File size: 2,104 Bytes
6a27cf6 f87dbde 6a27cf6 f87dbde 6a27cf6 f87dbde bff81ae f87dbde bff81ae f87dbde 01c40b7 f87dbde bff81ae f87dbde bff81ae f87dbde bff81ae f87dbde 6145115 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
license: apache-2.0
language:
- en
tags:
- depth-estimation
- colonoscopy
- medical-imaging
- video
- lora
- diffusion
library_name: transformers
base_model:
- tencent/DepthCrafter
- stabilityai/stable-video-diffusion-img2vid-xt
pipeline_tag: depth-estimation
---
# ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors
ColonCrafter builds upon [DepthCrafter](https://huggingface.co/tencent/DepthCrafter) and [Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) to provide temporally consistent depth predictions for colonoscopy video.
## Model Details
- **Model Type:** Video Depth Estimation (Diffusion-based)
- **Base Architecture:** DepthCrafter UNet with LoRA adaptation
- **LoRA Configuration:**
- Rank: 16
- Target modules: `to_q`, `to_k`, `to_v`, `to_out.0`
- Dropout: 0.1
- **Precision:** FP16
## Installation
Please refer to the installation instructions in our [repository](https://github.com/rajpurkarlab/ColonCrafter).
## Usage
```python
import torch
from src.depth.models.model import ColonCrafterInference
# Load the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ColonCrafterInference.from_pretrained(
"romainhardy/coloncrafter",
device=device
)
# Prepare video tensor: (N, C, H, W) in [0, 1] range
# video = ...
# Run inference
pred_depth, pred_disparity = model.predict_depth(
video,
num_inference_steps=1,
window_size=16,
overlap=8,
guidance_scale=1.0,
seed=42
)
```
## Citation
If you use this model in your research, please cite:
```bibtex
@article{hardy2025coloncrafter,
title={ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors},
author={Hardy, Romain and Berzin, Tyler and Rajpurkar, Pranav},
journal={arXiv preprint arXiv:2509.13525},
year={2025}
}
```
## Acknowledgments
This model builds upon [DepthCrafter](https://github.com/Tencent/DepthCrafter) and [Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt). |