HunyuanImage21 (#12333)

* add hunyuanimage2.1


---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/models/__init__.py

@@ -36,6 +36,8 @@ if is_torch_available():
     _import_structure["autoencoders.autoencoder_kl_cogvideox"] = ["AutoencoderKLCogVideoX"]
     _import_structure["autoencoders.autoencoder_kl_cosmos"] = ["AutoencoderKLCosmos"]
     _import_structure["autoencoders.autoencoder_kl_hunyuan_video"] = ["AutoencoderKLHunyuanVideo"]
+    _import_structure["autoencoders.autoencoder_kl_hunyuanimage"] = ["AutoencoderKLHunyuanImage"]
+    _import_structure["autoencoders.autoencoder_kl_hunyuanimage_refiner"] = ["AutoencoderKLHunyuanImageRefiner"]
     _import_structure["autoencoders.autoencoder_kl_ltx"] = ["AutoencoderKLLTXVideo"]
     _import_structure["autoencoders.autoencoder_kl_magvit"] = ["AutoencoderKLMagvit"]
     _import_structure["autoencoders.autoencoder_kl_mochi"] = ["AutoencoderKLMochi"]
@@ -91,6 +93,7 @@ if is_torch_available():
     _import_structure["transformers.transformer_hidream_image"] = ["HiDreamImageTransformer2DModel"]
     _import_structure["transformers.transformer_hunyuan_video"] = ["HunyuanVideoTransformer3DModel"]
     _import_structure["transformers.transformer_hunyuan_video_framepack"] = ["HunyuanVideoFramepackTransformer3DModel"]
+    _import_structure["transformers.transformer_hunyuanimage"] = ["HunyuanImageTransformer2DModel"]
     _import_structure["transformers.transformer_kandinsky"] = ["Kandinsky5Transformer3DModel"]
     _import_structure["transformers.transformer_ltx"] = ["LTXVideoTransformer3DModel"]
     _import_structure["transformers.transformer_lumina2"] = ["Lumina2Transformer2DModel"]
@@ -133,6 +136,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
             AutoencoderKLCosmos,
+            AutoencoderKLHunyuanImage,
+            AutoencoderKLHunyuanImageRefiner,
             AutoencoderKLHunyuanVideo,
             AutoencoderKLLTXVideo,
             AutoencoderKLMagvit,
@@ -182,6 +187,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
+            HunyuanImageTransformer2DModel,
             HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             Kandinsky5Transformer3DModel,

src/diffusers/models/autoencoders/__init__.py

@@ -5,6 +5,8 @@ from .autoencoder_kl_allegro import AutoencoderKLAllegro
 from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
 from .autoencoder_kl_cosmos import AutoencoderKLCosmos
 from .autoencoder_kl_hunyuan_video import AutoencoderKLHunyuanVideo
+from .autoencoder_kl_hunyuanimage import AutoencoderKLHunyuanImage
+from .autoencoder_kl_hunyuanimage_refiner import AutoencoderKLHunyuanImageRefiner
 from .autoencoder_kl_ltx import AutoencoderKLLTXVideo
 from .autoencoder_kl_magvit import AutoencoderKLMagvit
 from .autoencoder_kl_mochi import AutoencoderKLMochi

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py

+# Copyright 2025 The Hunyuan Team 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, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin
+from ...utils import logging
+from ...utils.accelerate_utils import apply_forward_hook
+from ..activations import get_activation
+from ..modeling_outputs import AutoencoderKLOutput
+from ..modeling_utils import ModelMixin
+from .vae import DecoderOutput, DiagonalGaussianDistribution
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class HunyuanImageResnetBlock(nn.Module):
+    r"""
+    Residual block with two convolutions and optional channel change.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        non_linearity (str, optional): Type of non-linearity to use. Default is "silu".
+    """
+
+    def __init__(self, in_channels: int, out_channels: int, non_linearity: str = "silu") -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.nonlinearity = get_activation(non_linearity)
+
+        # layers
+        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if in_channels != out_channels:
+            self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        else:
+            self.conv_shortcut = None
+
+    def forward(self, x):
+        # Apply shortcut connection
+        residual = x
+
+        # First normalization and activation
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+
+        if self.conv_shortcut is not None:
+            x = self.conv_shortcut(x)
+        # Add residual connection
+        return x + residual
+
+
+class HunyuanImageAttentionBlock(nn.Module):
+    r"""
+    Self-attention with a single head.
+
+    Args:
+        in_channels (int): The number of channels in the input tensor.
+    """
+
+    def __init__(self, in_channels: int):
+        super().__init__()
+
+        # layers
+        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.to_q = nn.Conv2d(in_channels, in_channels, 1)
+        self.to_k = nn.Conv2d(in_channels, in_channels, 1)
+        self.to_v = nn.Conv2d(in_channels, in_channels, 1)
+        self.proj = nn.Conv2d(in_channels, in_channels, 1)
+
+    def forward(self, x):
+        identity = x
+        x = self.norm(x)
+
+        # compute query, key, value
+        query = self.to_q(x)
+        key = self.to_k(x)
+        value = self.to_v(x)
+
+        batch_size, channels, height, width = query.shape
+        query = query.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
+        key = key.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
+        value = value.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
+
+        # apply attention
+        x = F.scaled_dot_product_attention(query, key, value)
+
+        x = x.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
+        # output projection
+        x = self.proj(x)
+
+        return x + identity
+
+
+class HunyuanImageDownsample(nn.Module):
+    """
+    Downsampling block for spatial reduction.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+    """
+
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        factor = 4
+        if out_channels % factor != 0:
+            raise ValueError(f"out_channels % factor != 0: {out_channels % factor}")
+
+        self.conv = nn.Conv2d(in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1)
+        self.group_size = factor * in_channels // out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.conv(x)
+
+        B, C, H, W = h.shape
+        h = h.reshape(B, C, H // 2, 2, W // 2, 2)
+        h = h.permute(0, 3, 5, 1, 2, 4)  # b, r1, r2, c, h, w
+        h = h.reshape(B, 4 * C, H // 2, W // 2)
+
+        B, C, H, W = x.shape
+        shortcut = x.reshape(B, C, H // 2, 2, W // 2, 2)
+        shortcut = shortcut.permute(0, 3, 5, 1, 2, 4)  # b, r1, r2, c, h, w
+        shortcut = shortcut.reshape(B, 4 * C, H // 2, W // 2)
+
+        B, C, H, W = shortcut.shape
+        shortcut = shortcut.view(B, h.shape[1], self.group_size, H, W).mean(dim=2)
+        return h + shortcut
+
+
+class HunyuanImageUpsample(nn.Module):
+    """
+    Upsampling block for spatial expansion.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+    """
+
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        factor = 4
+        self.conv = nn.Conv2d(in_channels, out_channels * factor, kernel_size=3, stride=1, padding=1)
+        self.repeats = factor * out_channels // in_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.conv(x)
+
+        B, C, H, W = h.shape
+        h = h.reshape(B, 2, 2, C // 4, H, W)  # b, r1, r2, c, h, w
+        h = h.permute(0, 3, 4, 1, 5, 2)  # b, c, h, r1, w, r2
+        h = h.reshape(B, C // 4, H * 2, W * 2)
+
+        shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
+
+        B, C, H, W = shortcut.shape
+        shortcut = shortcut.reshape(B, 2, 2, C // 4, H, W)  # b, r1, r2, c, h, w
+        shortcut = shortcut.permute(0, 3, 4, 1, 5, 2)  # b, c, h, r1, w, r2
+        shortcut = shortcut.reshape(B, C // 4, H * 2, W * 2)
+        return h + shortcut
+
+
+class HunyuanImageMidBlock(nn.Module):
+    """
+    Middle block for HunyuanImageVAE encoder and decoder.
+
+    Args:
+        in_channels (int): Number of input channels.
+        num_layers (int): Number of layers.
+    """
+
+    def __init__(self, in_channels: int, num_layers: int = 1):
+        super().__init__()
+
+        resnets = [HunyuanImageResnetBlock(in_channels=in_channels, out_channels=in_channels)]
+
+        attentions = []
+        for _ in range(num_layers):
+            attentions.append(HunyuanImageAttentionBlock(in_channels))
+            resnets.append(HunyuanImageResnetBlock(in_channels=in_channels, out_channels=in_channels))
+
+        self.resnets = nn.ModuleList(resnets)
+        self.attentions = nn.ModuleList(attentions)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.resnets[0](x)
+
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            x = attn(x)
+            x = resnet(x)
+
+        return x
+
+
+class HunyuanImageEncoder2D(nn.Module):
+    r"""
+    Encoder network that compresses input to latent representation.
+
+    Args:
+        in_channels (int): Number of input channels.
+        z_channels (int): Number of latent channels.
+        block_out_channels (list of int): Output channels for each block.
+        num_res_blocks (int): Number of residual blocks per block.
+        spatial_compression_ratio (int): Spatial downsampling factor.
+        non_linearity (str): Type of non-linearity to use. Default is "silu".
+        downsample_match_channel (bool): Whether to match channels during downsampling.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        z_channels: int,
+        block_out_channels: Tuple[int, ...],
+        num_res_blocks: int,
+        spatial_compression_ratio: int,
+        non_linearity: str = "silu",
+        downsample_match_channel: bool = True,
+    ):
+        super().__init__()
+        if block_out_channels[-1] % (2 * z_channels) != 0:
+            raise ValueError(
+                f"block_out_channels[-1 has to be divisible by 2 * out_channels, you have block_out_channels = {block_out_channels[-1]} and out_channels = {z_channels}"
+            )
+
+        self.in_channels = in_channels
+        self.z_channels = z_channels
+        self.block_out_channels = block_out_channels
+        self.num_res_blocks = num_res_blocks
+        self.spatial_compression_ratio = spatial_compression_ratio
+
+        self.group_size = block_out_channels[-1] // (2 * z_channels)
+        self.nonlinearity = get_activation(non_linearity)
+
+        # init block
+        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
+
+        # downsample blocks
+        self.down_blocks = nn.ModuleList([])
+
+        block_in_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            block_out_channel = block_out_channels[i]
+            # residual blocks
+            for _ in range(num_res_blocks):
+                self.down_blocks.append(
+                    HunyuanImageResnetBlock(in_channels=block_in_channel, out_channels=block_out_channel)
+                )
+                block_in_channel = block_out_channel
+
+            # downsample block
+            if i < np.log2(spatial_compression_ratio) and i != len(block_out_channels) - 1:
+                if downsample_match_channel:
+                    block_out_channel = block_out_channels[i + 1]
+                self.down_blocks.append(
+                    HunyuanImageDownsample(in_channels=block_in_channel, out_channels=block_out_channel)
+                )
+                block_in_channel = block_out_channel
+
+        # middle blocks
+        self.mid_block = HunyuanImageMidBlock(in_channels=block_out_channels[-1], num_layers=1)
+
+        # output blocks
+        # Output layers
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_out_channels[-1], eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_out_channels[-1], 2 * z_channels, kernel_size=3, stride=1, padding=1)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv_in(x)
+
+        ## downsamples
+        for down_block in self.down_blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                x = self._gradient_checkpointing_func(down_block, x)
+            else:
+                x = down_block(x)
+
+        ## middle
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            x = self._gradient_checkpointing_func(self.mid_block, x)
+        else:
+            x = self.mid_block(x)
+
+        ## head
+        B, C, H, W = x.shape
+        residual = x.view(B, C // self.group_size, self.group_size, H, W).mean(dim=2)
+
+        x = self.norm_out(x)
+        x = self.nonlinearity(x)
+        x = self.conv_out(x)
+        return x + residual
+
+
+class HunyuanImageDecoder2D(nn.Module):
+    r"""
+    Decoder network that reconstructs output from latent representation.
+
+    Args:
+    z_channels : int
+        Number of latent channels.
+    out_channels : int
+        Number of output channels.
+    block_out_channels : Tuple[int, ...]
+        Output channels for each block.
+    num_res_blocks : int
+        Number of residual blocks per block.
+    spatial_compression_ratio : int
+        Spatial upsampling factor.
+    upsample_match_channel : bool
+        Whether to match channels during upsampling.
+    non_linearity (str): Type of non-linearity to use. Default is "silu".
+    """
+
+    def __init__(
+        self,
+        z_channels: int,
+        out_channels: int,
+        block_out_channels: Tuple[int, ...],
+        num_res_blocks: int,
+        spatial_compression_ratio: int,
+        upsample_match_channel: bool = True,
+        non_linearity: str = "silu",
+    ):
+        super().__init__()
+        if block_out_channels[0] % z_channels != 0:
+            raise ValueError(
+                f"block_out_channels[0] should be divisible by z_channels but has block_out_channels[0] = {block_out_channels[0]} and z_channels = {z_channels}"
+            )
+
+        self.z_channels = z_channels
+        self.block_out_channels = block_out_channels
+        self.num_res_blocks = num_res_blocks
+        self.repeat = block_out_channels[0] // z_channels
+        self.spatial_compression_ratio = spatial_compression_ratio
+        self.nonlinearity = get_activation(non_linearity)
+
+        self.conv_in = nn.Conv2d(z_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
+
+        # Middle blocks with attention
+        self.mid_block = HunyuanImageMidBlock(in_channels=block_out_channels[0], num_layers=1)
+
+        # Upsampling blocks
+        block_in_channel = block_out_channels[0]
+        self.up_blocks = nn.ModuleList()
+        for i in range(len(block_out_channels)):
+            block_out_channel = block_out_channels[i]
+            for _ in range(self.num_res_blocks + 1):
+                self.up_blocks.append(
+                    HunyuanImageResnetBlock(in_channels=block_in_channel, out_channels=block_out_channel)
+                )
+                block_in_channel = block_out_channel
+
+            if i < np.log2(spatial_compression_ratio) and i != len(block_out_channels) - 1:
+                if upsample_match_channel:
+                    block_out_channel = block_out_channels[i + 1]
+                self.up_blocks.append(HunyuanImageUpsample(block_in_channel, block_out_channel))
+                block_in_channel = block_out_channel
+
+        # Output layers
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_out_channels[-1], eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.conv_in(x) + x.repeat_interleave(repeats=self.repeat, dim=1)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            h = self._gradient_checkpointing_func(self.mid_block, h)
+        else:
+            h = self.mid_block(h)
+
+        for up_block in self.up_blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                h = self._gradient_checkpointing_func(up_block, h)
+            else:
+                h = up_block(h)
+        h = self.norm_out(h)
+        h = self.nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class AutoencoderKLHunyuanImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+    r"""
+    A VAE model for 2D images with spatial tiling support.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+    """
+
+    _supports_gradient_checkpointing = False
+
+    # fmt: off
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        latent_channels: int,
+        block_out_channels: Tuple[int, ...],
+        layers_per_block: int,
+        spatial_compression_ratio: int,
+        sample_size: int,
+        scaling_factor: float = None,
+        downsample_match_channel: bool = True,
+        upsample_match_channel: bool = True,
+    ) -> None:
+    # fmt: on
+        super().__init__()
+
+        self.encoder = HunyuanImageEncoder2D(
+            in_channels=in_channels,
+            z_channels=latent_channels,
+            block_out_channels=block_out_channels,
+            num_res_blocks=layers_per_block,
+            spatial_compression_ratio=spatial_compression_ratio,
+            downsample_match_channel=downsample_match_channel,
+        )
+
+        self.decoder = HunyuanImageDecoder2D(
+            z_channels=latent_channels,
+            out_channels=out_channels,
+            block_out_channels=list(reversed(block_out_channels)),
+            num_res_blocks=layers_per_block,
+            spatial_compression_ratio=spatial_compression_ratio,
+            upsample_match_channel=upsample_match_channel,
+        )
+
+        # Tiling and slicing configuration
+        self.use_slicing = False
+        self.use_tiling = False
+
+        # Tiling parameters
+        self.tile_sample_min_size = sample_size
+        self.tile_latent_min_size = sample_size // spatial_compression_ratio
+        self.tile_overlap_factor = 0.25
+
+    def enable_tiling(
+        self,
+        tile_sample_min_size: Optional[int] = None,
+        tile_overlap_factor: Optional[float] = None,
+    ) -> None:
+        r"""
+        Enable spatial tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles
+        to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to
+        allow processing larger images.
+
+        Args:
+            tile_sample_min_size (`int`, *optional*):
+                The minimum size required for a sample to be separated into tiles across the spatial dimension.
+            tile_overlap_factor (`float`, *optional*):
+                The overlap factor required for a latent to be separated into tiles across the spatial dimension.
+        """
+        self.use_tiling = True
+        self.tile_sample_min_size = tile_sample_min_size or self.tile_sample_min_size
+        self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
+        self.tile_latent_min_size = self.tile_sample_min_size // self.config.spatial_compression_ratio
+
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    def _encode(self, x: torch.Tensor):
+
+        batch_size, num_channels, height, width = x.shape
+
+        if self.use_tiling and (width > self.tile_sample_min_size or height > self.tile_sample_min_size):
+            return self.tiled_encode(x)
+
+        enc = self.encoder(x)
+
+        return enc
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.Tensor, return_dict: bool = True
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        r"""
+        Encode a batch of images into latents.
+
+        Args:
+            x (`torch.Tensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+                The latent representations of the encoded videos. If `return_dict` is True, a
+                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
+        """
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self._encode(x)
+        posterior = DiagonalGaussianDistribution(h)
+
+        if not return_dict:
+            return (posterior,)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def _decode(self, z: torch.Tensor, return_dict: bool = True):
+
+        batch_size, num_channels, height, width = z.shape
+
+        if self.use_tiling and (width > self.tile_latent_min_size or height > self.tile_latent_min_size):
+            return self.tiled_decode(z, return_dict=return_dict)
+
+        dec = self.decoder(z)
+
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)
+
+    @apply_forward_hook
+    def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        r"""
+        Decode a batch of images.
+
+        Args:
+            z (`torch.Tensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z).sample
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (
+                y / blend_extent
+            )
+        return b
+
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (
+                x / blend_extent
+            )
+        return b
+
+    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Encode input using spatial tiling strategy.
+
+        Args:
+            x (`torch.Tensor`): Input tensor of shape (B, C, T, H, W).
+
+        Returns:
+            `torch.Tensor`:
+                The latent representation of the encoded images.
+        """
+        _, _, _, height, width = x.shape
+        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_latent_min_size - blend_extent
+
+        rows = []
+        for i in range(0, height, overlap_size):
+            row = []
+            for j in range(0, width, overlap_size):
+                tile = x[:, :, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
+                tile = self.encoder(tile)
+                row.append(tile)
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=-1))
+
+        moments = torch.cat(result_rows, dim=-2)
+
+        return moments
+
+    def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        """
+        Decode latent using spatial tiling strategy.
+
+        Args:
+            z (`torch.Tensor`): Latent tensor of shape (B, C, H, W).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        _, _, height, width = z.shape
+        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_sample_min_size - blend_extent
+
+        rows = []
+        for i in range(0, height, overlap_size):
+            row = []
+            for j in range(0, width, overlap_size):
+                tile = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
+                decoded = self.decoder(tile)
+                row.append(decoded)
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=-1))
+
+        dec = torch.cat(result_rows, dim=-2)
+        if not return_dict:
+            return (dec,)
+        return DecoderOutput(sample=dec)
+
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        """
+        Args:
+            sample (`torch.Tensor`): Input sample.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
+        """
+        posterior = self.encode(sample).latent_dist
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z, return_dict=return_dict)
+
+        return dec

