add tests

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>

docker/diffusers-pytorch-cuda/Dockerfile

@@ -1,8 +1,8 @@
-FROM nvidia/cuda:12.8.0-runtime-ubuntu22.04
+FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.11
+ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
@@ -32,12 +32,10 @@ RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-# Install torch, torchvision, and torchaudio together to ensure compatibility
 RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio \
-    --index-url https://download.pytorch.org/whl/cu128
+    torchaudio
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -1,8 +1,8 @@
-FROM nvidia/cuda:12.8.0-runtime-ubuntu22.04
+FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.11
+ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
@@ -32,12 +32,10 @@ RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-# Install torch, torchvision, and torchaudio together to ensure compatibility
 RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio \
-    --index-url https://download.pytorch.org/whl/cu128
+    torchaudio
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

src/diffusers/__init__.py

@@ -413,6 +413,9 @@ else:
     _import_structure["modular_pipelines"].extend(
         [
             "Flux2AutoBlocks",
+            "Flux2KleinAutoBlocks",
+            "Flux2KleinBaseAutoBlocks",
+            "Flux2KleinModularPipeline",
             "Flux2ModularPipeline",
             "FluxAutoBlocks",
             "FluxKontextAutoBlocks",
@@ -1146,6 +1149,9 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .modular_pipelines import (
             Flux2AutoBlocks,
+            Flux2KleinAutoBlocks,
+            Flux2KleinBaseAutoBlocks,
+            Flux2KleinModularPipeline,
             Flux2ModularPipeline,
             FluxAutoBlocks,
             FluxKontextAutoBlocks,

src/diffusers/models/transformers/transformer_longcat_image.py

@@ -406,7 +406,6 @@ class LongCatImageTransformer2DModel(
     """
 
     _supports_gradient_checkpointing = True
-    _repeated_blocks = ["LongCatImageTransformerBlock", "LongCatImageSingleTransformerBlock"]
 
     @register_to_config
     def __init__(

src/diffusers/modular_pipelines/__init__.py

@@ -54,7 +54,10 @@ else:
     ]
     _import_structure["flux2"] = [
         "Flux2AutoBlocks",
+        "Flux2KleinAutoBlocks",
+        "Flux2KleinBaseAutoBlocks",
         "Flux2ModularPipeline",
+        "Flux2KleinModularPipeline",
     ]
     _import_structure["qwenimage"] = [
         "QwenImageAutoBlocks",
@@ -81,7 +84,13 @@ 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 .flux2 import (
+            Flux2AutoBlocks,
+            Flux2KleinAutoBlocks,
+            Flux2KleinBaseAutoBlocks,
+            Flux2KleinModularPipeline,
+            Flux2ModularPipeline,
+        )
         from .modular_pipeline import (
             AutoPipelineBlocks,
             BlockState,

src/diffusers/modular_pipelines/flux2/__init__.py

@@ -43,7 +43,7 @@ else:
         "Flux2ProcessImagesInputStep",
         "Flux2TextInputStep",
     ]
-    _import_structure["modular_blocks"] = [
+    _import_structure["modular_blocks_flux2"] = [
         "ALL_BLOCKS",
         "AUTO_BLOCKS",
         "REMOTE_AUTO_BLOCKS",
@@ -51,10 +51,11 @@ else:
         "IMAGE_CONDITIONED_BLOCKS",
         "Flux2AutoBlocks",
         "Flux2AutoVaeEncoderStep",
-        "Flux2BeforeDenoiseStep",
+        "Flux2CoreDenoiseStep",
         "Flux2VaeEncoderSequentialStep",
     ]
-    _import_structure["modular_pipeline"] = ["Flux2ModularPipeline"]
+    _import_structure["modular_blocks_flux2_klein"] = ["Flux2KleinAutoBlocks", "Flux2KleinBaseAutoBlocks"]
+    _import_structure["modular_pipeline"] = ["Flux2ModularPipeline", "Flux2KleinModularPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -85,7 +86,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             Flux2ProcessImagesInputStep,
             Flux2TextInputStep,
         )
-        from .modular_blocks import (
+        from .modular_blocks_flux2 import (
             ALL_BLOCKS,
             AUTO_BLOCKS,
             IMAGE_CONDITIONED_BLOCKS,
@@ -93,10 +94,14 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             TEXT2IMAGE_BLOCKS,
             Flux2AutoBlocks,
             Flux2AutoVaeEncoderStep,
-            Flux2BeforeDenoiseStep,
+            Flux2CoreDenoiseStep,
             Flux2VaeEncoderSequentialStep,
         )
-        from .modular_pipeline import Flux2ModularPipeline
+        from .modular_blocks_flux2_klein import (
+            Flux2KleinAutoBlocks,
+            Flux2KleinBaseAutoBlocks,
+        )
+        from .modular_pipeline import Flux2KleinModularPipeline, Flux2ModularPipeline
 else:
     import sys
 

src/diffusers/modular_pipelines/flux2/before_denoise.py

@@ -129,17 +129,9 @@ class Flux2SetTimestepsStep(ModularPipelineBlocks):
             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
@@ -151,13 +143,12 @@ class Flux2SetTimestepsStep(ModularPipelineBlocks):
                 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
+        device = components._execution_device
 
         scheduler = components.scheduler
 
@@ -183,7 +174,7 @@ class Flux2SetTimestepsStep(ModularPipelineBlocks):
         timesteps, num_inference_steps = retrieve_timesteps(
             scheduler,
             num_inference_steps,
-            block_state.device,
+            device,
             timesteps=timesteps,
             sigmas=sigmas,
             mu=mu,
@@ -191,11 +182,6 @@ class Flux2SetTimestepsStep(ModularPipelineBlocks):
         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)
@@ -353,7 +339,6 @@ class Flux2RoPEInputsStep(ModularPipelineBlocks):
     def inputs(self) -> List[InputParam]:
         return [
             InputParam(name="prompt_embeds", required=True),
-            InputParam(name="latent_ids"),
         ]
 
