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# ApDepth: Aiming for Precise Monocular Depth Estimation Based on Diffusion Models

This repository is based on [Marigold](https://marigoldmonodepth.github.io), CVPR 2024 Best Paper: [**Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation**](https://arxiv.org/abs/2312.02145)

[![Website](doc/badges/badge-website.svg)](https://haruko386.github.io/research)
[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)
[![Static Badge](https://img.shields.io/badge/build-Haruko386-brightgreen?style=flat&logo=steam&logoColor=white&logoSize=auto&label=steam&labelColor=black&color=gray&cacheSeconds=3600)](https://steamcommunity.com/profiles/76561198217881431/)
[![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/developy/ApDepth)
[![Hugging Face Demo](https://img.shields.io/badge/🤗%20Hugging%20Face-Demo-purple)](https://huggingface.co/spaces/developy/ApDepth)

[Haruko386](https://haruko386.github.io/),
[Shuai Yuan](https://syjz.teacher.360eol.com/teacherBasic/preview?teacherId=23776)

![cover](doc/cover.jpg)

>We present **ApDepth**, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Based on Marigold. Its core innovation lies in addressing the deficiency of diffusion models in feature representation capability. Our model followed Marigold, derived from Stable Diffusion and fine-tuned with synthetic data: Hypersim and VKitti, achieved ideal results in object edge refinement.

## 📢 News
- 2025-10-09: We propose a novel diffusion-based deep estimation framework guided by pre-trained models.
- 2025-09-23: We change Marigold from **Stochastic multi-step generation** to **Deterministic one-step perception**
- 2025-08-10: Trying to make some optimizations in Feature Expression<br>
- 2025-05-08: Clone Marigold to local.<br>

## 🚀 Usage

**We offer several ways to interact with Marigold**:

1. A free online interactive demo is available here: <a href="https://huggingface.co/spaces/developy/ApDepth"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Demo-purple" height="18"></a>

2. If you just want to see the examples, visit our gallery: <a href="https://haruko386.github.io/research"><img src="doc/badges/badge-website.svg" height="16"></a>

3. Local development instructions with this codebase are given below.

## 🛠️ Setup

The inference code was tested on:

- Ubuntu 22.04 LTS, Python 3.12.9,  CUDA 11.8, GeForce RTX 4090 & GeForce RTX 5080 (pip)

### 🪧 A Note for Windows users

We recommend running the code in WSL2:

1. Install WSL following [installation guide](https://learn.microsoft.com/en-us/windows/wsl/install#install-wsl-command).
1. Install CUDA support for WSL following [installation guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl-2).
1. Find your drives in `/mnt/<drive letter>/`; check [WSL FAQ](https://learn.microsoft.com/en-us/windows/wsl/faq#how-do-i-access-my-c--drive-) for more details. Navigate to the working directory of choice. 

### 📦 Repository

Clone the repository (requires git):

```bash
git clone https://github.com/Haruko386/ApDepth.git
cd ApDepth
```

### 💻 Dependencies

 **Using Conda:** 
    Alternatively, create a Python native virtual environment and install dependencies into it:

    conda create -n apdepth python==3.12.9
    conda activate apdepth
    pip install -r requirements.txt

Keep the environment activated before running the inference script. 
Activate the environment again after restarting the terminal session.

## 🏃 Testing on your images

### 📷 Prepare images

1. Use selected images under `input`

1. Or place your images in a directory, for example, under `input/test-image`, and run the following inference command.

### 🎮 Run inference with paper setting

This setting corresponds to our paper. For academic comparison, please run with this setting.

```bash
python run.py \
    --checkpoint prs-eth/marigold-v1-0 \
    --denoise_steps 50 \
    --ensemble_size 10 \
    --input_rgb_dir input/in-the-wild_example \
    --output_dir output/in-the-wild_example
```

You can find all results in `output/in-the-wild_example`. Enjoy!

### ⚙️ Inference settings

The default settings are optimized for the best result. However, the behavior of the code can be customized:

- Trade-offs between the **accuracy** and **speed** (for both options, larger values result in better accuracy at the cost of slower inference.)
  - `--ensemble_size`: Number of inference passes in the ensemble. For LCM `ensemble_size` is more important than `denoise_steps`. Default: ~~10~~ 5 (for LCM).
  - `--denoise_steps`: Number of denoising steps of each inference pass. For the original (DDIM) version, it's recommended to use 10-50 steps, while for LCM 1-4 steps. When unassigned (`None`), will read default setting from model config. Default: ~~10 4 (for LCM)~~ `None`.

- By default, the inference script resizes input images to the *processing resolution*, and then resizes the prediction back to the original resolution. This gives the best quality, as Stable Diffusion, from which Marigold is derived, performs best at 768x768 resolution.  
  
