Merge branch 'main' into attention-backend-tests

* Feature: Add Mambo-G Guidance to Qwen-Image Pipeline

* change to guider implementation

* fix copied code residual

* Update src/diffusers/guiders/magnitude_aware_guidance.py

* Apply style fixes

---------

Co-authored-by: Pscgylotti <pscgylotti@github.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

src/diffusers/guiders/__init__.py

@@ -25,6 +25,7 @@ if is_torch_available():
     from .classifier_free_zero_star_guidance import ClassifierFreeZeroStarGuidance
     from .frequency_decoupled_guidance import FrequencyDecoupledGuidance
     from .guider_utils import BaseGuidance
+    from .magnitude_aware_guidance import MagnitudeAwareGuidance
     from .perturbed_attention_guidance import PerturbedAttentionGuidance
     from .skip_layer_guidance import SkipLayerGuidance
     from .smoothed_energy_guidance import SmoothedEnergyGuidance

src/diffusers/guiders/magnitude_aware_guidance.py

@@ -0,0 +1,159 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class MagnitudeAwareGuidance(BaseGuidance):
+    """
+    Magnitude-Aware Mitigation for Boosted Guidance (MAMBO-G): https://huggingface.co/papers/2508.03442
+
+    Args:
+        guidance_scale (`float`, defaults to `10.0`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        alpha (`float`, defaults to `8.0`):
+            The alpha parameter for the magnitude-aware guidance. Higher values cause more aggressive supression of
+            guidance scale when the magnitude of the guidance update is large.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.0`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `1.0`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 10.0,
+        alpha: float = 8.0,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+        enabled: bool = True,
+    ):
+        super().__init__(start, stop, enabled)
+
+        self.guidance_scale = guidance_scale
+        self.alpha = alpha
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
+            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
+            data_batches.append(data_batch)
+        return data_batches
+
+    def prepare_inputs_from_block_state(
+        self, data: "BlockState", input_fields: Dict[str, Union[str, Tuple[str, str]]]
+    ) -> List["BlockState"]:
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
+            data_batch = self._prepare_batch_from_block_state(input_fields, data, tuple_idx, input_prediction)
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput:
+        pred = None
+
+        if not self._is_mambo_g_enabled():
+            pred = pred_cond
+        else:
+            pred = mambo_guidance(
+                pred_cond,
+                pred_uncond,
+                self.guidance_scale,
+                self.alpha,
+                self.use_original_formulation,
+            )
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_mambo_g_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_mambo_g_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+
+def mambo_guidance(
+    pred_cond: torch.Tensor,
+    pred_uncond: torch.Tensor,
+    guidance_scale: float,
+    alpha: float = 8.0,
+    use_original_formulation: bool = False,
+):
+    dim = list(range(1, len(pred_cond.shape)))
+    diff = pred_cond - pred_uncond
+    ratio = torch.norm(diff, dim=dim, keepdim=True) / torch.norm(pred_uncond, dim=dim, keepdim=True)
+    guidance_scale_final = (
+        guidance_scale * torch.exp(-alpha * ratio)
+        if use_original_formulation
+        else 1.0 + (guidance_scale - 1.0) * torch.exp(-alpha * ratio)
+    )
+    pred = pred_cond if use_original_formulation else pred_uncond
+    pred = pred + guidance_scale_final * diff
+
+    return pred

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -231,7 +231,7 @@ class BlockState:
 
 class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
     """
-    Base class for all Pipeline Blocks: ConditionalPipelineBlocks, AutoPipelineBlocks, SequentialPipelineBlocks,
+    Base class for all Pipeline Blocks: PipelineBlock, AutoPipelineBlocks, SequentialPipelineBlocks,
     LoopSequentialPipelineBlocks
 
     [`ModularPipelineBlocks`] provides method to load and save the definition of pipeline blocks.
@@ -527,10 +527,9 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         )
 
 
-class ConditionalPipelineBlocks(ModularPipelineBlocks):
+class AutoPipelineBlocks(ModularPipelineBlocks):
     """
-    A Pipeline Blocks that conditionally selects a block to run based on the inputs.
-    Subclasses must implement the `select_block` method to define the logic for selecting the block.
+    A Pipeline Blocks that automatically selects a block to run based on the inputs.
 
     This class inherits from [`ModularPipelineBlocks`]. Check the superclass documentation for the generic methods the
     library implements for all the pipeline blocks (such as loading or saving etc.)
@@ -540,13 +539,12 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
     Attributes:
         block_classes: List of block classes to be used
         block_names: List of prefixes for each block
-        block_trigger_inputs: List of input names that select_block() uses to determine which block to run
+        block_trigger_inputs: List of input names that trigger specific blocks, with None for default
     """
 
     block_classes = []
     block_names = []
     block_trigger_inputs = []
-    default_block_name = None # name of the default block if no trigger inputs are provided, if None, this block can be skipped if no trigger inputs are provided
 
     def __init__(self):
         sub_blocks = InsertableDict()
@@ -556,15 +554,26 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
             else:
                 sub_blocks[block_name] = block
         self.sub_blocks = sub_blocks
-        if not (len(self.block_classes) == len(self.block_names)):
+        if not (len(self.block_classes) == len(self.block_names) == len(self.block_trigger_inputs)):
             raise ValueError(
-                f"In {self.__class__.__name__}, the number of block_classes and block_names must be the same."
+                f"In {self.__class__.__name__}, the number of block_classes, block_names, and block_trigger_inputs must be the same."
             )
-        if self.default_block_name is not None and self.default_block_name not in self.block_names:
+        default_blocks = [t for t in self.block_trigger_inputs if t is None]
+        # can only have 1 or 0 default block, and has to put in the last
+        # the order of blocks matters here because the first block with matching trigger will be dispatched
+        # e.g. blocks = [inpaint, img2img] and block_trigger_inputs = ["mask", "image"]
+        # as long as mask is provided, it is inpaint; if only image is provided, it is img2img
+        if len(default_blocks) > 1 or (len(default_blocks) == 1 and self.block_trigger_inputs[-1] is not None):
             raise ValueError(
-                f"In {self.__class__.__name__}, default_block_name '{self.default_block_name}' must be one of block_names: {self.block_names}"
+                f"In {self.__class__.__name__}, exactly one None must be specified as the last element "
+                "in block_trigger_inputs."
             )
 
+        # Map trigger inputs to block objects
+        self.trigger_to_block_map = dict(zip(self.block_trigger_inputs, self.sub_blocks.values()))
+        self.trigger_to_block_name_map = dict(zip(self.block_trigger_inputs, self.sub_blocks.keys()))
+        self.block_to_trigger_map = dict(zip(self.sub_blocks.keys(), self.block_trigger_inputs))
+
     @property
     def model_name(self):
         return next(iter(self.sub_blocks.values())).model_name
@@ -593,11 +602,8 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
 
     @property
     def required_inputs(self) -> List[str]:
-
-        # no default block means this conditional block can be skipped entirely
-        if self.default_block_name is None:
+        if None not in self.block_trigger_inputs:
             return []
-        
         first_block = next(iter(self.sub_blocks.values()))
         required_by_all = set(getattr(first_block, "required_inputs", set()))
 
@@ -608,7 +614,7 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
 
         return list(required_by_all)
 
-
+    # YiYi TODO: add test for this
     @property
     def inputs(self) -> List[Tuple[str, Any]]:
         named_inputs = [(name, block.inputs) for name, block in self.sub_blocks.items()]
@@ -633,69 +639,22 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
         combined_outputs = self.combine_outputs(*named_outputs)
         return combined_outputs
 
-    def _get_trigger_inputs(self) -> set:
-        """
-        Returns a set of all unique trigger input values found in this block and nested blocks.
-        """
-
-        def fn_recursive_get_trigger(blocks):
-            trigger_values = set()
-
-            if blocks is not None:
-                for name, block in blocks.items():
-                    # Check if current block has block_trigger_inputs
-                    if hasattr(block, "block_trigger_inputs") and block.block_trigger_inputs is not None:
-                        trigger_values.update(t for t in block.block_trigger_inputs if t is not None)
-
-                    # If block has sub_blocks, recursively check them
-                    if block.sub_blocks:
-                        nested_triggers = fn_recursive_get_trigger(block.sub_blocks)
-                        trigger_values.update(nested_triggers)
-
-            return trigger_values
-
-        # Start with this block's block_trigger_inputs
-        all_triggers = set(t for t in self.block_trigger_inputs if t is not None)
-        # Add nested triggers
-        all_triggers.update(fn_recursive_get_trigger(self.sub_blocks))
-
-        return all_triggers
-
-    @property
-    def trigger_inputs(self):
-        """All trigger inputs including from nested blocks."""
-        return self._get_trigger_inputs()
-
-    def select_block(self, **kwargs) -> Optional[str]:
-        """
-        Select the block to run based on the trigger inputs.
-        Subclasses must implement this method to define the logic for selecting the block.
-
-        Args:
-            **kwargs: Trigger input names and their values from the state.
-
-        Returns:
-            Optional[str]: The name of the block to run, or None to use default/skip.
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement the `select_block` method.")
-
     @torch.no_grad()
     def __call__(self, pipeline, state: PipelineState) -> PipelineState:
-        
-        trigger_kwargs = {name: state.get(name) for name in self.block_trigger_inputs if name is not None}
-        block_name = self.select_block(**trigger_kwargs)
+        # Find default block first (if any)
 
-        if block_name is None:
-            block_name = self.default_block_name
+        block = self.trigger_to_block_map.get(None)
+        for input_name in self.block_trigger_inputs:
+            if input_name is not None and state.get(input_name) is not None:
+                block = self.trigger_to_block_map[input_name]
+                break
 
-        if block_name is None:
-            logger.info(f"skipping conditional block: {self.__class__.__name__}")
+        if block is None:
+            logger.info(f"skipping auto block: {self.__class__.__name__}")
             return pipeline, state
-        
-        block = self.sub_blocks[block_name]
 
         try:
-            logger.info(f"Running block: {block.__class__.__name__}")
+            logger.info(f"Running block: {block.__class__.__name__}, trigger: {input_name}")
             return block(pipeline, state)
         except Exception as e:
             error_msg = (
@@ -706,6 +665,38 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
             logger.error(error_msg)
             raise
 
+    def _get_trigger_inputs(self):
+        """
+        Returns a set of all unique trigger input values found in the blocks. Returns: Set[str] containing all unique
+        block_trigger_inputs values
+        """
+
+        def fn_recursive_get_trigger(blocks):
+            trigger_values = set()
+
+            if blocks is not None:
+                for name, block in blocks.items():
+                    # Check if current block has trigger inputs(i.e. auto block)
+                    if hasattr(block, "block_trigger_inputs") and block.block_trigger_inputs is not None:
+                        # Add all non-None values from the trigger inputs list
+                        trigger_values.update(t for t in block.block_trigger_inputs if t is not None)
+
+                    # If block has sub_blocks, recursively check them
+                    if block.sub_blocks:
+                        nested_triggers = fn_recursive_get_trigger(block.sub_blocks)
+                        trigger_values.update(nested_triggers)
+
+            return trigger_values
+
+        trigger_inputs = set(self.block_trigger_inputs)
+        trigger_inputs.update(fn_recursive_get_trigger(self.sub_blocks))
+
+        return trigger_inputs
+
+    @property
+    def trigger_inputs(self):
+        return self._get_trigger_inputs()
+
     def __repr__(self):
         class_name = self.__class__.__name__
         base_class = self.__class__.__bases__[0].__name__
@@ -717,7 +708,7 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
             header += "\n"
             header += "  " + "=" * 100 + "\n"
             header += "  This pipeline contains blocks that are selected at runtime based on inputs.\n"
-            header += f"  Trigger Inputs: {sorted(self.trigger_inputs)}\n"
+            header += f"  Trigger Inputs: {[inp for inp in self.trigger_inputs if inp is not None]}\n"
             header += "  " + "=" * 100 + "\n\n"
 
         # Format description with proper indentation
@@ -738,20 +729,31 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
         expected_configs = getattr(self, "expected_configs", [])
         configs_str = format_configs(expected_configs, indent_level=2, add_empty_lines=False)
 