     @property
@@ -365,12 +350,6 @@ class Flux2RoPEInputsStep(ModularPipelineBlocks):
                 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
@@ -403,6 +382,72 @@ class Flux2RoPEInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+class Flux2KleinBaseRoPEInputsStep(ModularPipelineBlocks):
+    model_name = "flux2-klein"
+
+    @property
+    def description(self) -> str:
+        return "Step that prepares the 4D RoPE position IDs for Flux2-Klein base model 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="negative_prompt_embeds", required=False),
+        ]
+
+    @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="negative_txt_ids",
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="4D position IDs (T, H, W, L) for negative text tokens, 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)
+
+        block_state.negative_txt_ids = None
+        if block_state.negative_prompt_embeds is not None:
+            block_state.negative_txt_ids = self._prepare_text_ids(block_state.negative_prompt_embeds)
+            block_state.negative_txt_ids = block_state.negative_txt_ids.to(device)
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
 class Flux2PrepareImageLatentsStep(ModularPipelineBlocks):
     model_name = "flux2"
 
@@ -506,3 +551,42 @@ class Flux2PrepareImageLatentsStep(ModularPipelineBlocks):
 
         self.set_block_state(state, block_state)
         return components, state
+
+
+class Flux2PrepareGuidanceStep(ModularPipelineBlocks):
+    model_name = "flux2"
+
+    @property
+    def description(self) -> str:
+        return "Step that prepares the guidance scale tensor for Flux2 inference"
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("guidance_scale", default=4.0),
+            InputParam("num_images_per_prompt", default=1),
+            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("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)
+        device = components._execution_device
+        batch_size = block_state.batch_size * block_state.num_images_per_prompt
+        guidance = torch.full([1], block_state.guidance_scale, device=device, dtype=torch.float32)
+        guidance = guidance.expand(batch_size)
+        block_state.guidance = guidance
+
+        self.set_block_state(state, block_state)
+        return components, state

src/diffusers/modular_pipelines/flux2/decoders.py

@@ -29,29 +29,16 @@ from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class Flux2DecodeStep(ModularPipelineBlocks):
+class Flux2UnpackLatentsStep(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"
+        return "Step that unpacks the latents from the denoising step"
 
     @property
     def inputs(self) -> List[Tuple[str, Any]]:
         return [
-            InputParam("output_type", default="pil"),
             InputParam(
                 "latents",
                 required=True,
@@ -70,9 +57,9 @@ class Flux2DecodeStep(ModularPipelineBlocks):
     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",
+                "latents",
+                type_hint=torch.Tensor,
+                description="The denoise latents from denoising step, unpacked with position IDs.",
             )
         ]
 
@@ -107,6 +94,62 @@ class Flux2DecodeStep(ModularPipelineBlocks):
 
         return torch.stack(x_list, dim=0)
 
+    @torch.no_grad()
+    def __call__(self, components, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+
+        latents = block_state.latents
+        latent_ids = block_state.latent_ids
+
+        latents = self._unpack_latents_with_ids(latents, latent_ids)
+
+        block_state.latents = latents
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+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",
+            ),
+        ]
+
+    @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 _unpatchify_latents(latents):
         """Convert patchified latents back to regular format."""
@@ -121,26 +164,20 @@ class Flux2DecodeStep(ModularPipelineBlocks):
         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 = block_state.latents
 
-            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_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)
 
-            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
-            )
+        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

@@ -16,6 +16,8 @@ from typing import Any, List, Tuple
 
 import torch
 
+from ...configuration_utils import FrozenDict
+from ...guiders import ClassifierFreeGuidance
 from ...models import Flux2Transformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import is_torch_xla_available, logging
@@ -25,8 +27,8 @@ from ..modular_pipeline import (
     ModularPipelineBlocks,
     PipelineState,
 )
-from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
-from .modular_pipeline import Flux2ModularPipeline
+from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
+from .modular_pipeline import Flux2KleinModularPipeline, Flux2ModularPipeline
 
 
 if is_torch_xla_available():
@@ -134,6 +136,229 @@ class Flux2LoopDenoiser(ModularPipelineBlocks):
         return components, block_state
 
 
+# same as Flux2LoopDenoiser but guidance=None
+class Flux2KleinLoopDenoiser(ModularPipelineBlocks):
+    model_name = "flux2-klein"
+
+    @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(
+                "prompt_embeds",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Text embeddings from Qwen3",
+            ),
+            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: Flux2KleinModularPipeline, 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=None,
+            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
+
+
+# support CFG for Flux2-Klein base model
+class Flux2KleinBaseLoopDenoiser(ModularPipelineBlocks):
+    model_name = "flux2-klein"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("transformer", Flux2Transformer2DModel),
+            ComponentSpec(
+                "guider",
+                ClassifierFreeGuidance,
+                config=FrozenDict({"guidance_scale": 4.0}),
+                default_creation_method="from_config",
+            ),
+        ]
+
+    @property
+    def expected_configs(self) -> List[ConfigSpec]:
+        return [
+            ConfigSpec(name="is_distilled", default=False),
+        ]
+
+    @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(
+                "prompt_embeds",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Text embeddings from Qwen3",
+            ),
+            InputParam(
+                "negative_prompt_embeds",
+                required=False,
+                type_hint=torch.Tensor,
+                description="Negative text embeddings from Qwen3",
+            ),
+            InputParam(
+                "txt_ids",
+                required=True,
+                type_hint=torch.Tensor,
+                description="4D position IDs for text tokens (T, H, W, L)",
+            ),
+            InputParam(
+                "negative_txt_ids",
+                required=False,
+                type_hint=torch.Tensor,
+                description="4D position IDs for negative 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: Flux2KleinModularPipeline, 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)
+
+        guider_inputs = {
+            "encoder_hidden_states": (
+                getattr(block_state, "prompt_embeds", None),
+                getattr(block_state, "negative_prompt_embeds", None),
+            ),
+            "txt_ids": (
+                getattr(block_state, "txt_ids", None),
+                getattr(block_state, "negative_txt_ids", None),
+            ),
+        }
+
+        components.guider.set_state(step=i, num_inference_steps=block_state.num_inference_steps, timestep=t)
+        guider_state = components.guider.prepare_inputs(guider_inputs)
+
+        for guider_state_batch in guider_state:
+            components.guider.prepare_models(components.transformer)
+            cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
+
+            noise_pred = components.transformer(
+                hidden_states=latent_model_input,
+                timestep=timestep / 1000,
+                guidance=None,
+                img_ids=img_ids,
+                joint_attention_kwargs=block_state.joint_attention_kwargs,
+                return_dict=False,
+                **cond_kwargs,
+            )[0]
+            guider_state_batch.noise_pred = noise_pred[:, : latents.size(1)]
+            components.guider.cleanup_models(components.transformer)
+
+        # perform guidance
+        block_state.noise_pred = components.guider(guider_state)[0]
+
+        return components, block_state
+
+
 class Flux2LoopAfterDenoiser(ModularPipelineBlocks):
     model_name = "flux2"
 
@@ -250,3 +475,35 @@ class Flux2DenoiseStep(Flux2DenoiseLoopWrapper):
             " - `Flux2LoopAfterDenoiser`\n"
             "This block supports both text-to-image and image-conditioned generation."
         )
+
+
+class Flux2KleinDenoiseStep(Flux2DenoiseLoopWrapper):
+    block_classes = [Flux2KleinLoopDenoiser, 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"
+            " - `Flux2KleinLoopDenoiser`\n"
+            " - `Flux2LoopAfterDenoiser`\n"
+            "This block supports both text-to-image and image-conditioned generation."
+        )
+
+
+class Flux2KleinBaseDenoiseStep(Flux2DenoiseLoopWrapper):
+    block_classes = [Flux2KleinBaseLoopDenoiser, 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"
+            " - `Flux2KleinBaseLoopDenoiser`\n"
+            " - `Flux2LoopAfterDenoiser`\n"
+            "This block supports both text-to-image and image-conditioned generation."
+        )

src/diffusers/modular_pipelines/flux2/encoders.py

@@ -15,13 +15,15 @@
 from typing import List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoProcessor, Mistral3ForConditionalGeneration
+from transformers import AutoProcessor, Mistral3ForConditionalGeneration, Qwen2TokenizerFast, Qwen3ForCausalLM
 
+from ...configuration_utils import FrozenDict
+from ...guiders import ClassifierFreeGuidance
 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
+from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
+from .modular_pipeline import Flux2KleinModularPipeline, Flux2ModularPipeline
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -79,10 +81,8 @@ class Flux2TextEncoderStep(ModularPipelineBlocks):
     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
@@ -99,14 +99,7 @@ class Flux2TextEncoderStep(ModularPipelineBlocks):
     @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)):
+        if 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
@@ -165,10 +158,6 @@ class Flux2TextEncoderStep(ModularPipelineBlocks):
 
         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 = ""
@@ -205,7 +194,6 @@ class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
     def inputs(self) -> List[InputParam]:
         return [
             InputParam("prompt"),
-            InputParam("prompt_embeds", type_hint=torch.Tensor, required=False),
         ]
 
     @property
@@ -222,15 +210,8 @@ class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
     @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)}")
+        if 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(block_state.prompt)}")
 
     @torch.no_grad()
     def __call__(self, components: Flux2ModularPipeline, state: PipelineState) -> PipelineState:
@@ -244,10 +225,6 @@ class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
 
         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 = ""
@@ -270,6 +247,289 @@ class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
         return components, state
 