src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage_refiner.py

+# Copyright 2025 The Hunyuan Team 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, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils import logging
+from ...utils.accelerate_utils import apply_forward_hook
+from ..activations import get_activation
+from ..modeling_outputs import AutoencoderKLOutput
+from ..modeling_utils import ModelMixin
+from .vae import DecoderOutput, DiagonalGaussianDistribution
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class HunyuanImageRefinerCausalConv3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, Tuple[int, int, int]] = 3,
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        padding: Union[int, Tuple[int, int, int]] = 0,
+        dilation: Union[int, Tuple[int, int, int]] = 1,
+        bias: bool = True,
+        pad_mode: str = "replicate",
+    ) -> None:
+        super().__init__()
+
+        kernel_size = (kernel_size, kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size
+
+        self.pad_mode = pad_mode
+        self.time_causal_padding = (
+            kernel_size[0] // 2,
+            kernel_size[0] // 2,
+            kernel_size[1] // 2,
+            kernel_size[1] // 2,
+            kernel_size[2] - 1,
+            0,
+        )
+
+        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = F.pad(hidden_states, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(hidden_states)
+
+
+class HunyuanImageRefinerRMS_norm(nn.Module):
+    r"""
+    A custom RMS normalization layer.
+
+    Args:
+        dim (int): The number of dimensions to normalize over.
+        channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
+            Default is True.
+        images (bool, optional): Whether the input represents image data. Default is True.
+        bias (bool, optional): Whether to include a learnable bias term. Default is False.
+    """
+
+    def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0
+
+    def forward(self, x):
+        return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
+
+
+class HunyuanImageRefinerAttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = HunyuanImageRefinerRMS_norm(in_channels, images=False)
+
+        self.to_q = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.to_k = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.to_v = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.proj_out = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        identity = x
+
+        x = self.norm(x)
+
+        query = self.to_q(x)
+        key = self.to_k(x)
+        value = self.to_v(x)
+
+        batch_size, channels, frames, height, width = query.shape
+
+        query = query.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
+        key = key.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
+        value = value.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
+
+        x = nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=None)
+
+        # batch_size, 1, frames * height * width, channels
+
+        x = x.squeeze(1).reshape(batch_size, frames, height, width, channels).permute(0, 4, 1, 2, 3)
+        x = self.proj_out(x)
+
+        return x + identity
+
+
+class HunyuanImageRefinerUpsampleDCAE(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, add_temporal_upsample: bool = True):
+        super().__init__()
+        factor = 2 * 2 * 2 if add_temporal_upsample else 1 * 2 * 2
+        self.conv = HunyuanImageRefinerCausalConv3d(in_channels, out_channels * factor, kernel_size=3)
+
+        self.add_temporal_upsample = add_temporal_upsample
+        self.repeats = factor * out_channels // in_channels
+
+    @staticmethod
+    def _dcae_upsample_rearrange(tensor, r1=1, r2=2, r3=2):
+        """
+        Convert (b, r1*r2*r3*c, f, h, w) -> (b, c, r1*f, r2*h, r3*w)
+
+        Args:
+            tensor: Input tensor of shape (b, r1*r2*r3*c, f, h, w)
+            r1: temporal upsampling factor
+            r2: height upsampling factor
+            r3: width upsampling factor
+        """
+        b, packed_c, f, h, w = tensor.shape
+        factor = r1 * r2 * r3
+        c = packed_c // factor
+
+        tensor = tensor.view(b, r1, r2, r3, c, f, h, w)
+        tensor = tensor.permute(0, 4, 5, 1, 6, 2, 7, 3)
+        return tensor.reshape(b, c, f * r1, h * r2, w * r3)
+
+    def forward(self, x: torch.Tensor):
+        r1 = 2 if self.add_temporal_upsample else 1
+        h = self.conv(x)
+        if self.add_temporal_upsample:
+            h = self._dcae_upsample_rearrange(h, r1=1, r2=2, r3=2)
+            h = h[:, : h.shape[1] // 2]
+
+            # shortcut computation
+            shortcut = self._dcae_upsample_rearrange(x, r1=1, r2=2, r3=2)
+            shortcut = shortcut.repeat_interleave(repeats=self.repeats // 2, dim=1)
+
+        else:
+            h = self._dcae_upsample_rearrange(h, r1=r1, r2=2, r3=2)
+            shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
+            shortcut = self._dcae_upsample_rearrange(shortcut, r1=r1, r2=2, r3=2)
+        return h + shortcut
+
+
+class HunyuanImageRefinerDownsampleDCAE(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, add_temporal_downsample: bool = True):
+        super().__init__()
+        factor = 2 * 2 * 2 if add_temporal_downsample else 1 * 2 * 2
+        assert out_channels % factor == 0
+        # self.conv = Conv3d(in_channels, out_channels // factor, kernel_size=3, stride=1, padding=1)
+        self.conv = HunyuanImageRefinerCausalConv3d(in_channels, out_channels // factor, kernel_size=3)
+
+        self.add_temporal_downsample = add_temporal_downsample
+        self.group_size = factor * in_channels // out_channels
+
+    @staticmethod
+    def _dcae_downsample_rearrange(tensor, r1=1, r2=2, r3=2):
+        """
+        Convert (b, c, r1*f, r2*h, r3*w) -> (b, r1*r2*r3*c, f, h, w)
+
+        This packs spatial/temporal dimensions into channels (opposite of upsample)
+        """
+        b, c, packed_f, packed_h, packed_w = tensor.shape
+        f, h, w = packed_f // r1, packed_h // r2, packed_w // r3
+
+        tensor = tensor.view(b, c, f, r1, h, r2, w, r3)
+        tensor = tensor.permute(0, 3, 5, 7, 1, 2, 4, 6)
+        return tensor.reshape(b, r1 * r2 * r3 * c, f, h, w)
+
+    def forward(self, x: torch.Tensor):
+        r1 = 2 if self.add_temporal_downsample else 1
+        h = self.conv(x)
+        if self.add_temporal_downsample:
+            # h = rearrange(h, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
+            h = self._dcae_downsample_rearrange(h, r1=1, r2=2, r3=2)
+            h = torch.cat([h, h], dim=1)
+            # shortcut computation
+            # shortcut = rearrange(x, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
+            shortcut = self._dcae_downsample_rearrange(x, r1=1, r2=2, r3=2)
+            B, C, T, H, W = shortcut.shape
+            shortcut = shortcut.view(B, h.shape[1], self.group_size // 2, T, H, W).mean(dim=2)
+        else:
+            # h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
+            h = self._dcae_downsample_rearrange(h, r1=r1, r2=2, r3=2)
+            # shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
+            shortcut = self._dcae_downsample_rearrange(x, r1=r1, r2=2, r3=2)
+            B, C, T, H, W = shortcut.shape
+            shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
+
+        return h + shortcut
+
+
+class HunyuanImageRefinerResnetBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        non_linearity: str = "swish",
+    ) -> None:
+        super().__init__()
+        out_channels = out_channels or in_channels
+
+        self.nonlinearity = get_activation(non_linearity)
+
+        self.norm1 = HunyuanImageRefinerRMS_norm(in_channels, images=False)
+        self.conv1 = HunyuanImageRefinerCausalConv3d(in_channels, out_channels, kernel_size=3)
+
+        self.norm2 = HunyuanImageRefinerRMS_norm(out_channels, images=False)
+        self.conv2 = HunyuanImageRefinerCausalConv3d(out_channels, out_channels, kernel_size=3)
+
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            residual = self.conv_shortcut(residual)
+
+        return hidden_states + residual
+
+
+class HunyuanImageRefinerMidBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        num_layers: int = 1,
+        add_attention: bool = True,
+    ) -> None:
+        super().__init__()
+        self.add_attention = add_attention
+
+        # There is always at least one resnet
+        resnets = [
+            HunyuanImageRefinerResnetBlock(
+                in_channels=in_channels,
+                out_channels=in_channels,
+            )
+        ]
+        attentions = []
+
+        for _ in range(num_layers):
+            if self.add_attention:
+                attentions.append(HunyuanImageRefinerAttnBlock(in_channels))
+            else:
+                attentions.append(None)
+
+            resnets.append(
+                HunyuanImageRefinerResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.resnets[0](hidden_states)
+
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                hidden_states = attn(hidden_states)
+            hidden_states = resnet(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanImageRefinerDownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        downsample_out_channels: Optional[int] = None,
+        add_temporal_downsample: int = True,
+    ) -> None:
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                HunyuanImageRefinerResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if downsample_out_channels is not None:
+            self.downsamplers = nn.ModuleList(
+                [
+                    HunyuanImageRefinerDownsampleDCAE(
+                        out_channels,
+                        out_channels=downsample_out_channels,
+                        add_temporal_downsample=add_temporal_downsample,
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanImageRefinerUpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        upsample_out_channels: Optional[int] = None,
+        add_temporal_upsample: bool = True,
+    ) -> None:
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            input_channels = in_channels if i == 0 else out_channels
+
+            resnets.append(
+                HunyuanImageRefinerResnetBlock(
+                    in_channels=input_channels,
+                    out_channels=out_channels,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if upsample_out_channels is not None:
+            self.upsamplers = nn.ModuleList(
+                [
+                    HunyuanImageRefinerUpsampleDCAE(
+                        out_channels,
+                        out_channels=upsample_out_channels,
+                        add_temporal_upsample=add_temporal_upsample,
+                    )
+                ]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for resnet in self.resnets:
+                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states)
+
+        else:
+            for resnet in self.resnets:
+                hidden_states = resnet(hidden_states)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanImageRefinerEncoder3D(nn.Module):
+    r"""
+    3D vae encoder for HunyuanImageRefiner.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 64,
+        block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024, 1024),
+        layers_per_block: int = 2,
+        temporal_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 16,
+        downsample_match_channel: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.group_size = block_out_channels[-1] // self.out_channels
+
+        self.conv_in = HunyuanImageRefinerCausalConv3d(in_channels, block_out_channels[0], kernel_size=3)
+        self.mid_block = None
+        self.down_blocks = nn.ModuleList([])
+
+        input_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            add_spatial_downsample = i < np.log2(spatial_compression_ratio)
+            output_channel = block_out_channels[i]
+            if not add_spatial_downsample:
+                down_block = HunyuanImageRefinerDownBlock3D(
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    downsample_out_channels=None,
+                    add_temporal_downsample=False,
+                )
+                input_channel = output_channel
+            else:
+                add_temporal_downsample = i >= np.log2(spatial_compression_ratio // temporal_compression_ratio)
+                downsample_out_channels = block_out_channels[i + 1] if downsample_match_channel else output_channel
+                down_block = HunyuanImageRefinerDownBlock3D(
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    downsample_out_channels=downsample_out_channels,
+                    add_temporal_downsample=add_temporal_downsample,
+                )
+                input_channel = downsample_out_channels
+
+            self.down_blocks.append(down_block)
+
+        self.mid_block = HunyuanImageRefinerMidBlock(in_channels=block_out_channels[-1])
+
+        self.norm_out = HunyuanImageRefinerRMS_norm(block_out_channels[-1], images=False)
+        self.conv_act = nn.SiLU()
+        self.conv_out = HunyuanImageRefinerCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv_in(hidden_states)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for down_block in self.down_blocks:
+                hidden_states = self._gradient_checkpointing_func(down_block, hidden_states)
+
+            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
+        else:
+            for down_block in self.down_blocks:
+                hidden_states = down_block(hidden_states)
+
+            hidden_states = self.mid_block(hidden_states)
+
+        # short_cut = rearrange(hidden_states, "b (c r) f h w -> b c r f h w", r=self.group_size).mean(dim=2)
+        batch_size, _, frame, height, width = hidden_states.shape
+        short_cut = hidden_states.view(batch_size, -1, self.group_size, frame, height, width).mean(dim=2)
+
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        hidden_states += short_cut
+
+        return hidden_states
+
+
+class HunyuanImageRefinerDecoder3D(nn.Module):
+    r"""
+    Causal decoder for 3D video-like data used for HunyuanImage-2.1 Refiner.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 32,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int, ...] = (1024, 1024, 512, 256, 128),
+        layers_per_block: int = 2,
+        spatial_compression_ratio: int = 16,
+        temporal_compression_ratio: int = 4,
+        upsample_match_channel: bool = True,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.repeat = block_out_channels[0] // self.in_channels
+
+        self.conv_in = HunyuanImageRefinerCausalConv3d(self.in_channels, block_out_channels[0], kernel_size=3)
+        self.up_blocks = nn.ModuleList([])
+
+        # mid
+        self.mid_block = HunyuanImageRefinerMidBlock(in_channels=block_out_channels[0])
+
+        # up
+        input_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            output_channel = block_out_channels[i]
+
+            add_spatial_upsample = i < np.log2(spatial_compression_ratio)
+            add_temporal_upsample = i < np.log2(temporal_compression_ratio)
+            if add_spatial_upsample or add_temporal_upsample:
+                upsample_out_channels = block_out_channels[i + 1] if upsample_match_channel else output_channel
+                up_block = HunyuanImageRefinerUpBlock3D(
+                    num_layers=self.layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    upsample_out_channels=upsample_out_channels,
+                    add_temporal_upsample=add_temporal_upsample,
+                )
+                input_channel = upsample_out_channels
+            else:
+                up_block = HunyuanImageRefinerUpBlock3D(
+                    num_layers=self.layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    upsample_out_channels=None,
+                    add_temporal_upsample=False,
+                )
+                input_channel = output_channel
+
+            self.up_blocks.append(up_block)
+
+        # out
+        self.norm_out = HunyuanImageRefinerRMS_norm(block_out_channels[-1], images=False)
+        self.conv_act = nn.SiLU()
+        self.conv_out = HunyuanImageRefinerCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv_in(hidden_states) + hidden_states.repeat_interleave(repeats=self.repeat, dim=1)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
+
+            for up_block in self.up_blocks:
+                hidden_states = self._gradient_checkpointing_func(up_block, hidden_states)
+        else:
+            hidden_states = self.mid_block(hidden_states)
+
+            for up_block in self.up_blocks:
+                hidden_states = up_block(hidden_states)
+
+        # post-process
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        return hidden_states
+
+
+class AutoencoderKLHunyuanImageRefiner(ModelMixin, ConfigMixin):
+    r"""
+    A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
+    HunyuanImage-2.1 Refiner.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        latent_channels: int = 32,
+        block_out_channels: Tuple[int] = (128, 256, 512, 1024, 1024),
+        layers_per_block: int = 2,
+        spatial_compression_ratio: int = 16,
+        temporal_compression_ratio: int = 4,
+        downsample_match_channel: bool = True,
+        upsample_match_channel: bool = True,
+        scaling_factor: float = 1.03682,
+    ) -> None:
+        super().__init__()
+
+        self.encoder = HunyuanImageRefinerEncoder3D(
+            in_channels=in_channels,
+            out_channels=latent_channels * 2,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            temporal_compression_ratio=temporal_compression_ratio,
+            spatial_compression_ratio=spatial_compression_ratio,
+            downsample_match_channel=downsample_match_channel,
+        )
+
+        self.decoder = HunyuanImageRefinerDecoder3D(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            block_out_channels=list(reversed(block_out_channels)),
+            layers_per_block=layers_per_block,
+            temporal_compression_ratio=temporal_compression_ratio,
+            spatial_compression_ratio=spatial_compression_ratio,
+            upsample_match_channel=upsample_match_channel,
+        )
+
+        self.spatial_compression_ratio = spatial_compression_ratio
+        self.temporal_compression_ratio = temporal_compression_ratio
+
+        # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
+        # to perform decoding of a single video latent at a time.
+        self.use_slicing = False
+
+        # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
+        # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
+        # intermediate tiles together, the memory requirement can be lowered.
+        self.use_tiling = False
+
+        # The minimal tile height and width for spatial tiling to be used
+        self.tile_sample_min_height = 256
+        self.tile_sample_min_width = 256
+
+        # The minimal distance between two spatial tiles
+        self.tile_sample_stride_height = 192
+        self.tile_sample_stride_width = 192
+
+        self.tile_overlap_factor = 0.25
+
+    def enable_tiling(
+        self,
+        tile_sample_min_height: Optional[int] = None,
+        tile_sample_min_width: Optional[int] = None,
+        tile_sample_stride_height: Optional[float] = None,
+        tile_sample_stride_width: Optional[float] = None,
+        tile_overlap_factor: Optional[float] = None,
+    ) -> None:
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+
+        Args:
+            tile_sample_min_height (`int`, *optional*):
+                The minimum height required for a sample to be separated into tiles across the height dimension.
+            tile_sample_min_width (`int`, *optional*):
+                The minimum width required for a sample to be separated into tiles across the width dimension.
+            tile_sample_stride_height (`int`, *optional*):
+                The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
+                no tiling artifacts produced across the height dimension.
+            tile_sample_stride_width (`int`, *optional*):
+                The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling
+                artifacts produced across the width dimension.
+        """
+        self.use_tiling = True
+        self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
+        self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
+        self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
+        self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
+        self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
+
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    def _encode(self, x: torch.Tensor) -> torch.Tensor:
+        _, _, _, height, width = x.shape
+
+        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
+            return self.tiled_encode(x)
+
+        x = self.encoder(x)
+        return x
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.Tensor, return_dict: bool = True
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        r"""
+        Encode a batch of images into latents.
+
+        Args:
+            x (`torch.Tensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+                The latent representations of the encoded videos. If `return_dict` is True, a
+                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
+        """
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self._encode(x)
+
+        posterior = DiagonalGaussianDistribution(h)
+
+        if not return_dict:
+            return (posterior,)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def _decode(self, z: torch.Tensor) -> torch.Tensor:
+        _, _, _, height, width = z.shape
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+
+        if self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height):
+            return self.tiled_decode(z)
+
+        dec = self.decoder(z)
+
+        return dec
+
+    @apply_forward_hook
+    def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        r"""
+        Decode a batch of images.
+
+        Args:
+            z (`torch.Tensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [self._decode(z_slice) for z_slice in z.split(1)]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z)
+
+        if not return_dict:
+            return (decoded,)
+
+        return DecoderOutput(sample=decoded)
+
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
+                y / blend_extent
+            )
+        return b
+
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
+                x / blend_extent
+            )
+        return b
+
+    def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, x, :, :] * (
+                x / blend_extent
+            )
+        return b
+
+    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
+        r"""Encode a batch of images using a tiled encoder.
+
+        Args:
+            x (`torch.Tensor`): Input batch of videos.
+
+        Returns:
+            `torch.Tensor`:
+                The latent representation of the encoded videos.
+        """
+        _, _, _, height, width = x.shape
+
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        overlap_height = int(tile_latent_min_height * (1 - self.tile_overlap_factor))  # 256 * (1 - 0.25) = 192
+        overlap_width = int(tile_latent_min_width * (1 - self.tile_overlap_factor))  # 256 * (1 - 0.25) = 192
+        blend_height = int(tile_latent_min_height * self.tile_overlap_factor)  # 8 * 0.25 = 2
+        blend_width = int(tile_latent_min_width * self.tile_overlap_factor)  # 8 * 0.25 = 2
+        row_limit_height = tile_latent_min_height - blend_height  # 8 - 2 = 6
+        row_limit_width = tile_latent_min_width - blend_width  # 8 - 2 = 6
+
+        rows = []
+        for i in range(0, height, overlap_height):
+            row = []
+            for j in range(0, width, overlap_width):
+                tile = x[
+                    :,
+                    :,
+                    :,
+                    i : i + self.tile_sample_min_height,
+                    j : j + self.tile_sample_min_width,
+                ]
+                tile = self.encoder(tile)
+                row.append(tile)
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_width)
+                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
+            result_rows.append(torch.cat(result_row, dim=-1))
+        moments = torch.cat(result_rows, dim=-2)
+
+        return moments
+
+    def tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
+        r"""
+        Decode a batch of images using a tiled decoder.
+
+        Args:
+            z (`torch.Tensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+
+        _, _, _, height, width = z.shape
+        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        overlap_height = int(tile_latent_min_height * (1 - self.tile_overlap_factor))  # 8 * (1 - 0.25) = 6
+        overlap_width = int(tile_latent_min_width * (1 - self.tile_overlap_factor))  # 8 * (1 - 0.25) = 6
+        blend_height = int(tile_latent_min_height * self.tile_overlap_factor)  # 256 * 0.25 = 64
+        blend_width = int(tile_latent_min_width * self.tile_overlap_factor)  # 256 * 0.25 = 64
+        row_limit_height = tile_latent_min_height - blend_height  # 256 - 64 = 192
+        row_limit_width = tile_latent_min_width - blend_width  # 256 - 64 = 192
+
+        rows = []
+        for i in range(0, height, overlap_height):
+            row = []
+            for j in range(0, width, overlap_width):
+                tile = z[
+                    :,
+                    :,
+                    :,
+                    i : i + tile_latent_min_height,
+                    j : j + tile_latent_min_width,
+                ]
+                decoded = self.decoder(tile)
+                row.append(decoded)
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_width)
+                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
+            result_rows.append(torch.cat(result_row, dim=-1))
+        dec = torch.cat(result_rows, dim=-2)
+
+        return dec
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        r"""
+        Args:
+            sample (`torch.Tensor`): Input sample.
+            sample_posterior (`bool`, *optional*, defaults to `False`):
+                Whether to sample from the posterior.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
+        """
+        x = sample
+        posterior = self.encode(x).latent_dist
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z, return_dict=return_dict)
+        return dec

src/diffusers/models/transformers/__init__.py

@@ -27,6 +27,7 @@ if is_torch_available():
     from .transformer_hidream_image import HiDreamImageTransformer2DModel
     from .transformer_hunyuan_video import HunyuanVideoTransformer3DModel
     from .transformer_hunyuan_video_framepack import HunyuanVideoFramepackTransformer3DModel
+    from .transformer_hunyuanimage import HunyuanImageTransformer2DModel
     from .transformer_kandinsky import Kandinsky5Transformer3DModel
     from .transformer_ltx import LTXVideoTransformer3DModel
     from .transformer_lumina2 import Lumina2Transformer2DModel