-        # Blocks section 
+        # Blocks section - moved to the end with simplified format
         blocks_str = "  Sub-Blocks:\n"
         for i, (name, block) in enumerate(self.sub_blocks.items()):
-            if name == self.default_block_name:
-                addtional_str  = " [default]"
+            # Get trigger input for this block
+            trigger = None
+            if hasattr(self, "block_to_trigger_map"):
+                trigger = self.block_to_trigger_map.get(name)
+                # Format the trigger info
+                if trigger is None:
+                    trigger_str = "[default]"
+                elif isinstance(trigger, (list, tuple)):
+                    trigger_str = f"[trigger: {', '.join(str(t) for t in trigger)}]"
+                else:
+                    trigger_str = f"[trigger: {trigger}]"
+                # For AutoPipelineBlocks, add bullet points
+                blocks_str += f"    • {name} {trigger_str} ({block.__class__.__name__})\n"
             else:
-                addtional_str = ""
-            blocks_str += f"    • {name}{addtional_str} ({block.__class__.__name__})\n"
+                # For SequentialPipelineBlocks, show execution order
+                blocks_str += f"    [{i}] {name} ({block.__class__.__name__})\n"
 
             # Add block description
-            block_desc_lines = block.description.split("\n")
-            indented_desc = block_desc_lines[0]
-            if len(block_desc_lines) > 1:
-                indented_desc += "\n" + "\n".join("                   " + line for line in block_desc_lines[1:])
+            desc_lines = block.description.split("\n")
+            indented_desc = desc_lines[0]
+            if len(desc_lines) > 1:
+                indented_desc += "\n" + "\n".join("                   " + line for line in desc_lines[1:])
             blocks_str += f"       Description: {indented_desc}\n\n"
 
         # Build the representation with conditional sections
@@ -782,35 +784,6 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
         )
 
 
-class AutoPipelineBlocks(ConditionalPipelineBlocks):
-    """
-    A Pipeline Blocks that automatically selects a block to run based on the presence of trigger inputs.
-    """
-
-    def __init__(self):
-        super().__init__()
-
-        if not (len(self.block_classes) == len(self.block_names) == len(self.block_trigger_inputs)):
-            raise ValueError(
-                f"In {self.__class__.__name__}, the number of block_classes, block_names, and block_trigger_inputs must be the same."
-            )
-
-    @property
-    def default_block_name(self) -> Optional[str]:
-        """Derive default_block_name from block_trigger_inputs (None entry)."""
-        if None in self.block_trigger_inputs:
-            idx = self.block_trigger_inputs.index(None)
-            return self.block_names[idx]
-        return None
-
-    def select_block(self, **kwargs) -> Optional[str]:
-        """Select block based on which trigger input is present (not None)."""
-        for trigger_input, block_name in zip(self.block_trigger_inputs, self.block_names):
-            if trigger_input is not None and kwargs.get(trigger_input) is not None:
-                return block_name
-        return None
-
-
 class SequentialPipelineBlocks(ModularPipelineBlocks):
     """
     A Pipeline Blocks that combines multiple pipeline block classes into one. When called, it will call each block in
@@ -912,8 +885,7 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
 
             # Only add outputs if the block cannot be skipped
             should_add_outputs = True
-            if isinstance(block, ConditionalPipelineBlocks) and block.default_block_name is None:
-                # ConditionalPipelineBlocks without default can be skipped
+            if hasattr(block, "block_trigger_inputs") and None not in block.block_trigger_inputs:
                 should_add_outputs = False
 
             if should_add_outputs:
@@ -976,7 +948,8 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
 
     def _get_trigger_inputs(self):
         """
-        Returns a set of all unique trigger input values found in the blocks.
+        Returns a set of all unique trigger input values found in the blocks. Returns: Set[str] containing all unique
+        block_trigger_inputs values
         """
 
         def fn_recursive_get_trigger(blocks):
@@ -984,8 +957,9 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
 
             if blocks is not None:
                 for name, block in blocks.items():
-                    # Check if current block has block_trigger_inputs (ConditionalPipelineBlocks)
+                    # Check if current block has trigger inputs(i.e. auto block)
                     if hasattr(block, "block_trigger_inputs") and block.block_trigger_inputs is not None:
+                        # Add all non-None values from the trigger inputs list
                         trigger_values.update(t for t in block.block_trigger_inputs if t is not None)
 
                     # If block has sub_blocks, recursively check them
@@ -1001,85 +975,82 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
     def trigger_inputs(self):
         return self._get_trigger_inputs()
 
-    def _traverse_trigger_blocks(self, active_inputs):
-        """
-        Traverse blocks and select which ones would run given the active inputs.
+    def _traverse_trigger_blocks(self, trigger_inputs):
+        # Convert trigger_inputs to a set for easier manipulation
+        active_triggers = set(trigger_inputs)
 
-        Args:
-            active_inputs: Dict of input names to values that are "present"
-
-        Returns:
-            OrderedDict of block_name -> block that would execute
-        """
-
-        def fn_recursive_traverse(block, block_name, active_inputs):
+        def fn_recursive_traverse(block, block_name, active_triggers):
             result_blocks = OrderedDict()
 
-            # ConditionalPipelineBlocks (includes AutoPipelineBlocks)
-            if isinstance(block, ConditionalPipelineBlocks):
-                trigger_kwargs = {name: active_inputs.get(name) for name in block.block_trigger_inputs}
-                selected_block_name = block.select_block(**trigger_kwargs)
-
-                if selected_block_name is None:
-                    selected_block_name = block.default_block_name
-
-                if selected_block_name is None:
-                    return result_blocks
-
-                selected_block = block.sub_blocks[selected_block_name]
-
-                if selected_block.sub_blocks:
-                    result_blocks.update(fn_recursive_traverse(selected_block, block_name, active_inputs))
+            # sequential(include loopsequential) or PipelineBlock
+            if not hasattr(block, "block_trigger_inputs"):
+                if block.sub_blocks:
+                    # sequential or LoopSequentialPipelineBlocks (keep traversing)
+                    for sub_block_name, sub_block in block.sub_blocks.items():
+                        blocks_to_update = fn_recursive_traverse(sub_block, sub_block_name, active_triggers)
+                        blocks_to_update = fn_recursive_traverse(sub_block, sub_block_name, active_triggers)
+                        blocks_to_update = {f"{block_name}.{k}": v for k, v in blocks_to_update.items()}
+                        result_blocks.update(blocks_to_update)
                 else:
-                    result_blocks[block_name] = selected_block
-                    if hasattr(selected_block, "outputs"):
-                        for out in selected_block.outputs:
-                            active_inputs[out.name] = True
-
+                    # PipelineBlock
+                    result_blocks[block_name] = block
+                    # Add this block's output names to active triggers if defined
+                    if hasattr(block, "outputs"):
+                        active_triggers.update(out.name for out in block.outputs)
                 return result_blocks
 
-            # SequentialPipelineBlocks or LoopSequentialPipelineBlocks
-            if block.sub_blocks:
-                for sub_block_name, sub_block in block.sub_blocks.items():
-                    blocks_to_update = fn_recursive_traverse(sub_block, sub_block_name, active_inputs)
-                    blocks_to_update = {f"{block_name}.{k}": v for k, v in blocks_to_update.items()}
-                    result_blocks.update(blocks_to_update)
+            # auto
             else:
-                result_blocks[block_name] = block
-                if hasattr(block, "outputs"):
-                    for out in block.outputs:
-                        active_inputs[out.name] = True
+                # Find first block_trigger_input that matches any value in our active_triggers
+                this_block = None
+                for trigger_input in block.block_trigger_inputs:
+                    if trigger_input is not None and trigger_input in active_triggers:
+                        this_block = block.trigger_to_block_map[trigger_input]
+                        break
+
+                # If no matches found, try to get the default (None) block
+                if this_block is None and None in block.block_trigger_inputs:
+                    this_block = block.trigger_to_block_map[None]
+
+                if this_block is not None:
+                    # sequential/auto (keep traversing)
+                    if this_block.sub_blocks:
+                        result_blocks.update(fn_recursive_traverse(this_block, block_name, active_triggers))
+                    else:
+                        # PipelineBlock
+                        result_blocks[block_name] = this_block
+                        # Add this block's output names to active triggers if defined
+                        # YiYi TODO: do we need outputs here? can it just be intermediate_outputs? can we get rid of outputs attribute?
+                        if hasattr(this_block, "outputs"):
+                            active_triggers.update(out.name for out in this_block.outputs)
 
             return result_blocks
 
         all_blocks = OrderedDict()
         for block_name, block in self.sub_blocks.items():
-            blocks_to_update = fn_recursive_traverse(block, block_name, active_inputs)
+            blocks_to_update = fn_recursive_traverse(block, block_name, active_triggers)
             all_blocks.update(blocks_to_update)
         return all_blocks
 
-    def get_execution_blocks(self, **kwargs):
-        """
-        Get the blocks that would execute given the specified inputs.
+    def get_execution_blocks(self, *trigger_inputs):
+        trigger_inputs_all = self.trigger_inputs
 
-        Args:
-            **kwargs: Input names and values. Only trigger inputs affect block selection.
-                    Pass any inputs that would be non-None at runtime.
+        if trigger_inputs is not None:
+            if not isinstance(trigger_inputs, (list, tuple, set)):
+                trigger_inputs = [trigger_inputs]
+            invalid_inputs = [x for x in trigger_inputs if x not in trigger_inputs_all]
+            if invalid_inputs:
+                logger.warning(
+                    f"The following trigger inputs will be ignored as they are not supported: {invalid_inputs}"
+                )
+                trigger_inputs = [x for x in trigger_inputs if x in trigger_inputs_all]
 
-        Returns:
-            SequentialPipelineBlocks containing only the blocks that would execute
-        
-        Example:
-            # Get blocks for inpainting workflow
-            blocks = pipeline.get_execution_blocks(prompt="a cat", mask=mask, image=image)
-            
-            # Get blocks for text2image workflow
-            blocks = pipeline.get_execution_blocks(prompt="a cat")
-        """
-        # Filter out None values
-        active_inputs = {k: v for k, v in kwargs.items() if v is not None}
-        
-        blocks_triggered = self._traverse_trigger_blocks(active_inputs)
+        if trigger_inputs is None:
+            if None in trigger_inputs_all:
+                trigger_inputs = [None]
+            else:
+                trigger_inputs = [trigger_inputs_all[0]]
+        blocks_triggered = self._traverse_trigger_blocks(trigger_inputs)
         return SequentialPipelineBlocks.from_blocks_dict(blocks_triggered)
 
     def __repr__(self):
@@ -1096,7 +1067,7 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             header += f"  Trigger Inputs: {[inp for inp in self.trigger_inputs if inp is not None]}\n"
             # Get first trigger input as example
             example_input = next(t for t in self.trigger_inputs if t is not None)
-            header += f"  Use `get_execution_blocks()` to see selected blocks (e.g. `get_execution_blocks({example_input}=...)`).\n"
+            header += f"  Use `get_execution_blocks()` with input names to see selected blocks (e.g. `get_execution_blocks('{example_input}')`).\n"
             header += "  " + "=" * 100 + "\n\n"
 
         # Format description with proper indentation
@@ -1120,9 +1091,22 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
         # Blocks section - moved to the end with simplified format
         blocks_str = "  Sub-Blocks:\n"
         for i, (name, block) in enumerate(self.sub_blocks.items()):
-
-            # show execution order
-            blocks_str += f"    [{i}] {name} ({block.__class__.__name__})\n"
+            # Get trigger input for this block
+            trigger = None
+            if hasattr(self, "block_to_trigger_map"):
+                trigger = self.block_to_trigger_map.get(name)
+                # Format the trigger info
+                if trigger is None:
+                    trigger_str = "[default]"
+                elif isinstance(trigger, (list, tuple)):
+                    trigger_str = f"[trigger: {', '.join(str(t) for t in trigger)}]"
+                else:
+                    trigger_str = f"[trigger: {trigger}]"
+                # For AutoPipelineBlocks, add bullet points
+                blocks_str += f"    • {name} {trigger_str} ({block.__class__.__name__})\n"
+            else:
+                # For SequentialPipelineBlocks, show execution order
+                blocks_str += f"    [{i}] {name} ({block.__class__.__name__})\n"
 
             # Add block description
             desc_lines = block.description.split("\n")
@@ -1246,9 +1230,15 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
                 if inp.name not in outputs and inp not in inputs:
                     inputs.append(inp)
 