 
+class Flux2KleinTextEncoderStep(ModularPipelineBlocks):
+    model_name = "flux2-klein"
+
+    @property
+    def description(self) -> str:
+        return "Text Encoder step that generates text embeddings using Qwen3 to guide the image generation"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("text_encoder", Qwen3ForCausalLM),
+            ComponentSpec("tokenizer", Qwen2TokenizerFast),
+        ]
+
+    @property
+    def expected_configs(self) -> List[ConfigSpec]:
+        return [
+            ConfigSpec(name="is_distilled", default=True),
+        ]
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("prompt"),
+            InputParam("max_sequence_length", type_hint=int, default=512, required=False),
+            InputParam("text_encoder_out_layers", type_hint=Tuple[int], default=(9, 18, 27), 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 qwen3 used to guide the image generation",
+            ),
+        ]
+
+    @staticmethod
+    def check_inputs(block_state):
+        prompt = block_state.prompt
+
+        if 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
+    # Copied from diffusers.pipelines.flux2.pipeline_flux2_klein.Flux2KleinPipeline._get_qwen3_prompt_embeds
+    def _get_qwen3_prompt_embeds(
+        text_encoder: Qwen3ForCausalLM,
+        tokenizer: Qwen2TokenizerFast,
+        prompt: Union[str, List[str]],
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+        hidden_states_layers: List[int] = (9, 18, 27),
+    ):
+        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
+
+        all_input_ids = []
+        all_attention_masks = []
+
+        for single_prompt in prompt:
+            messages = [{"role": "user", "content": single_prompt}]
+            text = tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=False,
+            )
+            inputs = tokenizer(
+                text,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_sequence_length,
+            )
+
+            all_input_ids.append(inputs["input_ids"])
+            all_attention_masks.append(inputs["attention_mask"])
+
+        input_ids = torch.cat(all_input_ids, dim=0).to(device)
+        attention_mask = torch.cat(all_attention_masks, dim=0).to(device)
+
+        # Forward pass through the model
+        output = text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            use_cache=False,
+        )
+
+        # Only use outputs from intermediate layers and stack them
+        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: Flux2KleinModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        self.check_inputs(block_state)
+
+        device = components._execution_device
+
+        prompt = block_state.prompt
+        if prompt is None:
+            prompt = ""
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        block_state.prompt_embeds = self._get_qwen3_prompt_embeds(
+            text_encoder=components.text_encoder,
+            tokenizer=components.tokenizer,
+            prompt=prompt,
+            device=device,
+            max_sequence_length=block_state.max_sequence_length,
+            hidden_states_layers=block_state.text_encoder_out_layers,
+        )
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class Flux2KleinBaseTextEncoderStep(ModularPipelineBlocks):
+    model_name = "flux2-klein"
+
+    @property
+    def description(self) -> str:
+        return "Text Encoder step that generates text embeddings using Qwen3 to guide the image generation"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("text_encoder", Qwen3ForCausalLM),
+            ComponentSpec("tokenizer", Qwen2TokenizerFast),
+            ComponentSpec(
+                "guider",
+                ClassifierFreeGuidance,
+                config=FrozenDict({"guidance_scale": 4.0}),
+                default_creation_method="from_config",
+            ),
+        ]
+
+    @property
+    def expected_configs(self) -> List[ConfigSpec]:
+        return [
+            ConfigSpec(name="is_distilled", default=False),
+        ]
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("prompt"),
+            InputParam("max_sequence_length", type_hint=int, default=512, required=False),
+            InputParam("text_encoder_out_layers", type_hint=Tuple[int], default=(9, 18, 27), 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 qwen3 used to guide the image generation",
+            ),
+            OutputParam(
+                "negative_prompt_embeds",
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="Negative text embeddings from qwen3 used to guide the image generation",
+            ),
+        ]
+
+    @staticmethod
+    def check_inputs(block_state):
+        prompt = block_state.prompt
+
+        if 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
+    # Copied from diffusers.pipelines.flux2.pipeline_flux2_klein.Flux2KleinPipeline._get_qwen3_prompt_embeds
+    def _get_qwen3_prompt_embeds(
+        text_encoder: Qwen3ForCausalLM,
+        tokenizer: Qwen2TokenizerFast,
+        prompt: Union[str, List[str]],
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+        hidden_states_layers: List[int] = (9, 18, 27),
+    ):
+        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
+
+        all_input_ids = []
+        all_attention_masks = []
+
+        for single_prompt in prompt:
+            messages = [{"role": "user", "content": single_prompt}]
+            text = tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=False,
+            )
+            inputs = tokenizer(
+                text,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_sequence_length,
+            )
+
+            all_input_ids.append(inputs["input_ids"])
+            all_attention_masks.append(inputs["attention_mask"])
+
+        input_ids = torch.cat(all_input_ids, dim=0).to(device)
+        attention_mask = torch.cat(all_attention_masks, dim=0).to(device)
+
+        # Forward pass through the model
+        output = text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            use_cache=False,
+        )
+
+        # Only use outputs from intermediate layers and stack them
+        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: Flux2KleinModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        self.check_inputs(block_state)
+
+        device = components._execution_device
+
+        prompt = block_state.prompt
+        if prompt is None:
+            prompt = ""
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        block_state.prompt_embeds = self._get_qwen3_prompt_embeds(
+            text_encoder=components.text_encoder,
+            tokenizer=components.tokenizer,
+            prompt=prompt,
+            device=device,
+            max_sequence_length=block_state.max_sequence_length,
+            hidden_states_layers=block_state.text_encoder_out_layers,
+        )
+
+        if components.requires_unconditional_embeds:
+            negative_prompt = [""] * len(prompt)
+            block_state.negative_prompt_embeds = self._get_qwen3_prompt_embeds(
+                text_encoder=components.text_encoder,
+                tokenizer=components.tokenizer,
+                prompt=negative_prompt,
+                device=device,
+                max_sequence_length=block_state.max_sequence_length,
+                hidden_states_layers=block_state.text_encoder_out_layers,
+            )
+        else:
+            block_state.negative_prompt_embeds = None
+
+        self.set_block_state(state, block_state)
+        return components, state
+
+
 class Flux2VaeEncoderStep(ModularPipelineBlocks):
     model_name = "flux2"
 

src/diffusers/modular_pipelines/flux2/inputs.py

@@ -47,7 +47,7 @@ class Flux2TextInputStep(ModularPipelineBlocks):
                 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.",
+                description="Pre-generated text embeddings. Can be generated from text_encoder step.",
             ),
         ]
 
@@ -89,6 +89,90 @@ class Flux2TextInputStep(ModularPipelineBlocks):
         return components, state
 
 
+class Flux2KleinBaseTextInputStep(ModularPipelineBlocks):
+    model_name = "flux2-klein"
+
+    @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. Can be generated from text_encoder step.",
+            ),
+            InputParam(
+                "negative_prompt_embeds",
+                required=False,
+                kwargs_type="denoiser_input_fields",
+                type_hint=torch.Tensor,
+                description="Pre-generated negative text embeddings. 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",
+            ),
+            OutputParam(
+                "negative_prompt_embeds",
+                type_hint=torch.Tensor,
+                kwargs_type="denoiser_input_fields",
+                description="Negative 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
+        )
+
+        if block_state.negative_prompt_embeds is not None:
+            _, seq_len, _ = block_state.negative_prompt_embeds.shape
+            block_state.negative_prompt_embeds = block_state.negative_prompt_embeds.repeat(
+                1, block_state.num_images_per_prompt, 1
+            )
+            block_state.negative_prompt_embeds = block_state.negative_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"
 

src/diffusers/modular_pipelines/flux2/modular_blocks_flux2.py

@@ -12,16 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import List
+
+import PIL.Image
+import torch
+
 from ...utils import logging
 from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
-from ..modular_pipeline_utils import InsertableDict
+from ..modular_pipeline_utils import InsertableDict, OutputParam
 from .before_denoise import (
+    Flux2PrepareGuidanceStep,
     Flux2PrepareImageLatentsStep,
     Flux2PrepareLatentsStep,
     Flux2RoPEInputsStep,
     Flux2SetTimestepsStep,
 )
-from .decoders import Flux2DecodeStep
+from .decoders import Flux2DecodeStep, Flux2UnpackLatentsStep
 from .denoise import Flux2DenoiseStep
 from .encoders import (
     Flux2RemoteTextEncoderStep,
@@ -41,7 +47,6 @@ Flux2VaeEncoderBlocks = InsertableDict(
     [
         ("preprocess", Flux2ProcessImagesInputStep()),
         ("encode", Flux2VaeEncoderStep()),
-        ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
     ]
 )
 
@@ -72,33 +77,56 @@ class Flux2AutoVaeEncoderStep(AutoPipelineBlocks):
         )
 
