+
+    def test_sequential_offload_forward_pass_twice(self):
+        # TODO(YiYi) need to fix later
+        pass
+
+    def test_inference_batch_single_identical(self):
+        self._test_inference_batch_single_identical(
+            expected_max_diff=1e-3,
+        )
+
+    def test_save_load_optional_components(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        prompt = inputs["prompt"]
+        generator = inputs["generator"]
+        num_inference_steps = inputs["num_inference_steps"]
+        output_type = inputs["output_type"]
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+        ) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0)
+
+        (
+            prompt_embeds_2,
+            negative_prompt_embeds_2,
+            prompt_attention_mask_2,
+            negative_prompt_attention_mask_2,
+        ) = pipe.encode_prompt(
+            prompt,
+            device=torch_device,
+            dtype=torch.float32,
+            text_encoder_index=1,
+        )
+
+        # inputs with prompt converted to embeddings
+        inputs = {
+            "prompt_embeds": prompt_embeds,
+            "prompt_attention_mask": prompt_attention_mask,
+            "negative_prompt_embeds": negative_prompt_embeds,
+            "negative_prompt_attention_mask": negative_prompt_attention_mask,
+            "prompt_embeds_2": prompt_embeds_2,
+            "prompt_attention_mask_2": prompt_attention_mask_2,
+            "negative_prompt_embeds_2": negative_prompt_embeds_2,
+            "negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
+            "generator": generator,
+            "num_inference_steps": num_inference_steps,
+            "output_type": output_type,
+            "use_resolution_binning": False,
+        }
+
+        # set all optional components to None
+        for optional_component in pipe._optional_components:
+            setattr(pipe, optional_component, None)
+
+        output = pipe(**inputs)[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
+            pipe_loaded.to(torch_device)
+            pipe_loaded.set_progress_bar_config(disable=None)
+
+        for optional_component in pipe._optional_components:
+            self.assertTrue(
+                getattr(pipe_loaded, optional_component) is None,
+                f"`{optional_component}` did not stay set to None after loading.",
+            )
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        generator = inputs["generator"]
+        num_inference_steps = inputs["num_inference_steps"]
+        output_type = inputs["output_type"]
+
+        # inputs with prompt converted to embeddings
+        inputs = {
+            "prompt_embeds": prompt_embeds,
+            "prompt_attention_mask": prompt_attention_mask,
+            "negative_prompt_embeds": negative_prompt_embeds,
+            "negative_prompt_attention_mask": negative_prompt_attention_mask,
+            "prompt_embeds_2": prompt_embeds_2,
+            "prompt_attention_mask_2": prompt_attention_mask_2,
+            "negative_prompt_embeds_2": negative_prompt_embeds_2,
+            "negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
+            "generator": generator,
+            "num_inference_steps": num_inference_steps,
+            "output_type": output_type,
+            "use_resolution_binning": False,
+        }
+
+        output_loaded = pipe_loaded(**inputs)[0]
+
+        max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
+        self.assertLess(max_diff, 1e-4)
+
+
+@slow
+@require_torch_gpu
+class HunyuanDiTPipelineIntegrationTests(unittest.TestCase):
+    prompt = "一个宇航员在骑马"
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_hunyuan_dit_1024(self):
+        generator = torch.Generator("cpu").manual_seed(0)
+
+        pipe = HunyuanDiTPipeline.from_pretrained(
+            "XCLiu/HunyuanDiT-0523", revision="refs/pr/2", torch_dtype=torch.float16
+        )
+        pipe.enable_model_cpu_offload()
+        prompt = self.prompt
+
+        image = pipe(
+            prompt=prompt, height=1024, width=1024, generator=generator, num_inference_steps=2, output_type="np"
+        ).images
+
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = np.array(
+            [0.48388672, 0.33789062, 0.30737305, 0.47875977, 0.25097656, 0.30029297, 0.4440918, 0.26953125, 0.30078125]
+        )
+
+        max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
+        assert max_diff < 1e-3, f"Max diff is too high. got {image_slice.flatten()}"