src/diffusers/models/transformers/transformer_hunyuanimage.py

+# Copyright 2025 The Hunyuan Team 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 math
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.loaders import FromOriginalModelMixin
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import PeftAdapterMixin
+from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention import FeedForward
+from ..attention_dispatch import dispatch_attention_fn
+from ..attention_processor import Attention, AttentionProcessor
+from ..cache_utils import CacheMixin
+from ..embeddings import (
+    CombinedTimestepTextProjEmbeddings,
+    TimestepEmbedding,
+    Timesteps,
+    get_1d_rotary_pos_embed,
+)
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class HunyuanImageAttnProcessor:
+    _attention_backend = None
+    _parallel_config = None
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "HunyuanImageAttnProcessor 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,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if attn.add_q_proj is None and encoder_hidden_states is not None:
+            hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
+
+        # 1. QKV projections
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(2, (attn.heads, -1))  # batch_size, seq_len, heads, head_dim
+        key = key.unflatten(2, (attn.heads, -1))
+        value = value.unflatten(2, (attn.heads, -1))
+
+        # 2. QK normalization
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # 3. Rotational positional embeddings applied to latent stream
+        if image_rotary_emb is not None:
+            from ..embeddings import apply_rotary_emb
+
+            if attn.add_q_proj is None and encoder_hidden_states is not None:
+                query = torch.cat(
+                    [
+                        apply_rotary_emb(
+                            query[:, : -encoder_hidden_states.shape[1]], image_rotary_emb, sequence_dim=1
+                        ),
+                        query[:, -encoder_hidden_states.shape[1] :],
+                    ],
+                    dim=1,
+                )
+                key = torch.cat(
+                    [
+                        apply_rotary_emb(key[:, : -encoder_hidden_states.shape[1]], image_rotary_emb, sequence_dim=1),
+                        key[:, -encoder_hidden_states.shape[1] :],
+                    ],
+                    dim=1,
+                )
+            else:
+                query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
+                key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
+
+        # 4. Encoder condition QKV projection and normalization
+        if attn.add_q_proj is not None and encoder_hidden_states is not None:
+            encoder_query = attn.add_q_proj(encoder_hidden_states)
+            encoder_key = attn.add_k_proj(encoder_hidden_states)
+            encoder_value = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
+            encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
+            encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
+
+            if attn.norm_added_q is not None:
+                encoder_query = attn.norm_added_q(encoder_query)
+            if attn.norm_added_k is not None:
+                encoder_key = attn.norm_added_k(encoder_key)
+
+            query = torch.cat([query, encoder_query], dim=1)
+            key = torch.cat([key, encoder_key], dim=1)
+            value = torch.cat([value, encoder_value], dim=1)
+
+        # 5. Attention
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # 6. Output projection
+        if encoder_hidden_states is not None:
+            hidden_states, encoder_hidden_states = (
+                hidden_states[:, : -encoder_hidden_states.shape[1]],
+                hidden_states[:, -encoder_hidden_states.shape[1] :],
+            )
+
+            if getattr(attn, "to_out", None) is not None:
+                hidden_states = attn.to_out[0](hidden_states)
+                hidden_states = attn.to_out[1](hidden_states)
+
+            if getattr(attn, "to_add_out", None) is not None:
+                encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanImagePatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: Union[Tuple[int, int], Tuple[int, int, int]] = (16, 16),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+
+        if len(patch_size) == 2:
+            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        elif len(patch_size) == 3:
+            self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        else:
+            raise ValueError(f"patch_size must be a tuple of length 2 or 3, got {len(patch_size)}")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.proj(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        return hidden_states
+
+
+class HunyuanImageByT5TextProjection(nn.Module):
+    def __init__(self, in_features: int, hidden_size: int, out_features: int):
+        super().__init__()
+        self.norm = nn.LayerNorm(in_features)
+        self.linear_1 = nn.Linear(in_features, hidden_size)
+        self.linear_2 = nn.Linear(hidden_size, hidden_size)
+        self.linear_3 = nn.Linear(hidden_size, out_features)
+        self.act_fn = nn.GELU()
+
+    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.norm(encoder_hidden_states)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states = self.linear_3(hidden_states)
+        return hidden_states
+
+
+class HunyuanImageAdaNorm(nn.Module):
+    def __init__(self, in_features: int, out_features: Optional[int] = None) -> None:
+        super().__init__()
+
+        out_features = out_features or 2 * in_features
+        self.linear = nn.Linear(in_features, out_features)
+        self.nonlinearity = nn.SiLU()
+
+    def forward(
+        self, temb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        temb = self.linear(self.nonlinearity(temb))
+        gate_msa, gate_mlp = temb.chunk(2, dim=1)
+        gate_msa, gate_mlp = gate_msa.unsqueeze(1), gate_mlp.unsqueeze(1)
+        return gate_msa, gate_mlp
+
+
+class HunyuanImageCombinedTimeGuidanceEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        guidance_embeds: bool = False,
+        use_meanflow: bool = False,
+    ):
+        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.use_meanflow = use_meanflow
+
+        self.time_proj_r = None
+        self.timestep_embedder_r = None
+        if use_meanflow:
+            self.time_proj_r = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+            self.timestep_embedder_r = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+        self.guidance_embedder = None
+        if guidance_embeds:
+            self.guidance_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        timestep_r: Optional[torch.Tensor] = None,
+        guidance: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=timestep.dtype))
+
+        if timestep_r is not None:
+            timesteps_proj_r = self.time_proj_r(timestep_r)
+            timesteps_emb_r = self.timestep_embedder_r(timesteps_proj_r.to(dtype=timestep.dtype))
+            timesteps_emb = (timesteps_emb + timesteps_emb_r) / 2
+
+        if self.guidance_embedder is not None:
+            guidance_proj = self.time_proj(guidance)
+            guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=timestep.dtype))
+            conditioning = timesteps_emb + guidance_emb
+        else:
+            conditioning = timesteps_emb
+
+        return conditioning
+
+
+# IndividualTokenRefinerBlock
+@maybe_allow_in_graph
+class HunyuanImageIndividualTokenRefinerBlock(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,  # 28
+        attention_head_dim: int,  # 128
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            bias=attention_bias,
+        )
+
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.ff = FeedForward(hidden_size, mult=mlp_width_ratio, activation_fn="linear-silu", dropout=mlp_drop_rate)
+
+        self.norm_out = HunyuanImageAdaNorm(hidden_size, 2 * hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        norm_hidden_states = self.norm1(hidden_states)
+
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=None,
+            attention_mask=attention_mask,
+        )
+
+        gate_msa, gate_mlp = self.norm_out(temb)
+        hidden_states = hidden_states + attn_output * gate_msa
+
+        ff_output = self.ff(self.norm2(hidden_states))
+        hidden_states = hidden_states + ff_output * gate_mlp
+
+        return hidden_states
+
+
+class HunyuanImageIndividualTokenRefiner(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        num_layers: int,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.refiner_blocks = nn.ModuleList(
+            [
+                HunyuanImageIndividualTokenRefinerBlock(
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    mlp_width_ratio=mlp_width_ratio,
+                    mlp_drop_rate=mlp_drop_rate,
+                    attention_bias=attention_bias,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> None:
+        self_attn_mask = None
+        if attention_mask is not None:
+            batch_size = attention_mask.shape[0]
+            seq_len = attention_mask.shape[1]
+            attention_mask = attention_mask.to(hidden_states.device)
+            self_attn_mask_1 = attention_mask.view(batch_size, 1, 1, seq_len).repeat(1, 1, seq_len, 1)
+            self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
+            self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
+            self_attn_mask[:, :, :, 0] = True
+
+        for block in self.refiner_blocks:
+            hidden_states = block(hidden_states, temb, self_attn_mask)
+
+        return hidden_states
+
+
+# txt_in
+class HunyuanImageTokenRefiner(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        num_layers: int,
+        mlp_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
+            embedding_dim=hidden_size, pooled_projection_dim=in_channels
+        )
+        self.proj_in = nn.Linear(in_channels, hidden_size, bias=True)
+        self.token_refiner = HunyuanImageIndividualTokenRefiner(
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            num_layers=num_layers,
+            mlp_width_ratio=mlp_ratio,
+            mlp_drop_rate=mlp_drop_rate,
+            attention_bias=attention_bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+        if attention_mask is None:
+            pooled_hidden_states = hidden_states.mean(dim=1)
+        else:
+            original_dtype = hidden_states.dtype
+            mask_float = attention_mask.float().unsqueeze(-1)
+            pooled_hidden_states = (hidden_states * mask_float).sum(dim=1) / mask_float.sum(dim=1)
+            pooled_hidden_states = pooled_hidden_states.to(original_dtype)
+
+        temb = self.time_text_embed(timestep, pooled_hidden_states)
+        hidden_states = self.proj_in(hidden_states)
+        hidden_states = self.token_refiner(hidden_states, temb, attention_mask)
+
+        return hidden_states
+
+
+class HunyuanImageRotaryPosEmbed(nn.Module):
+    def __init__(
+        self, patch_size: Union[Tuple, List[int]], rope_dim: Union[Tuple, List[int]], theta: float = 256.0
+    ) -> None:
+        super().__init__()
+
+        if not isinstance(patch_size, (tuple, list)) or len(patch_size) not in [2, 3]:
+            raise ValueError(f"patch_size must be a tuple or list of length 2 or 3, got {patch_size}")
+
+        if not isinstance(rope_dim, (tuple, list)) or len(rope_dim) not in [2, 3]:
+            raise ValueError(f"rope_dim must be a tuple or list of length 2 or 3, got {rope_dim}")
+
+        if not len(patch_size) == len(rope_dim):
+            raise ValueError(f"patch_size and rope_dim must have the same length, got {patch_size} and {rope_dim}")
+
+        self.patch_size = patch_size
+        self.rope_dim = rope_dim
+        self.theta = theta
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if hidden_states.ndim == 5:
+            _, _, frame, height, width = hidden_states.shape
+            patch_size_frame, patch_size_height, patch_size_width = self.patch_size
+            rope_sizes = [frame // patch_size_frame, height // patch_size_height, width // patch_size_width]
+        elif hidden_states.ndim == 4:
+            _, _, height, width = hidden_states.shape
+            patch_size_height, patch_size_width = self.patch_size
+            rope_sizes = [height // patch_size_height, width // patch_size_width]
+        else:
+            raise ValueError(f"hidden_states must be a 4D or 5D tensor, got {hidden_states.shape}")
+
+        axes_grids = []
+        for i in range(len(rope_sizes)):
+            grid = torch.arange(0, rope_sizes[i], device=hidden_states.device, dtype=torch.float32)
+            axes_grids.append(grid)
+        grid = torch.meshgrid(*axes_grids, indexing="ij")  # dim x [H, W]
+        grid = torch.stack(grid, dim=0)  # [2, H, W]
+
+        freqs = []
+        for i in range(len(rope_sizes)):
+            freq = get_1d_rotary_pos_embed(self.rope_dim[i], grid[i].reshape(-1), self.theta, use_real=True)
+            freqs.append(freq)
+
+        freqs_cos = torch.cat([f[0] for f in freqs], dim=1)  # (W * H * T, D / 2)
+        freqs_sin = torch.cat([f[1] for f in freqs], dim=1)  # (W * H * T, D / 2)
+        return freqs_cos, freqs_sin
+
+
+@maybe_allow_in_graph
+class HunyuanImageSingleTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float = 4.0,
+        qk_norm: str = "rms_norm",
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+        mlp_dim = int(hidden_size * mlp_ratio)
+
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=hidden_size,
+            bias=True,
+            processor=HunyuanImageAttnProcessor(),
+            qk_norm=qk_norm,
+            eps=1e-6,
+            pre_only=True,
+        )
+
+        self.norm = AdaLayerNormZeroSingle(hidden_size, norm_type="layer_norm")
+        self.proj_mlp = nn.Linear(hidden_size, mlp_dim)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = nn.Linear(hidden_size + mlp_dim, hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        text_seq_length = encoder_hidden_states.shape[1]
+        hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
+
+        residual = hidden_states
+
+        # 1. Input normalization
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+
+        norm_hidden_states, norm_encoder_hidden_states = (
+            norm_hidden_states[:, :-text_seq_length, :],
+            norm_hidden_states[:, -text_seq_length:, :],
+        )
+
+        # 2. Attention
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            attention_mask=attention_mask,
+            image_rotary_emb=image_rotary_emb,
+        )
+        attn_output = torch.cat([attn_output, context_attn_output], dim=1)
+
+        # 3. Modulation and residual connection
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states = gate.unsqueeze(1) * self.proj_out(hidden_states)
+        hidden_states = hidden_states + residual
+
+        hidden_states, encoder_hidden_states = (
+            hidden_states[:, :-text_seq_length, :],
+            hidden_states[:, -text_seq_length:, :],
+        )
+        return hidden_states, encoder_hidden_states
+
+
+@maybe_allow_in_graph
+class HunyuanImageTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float,
+        qk_norm: str = "rms_norm",
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.norm1 = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
+        self.norm1_context = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
+
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            added_kv_proj_dim=hidden_size,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=hidden_size,
+            context_pre_only=False,
+            bias=True,
+            processor=HunyuanImageAttnProcessor(),
+            qk_norm=qk_norm,
+            eps=1e-6,
+        )
+
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
+
+        self.norm2_context = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.ff_context = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        *args,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 1. Input normalization
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
+        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
+            encoder_hidden_states, emb=temb
+        )
+
+        # 2. Joint attention
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            attention_mask=attention_mask,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        # 3. Modulation and residual connection
+        hidden_states = hidden_states + attn_output * gate_msa.unsqueeze(1)
+        encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa.unsqueeze(1)
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+
+        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+
+        hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
+        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
+
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
+    r"""
+    The Transformer model used in [HunyuanImage-2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1).
+
+    Args:
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, defaults to `16`):
+            The number of channels in the output.
+        num_attention_heads (`int`, defaults to `24`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of channels in each head.
+        num_layers (`int`, defaults to `20`):
+            The number of layers of dual-stream blocks to use.
+        num_single_layers (`int`, defaults to `40`):
+            The number of layers of single-stream blocks to use.
+        num_refiner_layers (`int`, defaults to `2`):
+            The number of layers of refiner blocks to use.
+        mlp_ratio (`float`, defaults to `4.0`):
+            The ratio of the hidden layer size to the input size in the feedforward network.
+        patch_size (`int`, defaults to `2`):
+            The size of the spatial patches to use in the patch embedding layer.
+        patch_size_t (`int`, defaults to `1`):
+            The size of the tmeporal patches to use in the patch embedding layer.
+        qk_norm (`str`, defaults to `rms_norm`):
+            The normalization to use for the query and key projections in the attention layers.
+        guidance_embeds (`bool`, defaults to `True`):
+            Whether to use guidance embeddings in the model.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        pooled_projection_dim (`int`, defaults to `768`):
+            The dimension of the pooled projection of the text embeddings.
+        rope_theta (`float`, defaults to `256.0`):
+            The value of theta to use in the RoPE layer.
+        rope_axes_dim (`Tuple[int]`, defaults to `(16, 56, 56)`):
+            The dimensions of the axes to use in the RoPE layer.
+        image_condition_type (`str`, *optional*, defaults to `None`):
+            The type of image conditioning to use. If `None`, no image conditioning is used. If `latent_concat`, the
+            image is concatenated to the latent stream. If `token_replace`, the image is used to replace first-frame
+            tokens in the latent stream and apply conditioning.
+    """
+
+    _supports_gradient_checkpointing = True
+    _skip_layerwise_casting_patterns = ["x_embedder", "context_embedder", "norm"]
+    _no_split_modules = [
+        "HunyuanImageTransformerBlock",
+        "HunyuanImageSingleTransformerBlock",
+        "HunyuanImagePatchEmbed",
+        "HunyuanImageTokenRefiner",
+    ]
+    _repeated_blocks = [
+        "HunyuanImageTransformerBlock",
+        "HunyuanImageSingleTransformerBlock",
+    ]
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 64,
+        out_channels: int = 64,
+        num_attention_heads: int = 28,
+        attention_head_dim: int = 128,
+        num_layers: int = 20,
+        num_single_layers: int = 40,
+        num_refiner_layers: int = 2,
+        mlp_ratio: float = 4.0,
+        patch_size: Tuple[int, int] = (1, 1),
+        qk_norm: str = "rms_norm",
+        guidance_embeds: bool = False,
+        text_embed_dim: int = 3584,
+        text_embed_2_dim: Optional[int] = None,
+        rope_theta: float = 256.0,
+        rope_axes_dim: Tuple[int] = (64, 64),
+        use_meanflow: bool = False,
+    ) -> None:
+        super().__init__()
+
+        if not (isinstance(patch_size, (tuple, list)) and len(patch_size) in [2, 3]):
+            raise ValueError(f"patch_size must be a tuple of length 2 or 3, got {patch_size}")
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        # 1. Latent and condition embedders
+        self.x_embedder = HunyuanImagePatchEmbed(patch_size, in_channels, inner_dim)
+        self.context_embedder = HunyuanImageTokenRefiner(
+            text_embed_dim, num_attention_heads, attention_head_dim, num_layers=num_refiner_layers
+        )
+
+        if text_embed_2_dim is not None:
+            self.context_embedder_2 = HunyuanImageByT5TextProjection(text_embed_2_dim, 2048, inner_dim)
+        else:
+            self.context_embedder_2 = None
+
+        self.time_guidance_embed = HunyuanImageCombinedTimeGuidanceEmbedding(inner_dim, guidance_embeds, use_meanflow)
+
+        # 2. RoPE
+        self.rope = HunyuanImageRotaryPosEmbed(patch_size, rope_axes_dim, rope_theta)
+
+        # 3. Dual stream transformer blocks
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                HunyuanImageTransformerBlock(
+                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 4. Single stream transformer blocks
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                HunyuanImageSingleTransformerBlock(
+                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
+                )
+                for _ in range(num_single_layers)
+            ]
+        )
+
+        # 5. Output projection
+        self.norm_out = AdaLayerNormContinuous(inner_dim, inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(inner_dim, math.prod(patch_size) * out_channels)
+
+        self.gradient_checkpointing = False
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        timestep_r: Optional[torch.LongTensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        encoder_attention_mask_2: Optional[torch.Tensor] = None,
+        guidance: Optional[torch.Tensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        if hidden_states.ndim == 4:
+            batch_size, channels, height, width = hidden_states.shape
+            sizes = (height, width)
+        elif hidden_states.ndim == 5:
+            batch_size, channels, frame, height, width = hidden_states.shape
+            sizes = (frame, height, width)
+        else:
+            raise ValueError(f"hidden_states must be a 4D or 5D tensor, got {hidden_states.shape}")
+
+        post_patch_sizes = tuple(d // p for d, p in zip(sizes, self.config.patch_size))
+
+        # 1. RoPE
+        image_rotary_emb = self.rope(hidden_states)
+
+        # 2. Conditional embeddings
+        encoder_attention_mask = encoder_attention_mask.bool()
+        temb = self.time_guidance_embed(timestep, guidance=guidance, timestep_r=timestep_r)
+        hidden_states = self.x_embedder(hidden_states)
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states, timestep, encoder_attention_mask)
+
+        if self.context_embedder_2 is not None and encoder_hidden_states_2 is not None:
+            encoder_hidden_states_2 = self.context_embedder_2(encoder_hidden_states_2)
+
+            encoder_attention_mask_2 = encoder_attention_mask_2.bool()
+
+            # reorder and combine text tokens: combine valid tokens first, then padding
+            new_encoder_hidden_states = []
+            new_encoder_attention_mask = []
+
+            for text, text_mask, text_2, text_mask_2 in zip(
+                encoder_hidden_states, encoder_attention_mask, encoder_hidden_states_2, encoder_attention_mask_2
+            ):
+                # Concatenate: [valid_mllm, valid_byt5, invalid_mllm, invalid_byt5]
+                new_encoder_hidden_states.append(
+                    torch.cat(
+                        [
+                            text_2[text_mask_2],  # valid byt5
+                            text[text_mask],  # valid mllm
+                            text_2[~text_mask_2],  # invalid byt5
+                            text[~text_mask],  # invalid mllm
+                        ],
+                        dim=0,
+                    )
+                )
+
+                # Apply same reordering to attention masks
+                new_encoder_attention_mask.append(
+                    torch.cat(
+                        [
+                            text_mask_2[text_mask_2],
+                            text_mask[text_mask],
+                            text_mask_2[~text_mask_2],
+                            text_mask[~text_mask],
+                        ],
+                        dim=0,
+                    )
+                )
+
+            encoder_hidden_states = torch.stack(new_encoder_hidden_states)
+            encoder_attention_mask = torch.stack(new_encoder_attention_mask)
+
+        attention_mask = torch.nn.functional.pad(encoder_attention_mask, (hidden_states.shape[1], 0), value=True)
+        attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+        # 3. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask=attention_mask,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+            for block in self.single_transformer_blocks:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask=attention_mask,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+        else:
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask=attention_mask,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+            for block in self.single_transformer_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask=attention_mask,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+        # 4. Output projection
+        hidden_states = self.norm_out(hidden_states, temb)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 5. unpatchify
+        # reshape: [batch_size, *post_patch_dims, channels, *patch_size]
+        out_channels = self.config.out_channels
+        reshape_dims = [batch_size] + list(post_patch_sizes) + [out_channels] + list(self.config.patch_size)
+        hidden_states = hidden_states.reshape(*reshape_dims)
+
+        # create permutation pattern: batch, channels, then interleave post_patch and patch dims
+        # For 4D: [0, 3, 1, 4, 2, 5] -> batch, channels, post_patch_height, patch_size_height, post_patch_width, patch_size_width
+        # For 5D: [0, 4, 1, 5, 2, 6, 3, 7] -> batch, channels, post_patch_frame, patch_size_frame, post_patch_height, patch_size_height, post_patch_width, patch_size_width
+        ndim = len(post_patch_sizes)
+        permute_pattern = [0, ndim + 1]  # batch, channels
+        for i in range(ndim):
+            permute_pattern.extend([i + 1, ndim + 2 + i])  # post_patch_sizes[i], patch_sizes[i]
+        hidden_states = hidden_states.permute(*permute_pattern)
+
+        # flatten patch dimensions: flatten each (post_patch_size, patch_size) pair
+        # batch_size, channels, post_patch_sizes[0] * patch_sizes[0], post_patch_sizes[1] * patch_sizes[1], ...
+        final_dims = [batch_size, out_channels] + [
+            post_patch * patch for post_patch, patch in zip(post_patch_sizes, self.config.patch_size)
+        ]
+        hidden_states = hidden_states.reshape(*final_dims)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (hidden_states,)
+
+        return Transformer2DModelOutput(sample=hidden_states)

