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CONTRIBUTING.md

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+# How to Contribute
+
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README.md

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+# Concept Decomposition for Visual Exploration and Inspiration
+
+<a href="https://inspirationtree.github.io/inspirationtree/"><img src="https://img.shields.io/static/v1?label=Project&message=Website&color=blue"></a>
+<a href="https://arxiv.org/abs/2305.18203"><img src="https://img.shields.io/badge/arXiv-2305.16311-b31b1b.svg"></a>
+<a href="https://www.apache.org/licenses/LICENSE-2.0.txt"><img src="https://img.shields.io/badge/License-Apache-yellow"></a>
+<!-- Official implementation. -->
+<br>
+
+
+<p align="center">
+<img src="repo_images/teaser.jpeg" width="90%"/>  
+  
+> <a href="https://inspirationtree.github.io/inspirationtree/">**Concept Decomposition for Visual Exploration and Inspiration**</a>
+>
+> <a href="https://yael-vinker.github.io/website/">Yael Vinker</a>,
+<a href="https://scholar.google.com/citations?user=imBjSgUAAAAJ&hl=ru">Andrey Voynov</a>,
+<a href="https://danielcohenor.com/">Daniel Cohen-Or</a>,
+<a href="https://faculty.runi.ac.il/arik/site/index.asp">Ariel Shamir</a>
+> <br>
+> Our method provides a tree-structured visual exploration space for a given unique concept. The nodes of the tree ("v_i") are newly learned textual vector embeddings, injected to the latent space of a pretrained text-to-image model. The nodes encode different aspects of the subject of interest. Through examining combinations within > and across trees, the different aspects can inspire the creation of new designs and concepts, as can be seen below.
+</p>
+
+# Setup
+```
+git clone https://github.com/yael-vinker/inspiration_tree.git
+```
+
+## Environment (with pip)
+Our code relies on the enviornment in the official [Stable Diffusion repository](https://github.com/CompVis/stable-diffusion). To set up their environment, please run:
+```
+python -m venv .tree_venv
+source .tree_venv/bin/activate
+pip install -r requirements.txt
+```
+Technical details:
+* CUDA 11.6
+* torch 1.7.1+cu110
+
+**Hugging Face Diffusers Library**  
+Our code relies on the [diffusers](https://github.com/huggingface/diffusers) library and the official [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4) model.
+
+# Usage
+<p align="center">
+<img src="repo_images/usage.jpg" width="50%"/>  
+<br>
+This code will allow you to generate a tree per concept, and play with the different prompts and combinations for the generated tree (under "inspiration_tree_playground.ipynb").
+</p>
+
+
+## Pretrained Models and Datasets
+As part of our code release and to assist with comparisons, we have also provided some of the trained models and datasets used in the paper. 
+
+All of our models (learned tokens from the paper) can be found undeer the "learned_tokens" directory. The notebook inspiration_tree_playground.ipynb shows how to load them and reproduce the results from the paper (under "Play with learned aspects from the paper"). 
+All datasets used from Textual Inversion can be found under "datasets".
+
+## Generate your tree
+The logic for generating the tree is under "main_multiseed.py", which runs the framwork for a <b>single node</b>.
+You can generate the tree by passing your own parameters to main_multiseed.py. An example is given in "run_decompose.sh":
+```
+python main_multiseed.py --parent_data_dir "cat_sculpture/" --node v0 --test_name "v0" --GPU_ID "${GPU_ID}" --multiprocess 0
+```
+Notes:
+- The "test_name" should be identical to the chosen node
+Results:
+- The results will be saved to "outputs/<parent_data_dir>/<node>"
+- In "final_samples.jpg" you can see a batch of random samples of the learned nodes
+- Under "consistency_test" we save the results of the seed selection procedure
+- Once training is finished, you can continue with splitting the generated nodes as well
+
+## Inference
+We made a notebook ("inspiration_tree_playground.ipynb") to play with the results, and this should be the most convenient option.
+Notes: 
+- The notebook shows the concepts learned in each node
+- You can also load checkpoints from early iterations
+- You can generate new concepts using natural language sentences
+
+# Acknowledgements
+Our code builds on the [diffusers implementation of textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) 
+
+## Citation
+If you find this useful for your research, please cite the following:
+```bibtex
+@article{vinker2023concept,
+  title={Concept Decomposition for Visual Exploration and Inspiration},
+  author={Yael Vinker and Andrey Voynov and Daniel Cohen-Or and Ariel Shamir},
+  journal={arXiv preprint arXiv:2305.18203},
+  year={2023}
+}
+```
+
+## Disclaimer
+
+This is not an officially supported Google product.