-            # Add this block's outputs
-            block_intermediate_outputs = [out.name for out in block.intermediate_outputs]
-            outputs.update(block_intermediate_outputs)
+            # Only add outputs if the block cannot be skipped
+            should_add_outputs = True
+            if hasattr(block, "block_trigger_inputs") and None not in block.block_trigger_inputs:
+                should_add_outputs = False
+
+            if should_add_outputs:
+                # Add this block's outputs
+                block_intermediate_outputs = [out.name for out in block.intermediate_outputs]
+                outputs.update(block_intermediate_outputs)
 
         for input_param in inputs:
             if input_param.name in self.required_inputs:
@@ -1305,14 +1295,6 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
                 sub_blocks[block_name] = block
         self.sub_blocks = sub_blocks
 
-        # Validate that sub_blocks are only leaf blocks
-        for block_name, block in self.sub_blocks.items():
-            if block.sub_blocks:
-                raise ValueError(
-                    f"In {self.__class__.__name__}, sub_blocks must be leaf blocks (no sub_blocks). "
-                    f"Block '{block_name}' ({block.__class__.__name__}) has sub_blocks."
-                )
-
     @classmethod
     def from_blocks_dict(cls, blocks_dict: Dict[str, Any]) -> "LoopSequentialPipelineBlocks":
         """

src/diffusers/modular_pipelines/qwenimage/__init__.py

@@ -21,16 +21,21 @@ except OptionalDependencyNotAvailable:
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["modular_blocks_qwenimage"] = [
+    _import_structure["encoders"] = ["QwenImageTextEncoderStep"]
+    _import_structure["modular_blocks"] = [
+        "ALL_BLOCKS",
         "AUTO_BLOCKS",
-        "QwenImageAutoBlocks",
-    ]
-    _import_structure["modular_blocks_qwenimage_edit"] = [
+        "CONTROLNET_BLOCKS",
         "EDIT_AUTO_BLOCKS",
-        "QwenImageEditAutoBlocks",
-    ]
-    _import_structure["modular_blocks_qwenimage_edit_plus"] = [
+        "EDIT_BLOCKS",
+        "EDIT_INPAINT_BLOCKS",
         "EDIT_PLUS_AUTO_BLOCKS",
+        "EDIT_PLUS_BLOCKS",
+        "IMAGE2IMAGE_BLOCKS",
+        "INPAINT_BLOCKS",
+        "TEXT2IMAGE_BLOCKS",
+        "QwenImageAutoBlocks",
+        "QwenImageEditAutoBlocks",
         "QwenImageEditPlusAutoBlocks",
     ]
     _import_structure["modular_pipeline"] = [
@@ -46,16 +51,23 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
     else:
-        from .modular_blocks_qwenimage import (
+        from .encoders import (
+            QwenImageTextEncoderStep,
+        )
+        from .modular_blocks import (
+            ALL_BLOCKS,
             AUTO_BLOCKS,
-            QwenImageAutoBlocks,
-        )
-        from .modular_blocks_qwenimage_edit import (
+            CONTROLNET_BLOCKS,
             EDIT_AUTO_BLOCKS,
-            QwenImageEditAutoBlocks,
-        )
-        from .modular_blocks_qwenimage_edit_plus import (
+            EDIT_BLOCKS,
+            EDIT_INPAINT_BLOCKS,
             EDIT_PLUS_AUTO_BLOCKS,
+            EDIT_PLUS_BLOCKS,
+            IMAGE2IMAGE_BLOCKS,
+            INPAINT_BLOCKS,
+            TEXT2IMAGE_BLOCKS,
+            QwenImageAutoBlocks,
+            QwenImageEditAutoBlocks,
             QwenImageEditPlusAutoBlocks,
         )
         from .modular_pipeline import (
@@ -74,4 +86,4 @@ else:
     )
 
     for name, value in _dummy_objects.items():
-        setattr(sys.modules[__name__], name, value)
+        setattr(sys.modules[__name__], name, value)

src/diffusers/modular_pipelines/qwenimage/before_denoise.py

@@ -639,65 +639,19 @@ class QwenImageEditRoPEInputsStep(ModularPipelineBlocks):
         return components, state
 
 
-class QwenImageEditPlusRoPEInputsStep(ModularPipelineBlocks):
-    """RoPE inputs step for Edit Plus that handles lists of image heights/widths."""
-
+class QwenImageEditPlusRoPEInputsStep(QwenImageEditRoPEInputsStep):
     model_name = "qwenimage-edit-plus"
 
-    @property
-    def description(self) -> str:
-        return (
-            "Step that prepares the RoPE inputs for denoising process. This is used in QwenImage Edit Plus.\n"
-            "Unlike Edit, Edit Plus handles lists of image_height/image_width for multiple reference images.\n"
-            "Should be placed after prepare_latents step."
-        )
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(name="batch_size", required=True),
-            InputParam(name="image_height", required=True, type_hint=List[int]),
-            InputParam(name="image_width", required=True, type_hint=List[int]),
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(name="prompt_embeds_mask"),
-            InputParam(name="negative_prompt_embeds_mask"),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                name="img_shapes",
-                type_hint=List[List[Tuple[int, int, int]]],
-                description="The shapes of the image latents, used for RoPE calculation",
-            ),
-            OutputParam(
-                name="txt_seq_lens",
-                kwargs_type="denoiser_input_fields",
-                type_hint=List[int],
-                description="The sequence lengths of the prompt embeds, used for RoPE calculation",
-            ),
-            OutputParam(
-                name="negative_txt_seq_lens",
-                kwargs_type="denoiser_input_fields",
-                type_hint=List[int],
-                description="The sequence lengths of the negative prompt embeds, used for RoPE calculation",
-            ),
-        ]
-
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
 
         vae_scale_factor = components.vae_scale_factor
-
-        # Edit Plus: image_height and image_width are lists
         block_state.img_shapes = [
             [
                 (1, block_state.height // vae_scale_factor // 2, block_state.width // vae_scale_factor // 2),
                 *[
-                    (1, img_height // vae_scale_factor // 2, img_width // vae_scale_factor // 2)
-                    for img_height, img_width in zip(block_state.image_height, block_state.image_width)
+                    (1, vae_height // vae_scale_factor // 2, vae_width // vae_scale_factor // 2)
+                    for vae_height, vae_width in zip(block_state.image_height, block_state.image_width)
                 ],
             ]
         ] * block_state.batch_size

src/diffusers/modular_pipelines/qwenimage/encoders.py

@@ -244,19 +244,18 @@ def encode_vae_image(
 class QwenImageEditResizeDynamicStep(ModularPipelineBlocks):
     model_name = "qwenimage"
 
-    def __init__(
-        self, 
-        input_name: str = "image", 
-        output_name: str = "resized_image",
-        target_area: int = 1024 * 1024,
-    ):
-        """Create a configurable step for resizing images to the target area while maintaining the aspect ratio.
+    def __init__(self, input_name: str = "image", output_name: str = "resized_image"):
+        """Create a configurable step for resizing images to the target area (1024 * 1024) while maintaining the aspect ratio.
+
+        This block resizes an input image tensor and exposes the resized result under configurable input and output
+        names. Use this when you need to wire the resize step to different image fields (e.g., "image",
+        "control_image")
+
         Args:
             input_name (str, optional): Name of the image field to read from the
                 pipeline state. Defaults to "image".
             output_name (str, optional): Name of the resized image field to write
                 back to the pipeline state. Defaults to "resized_image".
-            target_area (int, optional): Target area in pixels. Defaults to 1024*1024.
         """
         if not isinstance(input_name, str) or not isinstance(output_name, str):
             raise ValueError(
@@ -264,12 +263,11 @@ class QwenImageEditResizeDynamicStep(ModularPipelineBlocks):
             )
         self._image_input_name = input_name
         self._resized_image_output_name = output_name
-        self._target_area = target_area
         super().__init__()
 
     @property
     def description(self) -> str:
-        return f"Image Resize step that resize the {self._image_input_name} to the target area {self._target_area} while maintaining the aspect ratio."
+        return f"Image Resize step that resize the {self._image_input_name} to the target area (1024 * 1024) while maintaining the aspect ratio."
 
     @property
     def expected_components(self) -> List[ComponentSpec]:
@@ -322,67 +320,48 @@ class QwenImageEditResizeDynamicStep(ModularPipelineBlocks):
         self.set_block_state(state, block_state)
         return components, state
 
-class QwenImageEditPlusResizeDynamicStep(ModularPipelineBlocks):
-    """Resize each image independently based on its own aspect ratio. For QwenImage Edit Plus."""
 
-    model_name = "qwenimage-edit-plus"
+class QwenImageEditPlusResizeDynamicStep(QwenImageEditResizeDynamicStep):
+    model_name = "qwenimage"
 
     def __init__(
-        self, 
-        input_name: str = "image", 
+        self,
+        input_name: str = "image",
         output_name: str = "resized_image",
-        target_area: int = 1024 * 1024,
+        vae_image_output_name: str = "vae_image",
     ):
-        """Create a step for resizing images to a target area.
+        """Create a configurable step for resizing images to the target area (384 * 384) while maintaining the aspect ratio.
 
-        Each image is resized independently based on its own aspect ratio.
-        This is suitable for Edit Plus where multiple reference images can have different dimensions.
+        This block resizes an input image or a list input images and exposes the resized result under configurable
+        input and output names. Use this when you need to wire the resize step to different image fields (e.g.,
+        "image", "control_image")
 
         Args:
-            input_name (str, optional): Name of the image field to read. Defaults to "image".
-            output_name (str, optional): Name of the resized image field to write. Defaults to "resized_image".
-            target_area (int, optional): Target area in pixels. Defaults to 1024*1024.
+            input_name (str, optional): Name of the image field to read from the
+                pipeline state. Defaults to "image".
+            output_name (str, optional): Name of the resized image field to write
+                back to the pipeline state. Defaults to "resized_image".
+            vae_image_output_name (str, optional): Name of the image field
+                to write back to the pipeline state. This is used by the VAE encoder step later on. QwenImage Edit Plus
+                processes the input image(s) differently for the VL and the VAE.
         """
         if not isinstance(input_name, str) or not isinstance(output_name, str):
             raise ValueError(
                 f"input_name and output_name must be strings but are {type(input_name)} and {type(output_name)}"
             )
+        self.condition_image_size = 384 * 384
         self._image_input_name = input_name
         self._resized_image_output_name = output_name
-        self._target_area = target_area
+        self._vae_image_output_name = vae_image_output_name
         super().__init__()
 
-    @property
-    def description(self) -> str:
-        return (
-            f"Image Resize step that resizes {self._image_input_name} to target area {self._target_area}.\n"
-            "Each image is resized independently based on its own aspect ratio."
-        )
-
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        return [
-            ComponentSpec(
-                "image_resize_processor",
-                VaeImageProcessor,
-                config=FrozenDict({"vae_scale_factor": 16}),
-                default_creation_method="from_config",
-            ),
-        ]
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(
-                name=self._image_input_name, required=True, type_hint=torch.Tensor, description="The image(s) to resize"
-            ),
-        ]
-
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
-        return [
+        return super().intermediate_outputs + [
             OutputParam(
-                name=self._resized_image_output_name, type_hint=List[PIL.Image.Image], description="The resized images"
+                name=self._vae_image_output_name,
+                type_hint=List[PIL.Image.Image],
+                description="The images to be processed which will be further used by the VAE encoder.",
             ),
         ]
 
@@ -395,21 +374,26 @@ class QwenImageEditPlusResizeDynamicStep(ModularPipelineBlocks):
         if not is_valid_image_imagelist(images):
             raise ValueError(f"Images must be image or list of images but are {type(images)}")
 
-        if is_valid_image(images):
+        if (
+            not isinstance(images, torch.Tensor)
+            and isinstance(images, PIL.Image.Image)
+            and not isinstance(images, list)
+        ):
             images = [images]
 
-        # Resize each image independently based on its own aspect ratio
-        resized_images = []
-        for image in images:
-            image_width, image_height = image.size
-            calculated_width, calculated_height, _ = calculate_dimensions(
-                self._target_area, image_width / image_height
-            )
-            resized_images.append(
-                components.image_resize_processor.resize(image, height=calculated_height, width=calculated_width)
+        # TODO (sayakpaul): revisit this when the inputs are `torch.Tensor`s
+        condition_images = []
+        vae_images = []
+        for img in images:
+            image_width, image_height = img.size
+            condition_width, condition_height, _ = calculate_dimensions(
+                self.condition_image_size, image_width / image_height
             )
+            condition_images.append(components.image_resize_processor.resize(img, condition_height, condition_width))
+            vae_images.append(img)
 