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -130,8 +130,14 @@ class PipelineState:
         Allow attribute access to intermediate values. If an attribute is not found in the object, look for it in the
         intermediates dict.
         """
-        if name in self.values:
-            return self.values[name]
+        # Use object.__getattribute__ to avoid infinite recursion during deepcopy
+        try:
+            values = object.__getattribute__(self, "values")
+        except AttributeError:
+            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
+
+        if name in values:
+            return values[name]
         raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
 
     def __repr__(self):
@@ -2492,6 +2498,8 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         """
         if state is None:
             state = PipelineState()
+        else:
+            state = deepcopy(state)
 
         # Make a copy of the input kwargs
         passed_kwargs = kwargs.copy()

src/diffusers/modular_pipelines/qwenimage/denoise.py

@@ -238,19 +238,27 @@ class QwenImageLoopDenoiser(ModularPipelineBlocks):
 
     @torch.no_grad()
     def __call__(self, components: QwenImageModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
-        guider_input_fields = {
-            "encoder_hidden_states": ("prompt_embeds", "negative_prompt_embeds"),
-            "encoder_hidden_states_mask": ("prompt_embeds_mask", "negative_prompt_embeds_mask"),
-            "txt_seq_lens": ("txt_seq_lens", "negative_txt_seq_lens"),
+        guider_inputs = {
+            "encoder_hidden_states": (
+                getattr(block_state, "prompt_embeds", None),
+                getattr(block_state, "negative_prompt_embeds", None),
+            ),
+            "encoder_hidden_states_mask": (
+                getattr(block_state, "prompt_embeds_mask", None),
+                getattr(block_state, "negative_prompt_embeds_mask", None),
+            ),
+            "txt_seq_lens": (
+                getattr(block_state, "txt_seq_lens", None),
+                getattr(block_state, "negative_txt_seq_lens", None),
+            ),
         }
 
         components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
-        guider_state = components.guider.prepare_inputs(block_state, guider_input_fields)
+        guider_state = components.guider.prepare_inputs(guider_inputs)
 
         for guider_state_batch in guider_state:
             components.guider.prepare_models(components.transformer)
-            cond_kwargs = guider_state_batch.as_dict()
-            cond_kwargs = {k: v for k, v in cond_kwargs.items() if k in guider_input_fields}
+            cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
 
             # YiYi TODO: add cache context
             guider_state_batch.noise_pred = components.transformer(
@@ -328,19 +336,27 @@ class QwenImageEditLoopDenoiser(ModularPipelineBlocks):
 
     @torch.no_grad()
     def __call__(self, components: QwenImageModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
-        guider_input_fields = {
-            "encoder_hidden_states": ("prompt_embeds", "negative_prompt_embeds"),
-            "encoder_hidden_states_mask": ("prompt_embeds_mask", "negative_prompt_embeds_mask"),
-            "txt_seq_lens": ("txt_seq_lens", "negative_txt_seq_lens"),
+        guider_inputs = {
+            "encoder_hidden_states": (
+                getattr(block_state, "prompt_embeds", None),
+                getattr(block_state, "negative_prompt_embeds", None),
+            ),
+            "encoder_hidden_states_mask": (
+                getattr(block_state, "prompt_embeds_mask", None),
+                getattr(block_state, "negative_prompt_embeds_mask", None),
+            ),
+            "txt_seq_lens": (
+                getattr(block_state, "txt_seq_lens", None),
+                getattr(block_state, "negative_txt_seq_lens", None),
+            ),
         }
 
         components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
-        guider_state = components.guider.prepare_inputs(block_state, guider_input_fields)
+        guider_state = components.guider.prepare_inputs(guider_inputs)
 
         for guider_state_batch in guider_state:
             components.guider.prepare_models(components.transformer)
-            cond_kwargs = guider_state_batch.as_dict()
-            cond_kwargs = {k: v for k, v in cond_kwargs.items() if k in guider_input_fields}
+            cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
 
             # YiYi TODO: add cache context
             guider_state_batch.noise_pred = components.transformer(

src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py

@@ -201,27 +201,41 @@ class StableDiffusionXLLoopDenoiser(ModularPipelineBlocks):
     ) -> PipelineState:
         #  Map the keys we'll see on each `guider_state_batch` (e.g. guider_state_batch.prompt_embeds)
         #  to the corresponding (cond, uncond) fields on block_state. (e.g. block_state.prompt_embeds, block_state.negative_prompt_embeds)
-        guider_input_fields = {
-            "prompt_embeds": ("prompt_embeds", "negative_prompt_embeds"),
-            "time_ids": ("add_time_ids", "negative_add_time_ids"),
-            "text_embeds": ("pooled_prompt_embeds", "negative_pooled_prompt_embeds"),
-            "image_embeds": ("ip_adapter_embeds", "negative_ip_adapter_embeds"),
+        guider_inputs = {
+            "prompt_embeds": (
+                getattr(block_state, "prompt_embeds", None),
+                getattr(block_state, "negative_prompt_embeds", None),
+            ),
+            "time_ids": (
+                getattr(block_state, "add_time_ids", None),
+                getattr(block_state, "negative_add_time_ids", None),
+            ),
+            "text_embeds": (
+                getattr(block_state, "pooled_prompt_embeds", None),
+                getattr(block_state, "negative_pooled_prompt_embeds", None),
+            ),
+            "image_embeds": (
+                getattr(block_state, "ip_adapter_embeds", None),
+                getattr(block_state, "negative_ip_adapter_embeds", None),
+            ),
         }
 
         components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
 
-        # Prepare mini‐batches according to guidance method and `guider_input_fields`
-        # Each guider_state_batch will have .prompt_embeds, .time_ids, text_embeds, image_embeds.
-        # e.g. for CFG, we prepare two batches: one for uncond, one for cond
-        # for first batch, guider_state_batch.prompt_embeds correspond to block_state.prompt_embeds
-        # for second batch, guider_state_batch.prompt_embeds correspond to block_state.negative_prompt_embeds
-        guider_state = components.guider.prepare_inputs(block_state, guider_input_fields)
+        # The guider splits model inputs into separate batches for conditional/unconditional predictions.
+        # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}:
+        # you will get a guider_state with two batches:
+        #   guider_state = [
+        #       {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"},      # conditional batch
+        #       {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"},  # unconditional batch
+        #   ]
+        # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG).
+        guider_state = components.guider.prepare_inputs(guider_inputs)
 
         # run the denoiser for each guidance batch
         for guider_state_batch in guider_state:
             components.guider.prepare_models(components.unet)
-            cond_kwargs = guider_state_batch.as_dict()
-            cond_kwargs = {k: v for k, v in cond_kwargs.items() if k in guider_input_fields}
+            cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
             prompt_embeds = cond_kwargs.pop("prompt_embeds")
 
             # Predict the noise residual
@@ -344,11 +358,23 @@ class StableDiffusionXLControlNetLoopDenoiser(ModularPipelineBlocks):
 
         #  Map the keys we'll see on each `guider_state_batch` (e.g. guider_state_batch.prompt_embeds)
         #  to the corresponding (cond, uncond) fields on block_state. (e.g. block_state.prompt_embeds, block_state.negative_prompt_embeds)
-        guider_input_fields = {
-            "prompt_embeds": ("prompt_embeds", "negative_prompt_embeds"),
-            "time_ids": ("add_time_ids", "negative_add_time_ids"),
-            "text_embeds": ("pooled_prompt_embeds", "negative_pooled_prompt_embeds"),
-            "image_embeds": ("ip_adapter_embeds", "negative_ip_adapter_embeds"),
+        guider_inputs = {
+            "prompt_embeds": (
+                getattr(block_state, "prompt_embeds", None),
+                getattr(block_state, "negative_prompt_embeds", None),
+            ),
+            "time_ids": (
+                getattr(block_state, "add_time_ids", None),
+                getattr(block_state, "negative_add_time_ids", None),
+            ),
+            "text_embeds": (
+                getattr(block_state, "pooled_prompt_embeds", None),
+                getattr(block_state, "negative_pooled_prompt_embeds", None),
+            ),
+            "image_embeds": (
+                getattr(block_state, "ip_adapter_embeds", None),
+                getattr(block_state, "negative_ip_adapter_embeds", None),
+            ),
         }
 
         # cond_scale for the timestep (controlnet input)
@@ -369,12 +395,15 @@ class StableDiffusionXLControlNetLoopDenoiser(ModularPipelineBlocks):
         # guided denoiser step
         components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
 
