update

Author: DN6

docs/source/en/modular_diffusers/modular_diffusers_states.md

@@ -27,8 +27,7 @@ The [`~modular_pipelines.PipelineState`] is a global state container for all blo
 
 There are two dict's in [`~modular_pipelines.PipelineState`] for structuring data.
 
-- The `values` dict is an **mutable** state containing a copy of user provided input values and intermediate output values generated by blocks. Blocks can read and write to their `inputs`. If a block modifies an `input`, it will be reflected in the `values` dict after calling `set_block_state`.
-- The `kwargs_type`
+- The `values` dict is a **mutable** state containing a copy of user provided input values and intermediate output values generated by blocks. If a block modifies an `input`, it will be reflected in the `values` dict after calling `set_block_state`.
 
 ```py
 PipelineState(
@@ -74,5 +73,3 @@ def __call__(self, components, state):
 
 - `inputs`, a block can modify an input - like `block_state.image` - and this change can be propagated globally to [`~modular_pipelines.PipelineState`] by calling `set_block_state`.
 - `intermediate_outputs`, is a new variable that a block creates. It is added to the [`~modular_pipelines.PipelineState`]'s `values` dict and is available as for subsequent blocks or accessed by users as a final output from the pipeline.
-
-If a variable is modified in `block_state` but not declared as an `intermediate_outputs`, it won't be added to [`~modular_pipelines.PipelineState`].

docs/source/en/modular_diffusers/sequential_pipeline_blocks.md

@@ -33,10 +33,6 @@ class InputBlock(ModularPipelineBlocks):
             InputParam(name="num_images_per_prompt", type_hint=int, description="number of images per prompt"),
         ]
 
-    @property
-    def intermediate_inputs(self):
-        return []
-
     @property
     def intermediate_outputs(self):
         return [
@@ -68,11 +64,6 @@ class ImageEncoderBlock(ModularPipelineBlocks):
     def inputs(self):
         return [
             InputParam(name="image", type_hint="PIL.Image", description="raw input image to process"),
-        ]
-
-    @property
-    def intermediate_inputs(self):
-        return [
             InputParam(name="batch_size", type_hint=int),
         ]
 
@@ -89,6 +80,7 @@ class ImageEncoderBlock(ModularPipelineBlocks):
     def __call__(self, components, state):
         block_state = self.get_block_state(state)
         # Simulate processing the image
+        # This will change the state of the image from a PIL image to a tensor for all blocks
         block_state.image = torch.randn(1, 3, 512, 512)
         block_state.batch_size = block_state.batch_size * 2
         block_state.image_latents = torch.randn(1, 4, 64, 64)

src/diffusers/__init__.py

@@ -36,9 +36,7 @@
     "configuration_utils": ["ConfigMixin"],
     "guiders": [],
     "hooks": [],
-    "image_processor": [
-        "VaeImageProcessor"
-    ],
+    "image_processor": ["VaeImageProcessor"],
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
     "modular_pipelines": [],
@@ -64,9 +62,7 @@
         "is_unidecode_available",
         "logging",
     ],
-    "video_processor": [
-        "VideoProcessor"
-    ],
+    "video_processor": ["VideoProcessor"],
 }
 
 try: