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# Quickstart

Modular Diffusers is a framework for quickly building flexible and customizable pipelines. At the core of Modular Diffusers are [`ModularPipelineBlocks`] that can be combined with other blocks to adapt to new workflows. The blocks are converted into a [`ModularPipeline`], a friendly user-facing interface developers can use.

This doc will show you how to implement a [Differential Diffusion](https://differential-diffusion.github.io/) pipeline with the modular framework.

## ModularPipelineBlocks

[`ModularPipelineBlocks`] are *definitions* that specify the components, inputs, outputs, and computation logic for a single step in a pipeline. There are four types of blocks.

- [`ModularPipelineBlocks`] is the most basic block for a single step.
- [`SequentialPipelineBlocks`] is a multi-block that composes other blocks linearly. The outputs of one block are the inputs to the next block.
- [`LoopSequentialPipelineBlocks`] is a multi-block that runs iteratively and is designed for iterative workflows.
- [`AutoPipelineBlocks`] is a collection of blocks for different workflows and it selects which block to run based on the input. It is designed to conveniently package multiple workflows into a single pipeline.

[Differential Diffusion](https://differential-diffusion.github.io/) is an image-to-image workflow. Start with the `IMAGE2IMAGE_BLOCKS` preset, a collection of `ModularPipelineBlocks` for image-to-image generation.

```py
from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS
IMAGE2IMAGE_BLOCKS = InsertableDict([
    ("text_encoder", StableDiffusionXLTextEncoderStep),
    ("image_encoder", StableDiffusionXLVaeEncoderStep),
    ("input", StableDiffusionXLInputStep),
    ("set_timesteps", StableDiffusionXLImg2ImgSetTimestepsStep),
    ("prepare_latents", StableDiffusionXLImg2ImgPrepareLatentsStep),
    ("prepare_add_cond", StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep),
    ("denoise", StableDiffusionXLDenoiseStep),
    ("decode", StableDiffusionXLDecodeStep)
])
```

## Pipeline and block states

Modular Diffusers uses *state* to communicate data between blocks. There are two types of states.

- [`PipelineState`] is a global state that can be used to track all inputs and outputs across all blocks.
- [`BlockState`] is a local view of relevant variables from [`PipelineState`] for an individual block.

## Customizing blocks

[Differential Diffusion](https://differential-diffusion.github.io/) differs from standard image-to-image in its `prepare_latents` and `denoise` blocks. All the other blocks can be reused, but you'll need to modify these two.

Create placeholder `ModularPipelineBlocks` for `prepare_latents` and `denoise` by copying and modifying the existing ones.

Print the `denoise` block to see that it is composed of [`LoopSequentialPipelineBlocks`] with three sub-blocks, `before_denoiser`, `denoiser`, and `after_denoiser`. Only the `before_denoiser` sub-block needs to be modified to prepare the latent input for the denoiser based on the change map.

```py
denoise_blocks = IMAGE2IMAGE_BLOCKS["denoise"]()
print(denoise_blocks)
```

Replace the `StableDiffusionXLLoopBeforeDenoiser` sub-block with the new `SDXLDiffDiffLoopBeforeDenoiser` block.

```py
# Copy existing blocks as placeholders
class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks):
    """Copied from StableDiffusionXLImg2ImgPrepareLatentsStep - will modify later"""
    # ... same implementation as StableDiffusionXLImg2ImgPrepareLatentsStep

class SDXLDiffDiffDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
    block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLLoopDenoiser, StableDiffusionXLLoopAfterDenoiser]
    block_names = ["before_denoiser", "denoiser", "after_denoiser"]
```

### prepare_latents

The `prepare_latents` block requires the following changes.

- a processor to process the change map
- a new `inputs` to accept the user-provided change map, `timestep` for precomputing all the latents and `num_inference_steps` to create the mask for updating the image regions
- update the computation in the `__call__` method for processing the change map and creating the masks, and storing it in the [`BlockState`]

```diff
class SDXLDiffDiffPrepareLatentsStep(ModularPipelineBlocks):
    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec("vae", AutoencoderKL),
            ComponentSpec("scheduler", EulerDiscreteScheduler),
+           ComponentSpec("mask_processor", VaeImageProcessor, config=FrozenDict({"do_normalize": False, "do_convert_grayscale": True}))
        ]
    @property
    def inputs(self) -> List[Tuple[str, Any]]:
        return [
            InputParam("generator"),
+           InputParam("diffdiff_map", required=True),
-           InputParam("latent_timestep", required=True, type_hint=torch.Tensor),
+           InputParam("timesteps", type_hint=torch.Tensor),
+           InputParam("num_inference_steps", type_hint=int),
        ]

    @property
    def intermediate_outputs(self) -> List[OutputParam]:
        return [
+           OutputParam("original_latents", type_hint=torch.Tensor),
+           OutputParam("diffdiff_masks", type_hint=torch.Tensor),
        ]
    def __call__(self, components, state: PipelineState):
        # ... existing logic ...
+       # Process change map and create masks
+       diffdiff_map = components.mask_processor.preprocess(block_state.diffdiff_map, height=latent_height, width=latent_width)
+       thresholds = torch.arange(block_state.num_inference_steps, dtype=diffdiff_map.dtype) / block_state.num_inference_steps
+       block_state.diffdiff_masks = diffdiff_map > (thresholds + (block_state.denoising_start or 0))
+       block_state.original_latents = block_state.latents
```