-        # Prepare mini‐batches according to guidance method and `guider_input_fields`
-        # Each guider_state_batch will have .prompt_embeds, .time_ids, text_embeds, image_embeds.
-        # e.g. for CFG, we prepare two batches: one for uncond, one for cond
-        # for first batch, guider_state_batch.prompt_embeds correspond to block_state.prompt_embeds
-        # for second batch, guider_state_batch.prompt_embeds correspond to block_state.negative_prompt_embeds
-        guider_state = components.guider.prepare_inputs(block_state, guider_input_fields)
+        # The guider splits model inputs into separate batches for conditional/unconditional predictions.
+        # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}:
+        # you will get a guider_state with two batches:
+        #   guider_state = [
+        #       {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"},      # conditional batch
+        #       {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"},  # unconditional batch
+        #   ]
+        # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG).
+        guider_state = components.guider.prepare_inputs(guider_inputs)
 
         # run the denoiser for each guidance batch
         for guider_state_batch in guider_state:

src/diffusers/modular_pipelines/wan/denoise.py

@@ -94,25 +94,30 @@ class WanLoopDenoiser(ModularPipelineBlocks):
     ) -> PipelineState:
         #  Map the keys we'll see on each `guider_state_batch` (e.g. guider_state_batch.prompt_embeds)
         #  to the corresponding (cond, uncond) fields on block_state. (e.g. block_state.prompt_embeds, block_state.negative_prompt_embeds)
-        guider_input_fields = {
-            "prompt_embeds": ("prompt_embeds", "negative_prompt_embeds"),
+        guider_inputs = {
+            "prompt_embeds": (
+                getattr(block_state, "prompt_embeds", None),
+                getattr(block_state, "negative_prompt_embeds", None),
+            ),
         }
         transformer_dtype = components.transformer.dtype
 
         components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
 
-        # Prepare mini‐batches according to guidance method and `guider_input_fields`
-        # Each guider_state_batch will have .prompt_embeds, .time_ids, text_embeds, image_embeds.
-        # e.g. for CFG, we prepare two batches: one for uncond, one for cond
-        # for first batch, guider_state_batch.prompt_embeds correspond to block_state.prompt_embeds
-        # for second batch, guider_state_batch.prompt_embeds correspond to block_state.negative_prompt_embeds
-        guider_state = components.guider.prepare_inputs(block_state, guider_input_fields)
+        # The guider splits model inputs into separate batches for conditional/unconditional predictions.
+        # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}:
+        # you will get a guider_state with two batches:
+        #   guider_state = [
+        #       {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"},      # conditional batch
+        #       {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"},  # unconditional batch
+        #   ]
+        # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG).
+        guider_state = components.guider.prepare_inputs(guider_inputs)
 
         # run the denoiser for each guidance batch
         for guider_state_batch in guider_state:
             components.guider.prepare_models(components.transformer)
-            cond_kwargs = guider_state_batch.as_dict()
-            cond_kwargs = {k: v for k, v in cond_kwargs.items() if k in guider_input_fields}
+            cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
             prompt_embeds = cond_kwargs.pop("prompt_embeds")
 
             # Predict the noise residual

src/diffusers/pipelines/__init__.py

@@ -241,6 +241,7 @@ else:
         "HunyuanVideoImageToVideoPipeline",
         "HunyuanVideoFramepackPipeline",
     ]
+    _import_structure["hunyuan_image"] = ["HunyuanImagePipeline", "HunyuanImageRefinerPipeline"]
     _import_structure["kandinsky"] = [
         "KandinskyCombinedPipeline",
         "KandinskyImg2ImgCombinedPipeline",
@@ -640,6 +641,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ReduxImageEncoder,
         )
         from .hidream_image import HiDreamImagePipeline
+        from .hunyuan_image import HunyuanImagePipeline, HunyuanImageRefinerPipeline
         from .hunyuan_video import (
             HunyuanSkyreelsImageToVideoPipeline,
             HunyuanVideoFramepackPipeline,

src/diffusers/pipelines/hunyuan_image/__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_hunyuanimage"] = ["HunyuanImagePipeline"]
+    _import_structure["pipeline_hunyuanimage_refiner"] = ["HunyuanImageRefinerPipeline"]
+
+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_hunyuanimage import HunyuanImagePipeline
+        from .pipeline_hunyuanimage_refiner import HunyuanImageRefinerPipeline
+
+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/hunyuan_image/pipeline_hunyuanimage.py