 
-Flux2BeforeDenoiseBlocks = InsertableDict(
+Flux2CoreDenoiseBlocks = InsertableDict(
     [
+        ("input", Flux2TextInputStep()),
+        ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
         ("prepare_latents", Flux2PrepareLatentsStep()),
         ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_guidance", Flux2PrepareGuidanceStep()),
         ("prepare_rope_inputs", Flux2RoPEInputsStep()),
+        ("denoise", Flux2DenoiseStep()),
+        ("after_denoise", Flux2UnpackLatentsStep()),
     ]
 )
 
 
-class Flux2BeforeDenoiseStep(SequentialPipelineBlocks):
+class Flux2CoreDenoiseStep(SequentialPipelineBlocks):
     model_name = "flux2"
 
-    block_classes = Flux2BeforeDenoiseBlocks.values()
-    block_names = Flux2BeforeDenoiseBlocks.keys()
+    block_classes = Flux2CoreDenoiseBlocks.values()
+    block_names = Flux2CoreDenoiseBlocks.keys()
 
     @property
     def description(self):
-        return "Before denoise step that prepares the inputs for the denoise step in Flux2 generation."
+        return (
+            "Core denoise step that performs the denoising process for Flux2-dev.\n"
+            " - `Flux2TextInputStep` (input) standardizes the text inputs (prompt_embeds) for the denoising step.\n"
+            " - `Flux2PrepareImageLatentsStep` (prepare_image_latents) prepares the image latents and image_latent_ids for the denoising step.\n"
+            " - `Flux2PrepareLatentsStep` (prepare_latents) prepares the initial latents (latents) and latent_ids for the denoising step.\n"
+            " - `Flux2SetTimestepsStep` (set_timesteps) sets the timesteps for the denoising step.\n"
+            " - `Flux2PrepareGuidanceStep` (prepare_guidance) prepares the guidance tensor for the denoising step.\n"
+            " - `Flux2RoPEInputsStep` (prepare_rope_inputs) prepares the RoPE inputs (txt_ids) for the denoising step.\n"
+            " - `Flux2DenoiseStep` (denoise) iteratively denoises the latents.\n"
+            " - `Flux2UnpackLatentsStep` (after_denoise) unpacks the latents from the denoising step.\n"
+        )
+
+    @property
+    def outputs(self):
+        return [
+            OutputParam(
+                name="latents",
+                type_hint=torch.Tensor,
+                description="The latents from the denoising step.",
+            )
+        ]
 
 
 AUTO_BLOCKS = InsertableDict(
     [
         ("text_encoder", Flux2TextEncoderStep()),
-        ("text_input", Flux2TextInputStep()),
-        ("vae_image_encoder", Flux2AutoVaeEncoderStep()),
-        ("before_denoise", Flux2BeforeDenoiseStep()),
-        ("denoise", Flux2DenoiseStep()),
+        ("vae_encoder", Flux2AutoVaeEncoderStep()),
+        ("denoise", Flux2CoreDenoiseStep()),
         ("decode", Flux2DecodeStep()),
     ]
 )
@@ -107,10 +135,8 @@ AUTO_BLOCKS = InsertableDict(
 REMOTE_AUTO_BLOCKS = InsertableDict(
     [
         ("text_encoder", Flux2RemoteTextEncoderStep()),
-        ("text_input", Flux2TextInputStep()),
-        ("vae_image_encoder", Flux2AutoVaeEncoderStep()),
-        ("before_denoise", Flux2BeforeDenoiseStep()),
-        ("denoise", Flux2DenoiseStep()),
+        ("vae_encoder", Flux2AutoVaeEncoderStep()),
+        ("denoise", Flux2CoreDenoiseStep()),
         ("decode", Flux2DecodeStep()),
     ]
 )
@@ -130,6 +156,16 @@ class Flux2AutoBlocks(SequentialPipelineBlocks):
             "- For image-conditioned generation, you need to provide `image` (list of PIL images)."
         )
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam(
+                name="images",
+                type_hint=List[PIL.Image.Image],
+                description="The images from the decoding step.",
+            )
+        ]
+
 
 TEXT2IMAGE_BLOCKS = InsertableDict(
     [
@@ -137,8 +173,10 @@ TEXT2IMAGE_BLOCKS = InsertableDict(
         ("text_input", Flux2TextInputStep()),
         ("prepare_latents", Flux2PrepareLatentsStep()),
         ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_guidance", Flux2PrepareGuidanceStep()),
         ("prepare_rope_inputs", Flux2RoPEInputsStep()),
         ("denoise", Flux2DenoiseStep()),
+        ("after_denoise", Flux2UnpackLatentsStep()),
         ("decode", Flux2DecodeStep()),
     ]
 )
@@ -152,8 +190,10 @@ IMAGE_CONDITIONED_BLOCKS = InsertableDict(
         ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
         ("prepare_latents", Flux2PrepareLatentsStep()),
         ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_guidance", Flux2PrepareGuidanceStep()),
         ("prepare_rope_inputs", Flux2RoPEInputsStep()),
         ("denoise", Flux2DenoiseStep()),
+        ("after_denoise", Flux2UnpackLatentsStep()),
         ("decode", Flux2DecodeStep()),
     ]
 )

