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https://github.com/threestudio-project/threestudio

A unified framework for 3D content generation.
https://github.com/threestudio-project/threestudio

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A unified framework for 3D content generation.

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    threestudio

    






threestudio is a unified framework for 3D content creation from text prompts, single images, and few-shot images, by lifting 2D text-to-image generation models.






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👆 Results obtained from methods implemented by threestudio 👆 


| ProlificDreamer | DreamFusion | Magic3D | SJC | Latent-NeRF | Fantasia3D | TextMesh |




| Zero-1-to-3 | Magic123 | HiFA | SDI |




| InstructNeRF2NeRF | Control4D |






  

  

  

  

  

  






    Did not find what you want? Checkout threestudio-extension or submit a feature request here!






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| Animate-124 | 4D-fy | GeoDream | DreamCraft3D | Dreamwaltz | 3DFuse | Progressive3D |  GaussianDreamer |  Gaussian Splatting | MVDream | Mesh-Fitting |




## News

- 08/11/2024: Thank [Artem Lukoianov](https://github.com/ottogin) for implementation of [Score Distillation via Reparametrized DDIM](https://lukoianov.com/sdi)! Text-to-3D module is added to Threestudio as well as a notebook with 2D score distillation experiments.

- 21/10/2024: Thank [Amir Barda](https://github.com/amirbarda) for implementation of [MagicClay](https://github.com/amirbarda/MagicClay)! Follow the instructions on its website to give it a try.

- 12/03/2024: Thank [Matthew Kwak](https://github.com/mskwak01) and [Inès Hyeonsu Kim](https://github.com/Ines-Hyeonsu-Kim) for implementation of [3DFuse](https://github.com/KU-CVLAB/3DFuse-threestudio)! Follow the instructions on its website to give it a try.

- 08/03/2024: Thank [Xinhua Cheng](https://github.com/cxh0519/) for implementation of [GaussianDreamer](https://github.com/cxh0519/threestudio-gaussiandreamer)! Follow the instructions on its website to give it a try.

- 01/03/2024: Thank [Xinhua Cheng](https://github.com/cxh0519/) for implementation of [Progressive3D](https://github.com/cxh0519/Progressive3D)! Follow the instructions on its website to give it a try.

- 09/01/2024: Thank [Zehuan Huang](https://github.com/huanngzh) for implementation of 3D human avatar generation [Dreamwaltz](https://github.com/huanngzh/threestudio-dreamwaltz)! Follow the instructions on its website to give it a try.

- 06/01/2024: Thank [Baorui Ma](https://github.com/mabaorui) for implementation of [GeoGream extensions](https://github.com/baaivision/GeoDream/tree/threestudio)! Follow the instructions on its website to give it a try.

- 05/01/2024: Implemented HiFA. Follow the instructions [here](https://github.com/threestudio-project/threestudio#hifa-) to try all three variants.

- 23/12/2023: Thank [Yuyang Zhao](https://github.com/HeliosZhao) for implementation of image-to-4D generation extensions [Animate-124](https://github.com/HeliosZhao/Animate124/tree/threestudio)! Follow the instructions on the extensions website to give it a try.

- 18/12/2023: Implementation of [4D-fy](https://github.com/DSaurus/threestudio-4dfy) for 4D generation and [DreamCraft3D](https://github.com/DSaurus/threestudio-dreamcraft3D) for high-quality image-to-3D generation as the custom extensions! Follow the instructions on the extensions website to give it a try.

- 13/12/2023: Implementation supporting [Stable Zero123](https://stability.ai/news/stable-zero123-3d-generation) for 3D generation from a single image! Follow the instructions [here](https://github.com/threestudio-project/threestudio#stable-zero123) to give it a try.

- 30/11/2023: Implementation of [MVDream](https://github.com/DSaurus/threestudio-mvdream), [Gaussian Splatting](https://github.com/DSaurus/threestudio-3dgs) as the custom extensions. You can also use neural representation to fit a mesh by [Mesh-Fitting](https://github.com/DSaurus/threestudio-meshfitting).

- 30/11/2023: Implementation of [custom extension system](https://threestudio-project.github.io/threestudio-extensions/) and you can add your extensions in [this project](https://github.com/threestudio-project/threestudio-extensions).

- 25/06/2023: Implementation of [Magic123](https://guochengqian.github.io/project/magic123/)! Follow the instructions [here](https://github.com/threestudio-project/threestudio#magic123-) to give it a try.

- 06/07/2023: Join our [Discord server](https://discord.gg/ejer2MAB8N) for lively discussions!

- 03/07/2023: Try text-to-3D online in [HuggingFace Spaces](https://huggingface.co/spaces/bennyguo/threestudio) or using our [self-hosted service](http://t23-g-01.threestudio.ai) (GPU support from Tencent). To host the web interface locally, see [here](https://github.com/threestudio-project/threestudio#gradio-web-interface).

- 20/06/2023: Implementations of Instruct-NeRF2NeRF and Control4D for high-fidelity 3D editing! Follow the instructions for [Control4D](https://github.com/threestudio-project/threestudio#control4d-) and [Instruct-NeRF2NeRF](https://github.com/threestudio-project/threestudio#instructnerf2nerf-) to give it a try.

- 14/06/2023: Implementation of TextMesh! Follow the instructions [here](https://github.com/threestudio-project/threestudio#textmesh-) to give it a try.

- 14/06/2023: Implementation of [prompt debiasing](https://arxiv.org/abs/2303.15413) and [Perp-Neg](https://perp-neg.github.io/)! Follow the instructions [here](https://github.com/threestudio-project/threestudio#tips-on-improving-quality) to give it a try.

- 29/05/2023: An experimental implementation of using [Zero-1-to-3](https://zero123.cs.columbia.edu/) for 3D generation from a single image! Follow the instructions [here](https://github.com/threestudio-project/threestudio#zero-1-to-3-) to give it a try.

- 26/05/2023: Implementation of [ProlificDreamer](https://ml.cs.tsinghua.edu.cn/prolificdreamer/)! Follow the instructions [here](https://github.com/threestudio-project/threestudio#prolificdreamer-) to give it a try.

- 14/05/2023: You can experiment with the SDS loss on 2D images using our [2dplayground](2dplayground.ipynb).

- 13/05/2023: You can now try threestudio on [Google Colab](https://colab.research.google.com/github/threestudio-project/threestudio/blob/main/threestudio.ipynb)!

- 11/05/2023: We now support exporting textured meshes! See [here](https://github.com/threestudio-project/threestudio#export-meshes) for instructions.



![export-blender](https://github.com/threestudio-project/threestudio/assets/19284678/ccae2820-e702-484c-a43f-81678a365427)



## Installation



See [installation.md](docs/installation.md) for additional information, including installation via Docker.



