update

.gitignore

@@ -0,0 +1,165 @@
+#  find ./ -type f -name '*.pyc' -exec git rm -f {} \;
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+# lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+*.npy
+.vscode
+training
+# *.txt
+*.log
+
+core*
+
+tmp
+logs

README.md

@@ -1,122 +1,9 @@
-# GaussianAnything: arXiv 2024
-
-## setup the environment (the same env as LN3Diff)
-
-```bash
-conda create -n ga python=3.10
-conda activate ga
-pip intall -r requrements.txt # will install the surfel Gaussians environments automatically.
-```
-
-Then, install pytorch3d with 
-```bash
-pip install git+https://github.com/facebookresearch/pytorch3d.git@stable
-```
-
-
-### :dromedary_camel: TODO
-
-- [x] Release inference code and checkpoints.
-- [x] Release Training code.
-- [x] Release pre-extracted latent codes for 3D diffusion training.
-- [ ] Release Gradio Demo.
-- [ ] Release the evaluation code.
-- [ ] Lint the code.
-
-
-# Inference
-
-Be aware to change the $logdir in the bash file accordingly.
-
-To load the checkpoint automatically: please replace ```/mnt/sfs-common/yslan/open-source``` with ```yslan/GaussianAnything/ckpts/checkpoints```.
-
-
-
-## Text-2-3D:
-
-Please update the caption for 3D generation in ```datasets/caption-forpaper.txt```. T o change the number of samples to be generated, please change ```$num_samples``` in the bash file.
-
-**stage-1**:
-```
-bash shell_scripts/release/inference/t23d/stage1-t23d.sh
-```
-then, set the ```$stage_1_output_dir``` to the ```$logdir``` of the above stage.
-
-**stage-2**: 
-```
-bash shell_scripts/release/inference/t23d/stage2-t23d.sh
-```
-
-The results will be dumped to ```./logs/t23d/stage-2```
-
-## I23D (requires two stage generation):
-
-set the $data_dir accordingly. For some demo image, please download from [huggingfac.co/yslan/GaussianAnything/demo-img](https://huggingface.co/yslan/GaussianAnything/tree/main/demo-img).
-
-**stage-1**:
-```
-bash shell_scripts/release/inference/i23d/i23d-stage1.sh
-```
-
-then, set the $stage_1_output_dir to the $logdir of the above stage.
-
-**stage-2**: 
-```
-bash shell_scripts/release/inference/i23d/i23d-stage1.sh
-```
-
-## 3D VAE Reconstruction:
-
-To encode a 3D asset into the latent point cloud, please download the pre-trained VAE checkpoint from [huggingfac.co/yslan/gaussiananything/ckpts/vae/model_rec1965000.pt](https://huggingface.co/yslan/GaussianAnything/blob/main/ckpts/vae/model_rec1965000.pt) to ```./checkpoint/model_rec1965000.pt```.
-
-Then, run the inference script
-
-```bash 
-bash shell_scripts/release/inference/vae-3d.sh
-```
-
-This will encode the mulit-view 3D renderings in ```./assets/demo-image-for-i23d/for-vae-reconstruction/Animals/0``` into the point-cloud structured latent code, and export them (along with the 2dgs mesh) in ```./logs/latent_dir/```. The exported latent code will be used for efficient 3D diffusion training.
-
-
-
-# Training (Flow Matching 3D Generation)
-All the training is conducted on 8 A100 (80GiB) with BF16 enabled. For training on V100, please use FP32 training by setting ```--use_amp``` False in the bash file. Feel free to tune the ```$batch_size``` in the bash file accordingly to match your VRAM.
-
-To facilitate reproducing the performance, we have uploaded the pre-extracted poind cloud-structured latent codes to the [huggingfac.co/yslan/gaussiananything/dataset/latent.tar.gz](https://huggingface.co/yslan/GaussianAnything/blob/main/dataset/latent.tar.gz) (34GiB required). Please download the pre extracted point cloud latent codes, unzip and set the ```$mv_latent_dir``` in the bash file accordingly.
-
-
-## Text to 3D:
-Please donwload the 3D caption from hugging face [huggingfac.co/yslan/GaussianAnything/dataset/text_captions_3dtopia.json](https://huggingface.co/yslan/GaussianAnything/blob/main/dataset/text_captions_3dtopia.json), and put it under ```dataset```.
-
-
-Note that if you want to train a specific class of Objaverse, just manually change the code at ```datasets/g_buffer_objaverse.py:3043```.
-
-**stage-1 training (point cloud generation)**:
-
-```
-bash shell_scripts/release/train/stage2-t23d/t23d-pcd-gen.sh
-```
-
-**stage-2 training (point cloud-conditioned KL feature generation)**:
-
-```
-bash shell_scripts/release/train/stage2-t23d/t23d-klfeat-gen.sh
-```
-
-## (single-view) Image to 3D
-Please download g-buffer dataset first.
-
-**stage-1 training (point cloud generation)**:
-
-```
-bash shell_scripts/release/train/stage2-i23d/i23d-pcd-gen.sh
-```
-
-**stage-2 training (point cloud-conditioned KL feature generation)**:
-
-```
-bash shell_scripts/release/train/stage2-i23d/i23d-klfeat-gen.sh
-```
-
-<!-- # Training (3D-aware VAE)
-Since the  -->
+title: GaussianAnything-AIGC3D
+emoji: 🌖
+colorFrom: green
+colorTo: green
+sdk: gradio
+sdk_version: 4.43.0
+app_file: app.py
+pinned: true
+license: mit