-        setattr(block_state, self._resized_image_output_name, resized_images)
+        setattr(block_state, self._resized_image_output_name, condition_images)
+        setattr(block_state, self._vae_image_output_name, vae_images)
         self.set_block_state(state, block_state)
         return components, state
 
@@ -663,30 +647,8 @@ class QwenImageEditTextEncoderStep(ModularPipelineBlocks):
         return components, state
 
 
-class QwenImageEditPlusTextEncoderStep(ModularPipelineBlocks):
-    """Text encoder for QwenImage Edit Plus that handles multiple reference images."""
-
-    model_name = "qwenimage-edit-plus"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Text Encoder step for QwenImage Edit Plus that processes prompt and multiple images together "
-            "to generate text embeddings for guiding image generation."
-        )
-
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        return [
-            ComponentSpec("text_encoder", Qwen2_5_VLForConditionalGeneration),
-            ComponentSpec("processor", Qwen2VLProcessor),
-            ComponentSpec(
-                "guider",
-                ClassifierFreeGuidance,
-                config=FrozenDict({"guidance_scale": 4.0}),
-                default_creation_method="from_config",
-            ),
-        ]
+class QwenImageEditPlusTextEncoderStep(QwenImageEditTextEncoderStep):
+    model_name = "qwenimage"
 
     @property
     def expected_configs(self) -> List[ConfigSpec]:
@@ -702,60 +664,6 @@ class QwenImageEditPlusTextEncoderStep(ModularPipelineBlocks):
             ConfigSpec(name="prompt_template_encode_start_idx", default=64),
         ]
 
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(name="prompt", required=True, type_hint=str, description="The prompt to encode"),
-            InputParam(name="negative_prompt", type_hint=str, description="The negative prompt to encode"),
-            InputParam(
-                name="resized_cond_image",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The image(s) to encode, can be a single image or list of images, should be resized to 384x384 using resize step",
-            ),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                name="prompt_embeds",
-                kwargs_type="denoiser_input_fields",
-                type_hint=torch.Tensor,
-                description="The prompt embeddings",
-            ),
-            OutputParam(
-                name="prompt_embeds_mask",
-                kwargs_type="denoiser_input_fields",
-                type_hint=torch.Tensor,
-                description="The encoder attention mask",
-            ),
-            OutputParam(
-                name="negative_prompt_embeds",
-                kwargs_type="denoiser_input_fields",
-                type_hint=torch.Tensor,
-                description="The negative prompt embeddings",
-            ),
-            OutputParam(
-                name="negative_prompt_embeds_mask",
-                kwargs_type="denoiser_input_fields",
-                type_hint=torch.Tensor,
-                description="The negative prompt embeddings mask",
-            ),
-        ]
-
-    @staticmethod
-    def check_inputs(prompt, negative_prompt):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if (
-            negative_prompt is not None
-            and not isinstance(negative_prompt, str)
-            and not isinstance(negative_prompt, list)
-        ):
-            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
-
     @torch.no_grad()
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
         block_state = self.get_block_state(state)
@@ -768,7 +676,7 @@ class QwenImageEditPlusTextEncoderStep(ModularPipelineBlocks):
             components.text_encoder,
             components.processor,
             prompt=block_state.prompt,
-            image=block_state.resized_cond_image,
+            image=block_state.resized_image,
             prompt_template_encode=components.config.prompt_template_encode,
             img_template_encode=components.config.img_template_encode,
             prompt_template_encode_start_idx=components.config.prompt_template_encode_start_idx,
@@ -784,7 +692,7 @@ class QwenImageEditPlusTextEncoderStep(ModularPipelineBlocks):
                     components.text_encoder,
                     components.processor,
                     prompt=negative_prompt,
-                    image=block_state.resized_cond_image,
+                    image=block_state.resized_image,
                     prompt_template_encode=components.config.prompt_template_encode,
                     img_template_encode=components.config.img_template_encode,
                     prompt_template_encode_start_idx=components.config.prompt_template_encode_start_idx,
@@ -938,60 +846,60 @@ class QwenImageProcessImagesInputStep(ModularPipelineBlocks):
         self.set_block_state(state, block_state)
         return components, state
 
-class QwenImageEditPlusProcessImagesInputStep(ModularPipelineBlocks):
+
+class QwenImageEditPlusProcessImagesInputStep(QwenImageProcessImagesInputStep):
     model_name = "qwenimage-edit-plus"
 
+    def __init__(self):
+        self.vae_image_size = 1024 * 1024
+        super().__init__()
+
     @property
     def description(self) -> str:
-        return "Image Preprocess step. Images can be resized first using QwenImageEditResizeDynamicStep."
-
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        return [
-            ComponentSpec(
-                "image_processor",
-                VaeImageProcessor,
-                config=FrozenDict({"vae_scale_factor": 16}),
-                default_creation_method="from_config",
-            ),
-        ]
+        return "Image Preprocess step for QwenImage Edit Plus. Unlike QwenImage Edit, QwenImage Edit Plus doesn't use the same resized image for further preprocessing."
 
     @property
     def inputs(self) -> List[InputParam]:
-        return [InputParam("resized_image")]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [OutputParam(name="processed_image")]
+        return [InputParam("vae_image"), InputParam("image"), InputParam("height"), InputParam("width")]
 
     @torch.no_grad()
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
         block_state = self.get_block_state(state)
 
+        if block_state.vae_image is None and block_state.image is None:
+            raise ValueError("`vae_image` and `image` cannot be None at the same time")
 
-
-        image = block_state.resized_image
-
-        is_image_list = isinstance(image, list)
-        if not is_image_list:
-            image = [image]
-
-        processed_images = []
-        for img in image:
-            img_width, img_height = img.size
-            processed_images.append(components.image_processor.preprocess(image=img, height=img_height, width=img_width))
-        block_state.processed_image = processed_images
-        if is_image_list:
-            block_state.processed_image = processed_images
+        vae_image_sizes = None
+        if block_state.vae_image is None:
+            image = block_state.image
+            self.check_inputs(
+                height=block_state.height, width=block_state.width, vae_scale_factor=components.vae_scale_factor
+            )
+            height = block_state.height or components.default_height
+            width = block_state.width or components.default_width
+            block_state.processed_image = components.image_processor.preprocess(
+                image=image, height=height, width=width
+            )
         else:
-            block_state.processed_image = processed_images[0]
+            # QwenImage Edit Plus can allow multiple input images with varied resolutions
+            processed_images = []
+            vae_image_sizes = []
+            for img in block_state.vae_image:
+                width, height = img.size
+                vae_width, vae_height, _ = calculate_dimensions(self.vae_image_size, width / height)
+                vae_image_sizes.append((vae_width, vae_height))
+                processed_images.append(
+                    components.image_processor.preprocess(image=img, height=vae_height, width=vae_width)
+                )
+            block_state.processed_image = processed_images
+
+        block_state.vae_image_sizes = vae_image_sizes
 
         self.set_block_state(state, block_state)
         return components, state
 
-class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
-    """VAE encoder that handles both single images and lists of images with varied resolutions."""
 
+class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
     model_name = "qwenimage"
 
     def __init__(
@@ -1001,12 +909,21 @@ class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
     ):
         """Initialize a VAE encoder step for converting images to latent representations.
 
-        Handles both single images and lists of images. When input is a list, outputs a list of latents.
-        When input is a single tensor, outputs a single latent tensor.
+        Both the input and output names are configurable so this block can be configured to process to different image
+        inputs (e.g., "processed_image" -> "image_latents", "processed_control_image" -> "control_image_latents").
 
         Args:
-            input_name (str, optional): Name of the input image tensor or list. Defaults to "processed_image".
-            output_name (str, optional): Name of the output latent tensor or list. Defaults to "image_latents".
+            input_name (str, optional): Name of the input image tensor. Defaults to "processed_image".
+                Examples: "processed_image" or "processed_control_image"
+            output_name (str, optional): Name of the output latent tensor. Defaults to "image_latents".
+                Examples: "image_latents" or "control_image_latents"
+
+        Examples:
+            # Basic usage with default settings (includes image processor) QwenImageVaeEncoderDynamicStep()
+
+            # Custom input/output names for control image QwenImageVaeEncoderDynamicStep(
+                input_name="processed_control_image", output_name="control_image_latents"
+            )
         """
         self._image_input_name = input_name
         self._image_latents_output_name = output_name
@@ -1014,18 +931,17 @@ class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
 
     @property
     def description(self) -> str:
-        return (
-            f"VAE Encoder step that converts {self._image_input_name} into latent representations {self._image_latents_output_name}.\n"
-            "Handles both single images and lists of images with varied resolutions."
-        )
+        return f"Dynamic VAE Encoder step that converts {self._image_input_name} into latent representations {self._image_latents_output_name}.\n"
 
     @property
     def expected_components(self) -> List[ComponentSpec]:
-        return [ComponentSpec("vae", AutoencoderKLQwenImage)]
+        components = [ComponentSpec("vae", AutoencoderKLQwenImage)]
+        return components
 
     @property
     def inputs(self) -> List[InputParam]:
-        return [InputParam(self._image_input_name, required=True), InputParam("generator")]
+        inputs = [InputParam(self._image_input_name, required=True), InputParam("generator")]
+        return inputs
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
@@ -1033,7 +949,7 @@ class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
             OutputParam(
                 self._image_latents_output_name,
                 type_hint=torch.Tensor,
-                description="The latents representing the reference image(s). Single tensor or list depending on input.",
+                description="The latents representing the reference image",
             )
         ]
 
@@ -1045,11 +961,47 @@ class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
         dtype = components.vae.dtype
 
         image = getattr(block_state, self._image_input_name)
-        is_image_list = isinstance(image, list)
-        if not is_image_list:
-            image = [image]
 
-        # Handle both single image and list of images
+        # Encode image into latents
+        image_latents = encode_vae_image(
+            image=image,
+            vae=components.vae,
+            generator=block_state.generator,
+            device=device,
+            dtype=dtype,
+            latent_channels=components.num_channels_latents,
+        )
+        setattr(block_state, self._image_latents_output_name, image_latents)
+
+        self.set_block_state(state, block_state)
+
+        return components, state
+
+
+class QwenImageEditPlusVaeEncoderDynamicStep(QwenImageVaeEncoderDynamicStep):
+    model_name = "qwenimage-edit-plus"
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        # Each reference image latent can have varied resolutions hence we return this as a list.
+        return [
+            OutputParam(
+                self._image_latents_output_name,
+                type_hint=List[torch.Tensor],
+                description="The latents representing the reference image(s).",
+            )
+        ]
+
+    @torch.no_grad()
+    def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+
+        device = components._execution_device
+        dtype = components.vae.dtype
+
+        image = getattr(block_state, self._image_input_name)
+
+        # Encode image into latents
         image_latents = []
         for img in image:
             image_latents.append(
@@ -1062,12 +1014,9 @@ class QwenImageVaeEncoderDynamicStep(ModularPipelineBlocks):
                     latent_channels=components.num_channels_latents,
                 )
             )
-        if not is_image_list:
-            image_latents = image_latents[0]
 
         setattr(block_state, self._image_latents_output_name, image_latents)
 
-
         self.set_block_state(state, block_state)
 
         return components, state

src/diffusers/modular_pipelines/qwenimage/inputs.py

@@ -222,15 +222,36 @@ class QwenImageTextInputsStep(ModularPipelineBlocks):
 
 
 class QwenImageInputsDynamicStep(ModularPipelineBlocks):
-    """Input step for QwenImage: update height/width, expand batch, patchify."""
-
     model_name = "qwenimage"
 