src/diffusers/modular_pipelines/flux2/modular_blocks_flux2_klein.py

@@ -0,0 +1,232 @@
+# 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 PIL.Image
+import torch
+
+from ...utils import logging
+from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
+from ..modular_pipeline_utils import InsertableDict, OutputParam
+from .before_denoise import (
+    Flux2KleinBaseRoPEInputsStep,
+    Flux2PrepareImageLatentsStep,
+    Flux2PrepareLatentsStep,
+    Flux2RoPEInputsStep,
+    Flux2SetTimestepsStep,
+)
+from .decoders import Flux2DecodeStep, Flux2UnpackLatentsStep
+from .denoise import Flux2KleinBaseDenoiseStep, Flux2KleinDenoiseStep
+from .encoders import (
+    Flux2KleinBaseTextEncoderStep,
+    Flux2KleinTextEncoderStep,
+    Flux2VaeEncoderStep,
+)
+from .inputs import (
+    Flux2KleinBaseTextInputStep,
+    Flux2ProcessImagesInputStep,
+    Flux2TextInputStep,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+################
+# VAE encoder
+################
+
+Flux2KleinVaeEncoderBlocks = InsertableDict(
+    [
+        ("preprocess", Flux2ProcessImagesInputStep()),
+        ("encode", Flux2VaeEncoderStep()),
+    ]
+)
+
+
+class Flux2KleinVaeEncoderSequentialStep(SequentialPipelineBlocks):
+    model_name = "flux2"
+
+    block_classes = Flux2KleinVaeEncoderBlocks.values()
+    block_names = Flux2KleinVaeEncoderBlocks.keys()
+
+    @property
+    def description(self) -> str:
+        return "VAE encoder step that preprocesses and encodes the image inputs into their latent representations."
+
+
+class Flux2KleinAutoVaeEncoderStep(AutoPipelineBlocks):
+    block_classes = [Flux2KleinVaeEncoderSequentialStep]
+    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"
+            " - `Flux2KleinVaeEncoderSequentialStep` is used when `image` is provided.\n"
+            " - If `image` is not provided, step will be skipped."
+        )
+
+
+###
+### Core denoise
+###
+
+Flux2KleinCoreDenoiseBlocks = InsertableDict(
+    [
+        ("input", Flux2TextInputStep()),
+        ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
+        ("prepare_latents", Flux2PrepareLatentsStep()),
+        ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_rope_inputs", Flux2RoPEInputsStep()),
+        ("denoise", Flux2KleinDenoiseStep()),
+        ("after_denoise", Flux2UnpackLatentsStep()),
+    ]
+)
+
+
+class Flux2KleinCoreDenoiseStep(SequentialPipelineBlocks):
+    model_name = "flux2-klein"
+
+    block_classes = Flux2KleinCoreDenoiseBlocks.values()
+    block_names = Flux2KleinCoreDenoiseBlocks.keys()
+
+    @property
+    def description(self):
+        return (
+            "Core denoise step that performs the denoising process for Flux2-Klein (distilled model).\n"
+            " - `Flux2KleinTextInputStep` (input) standardizes the text inputs (prompt_embeds) for the denoising step.\n"
+            " - `Flux2PrepareImageLatentsStep` (prepare_image_latents) prepares the image latents  and image_latent_ids for the denoising step.\n"
+            " - `Flux2PrepareLatentsStep` (prepare_latents) prepares the initial latents (latents) and latent_ids for the denoising step.\n"
+            " - `Flux2SetTimestepsStep` (set_timesteps) sets the timesteps for the denoising step.\n"
+            " - `Flux2RoPEInputsStep` (prepare_rope_inputs) prepares the RoPE inputs (txt_ids) for the denoising step.\n"
+            " - `Flux2KleinDenoiseStep` (denoise) iteratively denoises the latents.\n"
+            " - `Flux2UnpackLatentsStep` (after_denoise) unpacks the latents from the denoising step.\n"
+        )
+
+    @property
+    def outputs(self):
+        return [
+            OutputParam(
+                name="latents",
+                type_hint=torch.Tensor,
+                description="The latents from the denoising step.",
+            )
+        ]
+
+
+Flux2KleinBaseCoreDenoiseBlocks = InsertableDict(
+    [
+        ("input", Flux2KleinBaseTextInputStep()),
+        ("prepare_latents", Flux2PrepareLatentsStep()),
+        ("prepare_image_latents", Flux2PrepareImageLatentsStep()),
+        ("set_timesteps", Flux2SetTimestepsStep()),
+        ("prepare_rope_inputs", Flux2KleinBaseRoPEInputsStep()),
+        ("denoise", Flux2KleinBaseDenoiseStep()),
+        ("after_denoise", Flux2UnpackLatentsStep()),
+    ]
+)
+
+
+class Flux2KleinBaseCoreDenoiseStep(SequentialPipelineBlocks):
+    model_name = "flux2-klein"
+    block_classes = Flux2KleinBaseCoreDenoiseBlocks.values()
+    block_names = Flux2KleinBaseCoreDenoiseBlocks.keys()
+
+    @property
+    def description(self):
+        return "Core denoise step that performs the denoising process for Flux2-Klein (base model)."
+        return (
+            "Core denoise step that performs the denoising process for Flux2-Klein (base model).\n"
+            " - `Flux2KleinBaseTextInputStep` (input) standardizes the text inputs (prompt_embeds + negative_prompt_embeds) for the denoising step.\n"
+            " - `Flux2PrepareImageLatentsStep` (prepare_image_latents) prepares the image latents and image_latent_ids for the denoising step.\n"
+            " - `Flux2PrepareLatentsStep` (prepare_latents) prepares the initial latents (latents) and latent_ids for the denoising step.\n"
+            " - `Flux2SetTimestepsStep` (set_timesteps) sets the timesteps for the denoising step.\n"
+            " - `Flux2KleinBaseRoPEInputsStep` (prepare_rope_inputs) prepares the RoPE inputs (txt_ids + negative_txt_ids) for the denoising step.\n"
+            " - `Flux2KleinBaseDenoiseStep` (denoise) iteratively denoises the latents using Classifier-Free Guidance.\n"
+            " - `Flux2UnpackLatentsStep` (after_denoise) unpacks the latents from the denoising step.\n"
+        )
+
+    @property
+    def outputs(self):
+        return [
+            OutputParam(
+                name="latents",
+                type_hint=torch.Tensor,
+                description="The latents from the denoising step.",
+            )
+        ]
+
+
+###
+### Auto blocks
+###
+class Flux2KleinAutoBlocks(SequentialPipelineBlocks):
+    model_name = "flux2-klein"
+    block_classes = [
+        Flux2KleinTextEncoderStep(),
+        Flux2KleinAutoVaeEncoderStep(),
+        Flux2KleinCoreDenoiseStep(),
+        Flux2DecodeStep(),
+    ]
+    block_names = ["text_encoder", "vae_encoder", "denoise", "decode"]
+
+    @property
+    def description(self):
+        return (
+            "Auto blocks that perform the text-to-image and image-conditioned generation using Flux2-Klein.\n"
+            + " - for image-conditioned generation, you need to provide `image` (list of PIL images).\n"
+            + " - for text-to-image generation, all you need to provide is `prompt`.\n"
+        )
+
+    @property
+    def outputs(self):
+        return [
+            OutputParam(
+                name="images",
+                type_hint=List[PIL.Image.Image],
+                description="The images from the decoding step.",
+            )
+        ]
+
+
+class Flux2KleinBaseAutoBlocks(SequentialPipelineBlocks):
+    model_name = "flux2-klein"
+    block_classes = [
+        Flux2KleinBaseTextEncoderStep(),
+        Flux2KleinAutoVaeEncoderStep(),
+        Flux2KleinBaseCoreDenoiseStep(),
+        Flux2DecodeStep(),
+    ]
+    block_names = ["text_encoder", "vae_encoder", "denoise", "decode"]
+
+    @property
+    def description(self):
+        return (
+            "Auto blocks that perform the text-to-image and image-conditioned generation using Flux2-Klein (base model).\n"
+            + " - for image-conditioned generation, you need to provide `image` (list of PIL images).\n"
+            + " - for text-to-image generation, all you need to provide is `prompt`.\n"
+        )
+
+    @property
+    def outputs(self):
+        return [
+            OutputParam(
+                name="images",
+                type_hint=List[PIL.Image.Image],
+                description="The images from the decoding step.",
+            )
+        ]

