Files
nerfstudio/docs/nerfology/methods/index.md
T
b17268e83b [WIP] dreamfusion implementation (#1008)
* starting the framework for dreamfusion

* Removed view direction from RGB to calculate albedo

* Started diffusion

* Added dreamfusion to CLI, removed direction embeddings

* Normals in dreamfusion

* Lambertian shading added

* Train normals in Dreamfusion

* Stable diffusion from Stable Dreamfusion

* Fix normal orientation loss

* Configurable light direction sampling

* adding density initalization at origin

* V1 datamanager

* Don't train normals for first 1k iters

* SDS loss added to dreamfusion

* Dreamfusion pipeline

* Background MLP and SphereCollider

* Normals prediction training fixed

* Sigmoid on background, prompt in config

* Shading hyperparameter adjustements

* Shading hyperparameter tuning

* Location based prompting, density initialization fades, LR change

* Cleaned imports, more config options

* Tuning, more config changes

* remove unused sampler, normalize predicted normals from network

* change position encoding to direction encoding for background color mlp

* Updating dreamfusion to use custom trainer, fixed bug, multi-gpu support (put diffusion on one specific device), merged with main

* Hard coding less, offloading to config

* Appeasing the linter

* First pass opacity / sparsity losses

* Small patches and configs

* Accidentally pushed logs

* Removing logs for real this time

* Transmittance loss bug fix

* 12 GB Dreamfusion!

* Lint

* Remove unecissary collider changes

* Start SD cleanup

* Cleanup SD code

* adding overhead location prompting

* Use lambertian renderer

* turn grad accum off by default, decrease background mlp size, add overhead text embedding

* Cleanup

* Fix normals vis

* change uniform sampler to prediction network, add option to jit unet for faster sds_loss

* Use shaders

* cleaning up jit code

* add warning if using non jit unet

* add proposal network optimizer to config

* speed up render and cleanup code

* Clean up datamanager

* Add distortion loss

* fix wanb

* adding interpolated location sampling

* Update configs

* interpolated location prompting option, lowering init density, transmittance loss interpolation flipped

* rename alphas mult to alphas loss mult

* Mess around with normals

* add option to use random bg color + bg mlp

* Remove Dreamfusion Trainer (#1423)

* Dreamfusion trainer no longer needed

* Added typing for grad scaler

* SDS return loss

* Fix CFG

* Cleanup

* Update configs

* Fix opacity loss, use unform sampling first round of prop

* add prompt edditing to viewer

* Choose your own diffusion

* remove dreamfusion pipeline file

* merging + remove dreamfusion pipeline file

* Update configs

* add stable diff utils

* cleanup

* cleanup

* Fix test

* positional prompting bugfix

* Warmups

* tune

* lint

* Remove dataparser

* merging + text embedding bugfix

* Set data path to launch viewer for DreamFusion (#1776)

* Set data path to launch viewer for DreamFusion

* Apply formatter

* Fallback to 'base_dir' if data manager has no path

* Make if statement non-inline

* Reformat

---------

Co-authored-by: Matthew Tancik <mattt4001@gmail.com>

* add deepfloyd option first try

* merging

* merging from main

* merging

* removing comments, cleaning code, making stable diff utils usable for deepfloyd, remove deepfloyd text encoder after use

* undo chagnes in data folder

* cleaning code, removing unneccesary changes from main, removed normals prediction network

* remove old scripts files, rename generative pipeline flag to no_input_data, revert to default histogram padding in proposal sampler

* changing pipeline flag

* move no input data flag from pipeline to datamanager

* undo config and merge in deepfloyd from main

* merging grad scaler pipeline addition

* merging + setting proposal net scheduler

* revert nerfacto config change

* typing + linting

* rename dreamfusion datamanager to random camras datamanager, check datamanager typing in pipeline and trainer

* fix test import

* fix test import

* changing datamanager check in trainer, add typing in random cameras datamanager

* add Tensor to randomcamerasdatamanager

* remove old comments

* resolve tyro warning

* update fields to use tcnn

* lint

* add packages for deepfloyd

* add versions

* docs first draft

* rename to generfacto, clean docs

* fix import error

* minor changes

* fix orientation loss bug, retune params

* cleanup

* typing

* trying docs fix

* fixing generative package imports

* fix pyright

* docs fix

* merge w main

* docs minor change

* tuning + bugfixes

* remove torch.backends

---------

Co-authored-by: Abhik Ahuja <ahujaabhik@berkeley.edu>
Co-authored-by: David McAllister <davidrmc2@gmail.com>
Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
Co-authored-by: Matt Tancik <mattt4001@gmail.com>
Co-authored-by: SeungWoo Yoo <dreamy1534@gmail.com>
Co-authored-by: ayaanzhaque <ayaanzhaque@gmail.com>
Co-authored-by: Ayaan Haque <ayaanzhaque@berkeley.edu>
2023-06-17 23:34:04 -07:00

2.1 KiB

Methods

We provide a set of pre implemented nerfstudio methods.

The goal of nerfstudio is to modularize the various NeRF techniques as much as possible.

As a result, many of the techniques from these pre-implemented methods can be mixed 🎨.

Running a method

It's easy!

ns-train {METHOD_NAME}

To list the available methods run:

ns-train --help

Implemented Methods

The following methods are supported in nerfstudio:

    :maxdepth: 1
    Instant-NGP<instant_ngp.md>
    Instruct-NeRF2NeRF<in2n.md>
    K-Planes<kplanes.md>
    LERF<lerf.md>
    Mip-NeRF<mipnerf.md>
    NeRF<nerf.md>
    Nerfacto<nerfacto.md>
    NeRFPlayer<nerfplayer.md>
    Tetra-NeRF<tetranerf.md>
    TensoRF<tensorf.md>
    Generfacto<generfacto.md>

(own_method_docs)=

Adding Your Own Method

If you're a researcher looking to develop new NeRF-related methods, we hope that you find nerfstudio to be a useful tool. We've provided documentation about integrating with the nerfstudio codebase, which you can find here.

We also welcome additions to the list of methods above. To do this, simply create a pull request with the following changes,

  1. Add a markdown file describing the model to the docs/nerfology/methods folder
  2. Update the above list of implement methods in this file.
  3. Add the method to the {ref}this<third_party_methods> list in docs/index.md.
  4. Add a new ExternalMethod entry to the nerfstudio/configs/external_methods.py file.

For the method description, please refer to the Instruct-NeRF2NeRF page as an example of the layout. Please try to include the following information:

  • Installation instructions
  • Instructions for running the method
  • Requirements (dataset, GPU, ect)
  • Method description (the more detailed the better, treat it like a blog post)

You are welcome to include assets (such as images or video) in the description, but please host them elsewhere.

:::{admonition} Note :class: note

Please ensure that the documentation is clear and easy to understand for other users who may want to try out your method. :::