[modular] i2i and t2i support for kontext modular

src/diffusers/__init__.py

@@ -386,6 +386,8 @@
     _import_structure["modular_pipelines"].extend(
         [
             "FluxAutoBlocks",
+            "FluxKontextAutoBlocks",
+            "FluxKontextModularPipeline",
             "FluxModularPipeline",
             "QwenImageAutoBlocks",
             "QwenImageEditAutoBlocks",
@@ -1050,6 +1052,8 @@
     else:
         from .modular_pipelines import (
             FluxAutoBlocks,
+            FluxKontextAutoBlocks,
+            FluxKontextModularPipeline,
             FluxModularPipeline,
             QwenImageAutoBlocks,
             QwenImageEditAutoBlocks,

src/diffusers/modular_pipelines/__init__.py

@@ -46,7 +46,12 @@
     ]
     _import_structure["stable_diffusion_xl"] = ["StableDiffusionXLAutoBlocks", "StableDiffusionXLModularPipeline"]
     _import_structure["wan"] = ["WanAutoBlocks", "WanModularPipeline"]
-    _import_structure["flux"] = ["FluxAutoBlocks", "FluxModularPipeline"]
+    _import_structure["flux"] = [
+        "FluxAutoBlocks",
+        "FluxModularPipeline",
+        "FluxKontextAutoBlocks",
+        "FluxKontextModularPipeline",
+    ]
     _import_structure["qwenimage"] = [
         "QwenImageAutoBlocks",
         "QwenImageModularPipeline",
@@ -65,7 +70,7 @@
         from ..utils.dummy_pt_objects import *  # noqa F403
     else:
         from .components_manager import ComponentsManager
-        from .flux import FluxAutoBlocks, FluxModularPipeline
+        from .flux import FluxAutoBlocks, FluxKontextAutoBlocks, FluxKontextModularPipeline, FluxModularPipeline
         from .modular_pipeline import (
             AutoPipelineBlocks,
             BlockState,

src/diffusers/modular_pipelines/flux/__init__.py

@@ -25,14 +25,18 @@
     _import_structure["modular_blocks"] = [
         "ALL_BLOCKS",
         "AUTO_BLOCKS",
+        "AUTO_BLOCKS_KONTTEXT",
+        "FLUX_KONTEXT_BLOCKS",
         "TEXT2IMAGE_BLOCKS",
         "FluxAutoBeforeDenoiseStep",
         "FluxAutoBlocks",
-        "FluxAutoBlocks",
         "FluxAutoDecodeStep",
         "FluxAutoDenoiseStep",
+        "FluxKontextAutoBlocks",
+        "FluxKontextAutoDenoiseStep",
+        "FluxKontextBeforeDenoiseStep",
     ]
-    _import_structure["modular_pipeline"] = ["FluxModularPipeline"]
+    _import_structure["modular_pipeline"] = ["FluxKontextModularPipeline", "FluxModularPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -45,13 +49,18 @@
         from .modular_blocks import (
             ALL_BLOCKS,
             AUTO_BLOCKS,
+            AUTO_BLOCKS_KONTTEXT,
+            FLUX_KONTEXT_BLOCKS,
             TEXT2IMAGE_BLOCKS,
             FluxAutoBeforeDenoiseStep,
             FluxAutoBlocks,
             FluxAutoDecodeStep,
             FluxAutoDenoiseStep,
+            FluxKontextAutoBlocks,
+            FluxKontextAutoDenoiseStep,
+            FluxKontextBeforeDenoiseStep,
         )
-        from .modular_pipeline import FluxModularPipeline
+        from .modular_pipeline import FluxKontextModularPipeline, FluxModularPipeline
 else:
     import sys
 

src/diffusers/modular_pipelines/flux/before_denoise.py

@@ -118,15 +118,6 @@ def retrieve_latents(
         raise AttributeError("Could not access latents of provided encoder_output")
 
 
-# TODO: align this with Qwen patchifier
-def _pack_latents(latents, batch_size, num_channels_latents, height, width):
-    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
-    latents = latents.permute(0, 2, 4, 1, 3, 5)
-    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
-
-    return latents
-
-
 def _get_initial_timesteps_and_optionals(
     transformer,
     scheduler,
@@ -398,9 +389,9 @@ def prepare_latents(
                 f" size of {batch_size}. Make sure the batch size matches the length of the generators."
             )
 
-        # TODO: move packing latents code to a patchifier
+        # TODO: move packing latents code to a patchifier similar to Qwen
         latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = _pack_latents(latents, batch_size, num_channels_latents, height, width)
+        latents = FluxPipeline._pack_latents(latents, batch_size, num_channels_latents, height, width)
 
         return latents
 
@@ -557,3 +548,73 @@ def __call__(self, components: FluxModularPipeline, state: PipelineState) -> Pip
         self.set_block_state(state, block_state)
 