src/diffusers/modular_pipelines/flux2/modular_pipeline.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 
+from typing import Any, Dict, Optional
+
 from ...loaders import Flux2LoraLoaderMixin
 from ...utils import logging
 from ..modular_pipeline import ModularPipeline
@@ -55,3 +57,56 @@ class Flux2ModularPipeline(ModularPipeline, Flux2LoraLoaderMixin):
         if getattr(self, "transformer", None):
             num_channels_latents = self.transformer.config.in_channels // 4
         return num_channels_latents
+
+
+class Flux2KleinModularPipeline(ModularPipeline, Flux2LoraLoaderMixin):
+    """
+    A ModularPipeline for Flux2-Klein.
+
+    > [!WARNING] > This is an experimental feature and is likely to change in the future.
+    """
+
+    default_blocks_name = "Flux2KleinBaseAutoBlocks"
+
+    def get_default_blocks_name(self, config_dict: Optional[Dict[str, Any]]) -> Optional[str]:
+        if config_dict is not None and "is_distilled" in config_dict and config_dict["is_distilled"]:
+            return "Flux2KleinAutoBlocks"
+        else:
+            return "Flux2KleinBaseAutoBlocks"
+
+    @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
+
+    @property
+    def requires_unconditional_embeds(self):
+        if hasattr(self.config, "is_distilled") and self.config.is_distilled:
+            return False
+
+        requires_unconditional_embeds = False
+        if hasattr(self, "guider") and self.guider is not None:
+            requires_unconditional_embeds = self.guider._enabled and self.guider.num_conditions > 1
+
+        return requires_unconditional_embeds

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -59,6 +59,7 @@ MODULAR_PIPELINE_MAPPING = OrderedDict(
         ("flux", "FluxModularPipeline"),
         ("flux-kontext", "FluxKontextModularPipeline"),
         ("flux2", "Flux2ModularPipeline"),
+        ("flux2-klein", "Flux2KleinModularPipeline"),
         ("qwenimage", "QwenImageModularPipeline"),
         ("qwenimage-edit", "QwenImageEditModularPipeline"),
         ("qwenimage-edit-plus", "QwenImageEditPlusModularPipeline"),

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -22,7 +22,6 @@ import flax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import logging
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -33,9 +32,6 @@ from .scheduling_utils_flax import (
 )
 
 
-logger = logging.get_logger(__name__)
-
-
 @flax.struct.dataclass
 class DDIMSchedulerState:
     common: CommonSchedulerState
@@ -129,10 +125,6 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type: str = "epsilon",
         dtype: jnp.dtype = jnp.float32,
     ):
-        logger.warning(
-            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
-            "recommend migrating to PyTorch classes or pinning your version of Diffusers."
-        )
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:
@@ -160,10 +152,7 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         )
 
     def scale_model_input(
-        self,
-        state: DDIMSchedulerState,
-        sample: jnp.ndarray,
-        timestep: Optional[int] = None,
+        self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
     ) -> jnp.ndarray:
         """
         Args:
@@ -201,9 +190,7 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
     def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep):
         alpha_prod_t = state.common.alphas_cumprod[timestep]
         alpha_prod_t_prev = jnp.where(
-            prev_timestep >= 0,
-            state.common.alphas_cumprod[prev_timestep],
-            state.final_alpha_cumprod,
+            prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod
         )
         beta_prod_t = 1 - alpha_prod_t
         beta_prod_t_prev = 1 - alpha_prod_t_prev

src/diffusers/schedulers/scheduling_ddim_inverse.py

@@ -99,7 +99,7 @@ def betas_for_alpha_bar(
 
 
 # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
-def rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:
+def rescale_zero_terminal_snr(betas):
     """
     Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
 
@@ -187,14 +187,14 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         num_train_timesteps: int = 1000,
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
-        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "linear",
+        beta_schedule: str = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         clip_sample: bool = True,
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
-        prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
+        prediction_type: str = "epsilon",
         clip_sample_range: float = 1.0,
-        timestep_spacing: Literal["leading", "trailing"] = "leading",
+        timestep_spacing: str = "leading",
         rescale_betas_zero_snr: bool = False,
         **kwargs,
     ):
@@ -210,15 +210,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(
-                    beta_start**0.5,
-                    beta_end**0.5,
-                    num_train_timesteps,
-                    dtype=torch.float32,
-                )
-                ** 2
-            )
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -264,11 +256,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def set_timesteps(
-        self,
-        num_inference_steps: int,
-        device: Optional[Union[str, torch.device]] = None,
-    ) -> None:
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -320,10 +308,20 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         Args:
             model_output (`torch.Tensor`):
                 The direct output from learned diffusion model.
-            timestep (`int`):
+            timestep (`float`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
+            eta (`float`):
+                The weight of noise for added noise in diffusion step.
+            use_clipped_model_output (`bool`, defaults to `False`):
+                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
+                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
+                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
+                `use_clipped_model_output` has no effect.
+            variance_noise (`torch.Tensor`):
+                Alternative to generating noise with `generator` by directly providing the noise for the variance
+                itself. Useful for methods such as [`CycleDiffusion`].
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or
                 `tuple`.
@@ -337,8 +335,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         # 1. get previous step value (=t+1)
         prev_timestep = timestep
         timestep = min(
-            timestep - self.config.num_train_timesteps // self.num_inference_steps,
-            self.config.num_train_timesteps - 1,
+            timestep - self.config.num_train_timesteps // self.num_inference_steps, self.config.num_train_timesteps - 1
         )
 
