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Instruct-GS2GS: Editing 3D Scenes with Instructions

This codebase is a fork of the original Instruct-NeRF2NeRF repository.

Installation

1. Install Nerfstudio dependencies

Instruct-GS2GS is build on Nerfstudio and therefore has the same dependency reqirements. Specfically PyTorch and tinycudann are required.

Follow the instructions at this link to create the environment and install dependencies. Only follow the commands up to tinycudann. After the dependencies have been installed, return here.

2. Installing Instruct-GS2GS

Once you have finished installing dependencies, including those for gsplat, you can install Instruct-GS2GS using the following command:

pip install git+https://github.com/cvachha/instruct-gs2gs

Optional: If you would like to work with the code directly, clone then install the repo:

git clone https://github.com/cvachha/instruct-gs2gs.git
cd instruct-gs2gs
pip install --upgrade pip setuptools
pip install -e .

3. Checking the install

The following command should include igs2gs as one of the options:

ns-train -h

Using Instruct-GS2GS

To edit a GS, you must first train a regular splatfacto scene using your data. To process your custom data, please refer to this documentation.

Once you have your custom data, you can train your initial GS with the following command:

ns-train splatfacto --data {PROCESSED_DATA_DIR}

For more details on training a GS, see Nerfstudio documentation.

Once you have trained your scene for 20k iterations, the checkpoints will be saved to the outputs directory. Copy the path to the nerfstudio_models folder. (Note: We noticed that training for 20k iterations rather than 30k seemed to run more reliably)

To start training for editing the GS, run the following command:

ns-train igs2gs --data {PROCESSED_DATA_DIR} --load-dir {outputs/.../nerfstudio_models} --pipeline.prompt {"prompt"} --pipeline.guidance-scale 12.5 --pipeline.image-guidance-scale 1.5

The {PROCESSED_DATA_DIR} must be the same path as used in training the original GS. Using the CLI commands, you can choose the prompt and the guidance scales used for InstructPix2Pix.

After the GS is trained, you can render the GS using the standard Nerfstudio workflow, found here.

Training Notes

Important Please note that training the GS on images with resolution larger than 512 will likely cause InstructPix2Pix to throw OOM errors. Moreover, it seems InstructPix2Pix performs significantly worse on images at higher resolution. We suggest training with a resolution that is around 512 (max dimension), so add the following tag to the end of both your splatfacto and igs2gs training command: nerfstudio-data --downscale-factor {2,4,6,8} to the end of your ns-train commands. Alternatively, you can downscale your dataset yourself and update your transforms.json file (scale down w, h, fl_x, fl_y, cx, cy), or you can use a smaller image scale provided by Nerfstudio.

If you have multiple GPUs, training can be sped up by placing InstructPix2Pix on a separate GPU. To do so, add --pipeline.ip2p-device cuda:{device-number} to your training command.

Currently, we set the max number of iterations for igs2gs training to be 7.5k iteratios. Most often, the edit will look good after ~5k iterations. If you would like to train for longer, just reload your last igs2gs checkpoint and continue training, or change --max-num-iterations 10000.

Tips

If your edit isn't working as you desire, it is likely because InstructPix2Pix struggles with your images and prompt. We recommend taking one of your training views and trying to edit it in 2D first with InstructPix2Pix, which can be done at this HuggingFace space. More tips on getting a good edit can be found here.

Extending Instruct-GS2GS

Issues

Please open Github issues for any installation/usage problems you run into. We've tried to support as broad a range of GPUs as possible, but it might be necessary to provide even more low-footprint versions. Please contribute with any changes to improve memory usage!

Code structure

To build off Instruct-GS2GS, we provide explanations of the core code components.

igs2gs_datamanager.py: This file is almost identical to the base_datamanager.py in Nerfstudio. The main difference is that the entire dataset tensor is pre-computed in the setup_train method as opposed to being sampled in the next_train method each time.

igs2gs_pipeline.py: This file builds on the pipeline module in Nerfstudio. The get_train_loss_dict method samples images and places edited images back into the dataset.

ip2p.py: This file houses the InstructPix2Pix model (using the diffusers implementation). The edit_image method is where an image is denoised using the diffusion model, and a variety of helper methods are contained in this file as well.

igs2gs.py: We overwrite the get_loss_dict method to use LPIPs loss and L1Loss.

Bibtex

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