update

src/diffusers/__init__.py

@@ -399,6 +399,8 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["modular_pipelines"].extend(
         [
+            "Flux2AutoBlocks",
+            "Flux2ModularPipeline",
             "FluxAutoBlocks",
             "FluxKontextAutoBlocks",
             "FluxKontextModularPipeline",
@@ -1091,6 +1093,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
     else:
         from .modular_pipelines import (
+            Flux2AutoBlocks,
+            Flux2ModularPipeline,
             FluxAutoBlocks,
             FluxKontextAutoBlocks,
             FluxKontextModularPipeline,

src/diffusers/modular_pipelines/__init__.py

@@ -52,6 +52,10 @@ else:
         "FluxKontextAutoBlocks",
         "FluxKontextModularPipeline",
     ]
+    _import_structure["flux2"] = [
+        "Flux2AutoBlocks",
+        "Flux2ModularPipeline",
+    ]
     _import_structure["qwenimage"] = [
         "QwenImageAutoBlocks",
         "QwenImageModularPipeline",
@@ -71,6 +75,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .components_manager import ComponentsManager
         from .flux import FluxAutoBlocks, FluxKontextAutoBlocks, FluxKontextModularPipeline, FluxModularPipeline
+        from .flux2 import Flux2AutoBlocks, Flux2ModularPipeline
         from .modular_pipeline import (
             AutoPipelineBlocks,
             BlockState,

src/diffusers/modular_pipelines/flux2/__init__.py

@@ -0,0 +1,111 @@
+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["encoders"] = [
+        "Flux2TextEncoderStep",
+        "Flux2RemoteTextEncoderStep",
+        "Flux2VaeEncoderStep",
+    ]
+    _import_structure["before_denoise"] = [
+        "Flux2SetTimestepsStep",
+        "Flux2PrepareLatentsStep",
+        "Flux2RoPEInputsStep",
+        "Flux2PrepareImageLatentsStep",
+    ]
+    _import_structure["denoise"] = [
+        "Flux2LoopDenoiser",
+        "Flux2LoopAfterDenoiser",
+        "Flux2DenoiseLoopWrapper",
+        "Flux2DenoiseStep",
+    ]
+    _import_structure["decoders"] = ["Flux2DecodeStep"]
+    _import_structure["inputs"] = [
+        "Flux2ProcessImagesInputStep",
+        "Flux2TextInputStep",
+    ]
+    _import_structure["modular_blocks"] = [
+        "ALL_BLOCKS",
+        "AUTO_BLOCKS",
+        "REMOTE_AUTO_BLOCKS",
+        "TEXT2IMAGE_BLOCKS",
+        "IMAGE_CONDITIONED_BLOCKS",
+        "Flux2AutoBlocks",
+        "Flux2AutoVaeEncoderStep",
+        "Flux2BeforeDenoiseStep",
+        "Flux2VaeEncoderSequentialStep",
+    ]
+    _import_structure["modular_pipeline"] = ["Flux2ModularPipeline"]
+
+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 *  # noqa F403
+    else:
+        from .before_denoise import (
+            Flux2PrepareImageLatentsStep,
+            Flux2PrepareLatentsStep,
+            Flux2RoPEInputsStep,
+            Flux2SetTimestepsStep,
+        )
+        from .decoders import Flux2DecodeStep
+        from .denoise import (
+            Flux2DenoiseLoopWrapper,
+            Flux2DenoiseStep,
+            Flux2LoopAfterDenoiser,
+            Flux2LoopDenoiser,
+        )
+        from .encoders import (
+            Flux2RemoteTextEncoderStep,
+            Flux2TextEncoderStep,
+            Flux2VaeEncoderStep,
+        )
+        from .inputs import (
+            Flux2ProcessImagesInputStep,
+            Flux2TextInputStep,
+        )
+        from .modular_blocks import (
+            ALL_BLOCKS,
+            AUTO_BLOCKS,
+            IMAGE_CONDITIONED_BLOCKS,
+            REMOTE_AUTO_BLOCKS,
+            TEXT2IMAGE_BLOCKS,
+            Flux2AutoBlocks,
+            Flux2AutoVaeEncoderStep,
+            Flux2BeforeDenoiseStep,
+            Flux2VaeEncoderSequentialStep,
+        )
+        from .modular_pipeline import Flux2ModularPipeline
+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/modular_pipelines/flux2/before_denoise.py