-    def __init__(
-        self,
-        image_latent_inputs: List[str] = ["image_latents"],
-        additional_batch_inputs: List[str] = [],
-    ):
+    def __init__(self, image_latent_inputs: List[str] = ["image_latents"], additional_batch_inputs: List[str] = []):
+        """Initialize a configurable step that standardizes the inputs for the denoising step. It:\n"
+
+        This step handles multiple common tasks to prepare inputs for the denoising step:
+        1. For encoded image latents, use it update height/width if None, patchifies, and expands batch size
+        2. For additional_batch_inputs: Only expands batch dimensions to match final batch size
+
+        This is a dynamic block that allows you to configure which inputs to process.
+
+        Args:
+            image_latent_inputs (List[str], optional): Names of image latent tensors to process.
+                These will be used to determine height/width, patchified, and batch-expanded. Can be a single string or
+                list of strings. Defaults to ["image_latents"]. Examples: ["image_latents"], ["control_image_latents"]
+            additional_batch_inputs (List[str], optional):
+                Names of additional conditional input tensors to expand batch size. These tensors will only have their
+                batch dimensions adjusted to match the final batch size. Can be a single string or list of strings.
+                Defaults to []. Examples: ["processed_mask_image"]
+
+        Examples:
+            # Configure to process image_latents (default behavior) QwenImageInputsDynamicStep()
+
+            # Configure to process multiple image latent inputs
+            QwenImageInputsDynamicStep(image_latent_inputs=["image_latents", "control_image_latents"])
+
+            # Configure to process image latents and additional batch inputs QwenImageInputsDynamicStep(
+                image_latent_inputs=["image_latents"], additional_batch_inputs=["processed_mask_image"]
+            )
+        """
         if not isinstance(image_latent_inputs, list):
             image_latent_inputs = [image_latent_inputs]
         if not isinstance(additional_batch_inputs, list):
@@ -242,12 +263,14 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
 
     @property
     def description(self) -> str:
+        # Functionality section
         summary_section = (
             "Input processing step that:\n"
-            "  1. For image latent inputs: Updates height/width if None, patchifies, and expands batch size\n"
+            "  1. For image latent inputs: Updates height/width if None, patchifies latents, and expands batch size\n"
             "  2. For additional batch inputs: Expands batch dimensions to match final batch size"
         )
 
+        # Inputs info
         inputs_info = ""
         if self._image_latent_inputs or self._additional_batch_inputs:
             inputs_info = "\n\nConfigured inputs:"
@@ -256,16 +279,11 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
             if self._additional_batch_inputs:
                 inputs_info += f"\n  - Additional batch inputs: {self._additional_batch_inputs}"
 
+        # Placement guidance
         placement_section = "\n\nThis block should be placed after the encoder steps and the text input step."
 
         return summary_section + inputs_info + placement_section
 
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        return [
-            ComponentSpec("pachifier", QwenImagePachifier, default_creation_method="from_config"),
-        ]
-
     @property
     def inputs(self) -> List[InputParam]:
         inputs = [
@@ -275,9 +293,11 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
             InputParam(name="width"),
         ]
 
+        # Add image latent inputs
         for image_latent_input_name in self._image_latent_inputs:
             inputs.append(InputParam(name=image_latent_input_name))
 
+        # Add additional batch inputs
         for input_name in self._additional_batch_inputs:
             inputs.append(InputParam(name=input_name))
 
@@ -290,16 +310,22 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
             OutputParam(name="image_width", type_hint=int, description="The width of the image latents"),
         ]
 
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("pachifier", QwenImagePachifier, default_creation_method="from_config"),
+        ]
+
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
 
-        # Process image latent inputs
+        # Process image latent inputs (height/width calculation, patchify, and batch expansion)
         for image_latent_input_name in self._image_latent_inputs:
             image_latent_tensor = getattr(block_state, image_latent_input_name)
             if image_latent_tensor is None:
                 continue
 
-            # 1. Calculate height/width from latents and update if not provided
+            # 1. Calculate height/width from latents
             height, width = calculate_dimension_from_latents(image_latent_tensor, components.vae_scale_factor)
             block_state.height = block_state.height or height
             block_state.width = block_state.width or width
@@ -309,7 +335,7 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
             if not hasattr(block_state, "image_width"):
                 block_state.image_width = width
 
-            # 2. Patchify
+            # 2. Patchify the image latent tensor
             image_latent_tensor = components.pachifier.pack_latents(image_latent_tensor)
 
             # 3. Expand batch size
@@ -328,6 +354,7 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
             if input_tensor is None:
                 continue
 
+            # Only expand batch size
             input_tensor = repeat_tensor_to_batch_size(
                 input_name=input_name,
                 input_tensor=input_tensor,
@@ -341,130 +368,63 @@ class QwenImageInputsDynamicStep(ModularPipelineBlocks):
         return components, state
 
 
-class QwenImageEditPlusInputsDynamicStep(ModularPipelineBlocks):
-    """Input step for QwenImage Edit Plus: handles list of latents with different sizes."""
-
+class QwenImageEditPlusInputsDynamicStep(QwenImageInputsDynamicStep):
     model_name = "qwenimage-edit-plus"
 
-    def __init__(
-        self,
-        image_latent_inputs: List[str] = ["image_latents"],
-        additional_batch_inputs: List[str] = [],
-    ):
-        if not isinstance(image_latent_inputs, list):
-            image_latent_inputs = [image_latent_inputs]
-        if not isinstance(additional_batch_inputs, list):
-            additional_batch_inputs = [additional_batch_inputs]
-
-        self._image_latent_inputs = image_latent_inputs
-        self._additional_batch_inputs = additional_batch_inputs
-        super().__init__()
-
-    @property
-    def description(self) -> str:
-        summary_section = (
-            "Input processing step for Edit Plus that:\n"
-            "  1. For image latent inputs (list): Collects heights/widths, patchifies each, concatenates, expands batch\n"
-            "  2. For additional batch inputs: Expands batch dimensions to match final batch size\n"
-            "  Height/width defaults to last image in the list."
-        )
-
-        inputs_info = ""
-        if self._image_latent_inputs or self._additional_batch_inputs:
-            inputs_info = "\n\nConfigured inputs:"
-            if self._image_latent_inputs:
-                inputs_info += f"\n  - Image latent inputs: {self._image_latent_inputs}"
-            if self._additional_batch_inputs:
-                inputs_info += f"\n  - Additional batch inputs: {self._additional_batch_inputs}"
-
-        placement_section = "\n\nThis block should be placed after the encoder steps and the text input step."
-
-        return summary_section + inputs_info + placement_section
-
-    @property
-    def expected_components(self) -> List[ComponentSpec]:
-        return [
-            ComponentSpec("pachifier", QwenImagePachifier, default_creation_method="from_config"),
-        ]
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        inputs = [
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="batch_size", required=True),
-            InputParam(name="height"),
-            InputParam(name="width"),
-        ]
-
-        for image_latent_input_name in self._image_latent_inputs:
-            inputs.append(InputParam(name=image_latent_input_name))
-
-        for input_name in self._additional_batch_inputs:
-            inputs.append(InputParam(name=input_name))
-
-        return inputs
-
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
         return [
-            OutputParam(name="image_height", type_hint=List[int], description="The heights of the image latents"),
-            OutputParam(name="image_width", type_hint=List[int], description="The widths of the image latents"),
+            OutputParam(name="image_height", type_hint=List[int], description="The height of the image latents"),
+            OutputParam(name="image_width", type_hint=List[int], description="The width of the image latents"),
         ]
 
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
 
-        # Process image latent inputs
+        # Process image latent inputs (height/width calculation, patchify, and batch expansion)
         for image_latent_input_name in self._image_latent_inputs:
             image_latent_tensor = getattr(block_state, image_latent_input_name)
             if image_latent_tensor is None:
                 continue
 
-            is_list = isinstance(image_latent_tensor, list)
-            if not is_list:
-                image_latent_tensor = [image_latent_tensor]
-
+            # Each image latent can have different size in QwenImage Edit Plus.
             image_heights = []
             image_widths = []
             packed_image_latent_tensors = []
 
-            for i, img_latent_tensor in enumerate(image_latent_tensor):
+            for img_latent_tensor in image_latent_tensor:
                 # 1. Calculate height/width from latents
                 height, width = calculate_dimension_from_latents(img_latent_tensor, components.vae_scale_factor)
                 image_heights.append(height)
                 image_widths.append(width)
 
-                # 2. Patchify
+                # 2. Patchify the image latent tensor
                 img_latent_tensor = components.pachifier.pack_latents(img_latent_tensor)
 
                 # 3. Expand batch size
                 img_latent_tensor = repeat_tensor_to_batch_size(
-                    input_name=f"{image_latent_input_name}[{i}]",
+                    input_name=image_latent_input_name,
                     input_tensor=img_latent_tensor,
                     num_images_per_prompt=block_state.num_images_per_prompt,
                     batch_size=block_state.batch_size,
                 )
                 packed_image_latent_tensors.append(img_latent_tensor)
 
-            # Concatenate all packed latents along dim=1
             packed_image_latent_tensors = torch.cat(packed_image_latent_tensors, dim=1)
-
-            # Output lists of heights/widths
             block_state.image_height = image_heights
             block_state.image_width = image_widths
+            setattr(block_state, image_latent_input_name, packed_image_latent_tensors)
 
-            # Default height/width from last image
             block_state.height = block_state.height or image_heights[-1]
             block_state.width = block_state.width or image_widths[-1]
 
-            setattr(block_state, image_latent_input_name, packed_image_latent_tensors)
-
         # Process additional batch inputs (only batch expansion)
         for input_name in self._additional_batch_inputs:
             input_tensor = getattr(block_state, input_name)
             if input_tensor is None:
                 continue
 
+            # Only expand batch size
             input_tensor = repeat_tensor_to_batch_size(
                 input_name=input_name,
                 input_tensor=input_tensor,
@@ -476,6 +436,8 @@ class QwenImageEditPlusInputsDynamicStep(ModularPipelineBlocks):
 
         self.set_block_state(state, block_state)
         return components, state
+
+
 class QwenImageControlNetInputsStep(ModularPipelineBlocks):
     model_name = "qwenimage"
 

