A beginner's guide to the Marigold model by Adirik on Replicate

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This is a simplified guide to an AI model called Marigold maintained by Adirik. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Model overview

The marigold model is a diffusion-based AI model for monocular depth estimation, developed by adirik and the team at Replicate. Unlike traditional depth estimation models that require specialized training, marigold leverages the rich visual knowledge stored in modern generative image models like Stable Diffusion. By fine-tuning this pre-trained model with synthetic data, marigold can perform zero-shot depth estimation on unseen real-world images, offering state-of-the-art results.

Similar depth estimation models like t2i-adapter-sdxl-depth-midas and ml-depth-pro also aim to infer depth information from a single image. However, marigold stands out by its unique approach of repurposing a diffusion-based generative model, which allows it to achieve state-of-the-art performance without requiring extensive specialized training.

Model inputs and outputs

The marigold model takes an image as input and outputs two depth map images - one grayscale and one spectral. The grayscale depth map provides a visually intuitive representation of the scene's depth, while the spectral depth map encodes depth information using a color gradient.

Inputs

image: The input image, which can be RGB or grayscale. For best results, an RGB image is recommended.
resize_input: Whether to resize the input image to a maximum resolution of 768 x 768 pixels. This parameter is set to True by default.
num_infer: The number of inferences to be performed. Increasing this number can improve the depth estimation quality, but will also increase the inference time.
denoise_steps: The number of denoising steps used during the inference process. More steps can lead to higher accuracy but slower inference speed.
regularizer_strength: A parameter used for the ensembling of multiple depth predictions.
reduction_method: The method used to merge the aligned depth maps, with options of "mean" or "median".
max_iter: The maximum number of optimization iterations used during the ensembling process.
seed: An optional seed value for reproducibility.

Outputs

Grayscale depth map: A depth map image represented in grayscale, where brighter pixels indicate closer distances and darker pixels indicate farther distances.
Spectral depth map: A depth map image represented using a color gradient, where the color encodes the depth information.