@@ -0,0 +1,508 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+
+from ...models import Flux2Transformer2DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import logging
+from ...utils.torch_utils import randn_tensor
+from ..modular_pipeline import ModularPipelineBlocks, PipelineState
+from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
+from .modular_pipeline import Flux2ModularPipeline
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def compute_empirical_mu(image_seq_len: int, num_steps: int) -> float:
+    """Compute empirical mu for Flux2 timestep scheduling."""
+    a1, b1 = 8.73809524e-05, 1.89833333
+    a2, b2 = 0.00016927, 0.45666666
+
+    if image_seq_len > 4300:
+        mu = a2 * image_seq_len + b2
+        return float(mu)
+
+    m_200 = a2 * image_seq_len + b2
+    m_10 = a1 * image_seq_len + b1
+
+    a = (m_200 - m_10) / 190.0
+    b = m_200 - 200.0 * a
+    mu = a * num_steps + b
+
+    return float(mu)
+
+
+# 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 Flux2SetTimestepsStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler),
+            ComponentSpec("transformer", Flux2Transformer2DModel),
+        ]
+
+    @property
+    def description(self) -> str:
+        return "Step that sets the scheduler's timesteps for Flux2 inference using empirical mu calculation"
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("num_inference_steps", default=50),
+            InputParam("timesteps"),
+            InputParam("sigmas"),
+            InputParam("guidance_scale", default=4.0),
+            InputParam("latents", type_hint=torch.Tensor),
+            InputParam("num_images_per_prompt", default=1),
+            InputParam("height", type_hint=int),
+            InputParam("width", type_hint=int),
+            InputParam(
+                "batch_size",
+                required=True,
+                type_hint=int,
+                description="Number of prompts, the final batch size of model inputs should be `batch_size * num_images_per_prompt`.",
+            ),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam("timesteps", type_hint=torch.Tensor, description="The timesteps to use for inference"),
+            OutputParam(
+                "num_inference_steps",
+                type_hint=int,
+                description="The number of denoising steps to perform at inference time",
+            ),
+            OutputParam("guidance", type_hint=torch.Tensor, description="Guidance scale tensor"),
+        ]
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        block_state.device = components._execution_device
+
+        scheduler = components.scheduler
+
+        height = block_state.height or components.default_height
+        width = block_state.width or components.default_width
+        vae_scale_factor = components.vae_scale_factor
+
+        latent_height = 2 * (int(height) // (vae_scale_factor * 2))
+        latent_width = 2 * (int(width) // (vae_scale_factor * 2))
+        image_seq_len = (latent_height // 2) * (latent_width // 2)
+
+        num_inference_steps = block_state.num_inference_steps
+        sigmas = block_state.sigmas
+        timesteps = block_state.timesteps
+
+        if timesteps is None and sigmas is None:
+            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        if hasattr(scheduler.config, "use_flow_sigmas") and scheduler.config.use_flow_sigmas:
+            sigmas = None
+
+        mu = compute_empirical_mu(image_seq_len=image_seq_len, num_steps=num_inference_steps)
+
+        timesteps, num_inference_steps = retrieve_timesteps(
+            scheduler,
+            num_inference_steps,
+            block_state.device,
+            timesteps=timesteps,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        block_state.timesteps = timesteps
+        block_state.num_inference_steps = num_inference_steps
+
+        batch_size = block_state.batch_size * block_state.num_images_per_prompt
+        guidance = torch.full([1], block_state.guidance_scale, device=block_state.device, dtype=torch.float32)
+        guidance = guidance.expand(batch_size)
+        block_state.guidance = guidance
+
+        components.scheduler.set_begin_index(0)
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2PrepareLatentsStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return []
+
+    @property
+    def description(self) -> str:
+        return "Prepare latents step that prepares the initial noise latents for Flux2 text-to-image generation"
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("height", type_hint=int),
+            InputParam("width", type_hint=int),
+            InputParam("latents", type_hint=Optional[torch.Tensor]),
+            InputParam("num_images_per_prompt", type_hint=int, default=1),
+            InputParam("generator"),
+            InputParam(
+                "batch_size",
+                required=True,
+                type_hint=int,
+                description="Number of prompts, the final batch size of model inputs should be `batch_size * num_images_per_prompt`.",
+            ),
+            InputParam("dtype", type_hint=torch.dtype, description="The dtype of the model inputs"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                "latents", type_hint=torch.Tensor, description="The initial latents to use for the denoising process"
+            ),
+            OutputParam("latent_ids", type_hint=torch.Tensor, description="Position IDs for the latents (for RoPE)"),
+        ]
+
+    @staticmethod
+    def check_inputs(components, block_state):
+        vae_scale_factor = components.vae_scale_factor
+        if (block_state.height is not None and block_state.height % (vae_scale_factor * 2) != 0) or (
+            block_state.width is not None and block_state.width % (vae_scale_factor * 2) != 0
+        ):
+            logger.warning(
+                f"`height` and `width` have to be divisible by {vae_scale_factor * 2} but are {block_state.height} and {block_state.width}."
+            )
+
+    @staticmethod
+    def _prepare_latent_ids(latents: torch.Tensor):
+        """
+        Generates 4D position coordinates (T, H, W, L) for latent tensors.
+
+        Args:
+            latents: Latent tensor of shape (B, C, H, W)
+
+        Returns:
+            Position IDs tensor of shape (B, H*W, 4)
+        """
+        batch_size, _, height, width = latents.shape
+
+        t = torch.arange(1)
+        h = torch.arange(height)
+        w = torch.arange(width)
+        l = torch.arange(1)
+
+        latent_ids = torch.cartesian_prod(t, h, w, l)
+        latent_ids = latent_ids.unsqueeze(0).expand(batch_size, -1, -1)
+
+        return latent_ids
+
+    @staticmethod
+    def _pack_latents(latents):
+        """Pack latents: (batch_size, num_channels, height, width) -> (batch_size, height * width, num_channels)"""
+        batch_size, num_channels, height, width = latents.shape
