https://github.com/replicate/cog-marigold
Cog wrapper for Marigold
https://github.com/replicate/cog-marigold
Last synced: 9 months ago
JSON representation
Cog wrapper for Marigold
- Host: GitHub
- URL: https://github.com/replicate/cog-marigold
- Owner: replicate
- License: other
- Created: 2023-12-15T07:19:14.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-21T09:53:29.000Z (about 2 years ago)
- Last Synced: 2025-05-01T04:46:38.303Z (9 months ago)
- Language: Python
- Size: 49 MB
- Stars: 7
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Cog wrapper for Marigold
A Cog wrapper for Marigold, a diffusion model and associated fine-tuning protocol for monocular depth estimation. See the original [repository](https://github.com/prs-eth/marigold), [paper](https://arxiv.org/abs/2312.02145) and [Replicate demo](https://replicate.com/adirik/marigold) for details.
## API Usage
You need to have Cog and Docker installed to run this model locally. Follow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of Marigold to [Replicate](https://replicate.com). To use the model, simply provide the image (ideally RGB or grayscale) you would like to perform depth estimation for. The API returns two depth map images - one grayscale and one spectral.
To build the docker image with cog and run a prediction:
```bash
cog predict -i image=@examples/files_bee.jpg
```
To start a server and send requests to your locally or remotely deployed API:
```bash
cog run -p 5000 python -m cog.server.http
```
Input parameters are as follows:
- **image:** RGB or grayscale input image for the model, use an RGB image for best results.
- **resize_input:** whether to resize the input image to max resolution of 768 x 768 pixels, default to `True`.
- **num_infer:** number of inferences to be performed. if >1, multiple depth predictions are ensembled. A higher number yields better results but runs slower.
- **denoise_steps:** number of inference denoising steps, more steps results in higher accuracy but slower inference speed.
- **regularizer_strength:** ensembling parameter, weight of optimization regularizer.
- **reduction_method:** ensembling parameter, method to merge aligned depth maps. Choose between `["mean", "median"]`.
- **max_iter:** ensembling parameter, max number of optimization iterations.
- **seed:** (optional) seed for reproducibility, set to random if left as `None`.
## References
```
@misc{ke2023repurposing,
title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
year={2023},
eprint={2312.02145},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```