Marigold Computer Vision
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All things Marigold • 17 items • Updated • 27
How to use prs-eth/marigold-disparity-affine-v0-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("prs-eth/marigold-disparity-affine-v0-1", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]The model is fine-tuned from the stable-diffusion-2 model as
described in our papers, in inverse depth (disparity) space, with "trailing" and "zero-snr":
@misc{ke2025marigold,
title={Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis},
author={Bingxin Ke and Kevin Qu and Tianfu Wang and Nando Metzger and Shengyu Huang and Bo Li and Anton Obukhov and Konrad Schindler},
year={2025},
eprint={2505.09358},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@InProceedings{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},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}