+        latents = latents.reshape(batch_size, num_channels, height * width).permute(0, 2, 1)
+        return latents
+
+    @staticmethod
+    def prepare_latents(
+        comp,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // (comp.vae_scale_factor * 2))
+        width = 2 * (int(width) // (comp.vae_scale_factor * 2))
+
+        shape = (batch_size, num_channels_latents * 4, height // 2, width // 2)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        return latents
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        block_state.height = block_state.height or components.default_height
+        block_state.width = block_state.width or components.default_width
+        block_state.device = components._execution_device
+        block_state.num_channels_latents = components.num_channels_latents
+
+        self.check_inputs(components, block_state)
+        batch_size = block_state.batch_size * block_state.num_images_per_prompt
+
+        latents = self.prepare_latents(
+            components,
+            batch_size,
+            block_state.num_channels_latents,
+            block_state.height,
+            block_state.width,
+            block_state.dtype,
+            block_state.device,
+            block_state.generator,
+            block_state.latents,
+        )
+
+        latent_ids = self._prepare_latent_ids(latents)
+        latent_ids = latent_ids.to(block_state.device)
+
+        latents = self._pack_latents(latents)
+
+        block_state.latents = latents
+        block_state.latent_ids = latent_ids
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2RoPEInputsStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return "Step that prepares the 4D RoPE position IDs for Flux2 denoising. Should be placed after text encoder and latent preparation steps."
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam(name="prompt_embeds", required=True),
+            InputParam(name="latent_ids"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                name="txt_ids",
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="4D position IDs (T, H, W, L) for text tokens, used for RoPE calculation.",
+            ),
+            OutputParam(
+                name="latent_ids",
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="4D position IDs (T, H, W, L) for image latents, used for RoPE calculation.",
+            ),
+        ]
+
+    @staticmethod
+    def _prepare_text_ids(x: torch.Tensor, t_coord: Optional[torch.Tensor] = None):
+        """Prepare 4D position IDs for text tokens."""
+        B, L, _ = x.shape
+        out_ids = []
+
+        for i in range(B):
+            t = torch.arange(1) if t_coord is None else t_coord[i]
+            h = torch.arange(1)
+            w = torch.arange(1)
+            seq_l = torch.arange(L)
+
+            coords = torch.cartesian_prod(t, h, w, seq_l)
+            out_ids.append(coords)
+
+        return torch.stack(out_ids)
+
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+
+        prompt_embeds = block_state.prompt_embeds
+        device = prompt_embeds.device
+
+        block_state.txt_ids = self._prepare_text_ids(prompt_embeds)
+        block_state.txt_ids = block_state.txt_ids.to(device)
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2PrepareImageLatentsStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return "Step that prepares image latents and their position IDs for Flux2 image conditioning."
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("image_latents", type_hint=List[torch.Tensor]),
+            InputParam("batch_size", required=True, type_hint=int),
+            InputParam("num_images_per_prompt", default=1, type_hint=int),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                "image_latents",
+                type_hint=torch.Tensor,
+                description="Packed image latents for conditioning",
+            ),
+            OutputParam(
+                "image_latent_ids",
+                type_hint=torch.Tensor,
+                description="Position IDs for image latents",
+            ),
+        ]
+
+    @staticmethod
+    def _prepare_image_ids(image_latents: List[torch.Tensor], scale: int = 10):
+        """
+        Generates 4D time-space coordinates (T, H, W, L) for a sequence of image latents.
+
+        Args:
+            image_latents: A list of image latent feature tensors of shape (1, C, H, W).
+            scale: Factor used to define the time separation between latents.
+
+        Returns:
+            Combined coordinate tensor of shape (1, N_total, 4)
+        """
+        if not isinstance(image_latents, list):
+            raise ValueError(f"Expected `image_latents` to be a list, got {type(image_latents)}.")
+
+        t_coords = [scale + scale * t for t in torch.arange(0, len(image_latents))]
+        t_coords = [t.view(-1) for t in t_coords]
+
+        image_latent_ids = []
+        for x, t in zip(image_latents, t_coords):
+            x = x.squeeze(0)
+            _, height, width = x.shape
+
+            x_ids = torch.cartesian_prod(t, torch.arange(height), torch.arange(width), torch.arange(1))
+            image_latent_ids.append(x_ids)
+
+        image_latent_ids = torch.cat(image_latent_ids, dim=0)
+        image_latent_ids = image_latent_ids.unsqueeze(0)
+
+        return image_latent_ids
+
+    @staticmethod
+    def _pack_latents(latents):
+        """Pack latents: (batch_size, num_channels, height, width) -> (batch_size, height * width, num_channels)"""
+        batch_size, num_channels, height, width = latents.shape
+        latents = latents.reshape(batch_size, num_channels, height * width).permute(0, 2, 1)
+        return latents
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        image_latents = block_state.image_latents
+
+        if image_latents is None:
+            block_state.image_latents = None
+            block_state.image_latent_ids = None
+            self.set_block_state(state, block_state)
+
+            return components, state
+
+        device = components._execution_device
+        batch_size = block_state.batch_size * block_state.num_images_per_prompt
+
+        image_latent_ids = self._prepare_image_ids(image_latents)
+
+        packed_latents = []
+        for latent in image_latents:
+            packed = self._pack_latents(latent)
+            packed = packed.squeeze(0)
+            packed_latents.append(packed)
+
+        image_latents = torch.cat(packed_latents, dim=0)
+        image_latents = image_latents.unsqueeze(0)
+
+        image_latents = image_latents.repeat(batch_size, 1, 1)
+        image_latent_ids = image_latent_ids.repeat(batch_size, 1, 1)
+        image_latent_ids = image_latent_ids.to(device)
+
+        block_state.image_latents = image_latents
+        block_state.image_latent_ids = image_latent_ids
+
+        self.set_block_state(state, block_state)
+        return components, state