### denoise

The `before_denoiser` sub-block requires the following changes.

- a new `inputs` to accept a `denoising_start` parameter,  `original_latents` and `diffdiff_masks` from the `prepare_latents` block
- update the computation in the `__call__` method for applying Differential Diffusion

```diff
class SDXLDiffDiffLoopBeforeDenoiser(ModularPipelineBlocks):
    @property
    def description(self) -> str:
        return (
            "Step within the denoising loop for differential diffusion that prepare the latent input for the denoiser"
        )

    @property
    def inputs(self) -> List[str]:
        return [
            InputParam("latents", required=True, type_hint=torch.Tensor),
+           InputParam("denoising_start"),
+           InputParam("original_latents", type_hint=torch.Tensor),
+           InputParam("diffdiff_masks", type_hint=torch.Tensor),
        ]

    def __call__(self, components, block_state, i, t):
+       # Apply differential diffusion logic
+       if i == 0 and block_state.denoising_start is None:
+           block_state.latents = block_state.original_latents[:1]
+       else:
+           block_state.mask = block_state.diffdiff_masks[i].unsqueeze(0).unsqueeze(1)
+           block_state.latents = block_state.original_latents[i] * block_state.mask + block_state.latents * (1 - block_state.mask)

        # ... rest of existing logic ...
```

## Assembling the blocks

You should have all the blocks you need at this point to create a [`ModularPipeline`].

Copy the existing `IMAGE2IMAGE_BLOCKS` preset and for the `set_timesteps` block, use the `set_timesteps` from the `TEXT2IMAGE_BLOCKS` because Differential Diffusion doesn't require a `strength` parameter.

Set the `prepare_latents` and `denoise` blocks to the `SDXLDiffDiffPrepareLatentsStep` and `SDXLDiffDiffDenoiseStep` blocks you just modified.

Call [`SequentialPipelineBlocks.from_blocks_dict`] on the blocks to create a `SequentialPipelineBlocks`.

```py
DIFFDIFF_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
DIFFDIFF_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
DIFFDIFF_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
DIFFDIFF_BLOCKS["denoise"] = SDXLDiffDiffDenoiseStep

dd_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_BLOCKS)
print(dd_blocks)
```

## ModularPipeline

Convert the [`SequentialPipelineBlocks`] into a [`ModularPipeline`] with the [`ModularPipeline.init_pipeline`] method. This initializes the expected components to load from a `modular_model_index.json` file. Explicitly load the components by calling [`ModularPipeline.load_components`].

It is a good idea to initialize the [`ComponentManager`] with the pipeline to help manage the different components. Once you call [`~ModularPipeline.load_components`], the components are registered to the [`ComponentManager`] and can be shared between workflows. The example below uses the `collection` argument to assign the components a `"diffdiff"` label for better organization.

```py
from diffusers.modular_pipelines import ComponentsManager

components = ComponentManager()

dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", components_manager=components, collection="diffdiff")
dd_pipeline.load_default_componenets(torch_dtype=torch.float16)
dd_pipeline.to("cuda")
```

## Adding workflows

Other workflows can be added to the [`ModularPipeline`] to support additional features without rewriting the entire pipeline from scratch.

This section demonstrates how to add an IP-Adapter or ControlNet.

### IP-Adapter

Stable Diffusion XL already has a preset IP-Adapter block that you can use and doesn't require any changes to the existing Differential Diffusion pipeline.

```py
from diffusers.modular_pipelines.stable_diffusion_xl.encoders import StableDiffusionXLAutoIPAdapterStep

ip_adapter_block = StableDiffusionXLAutoIPAdapterStep()
```

Use the [`sub_blocks.insert`] method to insert it into the [`ModularPipeline`]. The example below inserts the `ip_adapter_block` at position `0`. Print the pipeline to see that the `ip_adapter_block` is added and it requires an `ip_adapter_image`. This also added two components to the pipeline, the `image_encoder` and `feature_extractor`.

```py
dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0)
```