The following steps have been tested on Ubuntu20.04.



- You must have an NVIDIA graphics card with at least 6GB VRAM and have [CUDA](https://developer.nvidia.com/cuda-downloads) installed.

- Install `Python >= 3.8`.

- (Optional, Recommended) Create a virtual environment:



```sh

python3 -m virtualenv venv

. venv/bin/activate



# Newer pip versions, e.g. pip-23.x, can be much faster than old versions, e.g. pip-20.x.

# For instance, it caches the wheels of git packages to avoid unnecessarily rebuilding them later.

python3 -m pip install --upgrade pip

```



- Install `PyTorch >= 1.12`. We have tested on `torch1.12.1+cu113` and `torch2.0.0+cu118`, but other versions should also work fine.



```sh

# torch1.12.1+cu113

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113

# or torch2.0.0+cu118

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

```



- (Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions:



```sh

pip install ninja

```



- Install dependencies:



```sh

pip install -r requirements.txt

```



- (Optional) `tiny-cuda-nn` installation might require downgrading pip to 23.0.1



- (Optional, Recommended) The best-performing models in threestudio use the newly-released T2I model [DeepFloyd IF](https://github.com/deep-floyd/IF), which currently requires signing a license agreement. If you would like to use these models, you need to [accept the license on the model card of DeepFloyd IF](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0), and login into the Hugging Face hub in the terminal by `huggingface-cli login`.



- For contributors, see [here](https://github.com/threestudio-project/threestudio#contributing-to-threestudio).



## Quickstart



Here we show some basic usage of threestudio. First let's train a DreamFusion model to create a classic pancake bunny.



**If you are experiencing unstable connections with Hugging Face, we suggest you either (1) setting environment variable `TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 HF_HUB_OFFLINE=1` before your running command after all needed files have been fetched on the first run, to prevent from connecting to Hugging Face each time you run, or (2) downloading the guidance model you used to a local folder following [here](https://huggingface.co/docs/huggingface_hub/v0.14.1/guides/download#download-an-entire-repository) and [here](https://huggingface.co/docs/huggingface_hub/v0.14.1/guides/download#download-files-to-local-folder), and set `pretrained_model_name_or_path` of the guidance and the prompt processor to the local path.**



```sh

# if you have agreed the license of DeepFloyd IF and have >20GB VRAM

# please try this configuration for higher quality

python launch.py --config configs/dreamfusion-if.yaml --train --gpu 0 system.prompt_processor.prompt="a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes"

# otherwise you could try with the Stable Diffusion model, which fits in 6GB VRAM

python launch.py --config configs/dreamfusion-sd.yaml --train --gpu 0 system.prompt_processor.prompt="a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes"

```



threestudio uses [OmegaConf](https://github.com/omry/omegaconf) for flexible configurations. You can easily change any configuration in the YAML file by specifying arguments without `--`, for example the specified prompt in the above cases. For all supported configurations, please see our [documentation](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md).



The training lasts for 10,000 iterations. You can find visualizations of the current status in the trial directory which defaults to `[exp_root_dir]/[name]/[tag]@[timestamp]`, where `exp_root_dir` (`outputs/` by default), `name` and `tag` can be set in the configuration file. A 360-degree video will be generated after the training is completed. In training, press `ctrl+c` one time will stop training and head directly to the test stage which generates the video. Press `ctrl+c` the second time to fully quit the program.



### Multi-GPU training



Multi-GPU training is supported, but may still be [buggy](https://github.com/threestudio-project/threestudio/issues/195). Note that `data.batch_size` is the batch size **per rank (device)**. Also remember to



- Set `data.n_val_views` to be a multiple of the number of GPUs.

- Set a unique `tag` as timestamp is disabled in multi-GPU training and will not be appended after the tag. If you the same tag as previous trials, saved config files, code and visualizations will be overridden.



```sh

# this results in an effective batch size of 4 (number of GPUs) * 2 (data.batch_size) = 8

python launch.py --config configs/dreamfusion-if.yaml --train --gpu 0,1,2,3 system.prompt_processor.prompt="a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes" data.batch_size=2 data.n_val_views=4

```



If you define the `CUDA_VISIBLE_DEVICES` environment variable before you call `launch.py`, you don't need to specify `--gpu` - this will use all available GPUs from `CUDA_VISIBLE_DEVICES`. For instance, the following command will automatically use GPUs 3 and 4:



`CUDA_VISIBLE_DEVICES=3,4 python launch.py --config configs/dreamfusion-if.yaml --train system.prompt_processor.prompt="a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes"`



This is particularly useful if you run `launch.py` in a cluster using a command that automatically picks GPU(s) and exports their IDs through CUDA_VISIBLE_DEVICES, e.g. through SLURM:



```bash

cd git/threestudio

. venv/bin/activate

srun --account mod3d --partition=g40 --gpus=1 --job-name=3s_bunny python launch.py --config configs/dreamfusion-if.yaml --train system.prompt_processor.prompt="a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes"

```



### Resume from checkpoints



If you want to resume from a checkpoint, do:



```sh

# resume training from the last checkpoint, you may replace last.ckpt with any other checkpoints

python launch.py --config path/to/trial/dir/configs/parsed.yaml --train --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt

# if the training has completed, you can still continue training for a longer time by setting trainer.max_steps

python launch.py --config path/to/trial/dir/configs/parsed.yaml --train --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt trainer.max_steps=20000

# you can also perform testing using resumed checkpoints

python launch.py --config path/to/trial/dir/configs/parsed.yaml --test --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt

# note that the above commands use parsed configuration files from previous trials

# which will continue using the same trial directory

# if you want to save to a new trial directory, replace parsed.yaml with raw.yaml in the command



# only load weights from saved checkpoint but dont resume training (i.e. dont load optimizer state):

python launch.py --config path/to/trial/dir/configs/parsed.yaml --train --gpu 0 system.weights=path/to/trial/dir/ckpts/last.ckpt

```



### Export Meshes



To export the scene to texture meshes, use the `--export` option. We currently support exporting to obj+mtl, or obj with vertex colors.



```sh

# this uses default mesh-exporter configurations which exports obj+mtl

python launch.py --config path/to/trial/dir/configs/parsed.yaml --export --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt system.exporter_type=mesh-exporter

# specify system.exporter.fmt=obj to get obj with vertex colors

# you may also add system.exporter.save_uv=false to accelerate the process, suitable for a quick peek of the result

python launch.py --config path/to/trial/dir/configs/parsed.yaml --export --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt system.exporter_type=mesh-exporter system.exporter.fmt=obj

# for NeRF-based methods (DreamFusion, Magic3D coarse, Latent-NeRF, SJC)

# you may need to adjust the isosurface threshold (25 by default) to get satisfying outputs

# decrease the threshold if the extracted model is incomplete, increase if it is extruded

python launch.py --config path/to/trial/dir/configs/parsed.yaml --export --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt system.exporter_type=mesh-exporter system.geometry.isosurface_threshold=10.

# use marching cubes of higher resolutions to get more detailed models

python launch.py --config path/to/trial/dir/configs/parsed.yaml --export --gpu 0 resume=path/to/trial/dir/ckpts/last.ckpt system.exporter_type=mesh-exporter system.geometry.isosurface_method=mc-cpu system.geometry.isosurface_resolution=256

```



For all the options you can specify when exporting, see [the documentation](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#exporters).