src/diffusers/modular_pipelines/flux2/decoders.py

@@ -0,0 +1,146 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, List, Tuple, Union
+
+import numpy as np
+import PIL
+import torch
+
+from ...configuration_utils import FrozenDict
+from ...models import AutoencoderKLFlux2
+from ...pipelines.flux2.image_processor import Flux2ImageProcessor
+from ...utils import logging
+from ..modular_pipeline import ModularPipelineBlocks, PipelineState
+from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class Flux2DecodeStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("vae", AutoencoderKLFlux2),
+            ComponentSpec(
+                "image_processor",
+                Flux2ImageProcessor,
+                config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 32}),
+                default_creation_method="from_config",
+            ),
+        ]
+
+    @property
+    def description(self) -> str:
+        return "Step that decodes the denoised latents into images using Flux2 VAE with batch norm denormalization"
+
+    @property
+    def inputs(self) -> List[Tuple[str, Any]]:
+        return [
+            InputParam("output_type", default="pil"),
+            InputParam(
+                "latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The denoised latents from the denoising step",
+            ),
+            InputParam(
+                "latent_ids",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Position IDs for the latents, used for unpacking",
+            ),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[str]:
+        return [
+            OutputParam(
+                "images",
+                type_hint=Union[List[PIL.Image.Image], torch.Tensor, np.ndarray],
+                description="The generated images, can be a list of PIL.Image.Image, torch.Tensor or a numpy array",
+            )
+        ]
+
+    @staticmethod
+    def _unpack_latents_with_ids(x: torch.Tensor, x_ids: torch.Tensor) -> torch.Tensor:
+        """
+        Unpack latents using position IDs to scatter tokens into place.
+
+        Args:
+            x: Packed latents tensor of shape (B, seq_len, C)
+            x_ids: Position IDs tensor of shape (B, seq_len, 4) with (T, H, W, L) coordinates
+
+        Returns:
+            Unpacked latents tensor of shape (B, C, H, W)
+        """
+        x_list = []
+        for data, pos in zip(x, x_ids):
+            _, ch = data.shape  # noqa: F841
+            h_ids = pos[:, 1].to(torch.int64)
+            w_ids = pos[:, 2].to(torch.int64)
+
+            h = torch.max(h_ids) + 1
+            w = torch.max(w_ids) + 1
+
+            flat_ids = h_ids * w + w_ids
+
+            out = torch.zeros((h * w, ch), device=data.device, dtype=data.dtype)
+            out.scatter_(0, flat_ids.unsqueeze(1).expand(-1, ch), data)
+
+            out = out.view(h, w, ch).permute(2, 0, 1)
+            x_list.append(out)
+
+        return torch.stack(x_list, dim=0)
+
+    @staticmethod
+    def _unpatchify_latents(latents):
+        """Convert patchified latents back to regular format."""
+        batch_size, num_channels_latents, height, width = latents.shape
+        latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), 2, 2, height, width)
+        latents = latents.permute(0, 1, 4, 2, 5, 3)
+        latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), height * 2, width * 2)
+        return latents
+
+    @torch.no_grad()
+    def __call__(self, components, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        vae = components.vae
+
+        if block_state.output_type == "latent":
+            block_state.images = block_state.latents
+        else:
+            latents = block_state.latents
+            latent_ids = block_state.latent_ids
+
+            latents = self._unpack_latents_with_ids(latents, latent_ids)
+
+            latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
+            latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps).to(
+                latents.device, latents.dtype
+            )
+            latents = latents * latents_bn_std + latents_bn_mean
+
+            latents = self._unpatchify_latents(latents)
+
+            block_state.images = vae.decode(latents, return_dict=False)[0]
+            block_state.images = components.image_processor.postprocess(
+                block_state.images, output_type=block_state.output_type
+            )
+
+        self.set_block_state(state, block_state)
+        return components, state

src/diffusers/modular_pipelines/flux2/denoise.py

@@ -0,0 +1,252 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, List, Tuple
+
+import torch
+
+from ...models import Flux2Transformer2DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging
+from ..modular_pipeline import (
+    BlockState,
+    LoopSequentialPipelineBlocks,
+    ModularPipelineBlocks,
+    PipelineState,
+)
+from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
+from .modular_pipeline import Flux2ModularPipeline
+
+
+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
+
+
+class Flux2LoopDenoiser(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [ComponentSpec("transformer", Flux2Transformer2DModel)]
+
+    @property
+    def description(self) -> str:
+        return (
+            "Step within the denoising loop that denoises the latents for Flux2. "
+            "This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
+            "object (e.g. `Flux2DenoiseLoopWrapper`)"
+        )
+
+    @property
+    def inputs(self) -> List[Tuple[str, Any]]:
+        return [
+            InputParam("joint_attention_kwargs"),
+            InputParam(
+                "latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The latents to denoise. Shape: (B, seq_len, C)",
+            ),
+            InputParam(
+                "image_latents",
+                type_hint=torch.Tensor,
+                description="Packed image latents for conditioning. Shape: (B, img_seq_len, C)",
+            ),
+            InputParam(
+                "image_latent_ids",
+                type_hint=torch.Tensor,
+                description="Position IDs for image latents. Shape: (B, img_seq_len, 4)",
+            ),
+            InputParam(
+                "guidance",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Guidance scale as a tensor",
+            ),
+            InputParam(
+                "prompt_embeds",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Text embeddings from Mistral3",
+            ),
+            InputParam(
+                "txt_ids",
+                required=True,
+                type_hint=torch.Tensor,
+                description="4D position IDs for text tokens (T, H, W, L)",
+            ),
+            InputParam(
+                "latent_ids",
+                required=True,
+                type_hint=torch.Tensor,
+                description="4D position IDs for latent tokens (T, H, W, L)",
+            ),
+        ]
+
+    @torch.no_grad()
+    def __call__(
+        self, components: Flux2ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor
+    ) -> PipelineState:
+        latents = block_state.latents
+        latent_model_input = latents.to(components.transformer.dtype)
+        img_ids = block_state.latent_ids
+
+        image_latents = getattr(block_state, "image_latents", None)
+        if image_latents is not None:
+            latent_model_input = torch.cat([latents, image_latents], dim=1).to(components.transformer.dtype)
+            image_latent_ids = block_state.image_latent_ids
+            img_ids = torch.cat([img_ids, image_latent_ids], dim=1)
+
+        timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+        noise_pred = components.transformer(
+            hidden_states=latent_model_input,
+            timestep=timestep / 1000,
+            guidance=block_state.guidance,
+            encoder_hidden_states=block_state.prompt_embeds,
+            txt_ids=block_state.txt_ids,
+            img_ids=img_ids,
+            joint_attention_kwargs=block_state.joint_attention_kwargs,
+            return_dict=False,
+        )[0]
+
+        noise_pred = noise_pred[:, : latents.size(1)]
+        block_state.noise_pred = noise_pred
+
+        return components, block_state
+
+
+class Flux2LoopAfterDenoiser(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler)]
+
+    @property
+    def description(self) -> str:
+        return (
+            "Step within the denoising loop that updates the latents after denoising. "
+            "This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
+            "object (e.g. `Flux2DenoiseLoopWrapper`)"
+        )
+
+    @property
+    def inputs(self) -> List[Tuple[str, Any]]:
+        return []
+
+    @property
+    def intermediate_inputs(self) -> List[str]:
+        return [InputParam("generator")]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [OutputParam("latents", type_hint=torch.Tensor, description="The denoised latents")]
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, block_state: BlockState, i: int, t: torch.Tensor):
+        latents_dtype = block_state.latents.dtype
+        block_state.latents = components.scheduler.step(
+            block_state.noise_pred,
+            t,
+            block_state.latents,
+            return_dict=False,
+        )[0]
+
+        if block_state.latents.dtype != latents_dtype:
+            if torch.backends.mps.is_available():
+                block_state.latents = block_state.latents.to(latents_dtype)
+
+        return components, block_state
+
+
+class Flux2DenoiseLoopWrapper(LoopSequentialPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Pipeline block that iteratively denoises the latents over `timesteps`. "
+            "The specific steps within each iteration can be customized with `sub_blocks` attribute"
+        )
+
+    @property
+    def loop_expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler),
+            ComponentSpec("transformer", Flux2Transformer2DModel),
+        ]
+
+    @property
+    def loop_inputs(self) -> List[InputParam]:
+        return [
+            InputParam(
+                "timesteps",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The timesteps to use for the denoising process.",
+            ),
+            InputParam(
+                "num_inference_steps",
+                required=True,
+                type_hint=int,
+                description="The number of inference steps to use for the denoising process.",
+            ),
+        ]
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+
+        block_state.num_warmup_steps = max(
+            len(block_state.timesteps) - block_state.num_inference_steps * components.scheduler.order, 0
+        )
+
+        with self.progress_bar(total=block_state.num_inference_steps) as progress_bar:
+            for i, t in enumerate(block_state.timesteps):
+                components, block_state = self.loop_step(components, block_state, i=i, t=t)
+
+                if i == len(block_state.timesteps) - 1 or (
+                    (i + 1) > block_state.num_warmup_steps and (i + 1) % components.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2DenoiseStep(Flux2DenoiseLoopWrapper):
+    block_classes = [Flux2LoopDenoiser, Flux2LoopAfterDenoiser]
+    block_names = ["denoiser", "after_denoiser"]
+
+    @property
+    def description(self) -> str:
+        return (
+            "Denoise step that iteratively denoises the latents for Flux2. \n"
+            "Its loop logic is defined in `Flux2DenoiseLoopWrapper.__call__` method \n"
+            "At each iteration, it runs blocks defined in `sub_blocks` sequentially:\n"
+            " - `Flux2LoopDenoiser`\n"
+            " - `Flux2LoopAfterDenoiser`\n"
+            "This block supports both text-to-image and image-conditioned generation."
+        )