+# Copyright 2025 Hunyuan-Image Team 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
+import re
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import ByT5Tokenizer, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, T5EncoderModel
+
+from ...guiders import AdaptiveProjectedMixGuidance
+from ...image_processor import VaeImageProcessor
+from ...models import AutoencoderKLHunyuanImage, HunyuanImageTransformer2DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import HunyuanImagePipelineOutput
+
+
+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 HunyuanImagePipeline
+
+        >>> pipe = HunyuanImagePipeline.from_pretrained(
+        ...     "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", torch_dtype=torch.bfloat16
+        ... )
+        >>> pipe.to("cuda")
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(prompt, negative_prompt="", num_inference_steps=50).images[0]
+        >>> image.save("hunyuanimage.png")
+        ```
+"""
+
+
+def extract_glyph_text(prompt: str):
+    """
+    Extract text enclosed in quotes for glyph rendering.
+
+    Finds text in single quotes, double quotes, and Chinese quotes, then formats it for byT5 processing.
+
+    Args:
+        prompt: Input text prompt
+
+    Returns:
+        Formatted glyph text string or None if no quoted text found
+    """
+    text_prompt_texts = []
+    pattern_quote_single = r"\'(.*?)\'"
+    pattern_quote_double = r"\"(.*?)\""
+    pattern_quote_chinese_single = r"‘(.*?)’"
+    pattern_quote_chinese_double = r"“(.*?)”"
+
+    matches_quote_single = re.findall(pattern_quote_single, prompt)
+    matches_quote_double = re.findall(pattern_quote_double, prompt)
+    matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, prompt)
+    matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, prompt)
+
+    text_prompt_texts.extend(matches_quote_single)
+    text_prompt_texts.extend(matches_quote_double)
+    text_prompt_texts.extend(matches_quote_chinese_single)
+    text_prompt_texts.extend(matches_quote_chinese_double)
+
+    if text_prompt_texts:
+        glyph_text_formatted = ". ".join([f'Text "{text}"' for text in text_prompt_texts]) + ". "
+    else:
+        glyph_text_formatted = None
+
+    return glyph_text_formatted
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class HunyuanImagePipeline(DiffusionPipeline):
+    r"""
+    The HunyuanImage pipeline for text-to-image generation.
+
+    Args:
+        transformer ([`HunyuanImageTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLHunyuanImage`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`Qwen2.5-VL-7B-Instruct`]):
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant.
+        tokenizer (`Qwen2Tokenizer`): Tokenizer of class [Qwen2Tokenizer].
+        text_encoder_2 ([`T5EncoderModel`]):
+            [T5EncoderModel](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel)
+            variant.
+        tokenizer_2 (`ByT5Tokenizer`): Tokenizer of class [ByT5Tokenizer]
+        guider ([`AdaptiveProjectedMixGuidance`]):
+            [AdaptiveProjectedMixGuidance]to be used to guide the image generation.
+        ocr_guider ([`AdaptiveProjectedMixGuidance`], *optional*):
+            [AdaptiveProjectedMixGuidance] to be used to guide the image generation when text rendering is needed.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+    _optional_components = ["ocr_guider", "guider"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKLHunyuanImage,
+        text_encoder: Qwen2_5_VLForConditionalGeneration,
+        tokenizer: Qwen2Tokenizer,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: ByT5Tokenizer,
+        transformer: HunyuanImageTransformer2DModel,
+        guider: Optional[AdaptiveProjectedMixGuidance] = None,
+        ocr_guider: Optional[AdaptiveProjectedMixGuidance] = None,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_encoder_2=text_encoder_2,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+            scheduler=scheduler,
+            guider=guider,
+            ocr_guider=ocr_guider,
+        )
+
+        self.vae_scale_factor = self.vae.config.spatial_compression_ratio if getattr(self, "vae", None) else 32
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.tokenizer_max_length = 1000
+        self.tokenizer_2_max_length = 128
+        self.prompt_template_encode = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>"
+        self.prompt_template_encode_start_idx = 34
+        self.default_sample_size = 64
+
+    def _get_qwen_prompt_embeds(
+        self,
+        tokenizer: Qwen2Tokenizer,
+        text_encoder: Qwen2_5_VLForConditionalGeneration,
+        prompt: Union[str, List[str]] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        tokenizer_max_length: int = 1000,
+        template: str = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>",
+        drop_idx: int = 34,
+        hidden_state_skip_layer: int = 2,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        txt = [template.format(e) for e in prompt]
+        txt_tokens = tokenizer(
+            txt, max_length=tokenizer_max_length + drop_idx, padding="max_length", truncation=True, return_tensors="pt"
+        ).to(device)
+
+        encoder_hidden_states = text_encoder(
+            input_ids=txt_tokens.input_ids,
+            attention_mask=txt_tokens.attention_mask,
+            output_hidden_states=True,
+        )
+        prompt_embeds = encoder_hidden_states.hidden_states[-(hidden_state_skip_layer + 1)]
+
+        prompt_embeds = prompt_embeds[:, drop_idx:]
+        encoder_attention_mask = txt_tokens.attention_mask[:, drop_idx:]
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        encoder_attention_mask = encoder_attention_mask.to(device=device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def _get_byt5_prompt_embeds(
+        self,
+        tokenizer: ByT5Tokenizer,
+        text_encoder: T5EncoderModel,
+        prompt: str,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        tokenizer_max_length: int = 128,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or text_encoder.dtype
+
+        if isinstance(prompt, list):
+            raise ValueError("byt5 prompt should be a string")
+        elif prompt is None:
+            raise ValueError("byt5 prompt should not be None")
+
+        txt_tokens = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=tokenizer_max_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        ).to(device)
+
+        prompt_embeds = text_encoder(
+            input_ids=txt_tokens.input_ids,
+            attention_mask=txt_tokens.attention_mask.float(),
+        )[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        encoder_attention_mask = txt_tokens.attention_mask.to(device=device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        batch_size: int = 1,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        prompt_embeds_2: Optional[torch.Tensor] = None,
+        prompt_embeds_mask_2: Optional[torch.Tensor] = None,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            batch_size (`int`):
+                batch size of prompts, defaults to 1
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. If not provided, text embeddings will be generated from `prompt` input
+                argument.
+            prompt_embeds_mask (`torch.Tensor`, *optional*):
+                Pre-generated text mask. If not provided, text mask will be generated from `prompt` input argument.
+            prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated glyph text embeddings from ByT5. If not provided, will be generated from `prompt` input
+                argument using self.tokenizer_2 and self.text_encoder_2.
+            prompt_embeds_mask_2 (`torch.Tensor`, *optional*):
+                Pre-generated glyph text mask from ByT5. If not provided, will be generated from `prompt` input
+                argument using self.tokenizer_2 and self.text_encoder_2.
+        """
+        device = device or self._execution_device
+
+        if prompt is None:
+            prompt = [""] * batch_size
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(
+                tokenizer=self.tokenizer,
+                text_encoder=self.text_encoder,
+                prompt=prompt,
+                device=device,
+                tokenizer_max_length=self.tokenizer_max_length,
+                template=self.prompt_template_encode,
+                drop_idx=self.prompt_template_encode_start_idx,
+            )
+
+        if prompt_embeds_2 is None:
+            prompt_embeds_2_list = []
+            prompt_embeds_mask_2_list = []
+
+            glyph_texts = [extract_glyph_text(p) for p in prompt]
+            for glyph_text in glyph_texts:
+                if glyph_text is None:
+                    glyph_text_embeds = torch.zeros(
+                        (1, self.tokenizer_2_max_length, self.text_encoder_2.config.d_model), device=device
+                    )
+                    glyph_text_embeds_mask = torch.zeros(
+                        (1, self.tokenizer_2_max_length), device=device, dtype=torch.int64
+                    )
+                else:
+                    glyph_text_embeds, glyph_text_embeds_mask = self._get_byt5_prompt_embeds(
+                        tokenizer=self.tokenizer_2,
+                        text_encoder=self.text_encoder_2,
+                        prompt=glyph_text,
+                        device=device,
+                        tokenizer_max_length=self.tokenizer_2_max_length,
+                    )
+
+                prompt_embeds_2_list.append(glyph_text_embeds)
+                prompt_embeds_mask_2_list.append(glyph_text_embeds_mask)
+
+            prompt_embeds_2 = torch.cat(prompt_embeds_2_list, dim=0)
+            prompt_embeds_mask_2 = torch.cat(prompt_embeds_mask_2_list, dim=0)
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        _, seq_len_2, _ = prompt_embeds_2.shape
+        prompt_embeds_2 = prompt_embeds_2.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_2 = prompt_embeds_2.view(batch_size * num_images_per_prompt, seq_len_2, -1)
+        prompt_embeds_mask_2 = prompt_embeds_mask_2.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask_2 = prompt_embeds_mask_2.view(batch_size * num_images_per_prompt, seq_len_2)
+
+        return prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        prompt_embeds_2=None,
+        prompt_embeds_mask_2=None,
+        negative_prompt_embeds_2=None,
+        negative_prompt_embeds_mask_2=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        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 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 negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        if 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."
+            )
+
+        if prompt_embeds_2 is not None and prompt_embeds_mask_2 is None:
+            raise ValueError(
+                "If `prompt_embeds_2` are provided, `prompt_embeds_mask_2` also have to be passed. Make sure to generate `prompt_embeds_mask_2` from the same text encoder that was used to generate `prompt_embeds_2`."
+            )
+        if negative_prompt_embeds_2 is not None and negative_prompt_embeds_mask_2 is None:
+            raise ValueError(
+                "If `negative_prompt_embeds_2` are provided, `negative_prompt_embeds_mask_2` also have to be passed. Make sure to generate `negative_prompt_embeds_mask_2` from the same text encoder that was used to generate `negative_prompt_embeds_2`."
+            )
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = int(height) // self.vae_scale_factor
+        width = int(width) // self.vae_scale_factor
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @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,
+        negative_prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        distilled_guidance_scale: Optional[float] = 3.25,
+        sigmas: Optional[List[float]] = None,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        prompt_embeds_2: Optional[torch.Tensor] = None,
+        prompt_embeds_mask_2: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_2: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask_2: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined and negative_prompt_embeds is
+                not provided, will use an empty negative prompt. Ignored when not using guidance. ).
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            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.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            distilled_guidance_scale (`float`, *optional*, defaults to None):
+                A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance
+                where the guidance scale is applied during inference through noise prediction rescaling, guidance
+                distilled models take the guidance scale directly as an input parameter during forward pass. Guidance
+                is enabled by setting `distilled_guidance_scale > 1`. Higher guidance scale encourages to generate
+                images that are closely linked to the text `prompt`, usually at the expense of lower image quality. For
+                guidance distilled models, this parameter is required. For non-distilled models, this parameter will be
+                ignored.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            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.
+            prompt_embeds_mask (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings mask. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, text embeddings mask will be generated from `prompt` input argument.
+            prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings for ocr. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, text embeddings for ocr will be generated from `prompt` input argument.
+            prompt_embeds_mask_2 (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings mask for ocr. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, text embeddings mask for ocr 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.
+            negative_prompt_embeds_mask (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings mask. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative text embeddings mask will be generated from `negative_prompt`
+                input argument.
+            negative_prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings for ocr. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative text embeddings for ocr will be generated from `negative_prompt`
+                input argument.
+            negative_prompt_embeds_mask_2 (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings mask for ocr. Can be used to easily tweak text inputs, *e.g.*
+                prompt weighting. If not provided, negative text embeddings mask for ocr will be generated from
+                `negative_prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.qwenimage.QwenImagePipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.hunyuan_image.HunyuanImagePipelineOutput`] or `tuple`:
+            [`~pipelines.hunyuan_image.HunyuanImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is a list with the generated images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            prompt_embeds_2=prompt_embeds_2,
+            prompt_embeds_mask_2=prompt_embeds_mask_2,
+            negative_prompt_embeds_2=negative_prompt_embeds_2,
+            negative_prompt_embeds_mask_2=negative_prompt_embeds_mask_2,
+        )
+
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        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. prepare prompt embeds
+
+        prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2 = self.encode_prompt(
+            prompt=prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            device=device,
+            batch_size=batch_size,
+            num_images_per_prompt=num_images_per_prompt,
+            prompt_embeds_2=prompt_embeds_2,
+            prompt_embeds_mask_2=prompt_embeds_mask_2,
+        )
+
+        prompt_embeds = prompt_embeds.to(self.transformer.dtype)
+        prompt_embeds_2 = prompt_embeds_2.to(self.transformer.dtype)
+
+        # select guider
+        if not torch.all(prompt_embeds_2 == 0) and self.ocr_guider is not None:
+            # prompt contains ocr and pipeline has a guider for ocr
+            guider = self.ocr_guider
+        elif self.guider is not None:
+            guider = self.guider
+        # distilled model does not use guidance method, use default guider with enabled=False
+        else:
+            guider = AdaptiveProjectedMixGuidance(enabled=False)
+
+        if guider._enabled and guider.num_conditions > 1:
+            (
+                negative_prompt_embeds,
+                negative_prompt_embeds_mask,
+                negative_prompt_embeds_2,
+                negative_prompt_embeds_mask_2,
+            ) = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                device=device,
+                batch_size=batch_size,
+                num_images_per_prompt=num_images_per_prompt,
+                prompt_embeds_2=negative_prompt_embeds_2,
+                prompt_embeds_mask_2=negative_prompt_embeds_mask_2,
+            )
+
+            negative_prompt_embeds = negative_prompt_embeds.to(self.transformer.dtype)
+            negative_prompt_embeds_2 = negative_prompt_embeds_2.to(self.transformer.dtype)
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size=batch_size * num_images_per_prompt,
+            num_channels_latents=num_channels_latents,
+            height=height,
+            width=width,
+            dtype=prompt_embeds.dtype,
+            device=device,
+            generator=generator,
+            latents=latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance (for guidance-distilled model)
+        if self.transformer.config.guidance_embeds and distilled_guidance_scale is None:
+            raise ValueError("`distilled_guidance_scale` is required for guidance-distilled model.")
+
+        if self.transformer.config.guidance_embeds:
+            guidance = (
+                torch.tensor(
+                    [distilled_guidance_scale] * latents.shape[0], dtype=self.transformer.dtype, device=device
+                )
+                * 1000.0
+            )
+
+        else:
+            guidance = None
+
+        if self.attention_kwargs is None:
+            self._attention_kwargs = {}
+
+        # 6. Denoising loop
+        self.scheduler.set_begin_index(0)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                if self.transformer.config.use_meanflow:
+                    if i == len(timesteps) - 1:
+                        timestep_r = torch.tensor([0.0], device=device)
+                    else:
+                        timestep_r = timesteps[i + 1]
+                    timestep_r = timestep_r.expand(latents.shape[0]).to(latents.dtype)
+                else:
+                    timestep_r = None
+
+                # Step 1: Collect model inputs needed for the guidance method
+                # conditional inputs should always be first element in the tuple
+                guider_inputs = {
+                    "encoder_hidden_states": (prompt_embeds, negative_prompt_embeds),
+                    "encoder_attention_mask": (prompt_embeds_mask, negative_prompt_embeds_mask),
+                    "encoder_hidden_states_2": (prompt_embeds_2, negative_prompt_embeds_2),
+                    "encoder_attention_mask_2": (prompt_embeds_mask_2, negative_prompt_embeds_mask_2),
+                }
+
+                # Step 2: Update guider's internal state for this denoising step
+                guider.set_state(step=i, num_inference_steps=num_inference_steps, timestep=t)
+
+                # Step 3: Prepare batched model inputs based on the guidance method
+                # The guider splits model inputs into separate batches for conditional/unconditional predictions.
+                # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}:
+                # you will get a guider_state with two batches:
+                #   guider_state = [
+                #       {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"},      # conditional batch
+                #       {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"},  # unconditional batch
+                #   ]
+                # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG).
+                guider_state = guider.prepare_inputs(guider_inputs)
+                # Step 4: Run the denoiser for each batch