See [here](https://github.com/threestudio-project/threestudio#supported-models) for example running commands of all our supported models. Please refer to [here](https://github.com/threestudio-project/threestudio#tips-on-improving-quality) for tips on getting higher-quality results, and [here](https://github.com/threestudio-project/threestudio#vram-optimization) for reducing VRAM usage.



### Gradio Web Interface



Launch the Gradio web interface by



```

python gradio_app.py launch

```



Parameters:



- `--listen`: listens to all addresses by setting `server_name="0.0.0.0"` when launching the Gradio app.

- `--self-deploy`: enables changing arbitrary configurations directly from the web.

- `--save`: enables checkpoint saving.



For feature requests, bug reports, or discussions about technical problems, please [file an issue](https://github.com/threestudio-project/threestudio/issues/new). In case you want to discuss the generation quality or showcase your generation results, please feel free to participate in the [discussion panel](https://github.com/threestudio-project/threestudio/discussions).



## Supported Models



### Score Distillation via Reparametrized DDIM (SDI) [![arXiv](https://img.shields.io/badge/arXiv-2405.15891-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2405.15891)



SDI suggests to reconsider the approach to sampling the noise term in Dreamfusion. The paper demonstrates that score distillation process can be seen as a reparametrization of 2D image sampling algorithms. In that case the noise added on each step of score distillation should be of a very particular form. Noise in Dreamfusion (SDS), however, is sampled randomly, what causes over-blurring. SDI approximates the correct noise term by inverting the DDIM process.



Notable differences from the paper: N/A.



Pros:

* High quality of the textures

* Sharp geometric details



Cons:

* Slower than SDS (1.5x times) due to additional inversion. Still faster then prolific dreamer due to lower number of steps

* Requires more VRAM than SDS due to higher resolution rendering. Decrease the resolution to fit to smaller GPUs.



**Results obtained in threestudio (Stable Diffusion, 512x512)**



A_DSLR_photo_of_a_freshly_baked_round_loaf_of_sourdough_bread

a_photograph_of_a_knight



**Example running commands**

```sh

python launch.py --config configs/sdi.yaml --train --gpu 0 system.prompt_processor.prompt="pumpkin head zombie, skinny, highly detailed, photorealistic"



python launch.py --config configs/sdi.yaml --train --gpu 1 system.prompt_processor.prompt="a photograph of a ninja"



python launch.py --config configs/sdi.yaml --train --gpu 2 system.prompt_processor.prompt="a zoomed out DSLR photo of a hamburger"



python launch.py --config configs/sdi.yaml --train --gpu 3 system.prompt_processor.prompt="bagel filled with cream cheese and lox"

```



### ProlificDreamer [![arXiv](https://img.shields.io/badge/arXiv-2305.16213-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2305.16213)



**This is an unofficial experimental implementation! Please refer to [https://github.com/thu-ml/prolificdreamer](https://github.com/thu-ml/prolificdreamer) for official code release.**



**Results obtained by threestudio (Stable Diffusion, 256x256 Stage1)**



https://github.com/threestudio-project/threestudio/assets/19284678/27b42d8f-4aa4-4b47-8ea0-0f77db90fd1e



https://github.com/threestudio-project/threestudio/assets/19284678/ffcbbb01-3817-4663-a2bf-5e21a076bc3d



**Results obtained by threestudio (Stable Diffusion, 256x256 Stage1, 512x512 Stage2+3)**



https://github.com/threestudio-project/threestudio/assets/19284678/cfab881e-18dc-45fc-8384-7476f835b36e



Notable differences from the paper:



- ProlificDreamer adopts a two-stage sampling strategy with 64 coarse samples and 32 fine samples, while we only use 512 coarse samples.

- In the first stage, we only render 64x64 images at the first 5000 iterations. After that, as the empty space has been effectively pruned, rendering 512x512 images wouldn't cost too much VRAM.

- We currently don't support multiple particles.



```sh

# --------- Stage 1 (NeRF) --------- #

# object generation with 512x512 NeRF rendering, ~30GB VRAM

python launch.py --config configs/prolificdreamer.yaml --train --gpu 0 system.prompt_processor.prompt="a pineapple"

# if you don't have enough VRAM, try training with 64x64 NeRF rendering, ~15GB VRAM

python launch.py --config configs/prolificdreamer.yaml --train --gpu 0 system.prompt_processor.prompt="a pineapple" data.width=64 data.height=64 data.batch_size=1

# using the same model for pretrained and LoRA enables 64x64 training with <10GB VRAM

# but the quality is worse due to the use of an epsilon prediction model for LoRA training

python launch.py --config configs/prolificdreamer.yaml --train --gpu 0 system.prompt_processor.prompt="a pineapple" data.width=64 data.height=64 data.batch_size=1 system.guidance.pretrained_model_name_or_path_lora="stabilityai/stable-diffusion-2-1-base"

# Using patch-based renderer to reduce memory consume, 512x512 resolution, ~20GB VRAM

python launch.py --config configs/prolificdreamer-patch.yaml --train --gpu 0 system.prompt_processor.prompt="a pineapple"

# scene generation with 512x512 NeRF rendering, ~30GB VRAM

python launch.py --config configs/prolificdreamer-scene.yaml --train --gpu 0 system.prompt_processor.prompt="Inside of a smart home, realistic detailed photo, 4k"



# --------- Stage 2 (Geometry Refinement) --------- #

# refine geometry with 512x512 rasterization, Stable Diffusion SDS guidance

python launch.py --config configs/prolificdreamer-geometry.yaml --train --gpu 0 system.prompt_processor.prompt="a pineapple" system.geometry_convert_from=path/to/stage1/trial/dir/ckpts/last.ckpt



# --------- Stage 3 (Texturing) --------- #

# texturing with 512x512 rasterization, Stable Difusion VSD guidance

python launch.py --config configs/prolificdreamer-texture.yaml --train --gpu 0 system.prompt_processor.prompt="a pineapple" system.geometry_convert_from=path/to/stage2/trial/dir/ckpts/last.ckpt

```

### HiFA [![arXiv](https://img.shields.io/badge/arXiv-2209.14988-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2305.18766)

**This is a re-implementation, missing some improvements from the original paper(coarse-to-fine NeRF sampling, kernel smoothing). For original results, please refer to [https://github.com/JunzheJosephZhu/HiFA](https://github.com/JunzheJosephZhu/HiFA)**



HiFA is more like a suite of improvements including image space SDS, z-variance loss, and noise strength annealing. It is compatible with most optimization-based methods. Therefore, we provide three variants based on DreamFusion, ProlificDreamer, and Magic123. We provide a unified guidance config as well as an SDS/VSD guidance config for the DreamFusion and ProlificDreamer variants, both configs should achieve the same results. Additionally, we also make HiFA compatible with ProlificDreamer-scene.



**Results obtained by threestudio(Dreamfusion-HiFA, 512x512)**



https://github.com/threestudio-project/threestudio/assets/24391451/c0030c66-0691-4ec2-8b79-d933101864a0



**Results obtained by threestudio(ProlificDreamer-HiFA, 512x512)**



https://github.com/threestudio-project/threestudio/assets/24391451/ff5dc4d0-d7d7-4a73-964e-84b8c48e2907



**Results obtained by threestudio(Magic123-HiFA, 512x512)**



https://github.com/threestudio-project/threestudio/assets/24391451/eb6f2f74-9143-4e26-8429-e300ad2d2b80



**Example running commands**



```sh

# ------ DreamFusion-HiFA ------- # (similar to original paper)

python launch.py --config configs/hifa.yaml --train --gpu 0 system.prompt_processor.prompt="a plate of delicious tacos"

python launch.py --config configs/experimental/unified-guidance/hifa.yaml --train --gpu 0 system.prompt_processor.prompt="a plate of delicious tacos"

# ------ ProlificDreamer-HiFA ------- #

python launch.py --config configs/prolificdreamer-hifa.yaml --train --gpu 0 system.prompt_processor.prompt="a plate of delicious tacos"

python launch.py --config configs/experimental/unified-guidance/prolificdreamer-hifa.yaml --train --gpu 0 system.prompt_processor.prompt="a plate of delicious tacos"

# ------ ProlificDreamer-scene-HiFA ------- #

python launch.py --config configs/prolificdreamer-scene-hifa.yaml --train --gpu 0 system.prompt_processor.prompt="A DSLR photo of a hamburger inside a restaurant"

# ------ Magic123-HiFA ------ #

python launch.py --config configs/magic123-hifa-coarse-sd.yaml --train --gpu 0 data.image_path=load/images/firekeeper_rgba.png system.prompt_processor.prompt="a toy figure of firekeeper from dark souls"

# We included a config for magic123's refine stage, but didn't really run it, since the coarse stage result already looks pretty decent.

```



**Tips**



- If the generated object's color seems oversaturated, decrease lambda_sds_img(or lambda_sd_img if using unified guidance).

- If the generated object looks cloudy, increase lamda_z_variance. If the shape becomes corrupted, decrease lambda_z_variance.

- If the generated object overall seems to have high luminance, increase min_step_percent.

- Make sure sqrt_anneal and use_img_loss are both set to True.

- Check out the [original repo](https://github.com/JunzheJosephZhu/HiFA)! The results are better.

- **If you are using sqrt_anneal, make sure system.guidance.trainer_max_steps is equal to trainer.max_steps, so noise strength annealing works correctly**



### DreamFusion [![arXiv](https://img.shields.io/badge/arXiv-2209.14988-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2209.14988)



**Results obtained by threestudio (DeepFloyd IF, batch size 8)**



https://user-images.githubusercontent.com/19284678/236694848-38ae4ea4-554b-4c9d-b4c7-fba5bee3acb3.mp4



**Notable differences from the paper**



- We use open-source T2I models (StableDiffusion, DeepFloyd IF), while the paper uses Imagen.

- We use a guidance scale of 20 for DeepFloyd IF, while the paper uses 100 for Imagen.

- We do not use sigmoid to normalize the albedo color but simply scale the color from `[-1,1]` to `[0,1]`, as we find this help convergence.

- We use HashGrid encoding and uniformly sample points along rays, while the paper uses Integrated Positional Encoding and sampling strategy from MipNeRF360.

- We adopt camera settings and density initialization strategy from Magic3D, which is slightly different from the DreamFusion paper.

- Some hyperparameters are different, such as the weighting of loss terms.



**Example running commands**



```sh

# uses DeepFloyd IF, requires ~15GB VRAM to extract text embeddings and ~10GB VRAM in training

# here we adopt random background augmentation to improve geometry quality

python launch.py --config configs/dreamfusion-if.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.background.random_aug=true

# uses StableDiffusion, requires ~6GB VRAM in training

python launch.py --config configs/dreamfusion-sd.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

```



**Tips**



- DeepFloyd IF performs **way better than** StableDiffusion.

- Validation shows albedo color before `system.material.ambient_only_steps` and shaded color after that.

- Try increasing/decreasing `system.loss.lambda_sparsity` if your scene is stuffed with floaters/becoming empty.

- Try increasing/decreasing `system.loss.lambda_orient` if you object is foggy/over-smoothed.

- Try replacing the background to random colors with a probability 0.5 by setting `system.background.random_aug=true` if you find the model incorrectly treats the background as part of the object.

- DeepFloyd IF uses T5-XXL as its text encoder, which consumes ~15GB VRAM even when using 8-bit quantization. This is currently the bottleneck for training with less VRAM. If anyone knows how to run the text encoder with less VRAM, please file an issue. We're also trying to push the text encoder to [Replicate](https://replicate.com/) to enable extracting text embeddings via API, but are having some network connection issues. Please [contact bennyguo](mailto:[email protected]) if you would like to help out.



### Magic3D [![arXiv](https://img.shields.io/badge/arXiv-2211.10440-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2211.10440)



**Results obtained by threestudio (DeepFloyd IF, batch size 8; first row: coarse, second row: refine)**



https://user-images.githubusercontent.com/19284678/236694858-0ed6939e-cd7a-408f-a94b-406709ae90c0.mp4



**Notable differences from the paper**



- We use open-source T2I models (StableDiffusion, DeepFloyd IF) for the coarse stage, while the paper uses eDiff-I.

- In the coarse stage, we use a guidance scale of 20 for DeepFloyd IF, while the paper uses 100 for eDiff-I.

- In the coarse stage, we use analytic normal, while the paper uses predicted normal.

- In the coarse stage, we use orientation loss as in DreamFusion, while the paper does not.

- There are many things that are omitted from the paper such as the weighting of loss terms and the DMTet grid resolution, which could be different.