src/diffusers/modular_pipelines/flux2/encoders.py

@@ -0,0 +1,349 @@
+# Copyright 2025 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 List, Optional, Tuple, Union
+
+import torch
+from transformers import AutoProcessor, Mistral3ForConditionalGeneration
+
+from ...models import AutoencoderKLFlux2
+from ...utils import logging
+from ..modular_pipeline import ModularPipelineBlocks, PipelineState
+from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
+from .modular_pipeline import Flux2ModularPipeline
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def format_text_input(prompts: List[str], system_message: str = None):
+    """Format prompts for Mistral3 chat template."""
+    cleaned_txt = [prompt.replace("[IMG]", "") for prompt in prompts]
+
+    return [
+        [
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": system_message}],
+            },
+            {"role": "user", "content": [{"type": "text", "text": prompt}]},
+        ]
+        for prompt in cleaned_txt
+    ]
+
+
+# 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 Flux2TextEncoderStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    # fmt: off
+    DEFAULT_SYSTEM_MESSAGE = "You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object attribution and actions without speculation."
+    # fmt: on
+
+    @property
+    def description(self) -> str:
+        return "Text Encoder step that generates text embeddings using Mistral3 to guide the image generation"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("text_encoder", Mistral3ForConditionalGeneration),
+            ComponentSpec("tokenizer", AutoProcessor),
+        ]
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("prompt"),
+            InputParam("prompt_embeds", type_hint=torch.Tensor, required=False),
+            InputParam("max_sequence_length", type_hint=int, default=512, required=False),
+            InputParam("text_encoder_out_layers", type_hint=Tuple[int], default=(10, 20, 30), required=False),
+            InputParam("joint_attention_kwargs"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                "prompt_embeds",
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="Text embeddings from Mistral3 used to guide the image generation",
+            ),
+        ]
+
+    @staticmethod
+    def check_inputs(block_state):
+        prompt = block_state.prompt
+        prompt_embeds = getattr(block_state, "prompt_embeds", None)
+
+        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 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)}")
+
+    @staticmethod
+    def _get_mistral_3_prompt_embeds(
+        text_encoder: Mistral3ForConditionalGeneration,
+        tokenizer: AutoProcessor,
+        prompt: Union[str, List[str]],
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+        # fmt: off
+        system_message: str = "You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object attribution and actions without speculation.",
+        # fmt: on
+        hidden_states_layers: Tuple[int] = (10, 20, 30),
+    ):
+        dtype = text_encoder.dtype if dtype is None else dtype
+        device = text_encoder.device if device is None else device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        messages_batch = format_text_input(prompts=prompt, system_message=system_message)
+
+        inputs = tokenizer.apply_chat_template(
+            messages_batch,
+            add_generation_prompt=False,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=max_sequence_length,
+        )
+
+        input_ids = inputs["input_ids"].to(device)
+        attention_mask = inputs["attention_mask"].to(device)
+
+        output = text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            use_cache=False,
+        )
+
+        out = torch.stack([output.hidden_states[k] for k in hidden_states_layers], dim=1)
+        out = out.to(dtype=dtype, device=device)
+
+        batch_size, num_channels, seq_len, hidden_dim = out.shape
+        prompt_embeds = out.permute(0, 2, 1, 3).reshape(batch_size, seq_len, num_channels * hidden_dim)
+
+        return prompt_embeds
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        self.check_inputs(block_state)
+
+        block_state.device = components._execution_device
+
+        if block_state.prompt_embeds is not None:
+            self.set_block_state(state, block_state)
+            return components, state
+
+        prompt = block_state.prompt
+        if prompt is None:
+            prompt = ""
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        block_state.prompt_embeds = self._get_mistral_3_prompt_embeds(
+            text_encoder=components.text_encoder,
+            tokenizer=components.tokenizer,
+            prompt=prompt,
+            device=block_state.device,
+            max_sequence_length=block_state.max_sequence_length,
+            system_message=self.DEFAULT_SYSTEM_MESSAGE,
+            hidden_states_layers=block_state.text_encoder_out_layers,
+        )
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    REMOTE_URL = "https://remote-text-encoder-flux-2.huggingface.co/predict"
+
+    @property
+    def description(self) -> str:
+        return "Text Encoder step that generates text embeddings using a remote API endpoint"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return []
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("prompt"),
+            InputParam("prompt_embeds", type_hint=torch.Tensor, required=False),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                "prompt_embeds",
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="Text embeddings from remote API used to guide the image generation",
+            ),
+        ]
+
+    @staticmethod
+    def check_inputs(block_state):
+        prompt = block_state.prompt
+        prompt_embeds = getattr(block_state, "prompt_embeds", None)
+
+        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 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)}")
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        import io
+
+        import requests
+        from huggingface_hub import get_token
+
+        block_state = self.get_block_state(state)
+        self.check_inputs(block_state)
+
+        block_state.device = components._execution_device
+
+        if block_state.prompt_embeds is not None:
+            self.set_block_state(state, block_state)
+            return components, state
+
+        prompt = block_state.prompt
+        if prompt is None:
+            prompt = ""
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        response = requests.post(
+            self.REMOTE_URL,
+            json={"prompt": prompt},
+            headers={
+                "Authorization": f"Bearer {get_token()}",
+                "Content-Type": "application/json",
+            },
+        )
+        response.raise_for_status()
+
+        block_state.prompt_embeds = torch.load(io.BytesIO(response.content), weights_only=True)
+        block_state.prompt_embeds = block_state.prompt_embeds.to(block_state.device)
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2VaeEncoderStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return "VAE Encoder step that encodes preprocessed images into latent representations for Flux2."
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [ComponentSpec("vae", AutoencoderKLFlux2)]
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("condition_images", type_hint=List[torch.Tensor]),
+            InputParam("generator"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                "image_latents",
+                type_hint=List[torch.Tensor],
+                description="List of latent representations for each reference image",
+            ),
+        ]
+
+    @staticmethod
+    def _patchify_latents(latents):
+        """Convert latents to patchified format for Flux2."""
+        batch_size, num_channels_latents, height, width = latents.shape
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 1, 3, 5, 2, 4)
+        latents = latents.reshape(batch_size, num_channels_latents * 4, height // 2, width // 2)
+        return latents
+
+    def _encode_vae_image(self, vae: AutoencoderKLFlux2, image: torch.Tensor, generator: torch.Generator):
+        """Encode a single image using Flux2 VAE with batch norm normalization."""
+        if image.ndim != 4:
+            raise ValueError(f"Expected image dims 4, got {image.ndim}.")
+
+        image_latents = retrieve_latents(vae.encode(image), generator=generator, sample_mode="argmax")
+        image_latents = self._patchify_latents(image_latents)
+
+        latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
+        latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps)
+        latents_bn_std = latents_bn_std.to(image_latents.device, image_latents.dtype)
+        image_latents = (image_latents - latents_bn_mean) / latents_bn_std
+
+        return image_latents
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        condition_images = block_state.condition_images
+
+        if condition_images is None:
+            return components, state
+
+        device = components._execution_device
+        dtype = components.vae.dtype
+
+        image_latents = []
+        for image in condition_images:
+            image = image.to(device=device, dtype=dtype)
+            latent = self._encode_vae_image(
+                vae=components.vae,
+                image=image,
+                generator=block_state.generator,
+            )
+            image_latents.append(latent)
+
+        block_state.image_latents = image_latents
+
+        self.set_block_state(state, block_state)
+        return components, state