Call [`~ModularPipeline.init_pipeline`] to initialize a [`ModularPipeline`] and use [`~ModularPipeline.load_components`] to load the model components. Load and set the IP-Adapter to run the pipeline.

```py
dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
dd_pipeline.load_components(torch_dtype=torch.float16)
dd_pipeline.loader.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
dd_pipeline.loader.set_ip_adapter_scale(0.6)
dd_pipeline = dd_pipeline.to(device)

ip_adapter_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_orange.jpeg")
image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true")

prompt = "a green pear"
negative_prompt = "blurry"
generator = torch.Generator(device=device).manual_seed(42)

image = dd_pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=25,
    generator=generator,
    ip_adapter_image=ip_adapter_image,
    diffdiff_map=mask,
    image=image,
    output="images"
)[0]
```

### ControlNet

Stable Diffusion XL already has a preset ControlNet block that can readily be used.

```py
from diffusers.modular_pipelines.stable_diffusion_xl.modular_blocks import StableDiffusionXLAutoControlNetInputStep

control_input_block = StableDiffusionXLAutoControlNetInputStep()
```

However, it requires modifying the `denoise` block because that's where the ControlNet injects the control information into the UNet.

Modify the `denoise` block by replacing the `StableDiffusionXLLoopDenoiser` sub-block with the `StableDiffusionXLControlNetLoopDenoiser`.

```py
class SDXLDiffDiffControlNetDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
    block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLControlNetLoopDenoiser, StableDiffusionXLDenoiseLoopAfterDenoiser]
    block_names = ["before_denoiser", "denoiser", "after_denoiser"]

controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep()
```

Insert the `controlnet_input` block and replace the `denoise` block with the new `controlnet_denoise_block`. Initialize a [`ModularPipeline`] and [`~ModularPipeline.load_components`] into it.

```py
dd_blocks.sub_blocks.insert("controlnet_input", control_input_block, 7)
dd_blocks.sub_blocks["denoise"] = controlnet_denoise_block

dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
dd_pipeline.load_components(torch_dtype=torch.float16)
dd_pipeline = dd_pipeline.to(device)

control_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg")
image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true")

prompt = "a green pear"
negative_prompt = "blurry"
generator = torch.Generator(device=device).manual_seed(42)

image = dd_pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=25,
    generator=generator,
    control_image=control_image,
    controlnet_conditioning_scale=0.5,
    diffdiff_map=mask,
    image=image,
    output="images"
)[0]
```

### AutoPipelineBlocks

The Differential Diffusion, IP-Adapter, and ControlNet workflows can be bundled into a single [`ModularPipeline`] by using [`AutoPipelineBlocks`]. This allows automatically selecting which sub-blocks to run based on the inputs like `control_image` or `ip_adapter_image`. If none of these inputs are passed, then it defaults to the Differential Diffusion.

Use `block_trigger_inputs` to only run the `SDXLDiffDiffControlNetDenoiseStep` block if a `control_image` input is provided. Otherwise, the `SDXLDiffDiffDenoiseStep` is used.

```py
class SDXLDiffDiffAutoDenoiseStep(AutoPipelineBlocks):
    block_classes = [SDXLDiffDiffControlNetDenoiseStep, SDXLDiffDiffDenoiseStep]
    block_names = ["controlnet_denoise", "denoise"]
    block_trigger_inputs = ["controlnet_cond", None]
```

Add the `ip_adapter` and `controlnet_input` blocks.

```py
DIFFDIFF_AUTO_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
DIFFDIFF_AUTO_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
DIFFDIFF_AUTO_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
DIFFDIFF_AUTO_BLOCKS["denoise"] = SDXLDiffDiffAutoDenoiseStep
DIFFDIFF_AUTO_BLOCKS.insert("ip_adapter", StableDiffusionXLAutoIPAdapterStep, 0)
DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",StableDiffusionXLControlNetAutoInput, 7)
```

Call [`SequentialPipelineBlocks.from_blocks_dict`] to create a [`SequentialPipelineBlocks`] and create a [`ModularPipeline`] and load in the model components to run.

```py
dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS)
dd_pipeline = dd_auto_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
dd_pipeline.load_components(torch_dtype=torch.float16)
```

## Share

Add your [`ModularPipeline`] to the Hub with [`~ModularPipeline.save_pretrained`] and set `push_to_hub` argument to `True`.

```py
dd_pipeline.save_pretrained("YiYiXu/test_modular_doc", push_to_hub=True)
```

Other users can load the [`ModularPipeline`] with [`~ModularPipeline.from_pretrained`].

```py
import torch
from diffusers.modular_pipelines import ModularPipeline, ComponentsManager

components = ComponentsManager()

diffdiff_pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-diffdiff-0704", trust_remote_code=True, components_manager=components, collection="diffdiff")
diffdiff_pipeline.load_components(torch_dtype=torch.float16)
```