         # 2. compute alphas, betas
@@ -381,5 +378,5 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
 
-    def __len__(self) -> int:
+    def __len__(self):
         return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -226,7 +226,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         time_shift_type: Literal["exponential"] = "exponential",
         sigma_min: Optional[float] = None,
         sigma_max: Optional[float] = None,
-        shift_terminal: Optional[float] = None,
     ) -> None:
         if self.config.use_beta_sigmas and not is_scipy_available():
             raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
@@ -246,8 +245,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
-        if shift_terminal is not None and not use_flow_sigmas:
-            raise ValueError("`shift_terminal` is only supported when `use_flow_sigmas=True`.")
 
         if rescale_betas_zero_snr:
             self.betas = rescale_zero_terminal_snr(self.betas)
@@ -316,12 +313,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = begin_index
 
     def set_timesteps(
-        self,
-        num_inference_steps: Optional[int] = None,
-        device: Union[str, torch.device] = None,
-        sigmas: Optional[List[float]] = None,
-        mu: Optional[float] = None,
-    ):
+        self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None, mu: Optional[float] = None
+    ) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -330,24 +323,13 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 The number of diffusion steps used when generating samples with a pre-trained model.
             device (`str` or `torch.device`, *optional*):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-            sigmas (`List[float]`, *optional*):
-                Custom values for sigmas to be used for each diffusion step. If `None`, the sigmas are computed
-                automatically.
             mu (`float`, *optional*):
                 Optional mu parameter for dynamic shifting when using exponential time shift type.
         """
-        if self.config.use_dynamic_shifting and mu is None:
-            raise ValueError("`mu` must be passed when `use_dynamic_shifting` is set to be `True`")
-
-        if sigmas is not None:
-            if not self.config.use_flow_sigmas:
-                raise ValueError(
-                    "Passing `sigmas` is only supported when `use_flow_sigmas=True`. "
-                    "Please set `use_flow_sigmas=True` during scheduler initialization."
-                )
-            num_inference_steps = len(sigmas)
-
         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
+        if mu is not None:
+            assert self.config.use_dynamic_shifting and self.config.time_shift_type == "exponential"
+            self.config.flow_shift = np.exp(mu)
         if self.config.timestep_spacing == "linspace":
             timesteps = (
                 np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
@@ -372,9 +354,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
             )
 
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         if self.config.use_karras_sigmas:
-            if sigmas is None:
-                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -394,8 +375,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_exponential_sigmas:
-            if sigmas is None:
-                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -410,8 +389,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_beta_sigmas:
-            if sigmas is None:
-                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -426,18 +403,9 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_flow_sigmas:
-            if sigmas is None:
-                sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1]
-            if self.config.use_dynamic_shifting:
-                sigmas = self.time_shift(mu, 1.0, sigmas)
-            else:
-                sigmas = self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas)
-            if self.config.shift_terminal:
-                sigmas = self.stretch_shift_to_terminal(sigmas)
-            eps = 1e-6
-            if np.fabs(sigmas[0] - 1) < eps:
-                # to avoid inf torch.log(alpha_si) in multistep_uni_p_bh_update during first/second update
-                sigmas[0] -= eps
+            alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)
+            sigmas = 1.0 - alphas
+            sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = sigmas[-1]
@@ -449,8 +417,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         else:
-            if sigmas is None:
-                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
@@ -480,43 +446,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = None
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
-    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
-    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
-        if self.config.time_shift_type == "exponential":
-            return self._time_shift_exponential(mu, sigma, t)
-        elif self.config.time_shift_type == "linear":
-            return self._time_shift_linear(mu, sigma, t)
-
-    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.stretch_shift_to_terminal
-    def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor:
-        r"""
-        Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_terminal` config
-        value.
-
-        Reference:
-        https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51
-
-        Args:
-            t (`torch.Tensor`):
-                A tensor of timesteps to be stretched and shifted.
-
-        Returns:
-            `torch.Tensor`:
-                A tensor of adjusted timesteps such that the final value equals `self.config.shift_terminal`.
-        """
-        one_minus_z = 1 - t
-        scale_factor = one_minus_z[-1] / (1 - self.config.shift_terminal)
-        stretched_t = 1 - (one_minus_z / scale_factor)
-        return stretched_t
-
-    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_exponential
-    def _time_shift_exponential(self, mu, sigma, t):
-        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-
-    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_linear
-    def _time_shift_linear(self, mu, sigma, t):
-        return mu / (mu + (1 / t - 1) ** sigma)
-
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
     def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
         """

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -17,6 +17,51 @@ class Flux2AutoBlocks(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class Flux2KleinAutoBlocks(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 Flux2KleinBaseAutoBlocks(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 Flux2KleinModularPipeline(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"]
 

tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein.py

@@ -0,0 +1,91 @@
+# 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 (
+    Flux2KleinAutoBlocks,
+    Flux2KleinModularPipeline,
+)
+
+from ...testing_utils import floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-modular"
+
+    params = frozenset(["prompt", "height", "width"])
+    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,
+            "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 = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-modular"
+
+    params = frozenset(["prompt", "height", "width", "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,
+            "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/flux2/test_modular_pipeline_flux2_klein_base.py

@@ -0,0 +1,91 @@
+# 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 (
+    Flux2KleinBaseAutoBlocks,
+    Flux2KleinModularPipeline,
+)
+
+from ...testing_utils import floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinBaseAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-base-modular"
+
+    params = frozenset(["prompt", "height", "width"])
+    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,
+            "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 = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinBaseAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-base-modular"
+
+    params = frozenset(["prompt", "height", "width", "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,
+            "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