src/diffusers/modular_pipelines/flux2/inputs.py

@@ -0,0 +1,160 @@
+# Copyright 2025 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 List
+
+import torch
+
+from ...configuration_utils import FrozenDict
+from ...pipelines.flux2.image_processor import Flux2ImageProcessor
+from ...utils import logging
+from ..modular_pipeline import ModularPipelineBlocks, PipelineState
+from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
+from .modular_pipeline import Flux2ModularPipeline
+
+
+logger = logging.get_logger(__name__)
+
+
+class Flux2TextInputStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return (
+            "This step:\n"
+            "  1. Determines `batch_size` and `dtype` based on `prompt_embeds`\n"
+            "  2. Ensures all text embeddings have consistent batch sizes (batch_size * num_images_per_prompt)"
+        )
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("num_images_per_prompt", default=1),
+            InputParam(
+                "prompt_embeds",
+                required=True,
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="Pre-generated text embeddings from Mistral3. Can be generated from text_encoder step.",
+            ),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[str]:
+        return [
+            OutputParam(
+                "batch_size",
+                type_hint=int,
+                description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt",
+            ),
+            OutputParam(
+                "dtype",
+                type_hint=torch.dtype,
+                description="Data type of model tensor inputs (determined by `prompt_embeds`)",
+            ),
+            OutputParam(
+                "prompt_embeds",
+                type_hint=torch.Tensor,
+                kwargs_type="denoiser_input_fields",
+                description="Text embeddings used to guide the image generation",
+            ),
+        ]
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+
+        block_state.batch_size = block_state.prompt_embeds.shape[0]
+        block_state.dtype = block_state.prompt_embeds.dtype
+
+        _, seq_len, _ = block_state.prompt_embeds.shape
+        block_state.prompt_embeds = block_state.prompt_embeds.repeat(1, block_state.num_images_per_prompt, 1)
+        block_state.prompt_embeds = block_state.prompt_embeds.view(
+            block_state.batch_size * block_state.num_images_per_prompt, seq_len, -1
+        )
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2ProcessImagesInputStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return "Image preprocess step for Flux2. Validates and preprocesses reference images."
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec(
+                "image_processor",
+                Flux2ImageProcessor,
+                config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 32}),
+                default_creation_method="from_config",
+            ),
+        ]
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("image"),
+            InputParam("height"),
+            InputParam("width"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [OutputParam(name="condition_images", type_hint=List[torch.Tensor])]
+
+    @torch.no_grad()
+    def __call__(self, components: Flux2ModularPipeline, state: PipelineState):
+        block_state = self.get_block_state(state)
+        images = block_state.image
+
+        if images is None:
+            block_state.condition_images = None
+            self.set_block_state(state, block_state)
+            return components, state
+
+        if not isinstance(images, list):
+            images = [images]
+
+        condition_images = []
+        for img in images:
+            components.image_processor.check_image_input(img)
+
+            image_width, image_height = img.size
+            if image_width * image_height > 1024 * 1024:
+                img = components.image_processor._resize_to_target_area(img, 1024 * 1024)
+                image_width, image_height = img.size
+
+            multiple_of = components.vae_scale_factor * 2
+            image_width = (image_width // multiple_of) * multiple_of
+            image_height = (image_height // multiple_of) * multiple_of
+            condition_img = components.image_processor.preprocess(
+                img, height=image_height, width=image_width, resize_mode="crop"
+            )
+            condition_images.append(condition_img)
+
+            if block_state.height is None:
+                block_state.height = image_height
+            if block_state.width is None:
+                block_state.width = image_width
+
+        block_state.condition_images = condition_images
+
+        self.set_block_state(state, block_state)
+        return components, state

src/diffusers/modular_pipelines/flux2/modular_blocks.py

@@ -0,0 +1,166 @@
+# Copyright 2025 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 ...utils import logging
+from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
+from ..modular_pipeline_utils import InsertableDict
+from .before_denoise import (
+    Flux2PrepareImageLatentsStep,
+    Flux2PrepareLatentsStep,
+    Flux2RoPEInputsStep,
+    Flux2SetTimestepsStep,
+)
+from .decoders import Flux2DecodeStep
+from .denoise import Flux2DenoiseStep
+from .encoders import (
+    Flux2RemoteTextEncoderStep,
+    Flux2TextEncoderStep,
+    Flux2VaeEncoderStep,
+)
+from .inputs import (
+    Flux2ProcessImagesInputStep,
+    Flux2TextInputStep,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+Flux2VaeEncoderBlocks = InsertableDict(
+    [
+        ("preprocess", Flux2ProcessImagesInputStep()),
+        ("encode", Flux2VaeEncoderStep()),
+        ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
+    ]
+)
+
+
+class Flux2VaeEncoderSequentialStep(SequentialPipelineBlocks):
+    model_name = "flux2"
+
+    block_classes = Flux2VaeEncoderBlocks.values()
+    block_names = Flux2VaeEncoderBlocks.keys()
+
+    @property
+    def description(self) -> str:
+        return "VAE encoder step that preprocesses, encodes, and prepares image latents for Flux2 conditioning."
+
+
+class Flux2AutoVaeEncoderStep(AutoPipelineBlocks):
+    block_classes = [Flux2VaeEncoderSequentialStep]
+    block_names = ["img_conditioning"]
+    block_trigger_inputs = ["image"]
+
+    @property
+    def description(self):
+        return (
+            "VAE encoder step that encodes the image inputs into their latent representations.\n"
+            "This is an auto pipeline block that works for image conditioning tasks.\n"
+            " - `Flux2VaeEncoderSequentialStep` is used when `image` is provided.\n"
+            " - If `image` is not provided, step will be skipped."
+        )
+
+
+Flux2BeforeDenoiseBlocks = InsertableDict(
+    [
+        ("prepare_latents", Flux2PrepareLatentsStep()),
+        ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_rope_inputs", Flux2RoPEInputsStep()),
+    ]
+)
+
+
+class Flux2BeforeDenoiseStep(SequentialPipelineBlocks):
+    model_name = "flux2"
+
+    block_classes = Flux2BeforeDenoiseBlocks.values()
+    block_names = Flux2BeforeDenoiseBlocks.keys()
+
+    @property
+    def description(self):
+        return "Before denoise step that prepares the inputs for the denoise step in Flux2 generation."
+
+
+AUTO_BLOCKS = InsertableDict(
+    [
+        ("text_encoder", Flux2TextEncoderStep()),
+        ("text_input", Flux2TextInputStep()),
+        ("vae_image_encoder", Flux2AutoVaeEncoderStep()),
+        ("before_denoise", Flux2BeforeDenoiseStep()),
+        ("denoise", Flux2DenoiseStep()),
+        ("decode", Flux2DecodeStep()),
+    ]
+)
+
+
+REMOTE_AUTO_BLOCKS = InsertableDict(
+    [
+        ("text_encoder", Flux2RemoteTextEncoderStep()),
+        ("text_input", Flux2TextInputStep()),
+        ("vae_image_encoder", Flux2AutoVaeEncoderStep()),
+        ("before_denoise", Flux2BeforeDenoiseStep()),
+        ("denoise", Flux2DenoiseStep()),
+        ("decode", Flux2DecodeStep()),
+    ]
+)
+
+
+class Flux2AutoBlocks(SequentialPipelineBlocks):
+    model_name = "flux2"
+
+    block_classes = AUTO_BLOCKS.values()
+    block_names = AUTO_BLOCKS.keys()
+
+    @property
+    def description(self):
+        return (
+            "Auto Modular pipeline for text-to-image and image-conditioned generation using Flux2.\n"
+            "- For text-to-image generation, all you need to provide is `prompt`.\n"
+            "- For image-conditioned generation, you need to provide `image` (list of PIL images)."
+        )
+
+
+TEXT2IMAGE_BLOCKS = InsertableDict(
+    [
+        ("text_encoder", Flux2TextEncoderStep()),
+        ("text_input", Flux2TextInputStep()),
+        ("prepare_latents", Flux2PrepareLatentsStep()),
+        ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_rope_inputs", Flux2RoPEInputsStep()),
+        ("denoise", Flux2DenoiseStep()),
+        ("decode", Flux2DecodeStep()),
+    ]
+)
+
+IMAGE_CONDITIONED_BLOCKS = InsertableDict(
+    [
+        ("text_encoder", Flux2TextEncoderStep()),
+        ("text_input", Flux2TextInputStep()),
+        ("preprocess_images", Flux2ProcessImagesInputStep()),
+        ("vae_encoder", Flux2VaeEncoderStep()),
+        ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
+        ("prepare_latents", Flux2PrepareLatentsStep()),
+        ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_rope_inputs", Flux2RoPEInputsStep()),
+        ("denoise", Flux2DenoiseStep()),
+        ("decode", Flux2DecodeStep()),
+    ]
+)
+
+ALL_BLOCKS = {
+    "text2image": TEXT2IMAGE_BLOCKS,
+    "image_conditioned": IMAGE_CONDITIONED_BLOCKS,
+    "auto": AUTO_BLOCKS,
+    "remote": REMOTE_AUTO_BLOCKS,
+}

src/diffusers/modular_pipelines/flux2/modular_pipeline.py

@@ -0,0 +1,57 @@
+# Copyright 2025 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 ...loaders import Flux2LoraLoaderMixin
+from ...utils import logging
+from ..modular_pipeline import ModularPipeline
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class Flux2ModularPipeline(ModularPipeline, Flux2LoraLoaderMixin):
+    """
+    A ModularPipeline for Flux2.
+
+    > [!WARNING] > This is an experimental feature and is likely to change in the future.
+    """
+
+    default_blocks_name = "Flux2AutoBlocks"
+
+    @property
+    def default_height(self):
+        return self.default_sample_size * self.vae_scale_factor
+
+    @property
+    def default_width(self):
+        return self.default_sample_size * self.vae_scale_factor
+
+    @property
+    def default_sample_size(self):
+        return 128
+
+    @property
+    def vae_scale_factor(self):
+        vae_scale_factor = 8
+        if getattr(self, "vae", None) is not None:
+            vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        return vae_scale_factor
+
+    @property
+    def num_channels_latents(self):
+        num_channels_latents = 32
+        if getattr(self, "transformer", None):
+            num_channels_latents = self.transformer.config.in_channels // 4
+        return num_channels_latents

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -58,6 +58,7 @@ MODULAR_PIPELINE_MAPPING = OrderedDict(
         ("wan", "WanModularPipeline"),
         ("flux", "FluxModularPipeline"),
         ("flux-kontext", "FluxKontextModularPipeline"),
+        ("flux2", "Flux2ModularPipeline"),
         ("qwenimage", "QwenImageModularPipeline"),
         ("qwenimage-edit", "QwenImageEditModularPipeline"),
         ("qwenimage-edit-plus", "QwenImageEditPlusModularPipeline"),
@@ -1585,7 +1586,6 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         for name, config_spec in self._config_specs.items():
             default_configs[name] = config_spec.default
         self.register_to_config(**default_configs)
-
         self.register_to_config(_blocks_class_name=self.blocks.__class__.__name__ if self.blocks is not None else None)
 
     @property

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -2,6 +2,36 @@
 from ..utils import DummyObject, requires_backends
 
 
+class Flux2AutoBlocks(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 Flux2ModularPipeline(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 FluxAutoBlocks(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/modular_pipelines/flux2/test_modular_pipeline_flux2.py

@@ -0,0 +1,93 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+
+import numpy as np
+import PIL
+import pytest
+
+from diffusers.modular_pipelines import (
+    Flux2AutoBlocks,
+    Flux2ModularPipeline,
+)
+
+from ...testing_utils import floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2ModularPipeline
+    pipeline_blocks_class = Flux2AutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-modular"
+
+    params = frozenset(["prompt", "height", "width", "guidance_scale"])
+    batch_params = frozenset(["prompt"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            # TODO (Dhruv): Update text encoder config so that vocab_size matches tokenizer
+            "max_sequence_length": 8,  # bit of a hack to workaround vocab size mismatch
+            "text_encoder_out_layers": (1,),
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 4.0,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_float16_inference(self):
+        super().test_float16_inference(9e-2)
+
+
+class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2ModularPipeline
+    pipeline_blocks_class = Flux2AutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-modular"
+
+    params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
+    batch_params = frozenset(["prompt", "image"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            # TODO (Dhruv): Update text encoder config so that vocab_size matches tokenizer
+            "max_sequence_length": 8,  # bit of a hack to workaround vocab size mismatch
+            "text_encoder_out_layers": (1,),
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 4.0,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(torch_device)
+        image = image.cpu().permute(0, 2, 3, 1)[0]
+        init_image = PIL.Image.fromarray(np.uint8(image * 255)).convert("RGB")
+        inputs["image"] = init_image
+
+        return inputs
+
+    def test_float16_inference(self):
+        super().test_float16_inference(9e-2)
+
+    @pytest.mark.skip(reason="batched inference is currently not supported")
+    def test_inference_batch_single_identical(self, batch_size=2, expected_max_diff=0.0001):
+        return

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -165,7 +165,6 @@ class ModularPipelineTesterMixin:
         expected_max_diff=1e-4,
     ):
         pipe = self.get_pipeline().to(torch_device)
-
         inputs = self.get_dummy_inputs()
 
         # Reset generator in case it is has been used in self.get_dummy_inputs