         return components, state
+
+
+class FluxKontextRoPEInputsStep(ModularPipelineBlocks):
+    model_name = "flux_kontext"
+
+    @property
+    def description(self) -> str:
+        return "Step that prepares the RoPE inputs for the denoising process of Flux Kontext. Should be placed after text encoder and latent preparation steps."
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam(name="image_height"),
+            InputParam(name="image_width"),
+            InputParam(name="height"),
+            InputParam(name="width"),
+            InputParam(name="prompt_embeds"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                name="txt_ids",
+                kwargs_type="denoiser_input_fields",
+                type_hint=List[int],
+                description="The sequence lengths of the prompt embeds, used for RoPE calculation.",
+            ),
+            OutputParam(
+                name="img_ids",
+                kwargs_type="denoiser_input_fields",
+                type_hint=List[int],
+                description="The sequence lengths of the image latents, used for RoPE calculation.",
+            ),
+        ]
+
+    def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+
+        prompt_embeds = block_state.prompt_embeds
+        device, dtype = prompt_embeds.device, prompt_embeds.dtype
+        block_state.txt_ids = torch.zeros(prompt_embeds.shape[1], 3).to(
+            device=prompt_embeds.device, dtype=prompt_embeds.dtype
+        )
+
+        img_ids = None
+        if (
+            getattr(block_state, "image_height", None) is not None
+            and getattr(block_state, "image_width", None) is not None
+        ):
+            image_latent_height = 2 * (int(block_state.image_height) // (components.vae_scale_factor * 2))
+            image_latent_width = 2 * (int(block_state.width) // (components.vae_scale_factor * 2))
+            img_ids = FluxPipeline._prepare_latent_image_ids(
+                None, image_latent_height // 2, image_latent_width // 2, device, dtype
+            )
+            # image ids are the same as latent ids with the first dimension set to 1 instead of 0
+            img_ids[..., 0] = 1
+
+        height = 2 * (int(block_state.height) // (components.vae_scale_factor * 2))
+        width = 2 * (int(block_state.width) // (components.vae_scale_factor * 2))
+        latent_ids = FluxPipeline._prepare_latent_image_ids(None, height // 2, width // 2, device, dtype)
+
+        if img_ids is not None:
+            latent_ids = torch.cat([latent_ids, img_ids], dim=0)
+
+        block_state.img_ids = latent_ids
+
+        self.set_block_state(state, block_state)
+
+        return components, state

src/diffusers/modular_pipelines/flux/denoise.py

@@ -109,6 +109,96 @@ def __call__(
         return components, block_state
 
 
+class FluxKontextLoopDenoiser(ModularPipelineBlocks):
+    model_name = "flux_kontext"
+
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [ComponentSpec("transformer", FluxTransformer2DModel)]
+
+    @property
+    def description(self) -> str:
+        return (
+            "Step within the denoising loop that denoise the latents for Flux Kontext. "
+            "This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` "
+            "object (e.g. `FluxDenoiseLoopWrapper`)"
+        )
+
+    @property
+    def inputs(self) -> List[Tuple[str, Any]]:
+        return [
+            InputParam("joint_attention_kwargs"),
+            InputParam(
+                "latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
+            ),
+            InputParam(
+                "image_latents",
+                type_hint=torch.Tensor,
+                description="Image latents to use for the denoising process. Can be generated in prepare_latent step.",
+            ),
+            InputParam(
+                "guidance",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Guidance scale as a tensor",
+            ),
+            InputParam(
+                "prompt_embeds",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Prompt embeddings",
+            ),
+            InputParam(
+                "pooled_prompt_embeds",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Pooled prompt embeddings",
+            ),
+            InputParam(
+                "txt_ids",
+                required=True,
+                type_hint=torch.Tensor,
+                description="IDs computed from text sequence needed for RoPE",
+            ),
+            InputParam(
+                "img_ids",
+                required=True,
+                type_hint=torch.Tensor,
+                description="IDs computed from latent sequence needed for RoPE",
+            ),
+        ]
+
+    @torch.no_grad()
+    def __call__(
+        self, components: FluxModularPipeline, block_state: BlockState, i: int, t: torch.Tensor
+    ) -> PipelineState:
+        latents = block_state.latents
+        latent_model_input = latents
+        image_latents = block_state.image_latents
+        if image_latents is not None:
+            latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)
+
+        timestep = t.expand(latents.shape[0]).to(latents.dtype)
+        noise_pred = components.transformer(
+            hidden_states=latent_model_input,
+            timestep=timestep / 1000,
+            guidance=block_state.guidance,
+            encoder_hidden_states=block_state.prompt_embeds,
+            pooled_projections=block_state.pooled_prompt_embeds,
+            joint_attention_kwargs=block_state.joint_attention_kwargs,
+            txt_ids=block_state.txt_ids,
+            img_ids=block_state.img_ids,
+            return_dict=False,
+        )[0]
+        noise_pred = noise_pred[:, : latents.size(1)]
+        block_state.noise_pred = noise_pred
+
+        return components, block_state
+
+
 class FluxLoopAfterDenoiser(ModularPipelineBlocks):
     model_name = "flux"
 
@@ -221,3 +311,19 @@ def description(self) -> str:
             " - `FluxLoopAfterDenoiser`\n"
             "This block supports both text2image and img2img tasks."
         )
+
+
+class FluxKontextDenoiseStep(FluxDenoiseLoopWrapper):
+    block_classes = [FluxKontextLoopDenoiser, FluxLoopAfterDenoiser]
+    block_names = ["denoiser", "after_denoiser"]
+
+    @property
+    def description(self) -> str:
+        return (
+            "Denoise step that iteratively denoise the latents. \n"
+            "Its loop logic is defined in `FluxDenoiseLoopWrapper.__call__` method \n"
+            "At each iteration, it runs blocks defined in `sub_blocks` sequentially:\n"
+            " - `FluxKontextLoopDenoiser`\n"
+            " - `FluxLoopAfterDenoiser`\n"
+            "This block supports both text2image and img2img tasks."
+        )