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage.py

@@ -1,465 +0,0 @@
-# Copyright 2025 Qwen-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from ...utils import logging
-from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks, ConditionalPipelineBlocks
-from ..modular_pipeline_utils import InsertableDict
-from .before_denoise import (
-    QwenImageControlNetBeforeDenoiserStep,
-    QwenImageCreateMaskLatentsStep,
-    QwenImagePrepareLatentsStep,
-    QwenImagePrepareLatentsWithStrengthStep,
-    QwenImageRoPEInputsStep,
-    QwenImageSetTimestepsStep,
-    QwenImageSetTimestepsWithStrengthStep,
-)
-from .decoders import (
-    QwenImageAfterDenoiseStep,
-    QwenImageDecoderStep,
-    QwenImageInpaintProcessImagesOutputStep,
-    QwenImageProcessImagesOutputStep,
-)
-from .denoise import (
-    QwenImageControlNetDenoiseStep,
-    QwenImageDenoiseStep,
-    QwenImageInpaintControlNetDenoiseStep,
-    QwenImageInpaintDenoiseStep,
-    QwenImageLoopBeforeDenoiserControlNet,
-)
-from .encoders import (
-    QwenImageControlNetVaeEncoderStep,
-    QwenImageInpaintProcessImagesInputStep,
-    QwenImageProcessImagesInputStep,
-    QwenImageTextEncoderStep,
-    QwenImageVaeEncoderDynamicStep,
-)
-from .inputs import (
-    QwenImageControlNetInputsStep,
-    QwenImageInputsDynamicStep,
-    QwenImageTextInputsStep,
-)
-
-
-logger = logging.get_logger(__name__)
-
-
-
-# 1. VAE ENCODER
-
-# inpaint vae encoder
-class QwenImageInpaintVaeEncoderStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [QwenImageInpaintProcessImagesInputStep(), QwenImageVaeEncoderDynamicStep()]
-    block_names = ["preprocess", "encode"]
-
-    @property
-    def description(self) -> str:
-        return (
-            "This step is used for processing image and mask inputs for inpainting tasks. It:\n"
-            " - Resizes the image to the target size, based on `height` and `width`.\n"
-            " - Processes and updates `image` and `mask_image`.\n"
-            " - Creates `image_latents`."
-        )
-
-
-# img2img vae encoder
-class QwenImageImg2ImgVaeEncoderStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-
-    block_classes = [QwenImageProcessImagesInputStep(), QwenImageVaeEncoderDynamicStep()]
-    block_names = ["preprocess", "encode"]
-
-    @property
-    def description(self) -> str:
-        return "Vae encoder step that preprocess andencode the image inputs into their latent representations."
-
-
-# auto vae encoder
-class QwenImageAutoVaeEncoderStep(AutoPipelineBlocks):
-    block_classes = [QwenImageInpaintVaeEncoderStep, QwenImageImg2ImgVaeEncoderStep]
-    block_names = ["inpaint", "img2img"]
-    block_trigger_inputs = ["mask_image", "image"]
-
-    @property
-    def description(self):
-        return (
-            "Vae encoder step that encode the image inputs into their latent representations.\n"
-            + "This is an auto pipeline block.\n"
-            + " - `QwenImageInpaintVaeEncoderStep` (inpaint) is used when `mask_image` is provided.\n"
-            + " - `QwenImageImg2ImgVaeEncoderStep` (img2img) is used when `image` is provided.\n"
-            + " - if `mask_image` or `image` is not provided, step will be skipped."
-        )
-
-
-# optional controlnet vae encoder
-class QwenImageOptionalControlNetVaeEncoderStep(AutoPipelineBlocks):
-    block_classes = [QwenImageControlNetVaeEncoderStep]
-    block_names = ["controlnet"]
-    block_trigger_inputs = ["control_image"]
-
-    @property
-    def description(self):
-        return (
-            "Vae encoder step that encode the image inputs into their latent representations.\n"
-            + "This is an auto pipeline block.\n"
-            + " - `QwenImageControlNetVaeEncoderStep` (controlnet) is used when `control_image` is provided.\n"
-            + " - if `control_image` is not provided, step will be skipped."
-        )
-
-# 2. DENOISE
-# input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise
-
-# img2img input
-class QwenImageImg2ImgInputStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [QwenImageTextInputsStep(), QwenImageInputsDynamicStep(image_latent_inputs=["image_latents"])]
-    block_names = ["text_inputs", "additional_inputs"]
-
-    @property
-    def description(self):
-        return "Input step that prepares the inputs for the img2img denoising step. It:\n"
-        " - make sure the text embeddings have consistent batch size as well as the additional inputs (`image_latents`).\n"
-        " - update height/width based `image_latents`, patchify `image_latents`."
-
-
-# inpaint input
-class QwenImageInpaintInputStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [QwenImageTextInputsStep(), QwenImageInputsDynamicStep(image_latent_inputs=["image_latents"], additional_batch_inputs=["processed_mask_image"])]
-    block_names = ["text_inputs", "additional_inputs"]
-
-    @property
-    def description(self):
-        return "Input step that prepares the inputs for the inpainting denoising step. It:\n"
-        " - make sure the text embeddings have consistent batch size as well as the additional inputs (`image_latents` and `processed_mask_image`).\n"
-        " - update height/width based `image_latents`, patchify `image_latents`."
-
-# inpaint prepare latents
-class QwenImageInpaintPrepareLatentsStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [QwenImagePrepareLatentsWithStrengthStep(), QwenImageCreateMaskLatentsStep()]
-    block_names = ["add_noise_to_latents", "create_mask_latents"]
-
-    @property
-    def description(self) -> str:
-        return (
-            "This step prepares the latents/image_latents and mask inputs for the inpainting denoising step. It:\n"
-            " - Add noise to the image latents to create the latents input for the denoiser.\n"
-            " - Create the pachified latents `mask` based on the processedmask image.\n"
-        )
-
-# CoreDenoiseStep: 
-# (input +  prepare_latents + set_timesteps + prepare_rope_inputs + denoise + after_denoise)
-
-# 1. text2image
-class QwenImageCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [
-        QwenImageTextInputsStep(),
-        QwenImagePrepareLatentsStep(), 
-        QwenImageSetTimestepsStep(), 
-        QwenImageRoPEInputsStep(), 
-        QwenImageDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-    ]
-    block_names = [
-        "input",
-        "prepare_latents", 
-        "set_timesteps", 
-        "prepare_rope_inputs", 
-        "denoise", 
-        "after_denoise",
-    ]
-
-    @property
-    def description(self):
-        return "step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.)."
-
-
-# 2.inpaint
-class QwenImageInpaintCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [
-        QwenImageInpaintInputStep(),
-        QwenImagePrepareLatentsStep(), 
-        QwenImageSetTimestepsWithStrengthStep(), 
-        QwenImageInpaintPrepareLatentsStep(), 
-        QwenImageRoPEInputsStep(),
-        QwenImageInpaintDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-        ]
-    block_names = [
-        "input",
-        "prepare_latents",
-        "set_timesteps", 
-        "prepare_inpaint_latents", 
-        "prepare_rope_inputs",
-        "denoise",
-        "after_denoise",
-        ]
-
-    @property
-    def description(self):
-        return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task."
-
-
-# 3. img2img
-class QwenImageImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [
-        QwenImageImg2ImgInputStep(),
-        QwenImagePrepareLatentsStep(), 
-        QwenImageSetTimestepsWithStrengthStep(), 
-        QwenImagePrepareLatentsWithStrengthStep(), 
-        QwenImageRoPEInputsStep(),
-        QwenImageDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-        ]
-    block_names = [
-        "input",
-        "prepare_latents", 
-        "set_timesteps", 
-        "prepare_img2img_latents", 
-        "prepare_rope_inputs",
-        "denoise",
-        "after_denoise",
-        ]
-
-    @property
-    def description(self):
-        return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task."
-
-
-
-# 4. text2image + controlnet
-class QwenImageControlNetCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [
-        QwenImageTextInputsStep(),
-        QwenImageControlNetInputsStep(),
-        QwenImagePrepareLatentsStep(), 
-        QwenImageSetTimestepsStep(), 
-        QwenImageRoPEInputsStep(), 
-        QwenImageControlNetBeforeDenoiserStep(),
-        QwenImageControlNetDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-    ]
-    block_names = [
-        "input",
-        "controlnet_input",
-        "prepare_latents", 
-        "set_timesteps", 
-        "prepare_rope_inputs", 
-        "controlnet_before_denoise",
-        "controlnet_denoise",
-        "after_denoise",
-    ]
-
-    @property
-    def description(self):
-        return "step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.)."
-
-
-# 5. inpaint + controlnet
-class QwenImageControlNetInpaintCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [
-        QwenImageInpaintInputStep(),
-        QwenImageControlNetInputsStep(),
-        QwenImagePrepareLatentsStep(), 
-        QwenImageSetTimestepsWithStrengthStep(), 
-        QwenImageInpaintPrepareLatentsStep(), 
-        QwenImageRoPEInputsStep(),
-        QwenImageControlNetBeforeDenoiserStep(),
-        QwenImageInpaintControlNetDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-        ]
-    block_names = [
-        "input",
-        "controlnet_input",
-        "prepare_latents",
-        "set_timesteps", 
-        "prepare_inpaint_latents", 
-        "prepare_rope_inputs",
-        "controlnet_before_denoise",
-        "controlnet_denoise",
-        "after_denoise",
-        ]
-
-    @property
-    def description(self):
-        return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task."
-
-
-# 6. img2img + controlnet
-class QwenImageControlNetImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [
-        QwenImageImg2ImgInputStep(),
-        QwenImageControlNetInputsStep(),
-        QwenImagePrepareLatentsStep(), 
-        QwenImageSetTimestepsWithStrengthStep(), 
-        QwenImagePrepareLatentsWithStrengthStep(), 
-        QwenImageRoPEInputsStep(),
-        QwenImageControlNetBeforeDenoiserStep(),
-        QwenImageControlNetDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-        ]
-    block_names = [
-        "input",
-        "controlnet_input",
-        "prepare_latents", 
-        "set_timesteps", 
-        "prepare_img2img_latents", 
-        "prepare_rope_inputs",
-        "controlnet_before_denoise",
-        "controlnet_denoise",
-        "after_denoise",
-        ]
-
-    @property
-    def description(self):
-        return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task."
-
-
-# auto denoise
-# auto denoise step for controlnet tasks: works for all tasks with controlnet
-class QwenImageAutoCoreDenoiseStep(ConditionalPipelineBlocks):
-    block_classes = [
-        QwenImageCoreDenoiseStep,
-        QwenImageInpaintCoreDenoiseStep, 
-        QwenImageImg2ImgCoreDenoiseStep,
-        QwenImageControlNetCoreDenoiseStep,
-        QwenImageControlNetInpaintCoreDenoiseStep,
-        QwenImageControlNetImg2ImgCoreDenoiseStep,
-    ]
-    block_names = [
-        "text2image",
-        "inpaint",
-        "img2img",
-        "controlnet_text2image",
-        "controlnet_inpaint",
-        "controlnet_img2img"]
-    block_trigger_inputs = ["control_image_latents", "processed_mask_image", "image_latents"]
-    default_block_name = "text2image"
-
-    def select_block(self, control_image_latents=None, processed_mask_image=None, image_latents=None):
-
-        if control_image_latents is not None:
-            if processed_mask_image is not None:
-                return "controlnet_inpaint"
-            elif image_latents is not None:
-                return "controlnet_img2img"
-            else:
-                return "controlnet_text2image"
-        else:
-            if processed_mask_image is not None:
-                return "inpaint"
-            elif image_latents is not None:
-                return "img2img"
-            else:
-                return "text2image"
-
-    @property
-    def description(self):
-        return (
-            "Core step that performs the denoising process. \n"
-            + " - `QwenImageCoreDenoiseStep` (text2image) for text2image tasks.\n"
-            + " - `QwenImageInpaintCoreDenoiseStep` (inpaint) for inpaint tasks.\n"
-            + " - `QwenImageImg2ImgCoreDenoiseStep` (img2img) for img2img tasks.\n"
-            + " - `QwenImageControlNetCoreDenoiseStep` (controlnet_text2image) for text2image tasks with controlnet.\n"
-            + " - `QwenImageControlNetInpaintCoreDenoiseStep` (controlnet_inpaint) for inpaint tasks with controlnet.\n"
-            + " - `QwenImageControlNetImg2ImgCoreDenoiseStep` (controlnet_img2img) for img2img tasks with controlnet.\n"
-            + "This step support text-to-image, image-to-image, inpainting, and controlnet tasks for QwenImage:\n"
-            + " - for image-to-image generation, you need to provide `image_latents`\n"
-            + " - for inpainting, you need to provide `processed_mask_image` and `image_latents`\n"
-            + " - to run the controlnet workflow, you need to provide `control_image_latents`\n"
-            + " - for text-to-image generation, all you need to provide is prompt embeddings"
-        )
-
-
-# 4. DECODE
-
-## 1.1 text2image
-
-#### decode
-#### (standard decode step works for most tasks except for inpaint)
-
-class QwenImageDecodeStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [QwenImageDecoderStep(), QwenImageProcessImagesOutputStep()]
-    block_names = ["decode", "postprocess"]
-
-    @property
-    def description(self):
-        return "Decode step that decodes the latents to images and postprocess the generated image."
-
-
-
-#### inpaint decode
-
-class QwenImageInpaintDecodeStep(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-    block_classes = [QwenImageDecoderStep(), QwenImageInpaintProcessImagesOutputStep()]
-    block_names = ["decode", "postprocess"]
-
-    @property
-    def description(self):
-        return "Decode step that decodes the latents to images and postprocess the generated image, optional apply the mask overally to the original image."
-
-
-# auto decode step for inpaint and text2image tasks
-class QwenImageAutoDecodeStep(AutoPipelineBlocks):
-    block_classes = [QwenImageInpaintDecodeStep, QwenImageDecodeStep]
-    block_names = ["inpaint_decode", "decode"]
-    block_trigger_inputs = ["mask", None]
-
-    @property
-    def description(self):
-        return (
-            "Decode step that decode the latents into images. \n"
-            " This is an auto pipeline block that works for inpaint/text2image/img2img tasks, for both QwenImage and QwenImage-Edit.\n"
-            + " - `QwenImageInpaintDecodeStep` (inpaint) is used when `mask` is provided.\n"
-            + " - `QwenImageDecodeStep` (text2image/img2img) is used when `mask` is not provided.\n"
-        )
-
-
-
-## 1.10 QwenImage/auto block & presets
-AUTO_BLOCKS = InsertableDict(
-    [
-        ("text_encoder", QwenImageTextEncoderStep()),
-        ("vae_encoder", QwenImageAutoVaeEncoderStep()),
-        ("controlnet_vae_encoder", QwenImageOptionalControlNetVaeEncoderStep()),
-        ("denoise", QwenImageAutoCoreDenoiseStep()),
-        ("decode", QwenImageAutoDecodeStep()),
-    ]
-)
-
-
-class QwenImageAutoBlocks(SequentialPipelineBlocks):
-    model_name = "qwenimage"
-
-    block_classes = AUTO_BLOCKS.values()
-    block_names = AUTO_BLOCKS.keys()
-
-    @property
-    def description(self):
-        return (
-            "Auto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage.\n"
-            + "- for image-to-image generation, you need to provide `image`\n"
-            + "- for inpainting, you need to provide `mask_image` and `image`, optionally you can provide `padding_mask_crop` \n"
-            + "- to run the controlnet workflow, you need to provide `control_image`\n"
-            + "- for text-to-image generation, all you need to provide is `prompt`"
-        )