  - `--processing_res`: the processing resolution; set as 0 to process the input resolution directly. When unassigned (`None`), will read default setting from model config. Default: ~~768~~ `None`.
  - `--output_processing_res`: produce output at the processing resolution instead of upsampling it to the input resolution. Default: False.
  - `--resample_method`: the resampling method used to resize images and depth predictions. This can be one of `bilinear`, `bicubic`, or `nearest`. Default: `bilinear`.

- `--half_precision` or `--fp16`: Run with half-precision (16-bit float) to have faster speed and reduced VRAM usage, but might lead to suboptimal results.
- `--seed`: Random seed can be set to ensure additional reproducibility. Default: None (unseeded). Note: forcing `--batch_size 1` helps to increase reproducibility. To ensure full reproducibility, [deterministic mode](https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms) needs to be used.
- `--batch_size`: Batch size of repeated inference. Default: 0 (best value determined automatically).
- `--color_map`: [Colormap](https://matplotlib.org/stable/users/explain/colors/colormaps.html) used to colorize the depth prediction. Default: Spectral. Set to `None` to skip colored depth map generation.
- `--apple_silicon`: Use Apple Silicon MPS acceleration.

### ⬇ Checkpoint cache

By default, the [checkpoint](https://huggingface.co/prs-eth/marigold-v1-0) is stored in the Hugging Face cache.
The `HF_HOME` environment variable defines its location and can be overridden, e.g.:

```bash
export HF_HOME=$(pwd)/cache
```

Alternatively, use the following script to download the checkpoint weights locally:

```bash
bash script/download_weights.sh marigold-v1-0
# or LCM checkpoint
bash script/download_weights.sh marigold-lcm-v1-0
```

At inference, specify the checkpoint path:

```bash
python run.py \
    --checkpoint checkpoint/marigold-v1-0 \
    --denoise_steps 50 \
    --ensemble_size 10 \
    --input_rgb_dir input/in-the-wild_example\
    --output_dir output/in-the-wild_example
```

## 🦿 Evaluation on test datasets <a name="evaluation"></a>

Install additional dependencies:

```bash
pip install -r requirements+.txt -r requirements.txt
```

Set data directory variable (also needed in evaluation scripts) and download [evaluation datasets](https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset) into corresponding subfolders:

```bash
export BASE_DATA_DIR=<YOUR_DATA_DIR>  # Set target data directory

wget -r -np -nH --cut-dirs=4 -R "index.html*" -P ${BASE_DATA_DIR} https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset/
```

Run inference and evaluation scripts, for example:

```bash
# Run inference
bash script/eval/11_infer_nyu.sh

# Evaluate predictions
bash script/eval/12_eval_nyu.sh
```

Note: although the seed has been set, the results might still be slightly different on different hardware.

## 🏋️ Training

Based on the previously created environment, install extended requirements:

```bash
pip install -r requirements++.txt -r requirements+.txt -r requirements.txt
```

Set environment parameters for the data directory:

```bash
export BASE_DATA_DIR=YOUR_DATA_DIR  # directory of training data
export BASE_CKPT_DIR=YOUR_CHECKPOINT_DIR  # directory of pretrained checkpoint
```

Download Stable Diffusion v2 [checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2) into `${BASE_CKPT_DIR}`

Prepare for [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/dataset_preprocess/hypersim/README.md) for Hypersim preprocessing.

Run training script

```bash
python train.py --config config/train_marigold.yaml --no_wandb
```

Resume from a checkpoint, e.g.

```bash
python train.py --resume_run output/train_marigold/checkpoint/latest --no_wandb
```

Evaluating results

Only the U-Net is updated and saved during training. To use the inference pipeline with your training result, replace `unet` folder in Marigold checkpoints with that in the `checkpoint` output folder. Then refer to [this section](#evaluation) for evaluation.

**Note**: Although random seeds have been set, the training result might be slightly different on different hardwares. It's recommended to train without interruption.

## ✏️ Contributing

Please refer to [this](CONTRIBUTING.md) instruction.

## 🤔 Troubleshooting

| Problem                                                                                                                                      | Solution                                                       |
|----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------|
| (Windows) Invalid DOS bash script on WSL                                                                                                     | Run `dos2unix <script_name>` to convert script format          |
| (Windows) error on WSL: `Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory` | Run `export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH` |


## 🎓 Citation
Waitting for publishing⏱️
<!-- Please cite our paper:

```bibtex
@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}
}
``` -->

## 🎫 License

This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).

By downloading and using the code and model you agree to the terms in the  [LICENSE](LICENSE.txt).

[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)