+                # Each batch in guider_state represents a different conditioning (conditional, unconditional, etc.).
+                # We run the model once per batch and store the noise prediction in guider_state_batch.noise_pred.
+                for guider_state_batch in guider_state:
+                    guider.prepare_models(self.transformer)
+
+                    # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states)
+                    cond_kwargs = {
+                        input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()
+                    }
+
+                    # e.g. "pred_cond"/"pred_uncond"
+                    context_name = getattr(guider_state_batch, guider._identifier_key)
+                    with self.transformer.cache_context(context_name):
+                        # Run denoiser and store noise prediction in this batch
+                        guider_state_batch.noise_pred = self.transformer(
+                            hidden_states=latents,
+                            timestep=timestep,
+                            timestep_r=timestep_r,
+                            guidance=guidance,
+                            attention_kwargs=self.attention_kwargs,
+                            return_dict=False,
+                            **cond_kwargs,
+                        )[0]
+
+                    # Cleanup model (e.g., remove hooks)
+                    guider.cleanup_models(self.transformer)
+
+                # Step 5: Combine predictions using the guidance method
+                # The guider takes all noise predictions from guider_state and combines them according to the guidance algorithm.
+                # Continuing the CFG example, the guider receives:
+                #   guider_state = [
+                #       {"encoder_hidden_states": prompt_embeds, "noise_pred": noise_pred_cond, "__guidance_identifier__": "pred_cond"},      # batch 0
+                #       {"encoder_hidden_states": negative_prompt_embeds, "noise_pred": noise_pred_uncond, "__guidance_identifier__": "pred_uncond"},  # batch 1
+                #   ]
+                # And extracts predictions using the __guidance_identifier__:
+                #   pred_cond = guider_state[0]["noise_pred"]      # extracts noise_pred_cond
+                #   pred_uncond = guider_state[1]["noise_pred"]    # extracts noise_pred_uncond
+                # Then applies CFG formula:
+                #   noise_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond)
+                # Returns GuiderOutput(pred=noise_pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
+                noise_pred = guider(guider_state)[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                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)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self._current_timestep = None
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return HunyuanImagePipelineOutput(images=image)

src/diffusers/pipelines/hunyuan_image/pipeline_hunyuanimage_refiner.py

+# Copyright 2025 Hunyuan-Image Team 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 Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
+
+from ...guiders import AdaptiveProjectedMixGuidance
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...models import AutoencoderKLHunyuanImageRefiner, HunyuanImageTransformer2DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import HunyuanImagePipelineOutput
+
+
+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 HunyuanImageRefinerPipeline
+
+        >>> pipe = HunyuanImageRefinerPipeline.from_pretrained(
+        ...     "hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers", torch_dtype=torch.bfloat16
+        ... )
+        >>> pipe.to("cuda")
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> image = load_image("path/to/image.png")
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(prompt, image=image, num_inference_steps=4).images[0]
+        >>> image.save("hunyuanimage.png")
+        ```
+"""
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class HunyuanImageRefinerPipeline(DiffusionPipeline):
+    r"""
+    The HunyuanImage pipeline for text-to-image generation.
+
+    Args:
+        transformer ([`HunyuanImageTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLHunyuanImageRefiner`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`Qwen2.5-VL-7B-Instruct`]):
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant.
+        tokenizer (`Qwen2Tokenizer`): Tokenizer of class [Qwen2Tokenizer].
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+    _optional_components = ["guider"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKLHunyuanImageRefiner,
+        text_encoder: Qwen2_5_VLForConditionalGeneration,
+        tokenizer: Qwen2Tokenizer,
+        transformer: HunyuanImageTransformer2DModel,
+        guider: Optional[AdaptiveProjectedMixGuidance] = None,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            guider=guider,
+        )
+
+        self.vae_scale_factor = self.vae.config.spatial_compression_ratio if getattr(self, "vae", None) else 16
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.tokenizer_max_length = 256
+        self.prompt_template_encode = "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
+        self.prompt_template_encode_start_idx = 36
+        self.default_sample_size = 64
+        self.latent_channels = self.transformer.config.in_channels // 2 if getattr(self, "transformer", None) else 64
+
+    # Copied from diffusers.pipelines.hunyuan_image.pipeline_hunyuanimage.HunyuanImagePipeline._get_qwen_prompt_embeds
+    def _get_qwen_prompt_embeds(
+        self,
+        tokenizer: Qwen2Tokenizer,
+        text_encoder: Qwen2_5_VLForConditionalGeneration,
+        prompt: Union[str, List[str]] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        tokenizer_max_length: int = 1000,
+        template: str = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>",
+        drop_idx: int = 34,
+        hidden_state_skip_layer: int = 2,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        txt = [template.format(e) for e in prompt]
+        txt_tokens = tokenizer(
+            txt, max_length=tokenizer_max_length + drop_idx, padding="max_length", truncation=True, return_tensors="pt"
+        ).to(device)
+
+        encoder_hidden_states = text_encoder(
+            input_ids=txt_tokens.input_ids,
+            attention_mask=txt_tokens.attention_mask,
+            output_hidden_states=True,
+        )
+        prompt_embeds = encoder_hidden_states.hidden_states[-(hidden_state_skip_layer + 1)]
+
+        prompt_embeds = prompt_embeds[:, drop_idx:]
+        encoder_attention_mask = txt_tokens.attention_mask[:, drop_idx:]
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        encoder_attention_mask = encoder_attention_mask.to(device=device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def encode_prompt(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        device: Optional[torch.device] = None,
+        batch_size: int = 1,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            batch_size (`int`):
+                batch size of prompts, defaults to 1
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. If not provided, text embeddings will be generated from `prompt` input
+                argument.
+            prompt_embeds_mask (`torch.Tensor`, *optional*):
+                Pre-generated text mask. If not provided, text mask will be generated from `prompt` input argument.
+            prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated glyph text embeddings from ByT5. If not provided, will be generated from `prompt` input
+                argument using self.tokenizer_2 and self.text_encoder_2.
+            prompt_embeds_mask_2 (`torch.Tensor`, *optional*):
+                Pre-generated glyph text mask from ByT5. If not provided, will be generated from `prompt` input
+                argument using self.tokenizer_2 and self.text_encoder_2.
+        """
+        device = device or self._execution_device
+
+        if prompt is None:
+            prompt = [""] * batch_size
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(
+                tokenizer=self.tokenizer,
+                text_encoder=self.text_encoder,
+                prompt=prompt,
+                device=device,
+                tokenizer_max_length=self.tokenizer_max_length,
+                template=self.prompt_template_encode,
+                drop_idx=self.prompt_template_encode_start_idx,
+            )
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_embeds_mask
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        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 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 negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+    def prepare_latents(
+        self,
+        image_latents,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+        strength=0.25,
+    ):
+        height = int(height) // self.vae_scale_factor
+        width = int(width) // self.vae_scale_factor
+
+        shape = (batch_size, num_channels_latents, 1, height, width)
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // image_latents.shape[0]
+            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+            )
+
+        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."
+            )
+
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        cond_latents = strength * noise + (1 - strength) * image_latents
+
+        return latents, cond_latents
+
+    @staticmethod
+    def _reorder_image_tokens(image_latents):
+        image_latents = torch.cat((image_latents[:, :, :1], image_latents), dim=2)
+        batch_size, num_latent_channels, num_latent_frames, latent_height, latent_width = image_latents.shape
+        image_latents = image_latents.permute(0, 2, 1, 3, 4)
+        image_latents = image_latents.reshape(
+            batch_size, num_latent_frames // 2, num_latent_channels * 2, latent_height, latent_width
+        )
+        image_latents = image_latents.permute(0, 2, 1, 3, 4).contiguous()
+
+        return image_latents
+
+    @staticmethod
+    def _restore_image_tokens_order(latents):
+        """Restore image tokens order by splitting channels and removing first frame slice."""
+        batch_size, num_latent_channels, num_latent_frames, latent_height, latent_width = latents.shape
+
+        latents = latents.permute(0, 2, 1, 3, 4)  # B, F, C, H, W
+        latents = latents.reshape(
+            batch_size, num_latent_frames * 2, num_latent_channels // 2, latent_height, latent_width
+        )  # B, F*2, C//2, H, W
+
+        latents = latents.permute(0, 2, 1, 3, 4)  # B, C//2, F*2, H, W
+        # Remove first frame slice
+        latents = latents[:, :, 1:]
+
+        return latents
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        if isinstance(generator, list):
+            image_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="sample")
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode="sample")
+        image_latents = self._reorder_image_tokens(image_latents)
+
+        image_latents = image_latents * self.vae.config.scaling_factor
+
+        return image_latents
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @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,
+        negative_prompt: Union[str, List[str]] = None,
+        distilled_guidance_scale: Optional[float] = 3.25,
+        image: Optional[PipelineImageInput] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 4,
+        sigmas: Optional[List[float]] = None,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, will use an empty negative
+                prompt. Ignored when not using guidance.
+            distilled_guidance_scale (`float`, *optional*, defaults to None):
+                A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance
+                where the guidance scale is applied during inference through noise prediction rescaling, guidance
+                distilled models take the guidance scale directly as an input parameter during forward pass. Guidance
+                is enabled by setting `distilled_guidance_scale > 1`. Higher guidance scale encourages to generate
+                images that are closely linked to the text `prompt`, usually at the expense of lower image quality. For
+                guidance distilled models, this parameter is required. For non-distilled models, this parameter will be
+                ignored.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            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.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            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.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.qwenimage.QwenImagePipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.hunyuan_image.HunyuanImagePipelineOutput`] or `tuple`:
+            [`~pipelines.hunyuan_image.HunyuanImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is a list with the generated images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+        )
+
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        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. process image
+        if image is not None and isinstance(image, torch.Tensor) and image.shape[1] == self.latent_channels:
+            image_latents = image
+        else:
+            image = self.image_processor.preprocess(image, height, width)
+            image = image.unsqueeze(2).to(device, dtype=self.vae.dtype)
+            image_latents = self._encode_vae_image(image=image, generator=generator)
+
+        # 3.prepare prompt embeds
+
+        if self.guider is not None:
+            guider = self.guider
+        else:
+            # distilled model does not use guidance method, use default guider with enabled=False
+            guider = AdaptiveProjectedMixGuidance(enabled=False)
+
+        requires_unconditional_embeds = guider._enabled and guider.num_conditions > 1
+        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
+            prompt=prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            device=device,
+            batch_size=batch_size,
+            num_images_per_prompt=num_images_per_prompt,
+        )
+
+        prompt_embeds = prompt_embeds.to(self.transformer.dtype)
+
+        if requires_unconditional_embeds:
+            (
+                negative_prompt_embeds,
+                negative_prompt_embeds_mask,
+            ) = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                device=device,
+                batch_size=batch_size,
+                num_images_per_prompt=num_images_per_prompt,
+            )
+
+            negative_prompt_embeds = negative_prompt_embeds.to(self.transformer.dtype)
+
+        # 4. Prepare latent variables
+        latents, cond_latents = self.prepare_latents(
+            image_latents=image_latents,
+            batch_size=batch_size * num_images_per_prompt,
+            num_channels_latents=self.latent_channels,
+            height=height,
+            width=width,
+            dtype=prompt_embeds.dtype,
+            device=device,
+            generator=generator,
+            latents=latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance (this pipeline only supports guidance-distilled models)
+        if distilled_guidance_scale is None:
+            raise ValueError("`distilled_guidance_scale` is required for guidance-distilled model.")
+        guidance = (
+            torch.tensor([distilled_guidance_scale] * latents.shape[0], dtype=self.transformer.dtype, device=device)
+            * 1000.0
+        )
+
+        if self.attention_kwargs is None:
+            self._attention_kwargs = {}
+
+        # 6. Denoising loop
+        self.scheduler.set_begin_index(0)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                latent_model_input = torch.cat([latents, cond_latents], dim=1).to(self.transformer.dtype)
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                # Step 1: Collect model inputs needed for the guidance method
+                # conditional inputs should always be first element in the tuple
+                guider_inputs = {
+                    "encoder_hidden_states": (prompt_embeds, negative_prompt_embeds),
+                    "encoder_attention_mask": (prompt_embeds_mask, negative_prompt_embeds_mask),
+                }
+
+                # Step 2: Update guider's internal state for this denoising step
+                guider.set_state(step=i, num_inference_steps=num_inference_steps, timestep=t)
+
+                # Step 3: Prepare batched model inputs based on the guidance method
+                # The guider splits model inputs into separate batches for conditional/unconditional predictions.
+                # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}:
+                # you will get a guider_state with two batches:
+                #   guider_state = [
+                #       {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"},      # conditional batch
+                #       {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"},  # unconditional batch
+                #   ]
+                # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG).
+                guider_state = guider.prepare_inputs(guider_inputs)
+
+                # Step 4: Run the denoiser for each batch
+                # Each batch in guider_state represents a different conditioning (conditional, unconditional, etc.).
+                # We run the model once per batch and store the noise prediction in guider_state_batch.noise_pred.
+                for guider_state_batch in guider_state:
+                    guider.prepare_models(self.transformer)
+
+                    # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states)
+                    cond_kwargs = {
+                        input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()
+                    }
+
+                    # e.g. "pred_cond"/"pred_uncond"
+                    context_name = getattr(guider_state_batch, guider._identifier_key)
+                    with self.transformer.cache_context(context_name):
+                        # Run denoiser and store noise prediction in this batch
+                        guider_state_batch.noise_pred = self.transformer(
+                            hidden_states=latent_model_input,
+                            timestep=timestep,
+                            guidance=guidance,
+                            attention_kwargs=self.attention_kwargs,
+                            return_dict=False,
+                            **cond_kwargs,
+                        )[0]
+
+                    # Cleanup model (e.g., remove hooks)
+                    guider.cleanup_models(self.transformer)
+
+                # Step 5: Combine predictions using the guidance method
+                # The guider takes all noise predictions from guider_state and combines them according to the guidance algorithm.
+                # Continuing the CFG example, the guider receives:
+                #   guider_state = [
+                #       {"encoder_hidden_states": prompt_embeds, "noise_pred": noise_pred_cond, "__guidance_identifier__": "pred_cond"},      # batch 0
+                #       {"encoder_hidden_states": negative_prompt_embeds, "noise_pred": noise_pred_uncond, "__guidance_identifier__": "pred_uncond"},  # batch 1
+                #   ]
+                # And extracts predictions using the __guidance_identifier__:
+                #   pred_cond = guider_state[0]["noise_pred"]      # extracts noise_pred_cond
+                #   pred_uncond = guider_state[1]["noise_pred"]    # extracts noise_pred_uncond
+                # Then applies CFG formula:
+                #   noise_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond)
+                # Returns GuiderOutput(pred=noise_pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
+                noise_pred = guider(guider_state)[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                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)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self._current_timestep = None
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
+            latents = self._restore_image_tokens_order(latents)
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image.squeeze(2), output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return HunyuanImagePipelineOutput(images=image)