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage_edit.py

@@ -1,329 +0,0 @@
-# Copyright 2025 Qwen-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Optional
-
-from ...utils import logging
-from ..modular_pipeline import AutoPipelineBlocks, ConditionalPipelineBlocks, SequentialPipelineBlocks
-from ..modular_pipeline_utils import InsertableDict
-from .before_denoise import (
-    QwenImageCreateMaskLatentsStep,
-    QwenImageEditRoPEInputsStep,
-    QwenImagePrepareLatentsStep,
-    QwenImagePrepareLatentsWithStrengthStep,
-    QwenImageSetTimestepsStep,
-    QwenImageSetTimestepsWithStrengthStep,
-)
-from .decoders import (
-    QwenImageAfterDenoiseStep,
-    QwenImageDecoderStep,
-    QwenImageInpaintProcessImagesOutputStep,
-    QwenImageProcessImagesOutputStep,
-)
-from .denoise import (
-    QwenImageEditDenoiseStep,
-    QwenImageEditInpaintDenoiseStep,
-)
-from .encoders import (
-    QwenImageEditResizeDynamicStep,
-    QwenImageEditTextEncoderStep,
-    QwenImageInpaintProcessImagesInputStep,
-    QwenImageProcessImagesInputStep,
-    QwenImageVaeEncoderDynamicStep,
-)
-from .inputs import (
-    QwenImageInputsDynamicStep,
-    QwenImageTextInputsStep,
-)
-
-
-logger = logging.get_logger(__name__)
-
-
-# ====================
-# 1. TEXT ENCODER
-# ====================
-
-class QwenImageEditVLEncoderStep(SequentialPipelineBlocks):
-    """VL encoder that takes both image and text prompts."""
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageEditResizeDynamicStep(),
-        QwenImageEditTextEncoderStep(),
-    ]
-    block_names = ["resize", "encode"]
-
-    @property
-    def description(self) -> str:
-        return "QwenImage-Edit VL encoder step that encode the image and text prompts together."
-
-
-# ====================
-# 2. VAE ENCODER
-# ====================
-
-# Edit VAE encoder
-class QwenImageEditVaeEncoderStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageEditResizeDynamicStep(),
-        QwenImageProcessImagesInputStep(),
-        QwenImageVaeEncoderDynamicStep(),
-    ]
-    block_names = ["resize", "preprocess", "encode"]
-
-    @property
-    def description(self) -> str:
-        return "Vae encoder step that encode the image inputs into their latent representations."
-
-
-# Edit Inpaint VAE encoder
-class QwenImageEditInpaintVaeEncoderStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageEditResizeDynamicStep(),
-        QwenImageInpaintProcessImagesInputStep(),
-        QwenImageVaeEncoderDynamicStep(input_name="processed_image", output_name="image_latents"),
-    ]
-    block_names = ["resize", "preprocess", "encode"]
-
-    @property
-    def description(self) -> str:
-        return (
-            "This step is used for processing image and mask inputs for QwenImage-Edit inpaint tasks. It:\n"
-            " - resize the image for target area (1024 * 1024) while maintaining the aspect ratio.\n"
-            " - process the resized image and mask image.\n"
-            " - create image latents."
-        )
-
-
-# Auto VAE encoder
-class QwenImageEditAutoVaeEncoderStep(AutoPipelineBlocks):
-    block_classes = [QwenImageEditInpaintVaeEncoderStep, QwenImageEditVaeEncoderStep]
-    block_names = ["edit_inpaint", "edit"]
-    block_trigger_inputs = ["mask_image", "image"]
-
-    @property
-    def description(self):
-        return (
-            "Vae encoder step that encode the image inputs into their latent representations.\n"
-            "This is an auto pipeline block.\n"
-            " - `QwenImageEditInpaintVaeEncoderStep` (edit_inpaint) is used when `mask_image` is provided.\n"
-            " - `QwenImageEditVaeEncoderStep` (edit) is used when `image` is provided.\n"
-            " - if `mask_image` or `image` is not provided, step will be skipped."
-        )
-
-
-# ====================
-# 3. DENOISE - input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise
-# ====================
-
-# Edit input step
-class QwenImageEditInputStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageTextInputsStep(),
-        QwenImageInputsDynamicStep(image_latent_inputs=["image_latents"]),
-    ]
-    block_names = ["text_inputs", "additional_inputs"]
-
-    @property
-    def description(self):
-        return (
-            "Input step that prepares the inputs for the edit denoising step. It:\n"
-            " - make sure the text embeddings have consistent batch size as well as the additional inputs.\n"
-            " - update height/width based `image_latents`, patchify `image_latents`."
-        )
-
-
-# Edit Inpaint input step
-class QwenImageEditInpaintInputStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageTextInputsStep(),
-        QwenImageInputsDynamicStep(image_latent_inputs=["image_latents"], additional_batch_inputs=["processed_mask_image"]),
-    ]
-    block_names = ["text_inputs", "additional_inputs"]
-
-    @property
-    def description(self):
-        return (
-            "Input step that prepares the inputs for the edit inpaint denoising step. It:\n"
-            " - make sure the text embeddings have consistent batch size as well as the additional inputs.\n"
-            " - update height/width based `image_latents`, patchify `image_latents`."
-        )
-
-
-# Edit Inpaint prepare latents step
-class QwenImageEditInpaintPrepareLatentsStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [QwenImagePrepareLatentsWithStrengthStep(), QwenImageCreateMaskLatentsStep()]
-    block_names = ["add_noise_to_latents", "create_mask_latents"]
-
-    @property
-    def description(self) -> str:
-        return (
-            "This step prepares the latents/image_latents and mask inputs for the edit inpainting denoising step. It:\n"
-            " - Add noise to the image latents to create the latents input for the denoiser.\n"
-            " - Create the patchified latents `mask` based on the processed mask image.\n"
-        )
-
-
-# 1. Edit (img2img) core denoise
-class QwenImageEditCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageEditInputStep(),
-        QwenImagePrepareLatentsStep(),
-        QwenImageSetTimestepsStep(),
-        QwenImageEditRoPEInputsStep(),
-        QwenImageEditDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-    ]
-    block_names = [
-        "input",
-        "prepare_latents",
-        "set_timesteps",
-        "prepare_rope_inputs",
-        "denoise",
-        "after_denoise",
-    ]
-
-    @property
-    def description(self):
-        return "Core denoising workflow for QwenImage-Edit edit (img2img) task."
-
-
-# 2. Edit Inpaint core denoise
-class QwenImageEditInpaintCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [
-        QwenImageEditInpaintInputStep(),
-        QwenImagePrepareLatentsStep(),
-        QwenImageSetTimestepsWithStrengthStep(),
-        QwenImageEditInpaintPrepareLatentsStep(),
-        QwenImageEditRoPEInputsStep(),
-        QwenImageEditInpaintDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-    ]
-    block_names = [
-        "input",
-        "prepare_latents",
-        "set_timesteps",
-        "prepare_inpaint_latents",
-        "prepare_rope_inputs",
-        "denoise",
-        "after_denoise",
-    ]
-
-    @property
-    def description(self):
-        return "Core denoising workflow for QwenImage-Edit edit inpaint task."
-
-
-# Auto core denoise step
-class QwenImageEditAutoCoreDenoiseStep(ConditionalPipelineBlocks):
-    block_classes = [
-        QwenImageEditInpaintCoreDenoiseStep,
-        QwenImageEditCoreDenoiseStep,
-    ]
-    block_names = ["edit_inpaint", "edit"]
-    block_trigger_inputs = ["processed_mask_image", "image_latents"]
-    default_block_name = "edit"
-
-    def select_block(self, processed_mask_image=None, image_latents=None) -> Optional[str]:
-        if processed_mask_image is not None:
-            return "edit_inpaint"
-        elif image_latents is not None:
-            return "edit"
-        return None
-
-    @property
-    def description(self):
-        return (
-            "Auto core denoising step that selects the appropriate workflow based on inputs.\n"
-            " - `QwenImageEditInpaintCoreDenoiseStep` when `processed_mask_image` is provided\n"
-            " - `QwenImageEditCoreDenoiseStep` when `image_latents` is provided\n"
-            "Supports edit (img2img) and edit inpainting tasks for QwenImage-Edit."
-        )
-
-
-# ====================
-# 4. DECODE
-# ====================
-
-# Decode step (standard)
-class QwenImageEditDecodeStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [QwenImageDecoderStep(), QwenImageProcessImagesOutputStep()]
-    block_names = ["decode", "postprocess"]
-
-    @property
-    def description(self):
-        return "Decode step that decodes the latents to images and postprocess the generated image."
-
-
-# Inpaint decode step
-class QwenImageEditInpaintDecodeStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = [QwenImageDecoderStep(), QwenImageInpaintProcessImagesOutputStep()]
-    block_names = ["decode", "postprocess"]
-
-    @property
-    def description(self):
-        return "Decode step that decodes the latents to images and postprocess the generated image, optionally apply the mask overlay to the original image."
-
-
-# Auto decode step
-class QwenImageEditAutoDecodeStep(AutoPipelineBlocks):
-    block_classes = [QwenImageEditInpaintDecodeStep, QwenImageEditDecodeStep]
-    block_names = ["inpaint_decode", "decode"]
-    block_trigger_inputs = ["mask", None]
-
-    @property
-    def description(self):
-        return (
-            "Decode step that decode the latents into images.\n"
-            "This is an auto pipeline block.\n"
-            " - `QwenImageEditInpaintDecodeStep` (inpaint) is used when `mask` is provided.\n"
-            " - `QwenImageEditDecodeStep` (edit) is used when `mask` is not provided.\n"
-        )
-
-
-# ====================
-# 5. AUTO BLOCKS & PRESETS
-# ====================
-
-EDIT_AUTO_BLOCKS = InsertableDict(
-    [
-        ("text_encoder", QwenImageEditVLEncoderStep()),
-        ("vae_encoder", QwenImageEditAutoVaeEncoderStep()),
-        ("denoise", QwenImageEditAutoCoreDenoiseStep()),
-        ("decode", QwenImageEditAutoDecodeStep()),
-    ]
-)
-
-
-class QwenImageEditAutoBlocks(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit"
-    block_classes = EDIT_AUTO_BLOCKS.values()
-    block_names = EDIT_AUTO_BLOCKS.keys()
-
-    @property
-    def description(self):
-        return (
-            "Auto Modular pipeline for edit (img2img) and edit inpaint tasks using QwenImage-Edit.\n"
-            "- for edit (img2img) generation, you need to provide `image`\n"
-            "- for edit inpainting, you need to provide `mask_image` and `image`, optionally you can provide `padding_mask_crop`\n"
-        )

