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# Marigold Computer Vision

![marigold](https://marigoldmonodepth.github.io/images/teaser_collage_compressed.jpg)

Marigold was proposed in 
[Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://huggingface.co/papers/2312.02145), 
a CVPR 2024 Oral paper by 
[Bingxin Ke](http://www.kebingxin.com/), 
[Anton Obukhov](https://www.obukhov.ai/), 
[Shengyu Huang](https://shengyuh.github.io/), 
[Nando Metzger](https://nandometzger.github.io/), 
[Rodrigo Caye Daudt](https://rcdaudt.github.io/), and 
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en).
The core idea is to **repurpose the generative prior of Text-to-Image Latent Diffusion Models (LDMs) for traditional 
computer vision tasks**.
This approach was explored by fine-tuning Stable Diffusion for **Monocular Depth Estimation**, as demonstrated in the 
teaser above.

Marigold was later extended in the follow-up paper, 
[Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis](https://huggingface.co/papers/2312.02145), 
authored by 
[Bingxin Ke](http://www.kebingxin.com/), 
[Kevin Qu](https://www.linkedin.com/in/kevin-qu-b3417621b/?locale=en_US), 
[Tianfu Wang](https://tianfwang.github.io/), 
[Nando Metzger](https://nandometzger.github.io/), 
[Shengyu Huang](https://shengyuh.github.io/), 
[Bo Li](https://www.linkedin.com/in/bobboli0202/), 
[Anton Obukhov](https://www.obukhov.ai/), and 
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en).
This work expanded Marigold to support new modalities such as **Surface Normals** and **Intrinsic Image Decomposition** 
(IID), introduced a training protocol for **Latent Consistency Models** (LCM), and demonstrated **High-Resolution** (HR) 
processing capability.

> [!TIP]
> The early Marigold models (`v1-0` and earlier) were optimized for best results with at least 10 inference steps.
> LCM models were later developed to enable high-quality inference in just 1 to 4 steps.
> Marigold models `v1-1` and later use the DDIM scheduler to achieve optimal 
> results in as few as 1 to 4 steps.

## Available Pipelines

Each pipeline is tailored for a specific computer vision task, processing an input RGB image and generating a 
corresponding prediction.
Currently, the following computer vision tasks are implemented:

| Pipeline                                                                                                                                          | Recommended Model Checkpoints                                                                                                                                                                           |                              Spaces (Interactive Apps)                               | Predicted Modalities                                                                                                                                                               |
|---------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py)           | [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1)                                                                                                                       |          [Depth Estimation](https://huggingface.co/spaces/prs-eth/marigold)          | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity)                                                                   |
| [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py)       | [prs-eth/marigold-normals-v1-1](https://huggingface.co/prs-eth/marigold-normals-v1-1)                                                                                                                   | [Surface Normals Estimation](https://huggingface.co/spaces/prs-eth/marigold-normals) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping)                                                                                                                    |
| [MarigoldIntrinsicsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_intrinsics.py) | [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1),<br>[prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1) | [Intrinsic Image Decomposition](https://huggingface.co/spaces/prs-eth/marigold-iid)  | [Albedo](https://en.wikipedia.org/wiki/Albedo), [Materials](https://www.n.aiq3d.com/wiki/roughnessmetalnessao-map), [Lighting](https://en.wikipedia.org/wiki/Diffuse_reflection)   |

## Available Checkpoints

All original checkpoints are available under the [PRS-ETH](https://huggingface.co/prs-eth/) organization on Hugging Face.
They are designed for use with diffusers pipelines and the [original codebase](https://github.com/prs-eth/marigold), which can also be used to train 
new model checkpoints.
The following is a summary of the recommended checkpoints, all of which produce reliable results with 1 to 4 steps. 

| Checkpoint                                                                                          | Modality     | Comment                                                                                                                                                                              |
|-----------------------------------------------------------------------------------------------------|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1)                   | Depth        | Affine-invariant depth prediction assigns each pixel a value between 0 (near plane) and 1 (far plane), with both planes determined by the model during inference.                    |
| [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1)               | Normals      | The surface normals predictions are unit-length 3D vectors in the screen space camera, with values in the range from -1 to 1.                                                        |
| [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1) | Intrinsics   | InteriorVerse decomposition is comprised of Albedo and two BRDF material properties: Roughness and Metallicity.                                                                      | 
| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1)     | Intrinsics   | HyperSim decomposition of an image $I$ is comprised of Albedo $A$, Diffuse shading $S$, and Non-diffuse residual $R$: $I = A*S+R$. |

> [!TIP]
> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff 
> between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to 
> efficiently load the same components into multiple pipelines. 
> Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section 
> [here](../../using-diffusers/svd#reduce-memory-usage).

> [!WARNING]
> Marigold pipelines were designed and tested with the scheduler embedded in the model checkpoint.
> The optimal number of inference steps varies by scheduler, with no universal value that works best across all cases.
> To accommodate this, the `num_inference_steps` parameter in the pipeline's `__call__` method defaults to `None` (see the 
> API reference).
> Unless set explicitly, it inherits the value from the `default_denoising_steps` field in the checkpoint configuration 
> file (`model_index.json`).
> This ensures high-quality predictions when invoking the pipeline with only the `image` argument.

See also Marigold [usage examples](../../using-diffusers/marigold_usage).

## Marigold Depth Prediction API

[[autodoc]] MarigoldDepthPipeline
	- __call__

[[autodoc]] pipelines.marigold.pipeline_marigold_depth.MarigoldDepthOutput

[[autodoc]] pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_depth

## Marigold Normals Estimation API
[[autodoc]] MarigoldNormalsPipeline
	- __call__

[[autodoc]] pipelines.marigold.pipeline_marigold_normals.MarigoldNormalsOutput

[[autodoc]] pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_normals

## Marigold Intrinsic Image Decomposition API

[[autodoc]] MarigoldIntrinsicsPipeline
	- __call__

[[autodoc]] pipelines.marigold.pipeline_marigold_intrinsics.MarigoldIntrinsicsOutput

[[autodoc]] pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_intrinsics