src/diffusers/pipelines/hunyuan_image/pipeline_output.py

+from dataclasses import dataclass
+from typing import List, Union
+
+import numpy as np
+import PIL.Image
+
+from ...utils import BaseOutput
+
+
+@dataclass
+class HunyuanImagePipelineOutput(BaseOutput):
+    """
+    Output class for HunyuanImage pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -76,6 +76,7 @@ LOADABLE_CLASSES = {
         "SchedulerMixin": ["save_pretrained", "from_pretrained"],
         "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
         "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],
+        "BaseGuidance": ["save_pretrained", "from_pretrained"],
     },
     "transformers": {
         "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],

src/diffusers/utils/dummy_pt_objects.py

@@ -17,6 +17,21 @@ class AdaptiveProjectedGuidance(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class AdaptiveProjectedMixGuidance(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 AutoGuidance(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -32,6 +47,21 @@ class AutoGuidance(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class BaseGuidance(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 ClassifierFreeGuidance(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -378,6 +408,36 @@ class AutoencoderKLCosmos(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class AutoencoderKLHunyuanImage(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 AutoencoderKLHunyuanImageRefiner(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 AutoencoderKLHunyuanVideo(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -858,6 +918,21 @@ class HunyuanDiT2DMultiControlNetModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class HunyuanImageTransformer2DModel(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 HunyuanVideoFramepackTransformer3DModel(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1037,6 +1037,36 @@ class HunyuanDiTPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class HunyuanImagePipeline(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 HunyuanImageRefinerPipeline(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 HunyuanSkyreelsImageToVideoPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/hunyuan_image_21/test_hunyuanimage.py

+# Copyright 2025 The HuggingFace Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+from transformers import (
+    ByT5Tokenizer,
+    Qwen2_5_VLConfig,
+    Qwen2_5_VLForConditionalGeneration,
+    Qwen2Tokenizer,
+    T5Config,
+    T5EncoderModel,
+)
+
+from diffusers import (
+    AdaptiveProjectedMixGuidance,
+    AutoencoderKLHunyuanImage,
+    FlowMatchEulerDiscreteScheduler,
+    HunyuanImagePipeline,
+    HunyuanImageTransformer2DModel,
+)
+
+from ...testing_utils import enable_full_determinism
+from ..test_pipelines_common import (
+    FirstBlockCacheTesterMixin,
+    PipelineTesterMixin,
+    to_np,
+)
+
+
+enable_full_determinism()
+
+
+class HunyuanImagePipelineFastTests(
+    PipelineTesterMixin,
+    FirstBlockCacheTesterMixin,
+    unittest.TestCase,
+):
+    pipeline_class = HunyuanImagePipeline
+    params = frozenset(["prompt", "height", "width"])
+    batch_params = frozenset(["prompt", "negative_prompt"])
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+
+    test_xformers_attention = False
+    test_layerwise_casting = True
+    test_group_offloading = True
+    test_attention_slicing = False
+    supports_dduf = False
+
+    def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1, guidance_embeds: bool = False):
+        torch.manual_seed(0)
+        transformer = HunyuanImageTransformer2DModel(
+            in_channels=4,
+            out_channels=4,
+            num_attention_heads=4,
+            attention_head_dim=8,
+            num_layers=num_layers,
+            num_single_layers=num_single_layers,
+            num_refiner_layers=1,
+            patch_size=(1, 1),
+            guidance_embeds=guidance_embeds,
+            text_embed_dim=32,
+            text_embed_2_dim=32,
+            rope_axes_dim=(4, 4),
+        )
+
+        torch.manual_seed(0)
+        vae = AutoencoderKLHunyuanImage(
+            in_channels=3,
+            out_channels=3,
+            latent_channels=4,
+            block_out_channels=(32, 64, 64, 64),
+            layers_per_block=1,
+            scaling_factor=0.476986,
+            spatial_compression_ratio=8,
+            sample_size=128,
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
+
+        if not guidance_embeds:
+            torch.manual_seed(0)
+            guider = AdaptiveProjectedMixGuidance(adaptive_projected_guidance_start_step=2)
+            ocr_guider = AdaptiveProjectedMixGuidance(adaptive_projected_guidance_start_step=3)
+        else:
+            guider = None
+            ocr_guider = None
+        torch.manual_seed(0)
+        config = Qwen2_5_VLConfig(
+            text_config={
+                "hidden_size": 32,
+                "intermediate_size": 32,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 2,
+                "num_key_value_heads": 2,
+                "rope_scaling": {
+                    "mrope_section": [2, 2, 4],
+                    "rope_type": "default",
+                    "type": "default",
+                },
+                "rope_theta": 1000000.0,
+            },
+            vision_config={
+                "depth": 2,
+                "hidden_size": 32,
+                "intermediate_size": 32,
+                "num_heads": 2,
+                "out_hidden_size": 32,
+            },
+            hidden_size=32,
+            vocab_size=152064,
+            vision_end_token_id=151653,
+            vision_start_token_id=151652,
+            vision_token_id=151654,
+        )
+        text_encoder = Qwen2_5_VLForConditionalGeneration(config)
+        tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration")
+
+        torch.manual_seed(0)
+        t5_config = T5Config(
+            d_model=32,
+            d_kv=4,
+            d_ff=16,
+            num_layers=2,
+            num_heads=2,
+            relative_attention_num_buckets=8,
+            relative_attention_max_distance=32,
+            vocab_size=256,
+            feed_forward_proj="gated-gelu",
+            dense_act_fn="gelu_new",
+            is_encoder_decoder=False,
+            use_cache=False,
+            tie_word_embeddings=False,
+        )
+        text_encoder_2 = T5EncoderModel(t5_config)
+        tokenizer_2 = ByT5Tokenizer()
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer": tokenizer,
+            "tokenizer_2": tokenizer_2,
+            "guider": guider,
+            "ocr_guider": ocr_guider,
+        }
+        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": 5,
+            "height": 16,
+            "width": 16,
+            "output_type": "pt",
+        }
+        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
+        generated_image = image[0]
+        self.assertEqual(generated_image.shape, (3, 16, 16))
+
+        expected_slice_np = np.array(
+            [0.6252659, 0.51482046, 0.60799813, 0.59267783, 0.488082, 0.5857634, 0.523781, 0.58028054, 0.5674121]
+        )
+        output_slice = generated_image[0, -3:, -3:].flatten().cpu().numpy()
+
+        self.assertTrue(
+            np.abs(output_slice - expected_slice_np).max() < 1e-3,
+            f"output_slice: {output_slice}, expected_slice_np: {expected_slice_np}",
+        )
+
+    def test_inference_guider(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        pipe.guider = pipe.guider.new(guidance_scale=1000)
+        pipe.ocr_guider = pipe.ocr_guider.new(guidance_scale=1000)
+
+        inputs = self.get_dummy_inputs(device)
+        image = pipe(**inputs).images
+        generated_image = image[0]
+        self.assertEqual(generated_image.shape, (3, 16, 16))
+
+        expected_slice_np = np.array(
+            [0.61494756, 0.49616697, 0.60327923, 0.6115793, 0.49047345, 0.56977504, 0.53066164, 0.58880305, 0.5570612]
+        )
+        output_slice = generated_image[0, -3:, -3:].flatten().cpu().numpy()
+
+        self.assertTrue(
+            np.abs(output_slice - expected_slice_np).max() < 1e-3,
+            f"output_slice: {output_slice}, expected_slice_np: {expected_slice_np}",
+        )
+
+    def test_inference_with_distilled_guidance(self):
+        device = "cpu"
+
+        components = self.get_dummy_components(guidance_embeds=True)
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        inputs["distilled_guidance_scale"] = 3.5
+        image = pipe(**inputs).images
+        generated_image = image[0]
+        self.assertEqual(generated_image.shape, (3, 16, 16))
+
+        expected_slice_np = np.array(
+            [0.63667065, 0.5187377, 0.66757566, 0.6320319, 0.4913387, 0.54813194, 0.5335031, 0.5736143, 0.5461346]
+        )
+        output_slice = generated_image[0, -3:, -3:].flatten().cpu().numpy()
+
+        self.assertTrue(
+            np.abs(output_slice - expected_slice_np).max() < 1e-3,
+            f"output_slice: {output_slice}, expected_slice_np: {expected_slice_np}",
+        )
+
+    def test_vae_tiling(self, expected_diff_max: float = 0.2):
+        generator_device = "cpu"
+        components = self.get_dummy_components()
+
+        pipe = self.pipeline_class(**components)
+        pipe.to("cpu")
+        pipe.set_progress_bar_config(disable=None)
+
+        # Without tiling
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_without_tiling = pipe(**inputs)[0]
+
+        # With tiling
+        pipe.vae.enable_tiling(tile_sample_min_size=96)
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_with_tiling = pipe(**inputs)[0]
+
+        self.assertLess(
+            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
+            expected_diff_max,
+            "VAE tiling should not affect the inference results",
+        )
+
+    @unittest.skip("TODO: Test not supported for now because needs to be adjusted to work with guiders.")
+    def test_encode_prompt_works_in_isolation(self):
+        pass