**Example running commands**



First train the coarse stage NeRF:



```sh

# uses DeepFloyd IF, requires ~15GB VRAM to extract text embeddings and ~10GB VRAM in training

python launch.py --config configs/magic3d-coarse-if.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

# uses StableDiffusion, requires ~6GB VRAM in training

python launch.py --config configs/magic3d-coarse-sd.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

```



Then convert the NeRF from the coarse stage to DMTet and train with differentiable rasterization:



```sh

# the refinement stage uses StableDiffusion, and requires ~5GB VRAM in training

python launch.py --config configs/magic3d-refine-sd.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.geometry_convert_from=path/to/coarse/stage/trial/dir/ckpts/last.ckpt

# if you're unsatisfied with the surface extracted using the default threshold (25)

# you can specify a threshold value using `system.geometry_convert_override`

# decrease the value if the extracted surface is incomplete, increase if it is extruded

python launch.py --config configs/magic3d-refine-sd.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.geometry_convert_from=path/to/coarse/stage/trial/dir/ckpts/last.ckpt system.geometry_convert_override.isosurface_threshold=10.

```



**Tips**



- For the coarse stage, DeepFloyd IF performs **way better than** StableDiffusion.

- Magic3D uses a neural network to predict the surface normal, which may not resemble the true geometric normal and degrade geometry quality, so we use analytic normal instead.

- Try increasing/decreasing `system.loss.lambda_sparsity` if your scene is stuffed with floaters/becoming empty.

- Try increasing/decreasing `system.loss.lambda_orient` if you object is foggy/over-smoothed.

- Try replacing the background with random colors with a probability 0.5 by setting `system.background.random_aug=true` if you find the model incorrectly treats the background as part of the object.



### Score Jacobian Chaining [![arXiv](https://img.shields.io/badge/arXiv-2212.00774-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2212.00774)



**Results obtained by threestudio (Stable Diffusion)**



https://user-images.githubusercontent.com/19284678/236694871-87a247c1-2d3d-4cbf-89df-450bfeac3aca.mp4



Notable differences from the paper: N/A.



**Example running commands**



```sh

# train with sjc guidance in latent space

python launch.py --config configs/sjc.yaml --train --gpu 0 system.prompt_processor.prompt="A high quality photo of a delicious burger"

# train with sjc guidance in latent space, trump figure

python launch.py --config configs/sjc.yaml --train --gpu 0 system.prompt_processor.prompt="Trump figure" trainer.max_steps=30000 system.loss.lambda_emptiness="[15000,10000.0,200000.0,15001]" system.optimizer.params.background.lr=0.05 seed=42

```



**Tips**



- SJC uses subpixel rendering which decodes a `128x128` latent feature map for better visualization quality. You can turn off this feature by `system.subpixel_rendering=false` to save VRAM in validation/testing.



### Latent-NeRF [![arXiv](https://img.shields.io/badge/arXiv-2211.07600-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2211.07600)



**Results obtained by threestudio (Stable Diffusion)**



https://user-images.githubusercontent.com/19284678/236694876-5a270347-6a41-4429-8909-44c90c554e06.mp4



Notable differences from the paper: N/A.



We currently only implement Latent-NeRF for text-guided and Sketch-Shape for (text,shape)-guided 3D generation. Latent-Paint is not implemented yet.



**Example running commands**



```sh

# train Latent-NeRF in Stable Diffusion latent space

python launch.py --config configs/latentnerf.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger"

# refine Latent-NeRF in RGB space

python launch.py --config configs/latentnerf-refine.yaml --train --gpu 0 system.prompt_processor.prompt="a delicious hamburger" system.weights=path/to/latent/stage/trial/dir/ckpts/last.ckpt



# train Sketch-Shape in Stable Diffusion latent space

python launch.py --config configs/sketchshape.yaml --train --gpu 0 system.guide_shape=load/shapes/teddy.obj system.prompt_processor.prompt="a teddy bear in a tuxedo"

# refine Sketch-Shape in RGB space

python launch.py --config configs/sketchshape-refine.yaml --train --gpu 0 system.guide_shape=load/shapes/teddy.obj system.prompt_processor.prompt="a teddy bear in a tuxedo" system.weights=path/to/latent/stage/trial/dir/ckpts/last.ckpt

```



### Fantasia3D [![arXiv](https://img.shields.io/badge/arXiv-2303.13873-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2303.13873)



**Results obtained by threestudio (Stable Diffusion)**



https://user-images.githubusercontent.com/19284678/236694880-33b0db21-4530-47f1-9c3b-c70357bc84b3.mp4



**Results obtained by threestudio (Stable Diffusion, mesh initialization)**



https://github.com/threestudio-project/threestudio/assets/19284678/762903c1-665b-47b5-a2c2-bd7021a9e548.mp4





threestudio




Notable differences from the paper:



- We enable tangent-space normal perturbation by default, which can be turned off by appending `system.material.use_bump=false`.



**Example running commands**



```sh

# --------- Geometry --------- #

python launch.py --config configs/fantasia3d.yaml --train --gpu 0 system.prompt_processor.prompt="a DSLR photo of an ice cream sundae"

# Fantasia3D highly relies on the initialized SDF shape

# the default shape is a sphere with radius 0.5

# change the shape initialization to match your input prompt

python launch.py --config configs/fantasia3d.yaml --train --gpu 0 system.prompt_processor.prompt="The leaning tower of Pisa" system.geometry.shape_init=ellipsoid system.geometry.shape_init_params="[0.3,0.3,0.8]"

# or you can initialize from a mesh

# here shape_init_params is the scale of the shape

# also make sure to input the correct up and front axis (in +x, +y, +z, -x, -y, -z)

python launch.py --config configs/fantasia3d.yaml --train --gpu 0 system.prompt_processor.prompt="hulk" system.geometry.shape_init=mesh:load/shapes/human.obj system.geometry.shape_init_params=0.9 system.geometry.shape_init_mesh_up=+y system.geometry.shape_init_mesh_front=+z

# --------- Texture --------- #

# to train PBR texture continued from a geometry checkpoint:

python launch.py --config configs/fantasia3d-texture.yaml --train --gpu 0 system.prompt_processor.prompt="a DSLR photo of an ice cream sundae" system.geometry_convert_from=path/to/geometry/stage/trial/dir/ckpts/last.ckpt

```



**Tips**



- If you find the shape easily diverge in early training stages, you may use a lower guidance scale by setting `system.guidance.guidance_scale=30.`.



### TextMesh [![arXiv](https://img.shields.io/badge/arXiv-2304.12439-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2304.12439)



**Results obtained by threestudio (DeepFloyd IF, batch size 4)**



https://github.com/threestudio-project/threestudio/assets/19284678/72217cdd-765a-475b-92d0-4ab62bf0f57a



**Notable differences from the paper**



- Most of the settings are the same as the DreamFusion model. Please refer to the notable differences of the DreamFusion model.

- We use NeuS as the geometry representation while the original paper uses VolSDF.

- We adopt techniques from [Neuralangelo](https://arxiv.org/abs/2306.03092) to stabilize normal computation when using hash grids.

- We currently only implemented the coarse stage of TextMesh.



**Example running commands**



```sh

# uses DeepFloyd IF, requires ~15GB VRAM

python launch.py --config configs/textmesh-if.yaml --train --gpu 0 system.prompt_processor.prompt="lib:cowboy_boots"

```



**Tips**



- TextMesh uses a surface-based geometry representation, so you don't need to manually tune the isosurface threshold when exporting meshes!



### Control4D [![arXiv](https://img.shields.io/badge/arXiv-2305.20082-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2305.20082)



**This is an experimental implementation of Control4D using threestudio! Control4D will release the full code including static and dynamic editing after paper acceptance.**



**Results obtained by threestudio (512x512)**



https://github.com/threestudio-project/threestudio/assets/24589363/97d9aadd-32c7-488f-9543-6951b285d588



We currently don't support dynamic editing.



Download the data sample of control4D using this [link](https://mailstsinghuaeducn-my.sharepoint.com/:u:/g/personal/shaorz20_mails_tsinghua_edu_cn/EcqOaEuNwH1KpR0JTzL4Ur0BO_iJr8RiY2rNAGVC7h3fng?e=Dyr2gu).



**Example running commands**



```sh

# --------- Control4D --------- #

# static editing with 128x128 NeRF + 512x512 GAN rendering, ~20GB VRAM

python launch.py --config configs/control4d-static.yaml --train --gpu 0 data.dataroot="YOUR_DATAROOT/twindom" system.prompt_processor.prompt="Elon Musk wearing red shirt, RAW photo, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3"

```



### InstructNeRF2NeRF [![arXiv](https://img.shields.io/badge/arXiv-2303.12789-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2303.12789)



**Results obtained by threestudio**



https://github.com/threestudio-project/threestudio/assets/24589363/7aa43a2d-87d7-4ef5-94b6-f778ddb041b5



Download the data sample of InstructNeRF2NeRF using this [link](https://mailstsinghuaeducn-my.sharepoint.com/:u:/g/personal/shaorz20_mails_tsinghua_edu_cn/EbNazeNAYsBIvxGeXuCmOXgBiLv8KM-hfRNbNS7DtTvSvA?e=C1k4bM).



**Example running commands**



```sh

# --------- InstructNeRF2NeRF --------- #

# 3D editing with NeRF patch-based rendering, ~20GB VRAM

python launch.py --config configs/instructnerf2nerf.yaml --train --gpu 0 data.dataroot="YOUR_DATAROOT/face" data.camera_layout="front" data.camera_distance=1 data.eval_interpolation=[1,3,50] system.prompt_processor.prompt="Turn him into Albert Einstein"

```



### Magic123 [![arXiv](https://img.shields.io/badge/arXiv-2306.17843-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2306.17843)



**Results obtained by threestudio (Zero123 + Stable Diffusion)**



https://github.com/threestudio-project/threestudio/assets/19284678/335a58a8-8fee-485b-ac27-c55a16f4a673



**Notable differences from the paper**

- This is an unofficial re-implementation which shares the same overall idea with the [official implementation](https://github.com/guochengqian/Magic123) but differs in some aspects like hyperparameters.

- Textual Inversion is not supported, which means a text prompt is needed for training.



**Example running commands**



First train the coarse stage NeRF:



```sh

# Zero123 + Stable Diffusion, ~12GB VRAM

# data.image_path must point to a 4-channel RGBA image

# system.prompt_proessor.prompt must be specified

python launch.py --config configs/magic123-coarse-sd.yaml --train --gpu 0 data.image_path=load/images/hamburger_rgba.png system.prompt_processor.prompt="a delicious hamburger"

```



Then convert the NeRF from the coarse stage to DMTet and train with differentiable rasterization:



```sh

# Zero123 + Stable Diffusion, ~10GB VRAM

# data.image_path must point to a 4-channel RGBA image

# system.prompt_proessor.prompt must be specified

python launch.py --config configs/magic123-refine-sd.yaml --train --gpu 0 data.image_path=load/images/hamburger_rgba.png system.prompt_processor.prompt="a delicious hamburger" system.geometry_convert_from=path/to/coarse/stage/trial/dir/ckpts/last.ckpt

# if you're unsatisfied with the surface extracted using the default threshold (25)

# you can specify a threshold value using `system.geometry_convert_override`

# decrease the value if the extracted surface is incomplete, increase if it is extruded

python launch.py --config configs/magic123-refine-sd.yaml --train --gpu 0 data.image_path=load/images/hamburger_rgba.png system.prompt_processor.prompt="a delicious hamburger" system.geometry_convert_from=path/to/coarse/stage/trial/dir/ckpts/last.ckpt system.geometry_convert_override.isosurface_threshold=10.

```



**Tips**



- If the image contains non-front-facing objects, specifying the approximate elevation and azimuth angle by setting `data.default_elevation_deg` and `data.default_azimuth_deg` can be helpful. In threestudio, top is elevation +90 and bottom is elevation -90; left is azimuth -90 and right is azimuth +90.



### Stable Zero123



**Installation**



Download pretrained Stable Zero123 checkpoint `stable-zero123.ckpt` into `load/zero123` from https://huggingface.co/stabilityai/stable-zero123



**Results obtained by threestudio (Stable Zero123 vs Zero123-XL)**

![Final_video_v01](https://github.com/threestudio-project/threestudio/assets/22424247/bf2d2213-5027-489c-a6ba-1c56c14ee8b7)



**Direct multi-view images generation**

If you only want to generate multi-view images, please refer to [threestudio-mvimg-gen](https://github.com/DSaurus/threestudio-mvimg-gen). This extension can use Stable Zero123 to directly generate images from multi-view perspectives.