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage_edit_plus.py

@@ -1,175 +0,0 @@
-# Copyright 2025 Qwen-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from ...utils import logging
-from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
-from ..modular_pipeline_utils import InsertableDict
-from .before_denoise import (
-    QwenImageEditPlusRoPEInputsStep,
-    QwenImagePrepareLatentsStep,
-    QwenImageSetTimestepsStep,
-)
-from .decoders import (
-    QwenImageAfterDenoiseStep,
-    QwenImageDecoderStep,
-    QwenImageProcessImagesOutputStep,
-)
-from .denoise import (
-    QwenImageEditDenoiseStep,
-)
-from .encoders import (
-    QwenImageEditPlusResizeDynamicStep,
-    QwenImageEditPlusTextEncoderStep,
-    QwenImageEditPlusProcessImagesInputStep,
-    QwenImageVaeEncoderDynamicStep,
-)
-from .inputs import (
-    QwenImageEditPlusInputsDynamicStep,
-    QwenImageTextInputsStep,
-)
-
-
-logger = logging.get_logger(__name__)
-
-
-# ====================
-# 1. TEXT ENCODER
-# ====================
-
-class QwenImageEditPlusVLEncoderStep(SequentialPipelineBlocks):
-    """VL encoder that takes both image and text prompts. Uses 384x384 target area."""
-    model_name = "qwenimage-edit-plus"
-    block_classes = [
-        QwenImageEditPlusResizeDynamicStep(target_area=384 * 384, output_name="resized_cond_image"),
-        QwenImageEditPlusTextEncoderStep(),
-    ]
-    block_names = ["resize", "encode"]
-
-    @property
-    def description(self) -> str:
-        return "QwenImage-Edit Plus VL encoder step that encodes the image and text prompts together."
-
-
-# ====================
-# 2. VAE ENCODER
-# ====================
-
-class QwenImageEditPlusVaeEncoderStep(SequentialPipelineBlocks):
-    """VAE encoder that handles multiple images with different sizes. Uses 1024x1024 target area."""
-    model_name = "qwenimage-edit-plus"
-    block_classes = [
-        QwenImageEditPlusResizeDynamicStep(target_area=1024 * 1024, output_name="resized_image"),
-        QwenImageEditPlusProcessImagesInputStep(),
-        QwenImageVaeEncoderDynamicStep(),
-    ]
-    block_names = ["resize", "preprocess", "encode"]
-
-    @property
-    def description(self) -> str:
-        return (
-            "VAE encoder step that encodes image inputs into latent representations.\n"
-            "Each image is resized independently based on its own aspect ratio to 1024x1024 target area."
-        )
-
-
-# ====================
-# 3. DENOISE - input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise
-# ====================
-
-# Edit Plus input step
-class QwenImageEditPlusInputStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit-plus"
-    block_classes = [
-        QwenImageTextInputsStep(),
-        QwenImageEditPlusInputsDynamicStep(image_latent_inputs=["image_latents"]),
-    ]
-    block_names = ["text_inputs", "additional_inputs"]
-
-    @property
-    def description(self):
-        return (
-            "Input step that prepares the inputs for the Edit Plus denoising step. It:\n"
-            " - Standardizes text embeddings batch size.\n"
-            " - Processes list of image latents: patchifies, concatenates along dim=1, expands batch.\n"
-            " - Outputs lists of image_height/image_width for RoPE calculation.\n"
-            " - Defaults height/width from last image in the list."
-        )
-
-
-# Edit Plus core denoise
-class QwenImageEditPlusCoreDenoiseStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit-plus"
-    block_classes = [
-        QwenImageEditPlusInputStep(),
-        QwenImagePrepareLatentsStep(),
-        QwenImageSetTimestepsStep(),
-        QwenImageEditPlusRoPEInputsStep(),
-        QwenImageEditDenoiseStep(),
-        QwenImageAfterDenoiseStep(),
-    ]
-    block_names = [
-        "input",
-        "prepare_latents",
-        "set_timesteps",
-        "prepare_rope_inputs",
-        "denoise",
-        "after_denoise",
-    ]
-
-    @property
-    def description(self):
-        return "Core denoising workflow for QwenImage-Edit Plus edit (img2img) task."
-
-
-# ====================
-# 4. DECODE
-# ====================
-
-class QwenImageEditPlusDecodeStep(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit-plus"
-    block_classes = [QwenImageDecoderStep(), QwenImageProcessImagesOutputStep()]
-    block_names = ["decode", "postprocess"]
-
-    @property
-    def description(self):
-        return "Decode step that decodes the latents to images and postprocesses the generated image."
-
-
-# ====================
-# 5. AUTO BLOCKS & PRESETS
-# ====================
-
-EDIT_PLUS_AUTO_BLOCKS = InsertableDict(
-    [
-        ("text_encoder", QwenImageEditPlusVLEncoderStep()),
-        ("vae_encoder", QwenImageEditPlusVaeEncoderStep()),
-        ("denoise", QwenImageEditPlusCoreDenoiseStep()),
-        ("decode", QwenImageEditPlusDecodeStep()),
-    ]
-)
-
-
-class QwenImageEditPlusAutoBlocks(SequentialPipelineBlocks):
-    model_name = "qwenimage-edit-plus"
-    block_classes = EDIT_PLUS_AUTO_BLOCKS.values()
-    block_names = EDIT_PLUS_AUTO_BLOCKS.keys()
-
-    @property
-    def description(self):
-        return (
-            "Auto Modular pipeline for edit (img2img) tasks using QwenImage-Edit Plus.\n"
-            "- `image` is required input (can be single image or list of images).\n"
-            "- Each image is resized independently based on its own aspect ratio.\n"
-            "- VL encoder uses 384x384 target area, VAE encoder uses 1024x1024 target area."
-        )

tests/others/test_attention_backends.py

@@ -11,8 +11,7 @@ export RUN_ATTENTION_BACKEND_TESTS=yes
 pytest tests/others/test_attention_backends.py
 ```
 
-Tests were conducted on an H100 with PyTorch 2.8.0 (CUDA 12.9). Slices for the compilation tests in
-"native" variants were obtained with a torch nightly version (2.10.0.dev20250924+cu128).
+Tests were conducted on an H100 with PyTorch 2.9.1 (CUDA 12.9).
 
 Tests for aiter backend were conducted and slices for the aiter backend tests collected on a MI355X
 with torch 2025-09-25 nightly version (ad2f7315ca66b42497047bb7951f696b50f1e81b) and
@@ -24,6 +23,8 @@ import os
 import pytest
 import torch
 
+from ..testing_utils import numpy_cosine_similarity_distance
+
 
 pytestmark = pytest.mark.skipif(
     os.getenv("RUN_ATTENTION_BACKEND_TESTS", "false") == "false", reason="Feature not mature enough."
@@ -36,23 +37,28 @@ from diffusers.utils import is_torch_version  # noqa: E402
 FORWARD_CASES = [
     (
         "flash_hub",
-        torch.tensor([0.0820, 0.0859, 0.0918, 0.1016, 0.0957, 0.0996, 0.0996, 0.1016, 0.2188, 0.2266, 0.2363, 0.2500, 0.2539, 0.2461, 0.2422, 0.2695], dtype=torch.bfloat16)
+        torch.tensor([0.0820, 0.0859, 0.0918, 0.1016, 0.0957, 0.0996, 0.0996, 0.1016, 0.2188, 0.2266, 0.2363, 0.2500, 0.2539, 0.2461, 0.2422, 0.2695], dtype=torch.bfloat16),
+        1e-4
     ),
     (
         "_flash_3_hub",
         torch.tensor([0.0820, 0.0859, 0.0938, 0.1016, 0.0977, 0.0996, 0.1016, 0.1016, 0.2188, 0.2246, 0.2344, 0.2480, 0.2539, 0.2480, 0.2441, 0.2715], dtype=torch.bfloat16),
+        1e-4
     ),
     (
         "native",
-        torch.tensor([0.0820, 0.0859, 0.0938, 0.1016, 0.0957, 0.0996, 0.0996, 0.1016, 0.2188, 0.2266, 0.2363, 0.2500, 0.2539, 0.2480, 0.2461, 0.2734], dtype=torch.bfloat16)
-        ),
+        torch.tensor([0.0820, 0.0859, 0.0938, 0.1016, 0.0957, 0.0996, 0.0996, 0.1016, 0.2188, 0.2266, 0.2363, 0.2500, 0.2539, 0.2480, 0.2461, 0.2734], dtype=torch.bfloat16),
+        1e-4
+    ),
     (
         "_native_cudnn",
         torch.tensor([0.0781, 0.0840, 0.0879, 0.0957, 0.0898, 0.0957, 0.0957, 0.0977, 0.2168, 0.2246, 0.2324, 0.2500, 0.2539, 0.2480, 0.2441, 0.2695], dtype=torch.bfloat16),
+        5e-4
     ),
     (
         "aiter",
         torch.tensor([0.0781, 0.0820, 0.0879, 0.0957, 0.0898, 0.0938, 0.0957, 0.0957, 0.2285, 0.2363, 0.2461, 0.2637, 0.2695, 0.2617, 0.2617, 0.2891], dtype=torch.bfloat16),
+        1e-4
     )
 ]
 
@@ -60,27 +66,32 @@ COMPILE_CASES = [
     (
         "flash_hub",
         torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0488, 0.0586, 0.0605, 0.0586, 0.2324, 0.2422, 0.2539, 0.2734, 0.2832, 0.2812, 0.2773, 0.3047], dtype=torch.bfloat16),
-        True
+        True,
+        1e-4
     ),
     (
         "_flash_3_hub",
         torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0508, 0.0605, 0.0625, 0.0605, 0.2344, 0.2461, 0.2578, 0.2734, 0.2852, 0.2812, 0.2773, 0.3047], dtype=torch.bfloat16),
         True,
+        1e-4
     ),
     (
         "native",
         torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0508, 0.0605, 0.0605, 0.0605, 0.2344, 0.2461, 0.2578, 0.2773, 0.2871, 0.2832, 0.2773, 0.3066], dtype=torch.bfloat16),
         True,
+        1e-4
     ),
     (
         "_native_cudnn",
         torch.tensor([0.0410, 0.0410, 0.0430, 0.0508, 0.0488, 0.0586, 0.0605, 0.0586, 0.2344, 0.2461, 0.2578, 0.2773, 0.2871, 0.2832, 0.2793, 0.3086], dtype=torch.bfloat16),
         True,
+        5e-4,
     ),
     (
         "aiter",
         torch.tensor([0.0391, 0.0391, 0.0430, 0.0488, 0.0469, 0.0566, 0.0586, 0.0566, 0.2402, 0.2539, 0.2637, 0.2812, 0.2930, 0.2910, 0.2891, 0.3164], dtype=torch.bfloat16),
         True,
+        1e-4
     )
 ]
 # fmt: on
@@ -104,11 +115,11 @@ def _backend_is_probably_supported(pipe, name: str):
         return False
 
 
-def _check_if_slices_match(output, expected_slice):
+def _check_if_slices_match(output, expected_slice, expected_diff=1e-4):
     img = output.images.detach().cpu()
     generated_slice = img.flatten()
     generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
-    assert torch.allclose(generated_slice, expected_slice, atol=1e-4)
+    assert numpy_cosine_similarity_distance(generated_slice, expected_slice) < expected_diff
 
 
 @pytest.fixture(scope="session")
@@ -126,23 +137,23 @@ def pipe(device):
     return pipe
 
 
-@pytest.mark.parametrize("backend_name,expected_slice", FORWARD_CASES, ids=[c[0] for c in FORWARD_CASES])
-def test_forward(pipe, backend_name, expected_slice):
+@pytest.mark.parametrize("backend_name,expected_slice,expected_diff", FORWARD_CASES, ids=[c[0] for c in FORWARD_CASES])
+def test_forward(pipe, backend_name, expected_slice, expected_diff):
     out = _backend_is_probably_supported(pipe, backend_name)
     if isinstance(out, bool):
         pytest.xfail(f"Backend '{backend_name}' not supported in this environment.")
 
     modified_pipe = out[0]
     out = modified_pipe(**INFER_KW, generator=torch.manual_seed(0))
-    _check_if_slices_match(out, expected_slice)
+    _check_if_slices_match(out, expected_slice, expected_diff)
 
 
 @pytest.mark.parametrize(
-    "backend_name,expected_slice,error_on_recompile",
+    "backend_name,expected_slice,error_on_recompile,expected_diff",
     COMPILE_CASES,
     ids=[c[0] for c in COMPILE_CASES],
 )
-def test_forward_with_compile(pipe, backend_name, expected_slice, error_on_recompile):
+def test_forward_with_compile(pipe, backend_name, expected_slice, error_on_recompile, expected_diff):
     if "native" in backend_name and error_on_recompile and not is_torch_version(">=", "2.9.0"):
         pytest.xfail(f"Test with {backend_name=} is compatible with a higher version of torch.")
 
@@ -160,4 +171,4 @@ def test_forward_with_compile(pipe, backend_name, expected_slice, error_on_recom
     ):
         out = modified_pipe(**INFER_KW, generator=torch.manual_seed(0))
 
-    _check_if_slices_match(out, expected_slice)
+    _check_if_slices_match(out, expected_slice, expected_diff)

tests/testing_utils.py

@@ -131,6 +131,10 @@ def torch_all_close(a, b, *args, **kwargs):
 
 
 def numpy_cosine_similarity_distance(a, b):
+    if isinstance(a, torch.Tensor):
+        a = a.detach().cpu().float().numpy()
+    if isinstance(b, torch.Tensor):
+        b = b.detach().cpu().float().numpy()
     similarity = np.dot(a, b) / (norm(a) * norm(b))
     distance = 1.0 - similarity.mean()