Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Release new model and fix some issues #9

Merged
merged 1 commit into from
Mar 4, 2025
Merged

Release new model and fix some issues #9

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 9 additions & 10 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,11 +1,9 @@
# Open Driving World Models (OpenDWM)

[[中文简介](README_intro_zh.md)]
[![Youtube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/j9RRj-xzOA4) [<img src=https://img.shields.io/badge/%E4%B8%AD%E6%96%87%E7%AE%80%E4%BB%8B-blue>](README_intro_zh.md)

https://github.com/user-attachments/assets/649d3b81-3b1f-44f9-9f51-4d1ed7756476

[Video link](https://youtu.be/j9RRj-xzOA4)

Welcome to the OpenDWM project! This is an open-source initiative, focusing on autonomous driving video generation. Our mission is to provide a high-quality, controllable tool for generating autonomous driving videos using the latest technology. We aim to build a codebase that is both user-friendly and highly reusable, and hope to continuously improve the project through the collective wisdom of the community.

The driving world models generate multi-view images or videos of autonomous driving scenes based on text and road environment layout conditions. Whether it's the environment, weather conditions, vehicle type, or driving path, you can adjust them according to your needs.
Expand Down Expand Up @@ -49,6 +47,7 @@ python -m pip install torch==2.5.1 torchvision==0.20.1
```

Clone the repository, then install the dependencies.

```
cd DWM
git submodule update --init --recursive
Expand All @@ -62,27 +61,27 @@ Our cross-view temporal SD (CTSD) pipeline support loading the pretrained SD 2.1
| Base model | Text conditioned <br/> driving generation | Text and layout (box, map) <br/> conditioned driving generation |
| :-: | :-: | :-: |
| [SD 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) | [Config](configs/ctsd/multi_datasets/ctsd_21_tirda_nwao.json), [Download](http://103.237.29.236:10030/ctsd_21_tirda_nwao_30k.pth) | [Config](configs/ctsd/multi_datasets/ctsd_21_tirda_bm_nwa.json), [Download](http://103.237.29.236:10030/ctsd_21_tirda_bm_nwa_30k.pth) |
| [SD 3.0](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) | | [UniMLVG Config](configs/ctsd/unimlvg/unimlvg_stage3_tirda_nwa.json), [Download](http://103.237.29.236:10030/ctsd_unimlvg_tirda_bm_nwa_60k.pth) |
| [SD 3.5](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) | [Config](configs/ctsd/multi_datasets/ctsd_35_tirda_nwao.json), [Download](http://103.237.29.236:10030/ctsd_35_tirda_nwao_20k.pth) | [Config](configs/ctsd/multi_datasets/ctsd_35_tirda_bm_nwa.json), Released by 2025-3-1 |
| [SD 3.0](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) | | [UniMLVG Config](configs/ctsd/unimlvg/ctsd_unimlvg_stage3_tirda_bm_nwa.json), [Download](http://103.237.29.236:10030/ctsd_unimlvg_tirda_bm_nwa_60k.pth) |
| [SD 3.5](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) | [Config](configs/ctsd/multi_datasets/ctsd_35_tirda_nwao.json), [Download](http://103.237.29.236:10030/ctsd_35_tirda_nwao_20k.pth) | [Config](configs/ctsd/multi_datasets/ctsd_35_tirda_bm_nwao.json), [Download](http://103.237.29.236:10030/ctsd_35_tirda_bm_nwao_40k.pth) |

## Examples

### T2I, T2V generation with CTSD pipeline

Download base model (for VAE, text encoders, scheduler config) and driving generation model checkpoint, and edit the [path](examples/ctsd_35_6views_image_generation.json#L102) and [prompts](examples/ctsd_35_6views_image_generation.json#L221) in the JSON config, then run this command.

```
```bash
PYTHONPATH=src python examples/ctsd_generation_example.py -c examples/ctsd_35_6views_image_generation.json -o output/ctsd_35_6views_image_generation
```

### Layout conditioned T2V generation with CTSD pipeline

1. Download base model (for VAE, text encoders, scheduler config) and driving generation model checkpoint, and edit the [path](examples/ctsd_21_6views_video_generation_with_layout.json#L119) in the JSON config.
2. Download layout resource package [nuscenes_scene-0627_package.zip](http://103.237.29.236:10030/nuscenes_scene-0627_package.zip) and unzip to the `{RESOURCE_PATH}`. Then edit the meta [path](examples/ctsd_21_6views_video_generation_with_layout.json#L129) as `{RESOURCE_PATH}/data.json` in the JSON config.
1. Download base model (for VAE, text encoders, scheduler config) and driving generation model checkpoint, and edit the [path](examples/ctsd_35_6views_video_generation_with_layout.json#L156) in the JSON config.
2. Download layout resource package ([nuscenes_scene-0627_package.zip](http://103.237.29.236:10030/nuscenes_scene-0627_package.zip), or [carla_town04_package](http://103.237.29.236:10030/carla_town04_package.zip)) and unzip to the `{RESOURCE_PATH}`. Then edit the meta [path](examples/ctsd_35_6views_video_generation_with_layout.json#L162) as `{RESOURCE_PATH}/data.json` in the JSON config.
3. Run this command to generate the video.

```
PYTHONPATH=src python src/dwm/preview.py -c examples/ctsd_unimlvg_6views_video_generation.json -o output/ctsd_unimlvg_6views_video_generation
```bash
PYTHONPATH=src python src/dwm/preview.py -c examples/ctsd_35_6views_video_generation_with_layout.json -o output/ctsd_35_6views_video_generation_with_layout
```

## Train
Expand Down
Loading