**Example running commands**



1. Take an image of your choice, or generate it from text using your favourite AI image generator such as SDXL Turbo (https://clipdrop.co/stable-diffusion-turbo) E.g. "A simple 3D render of a friendly dog"

2. Remove its background using Clipdrop (https://clipdrop.co/remove-background)

3. Save to `load/images/`, preferably with `_rgba.png` as the suffix

4. Run Zero-1-to-3 with the Stable Zero123 ckpt:

```sh

python launch.py --config configs/stable-zero123.yaml --train --gpu 0 data.image_path=./load/images/hamburger_rgba.png

```



**IMPORTANT NOTE: This is an experimental implementation and we're constantly improving the quality.**



**IMPORTANT NOTE: This implementation extends the Zero-1-to-3 implementation below, and is heavily inspired from the Zero-1-to-3 implementation in [https://github.com/ashawkey/stable-dreamfusion](stable-dreamfusion)! `extern/ldm_zero123` is borrowed from `stable-dreamfusion/ldm`.**



### Zero-1-to-3 [![arXiv](https://img.shields.io/badge/arXiv-2303.11328-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2303.11328)



**Installation**



Download pretrained Zero123XL weights into `load/zero123`:



```sh

cd load/zero123

wget https://zero123.cs.columbia.edu/assets/zero123-xl.ckpt

```



**Results obtained by threestudio (Zero-1-to-3)**



https://github.com/threestudio-project/threestudio/assets/22424247/f4e7b66f-7a46-4f9f-8fcd-750300cef651



**IMPORTANT NOTE: This is an experimental implementation and we're constantly improving the quality.**



**IMPORTANT NOTE: This implementation is heavily inspired from the Zero-1-to-3 implementation in [https://github.com/ashawkey/stable-dreamfusion](stable-dreamfusion)! `extern/ldm_zero123` is borrowed from `stable-dreamfusion/ldm`.**



**Example running commands**



1. Take an image of your choice, or generate it from text using your favourite AI image generator such as Stable Diffusion XL (https://clipdrop.co/stable-diffusion) E.g. "A simple 3D render of a friendly dog"

2. Remove its background using Clipdrop (https://clipdrop.co/remove-background)

3. Save to `load/images/`, preferably with `_rgba.png` as the suffix

4. Run Zero-1-to-3:

```sh

python launch.py --config configs/zero123.yaml --train --gpu 0 data.image_path=./load/images/dog1_rgba.png

```



For more scripts for Zero-1-to-3, please check `threestudio/scripts/run_zero123.sh`.



Previous Zero-1-to-3 weights are available at `https://huggingface.co/cvlab/zero123-weights/`. You can download them to `load/zero123` as above, and replace the path at `system.guidance.pretrained_model_name_or_path`.



**Guidance evaluation**



Also includes evaluation of the guidance during training. If `system.freq.guidance_eval` is set to a value > 0, this will save rendered image, noisy image (noise added mentioned at top left), 1-step-denoised image, 1-step prediction of original image, fully denoised image. For example:



![it143-train](https://github.com/threestudio-project/threestudio/assets/22424247/c8e7d835-4937-4852-bfb0-3e906e6b66b7)



### More to come, please stay tuned.



- [ ] [Dream3D](https://bluestyle97.github.io/dream3d/) [![arXiv](https://img.shields.io/badge/arXiv-2212.14704-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2212.14704)

- [ ] [DreamAvatar](https://yukangcao.github.io/DreamAvatar/) [![arXiv](https://img.shields.io/badge/arXiv-2304.00916-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2304.00916)



**If you would like to contribute a new method to threestudio, see [here](https://github.com/threestudio-project/threestudio#contributing-to-threestudio).**



## Prompt Library



For easier comparison, we collect the 397 preset prompts from the website of [DreamFusion](https://dreamfusion3d.github.io/gallery.html) in [this file](https://github.com/threestudio-project/threestudio/blob/main/load/prompt_library.json). You can use these prompts by setting `system.prompt_processor.prompt=lib:keyword1_keyword2_..._keywordN`. Note that the prompt should starts with `lib:` and all the keywords are separated by `_`. The prompt processor will match the keywords to all the prompts in the library, and will only succeed if there's **exactly one match**. The used prompt will be printed to the console. Also note that you can't use this syntax to point to every prompt in the library, as there are prompts that are subset of other prompts lmao. We will enhance the use of this feature.



## Tips on Improving Quality



It's important to note that existing techniques that lift 2D T2I models to 3D cannot consistently produce satisfying results. Results from great papers like DreamFusion and Magic3D are (to some extent) cherry-pickled, so don't be frustrated if you do not get what you expected on your first trial. Here are some tips that may help you improve the generation quality:



- **Increase batch size**. Large batch sizes help convergence and improve the 3D consistency of the geometry. State-of-the-art methods claim using large batch sizes: DreamFusion uses a batch size of 4; Magic3D uses a batch size of 32; Fantasia3D uses a batch size of 24; some results shown above use a batch size of 8. You can easily change the batch size by setting `data.batch_size=N`. Increasing the batch size requires more VRAM. If you have limited VRAM but still want the benefit of large batch sizes, you may use [gradient accumulation provided by PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/advanced/training_tricks.html#accumulate-gradients) by setting `trainer.accumulate_grad_batches=N`. This will accumulate the gradient of several batches and achieve a large effective batch size. Note that if you use gradient accumulation, you may need to multiply all step values by N times in your config, such as values that have the name `X_steps` and `trainer.val_check_interval`, since now N batches equal to a large batch.

- **Train longer.** This helps if you can already obtain reasonable results and would like to enhance the details. If the result is still a mess after several thousand steps, training for a longer time often won't help. You can set the total training iterations by `trainer.max_steps=N`.

- **Try different seeds.** This is a simple solution if your results have correct overall geometry but suffer from the multi-face Janus problem. You can change the seed by setting `seed=N`. Good luck!

- **Tuning regularization weights.** Some methods have regularization terms which can be essential to obtaining good geometry. Try tuning the weights of these regularizations by setting `system.loss.lambda_X=value`. The specific values depend on your situation, you may refer to [tips for each supported model](https://github.com/threestudio-project/threestudio#supported-models) for more detailed instructions.

- **Try debiasing methods.** When conventional SDS techniques like DreamFusion, Magic3D, SJC, and others fail to produce the desired 3D results, Debiased Score Distillation Sampling (D-SDS) can be a solution. D-SDS is devised to tackle challenges such as artifacts or the Janus problem, employing two strategies: score debiasing and prompt debiasing. You can activate score debiasing by just setting `system.guidance.grad_clip=[0,0.5,2.0,10000]`, where the order is `start_step, start_value, end_value, end_step`. You can enable prompt debiasing by setting `system.prompt_processor.use_prompt_debiasing=true`. When using prompt debiasing, it's recommended to set a list of indices for words that should potentially be removed by `system.prompt_processor.prompt_debiasing_mask_ids=[i1,i2,...]`. For example, if the prompt is `a smiling dog` and you only want to remove the word `smiling` for certain views, you should set it to `[1]`. You could also manually specify the prompt for each view by setting `system.prompt_processor.prompt_side`, `system.prompt_processor.prompt_back` and `system.prompt_processor.prompt_overhead`. For a detailed explanation of these techniques, refer to [the D-SDS paper](https://arxiv.org/abs/2303.15413) or check out [the project page](https://susunghong.github.io/Debiased-Score-Distillation-Sampling/).

- **Try Perp-Neg.** The [Perp-Neg algorithm](https://perp-neg.github.io/) can potentially alleviate the multi-face Janus problem. We now support Perp-Neg for `stable-diffusion-guidance` and `deep-floyd-guidance` by setting `system.prompt_processor.use_perp_neg=true`.



## VRAM Optimization



If you encounter CUDA OOM error, try the following in order (roughly sorted by recommendation) to meet your VRAM requirement.



- If you only encounter OOM at validation/test time, you can set `system.cleanup_after_validation_step=true` and `system.cleanup_after_test_step=true` to free memory after each validation/test step. This will slow down validation/testing.

- Use a smaller batch size or use gradient accumulation as demonstrated [here](https://github.com/threestudio-project/threestudio#tips-on-improving-quality).

- If you are using PyTorch1.x, enable [memory efficient attention](https://huggingface.co/docs/diffusers/optimization/fp16#memory-efficient-attention) by setting `system.guidance.enable_memory_efficient_attention=true`. PyTorch2.0 has built-in support for this optimization and is enabled by default.

- Enable [attention slicing](https://huggingface.co/docs/diffusers/optimization/fp16#sliced-attention-for-additional-memory-savings) by setting `system.guidance.enable_attention_slicing=true`. This will slow down training by ~20%.

- If you are using StableDiffusionGuidance, you can use [Token Merging](https://github.com/dbolya/tomesd) to **drastically** speed up computation and save memory. You can easily enable Token Merging by setting `system.guidance.token_merging=true`. You can also customize the Token Merging behavior by setting the parameters [here](https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L183-L213) to `system.guidance.token_merging_params`. Note that Token Merging may degrade generation quality.

- Enable [sequential CPU offload](https://huggingface.co/docs/diffusers/optimization/fp16#offloading-to-cpu-with-accelerate-for-memory-savings) by setting `system.guidance.enable_sequential_cpu_offload=true`. This could save a lot of VRAM but will make the training **extremely slow**.



## Documentation



threestudio use [OmegaConf](https://github.com/omry/omegaconf) to manage configurations. You can literally change anything inside the yaml configuration file or by adding command line arguments without `--`. We list all arguments that you can change in the configuration in our [documentation](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md). Happy experimenting!



## wandb (Weights & Biases) logging



To enable the (experimental) wandb support, set `system.loggers.wandb.enable=true`, e.g.:



```bash

python launch.py --config configs/zero123.yaml --train --gpu 0 system.loggers.wandb.enable=true`

```



If you're using a corporate wandb server, you may first need to login to your wandb instance, e.g.:

`wandb login --host=https://COMPANY_XYZ.wandb.io --relogin`



By default the runs will have a random name, recorded in the `threestudio` project. You can override them to give a more descriptive name, e.g.:



`python launch.py --config configs/zero123.yaml --train --gpu 0 system.loggers.wandb.enable=true system.loggers.wandb.name="zero123xl_accum;bs=4;lr=0.05"`



## Contributing to threestudio



- Fork the repository and create your branch from `main`.

- Install development dependencies:



```sh

pip install -r requirements-dev.txt

```



- If you are using VSCode as the text editor: (1) Install `editorconfig` extension. (2) Set the default linter to mypy to enable static type checking. (3) Set the default formatter to black. You could either manually format the document or let the editor format the document each time it is saved by setting `"editor.formatOnSave": true`.



- Run `pre-commit install` to install pre-commit hooks which will automatically format the files before commit.



- Make changes to the code, update README and DOCUMENTATION if needed, and open a pull request.



### Code Structure



Here we just briefly introduce the code structure of this project. We will make more detailed documentation about this in the future.



- All methods are implemented as a subclass of `BaseSystem` (in `systems/base.py`). There typically are six modules inside a system: geometry, material, background, renderer, guidance, and prompt_processor. All modules are subclass of `BaseModule` (in `utils/base.py`) except for guidance, and prompt_processor, which are subclass of `BaseObject` to prevent them from being treated as model parameters and better control their behavior in multi-GPU settings.

- All systems, modules, and data modules have their configurations in their own dataclasses.

- Base configurations for the whole project can be found in `utils/config.py`. In the `ExperimentConfig` dataclass, `data`, `system`, and module configurations under `system` are parsed to configurations of each class mentioned above. These configurations are strictly typed, which means you can only use defined properties in the dataclass and stick to the defined type of each property. This configuration paradigm (1) naturally supports default values for properties; (2) effectively prevents wrong assignments of these properties (say typos in the yaml file) or inappropriate usage at runtime.

- This projects use both static and runtime type checking. For more details, see `utils/typing.py`.

- To update anything of a module at each training step, simply make it inherit to `Updateable` (see `utils/base.py`). At the beginning of each iteration, an `Updateable` will update itself, and update all its attributes that are also `Updateable`. Note that subclasses of `BaseSystem`, `BaseModule` and `BaseObject` are by default inherited to `Updateable`.



## Known Problems



- Gradients of Vanilla MLP parameters are empty in AMP (temporarily fixed by disabling autocast).

- FullyFused MLP may cause NaNs in 32 precision.



## Credits



threestudio is built on the following amazing open-source projects:



- **[Lightning](https://github.com/Lightning-AI/lightning)** Framework for creating highly organized PyTorch code.

- **[OmegaConf](https://github.com/omry/omegaconf)** Flexible Python configuration system.

- **[NerfAcc](https://github.com/KAIR-BAIR/nerfacc)** Plug-and-play NeRF acceleration.



The following repositories greatly inspire threestudio:



- **[Stable-DreamFusion](https://github.com/ashawkey/stable-dreamfusion)**

- **[Latent-NeRF](https://github.com/eladrich/latent-nerf)**

- **[Score Jacobian Chaining](https://github.com/pals-ttic/sjc)**

- **[Fantasia3D.unofficial](https://github.com/ashawkey/fantasia3d.unofficial)**



Thanks to the maintainers of these projects for their contribution to the community!



## Citing threestudio



If you find threestudio helpful, please consider citing:



```

@Misc{threestudio2023,

  author =       {Yuan-Chen Guo and Ying-Tian Liu and Ruizhi Shao and Christian Laforte and Vikram Voleti and Guan Luo and Chia-Hao Chen and Zi-Xin Zou and Chen Wang and Yan-Pei Cao and Song-Hai Zhang},

  title =        {threestudio: A unified framework for 3D content generation},

  howpublished = {\url{https://github.com/threestudio-project/threestudio}},

  year =         {2023}

}

```