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- .gitattributes +24 -0
- CONTRIBUTING.md +33 -0
- LICENSE +201 -0
- README.md +93 -0
- datasets/buddha/IMG_4201 Large.png +3 -0
- datasets/buddha/IMG_4202 Large.png +3 -0
- datasets/buddha/IMG_4203 Large.png +3 -0
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- datasets/bull1/IMG_4232 Large.png +3 -0
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- datasets/canada_bear/IMG_4224 Large.png +3 -0
- datasets/canada_bear/IMG_4225 Large.png +3 -0
- datasets/canada_bear/IMG_4226 Large.png +3 -0
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- datasets/canada_bear/IMG_4229 Large.png +3 -0
- datasets/green_dall/IMG_4182 Large.png +3 -0
- datasets/green_dall/IMG_4183 Large.png +3 -0
- datasets/green_dall/IMG_4186 Large.png +3 -0
- datasets/green_dall/IMG_4187 Large.png +3 -0
- datasets/green_dall/IMG_4189 Large.png +3 -0
- datasets/green_dall/IMG_4190 Large.png +3 -0
- datasets/green_dall/IMG_4192 Large.png +3 -0
- datasets/mug_buildings/a.jpeg +0 -0
- datasets/mug_buildings/b.jpeg +0 -0
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- input_concepts/cat_sculpture/v0/1.jpeg +0 -0
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- input_concepts/cat_sculpture/v1/7.jpg +0 -0
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CONTRIBUTING.md
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# How to Contribute
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## Before you begin
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LICENSE
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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README.md
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@@ -0,0 +1,93 @@
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|
1 |
+
# Concept Decomposition for Visual Exploration and Inspiration
|
2 |
+
|
3 |
+
<a href="https://inspirationtree.github.io/inspirationtree/"><img src="https://img.shields.io/static/v1?label=Project&message=Website&color=blue"></a>
|
4 |
+
<a href="https://arxiv.org/abs/2305.18203"><img src="https://img.shields.io/badge/arXiv-2305.16311-b31b1b.svg"></a>
|
5 |
+
<a href="https://www.apache.org/licenses/LICENSE-2.0.txt"><img src="https://img.shields.io/badge/License-Apache-yellow"></a>
|
6 |
+
<!-- Official implementation. -->
|
7 |
+
<br>
|
8 |
+
|
9 |
+
|
10 |
+
<p align="center">
|
11 |
+
<img src="repo_images/teaser.jpeg" width="90%"/>
|
12 |
+
|
13 |
+
> <a href="https://inspirationtree.github.io/inspirationtree/">**Concept Decomposition for Visual Exploration and Inspiration**</a>
|
14 |
+
>
|
15 |
+
> <a href="https://yael-vinker.github.io/website/">Yael Vinker</a>,
|
16 |
+
<a href="https://scholar.google.com/citations?user=imBjSgUAAAAJ&hl=ru">Andrey Voynov</a>,
|
17 |
+
<a href="https://danielcohenor.com/">Daniel Cohen-Or</a>,
|
18 |
+
<a href="https://faculty.runi.ac.il/arik/site/index.asp">Ariel Shamir</a>
|
19 |
+
> <br>
|
20 |
+
> 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.
|
21 |
+
</p>
|
22 |
+
|
23 |
+
# Setup
|
24 |
+
```
|
25 |
+
git clone https://github.com/yael-vinker/inspiration_tree.git
|
26 |
+
```
|
27 |
+
|
28 |
+
## Environment (with pip)
|
29 |
+
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:
|
30 |
+
```
|
31 |
+
python -m venv .tree_venv
|
32 |
+
source .tree_venv/bin/activate
|
33 |
+
pip install -r requirements.txt
|
34 |
+
```
|
35 |
+
Technical details:
|
36 |
+
* CUDA 11.6
|
37 |
+
* torch 1.7.1+cu110
|
38 |
+
|
39 |
+
**Hugging Face Diffusers Library**
|
40 |
+
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.
|
41 |
+
|
42 |
+
# Usage
|
43 |
+
<p align="center">
|
44 |
+
<img src="repo_images/usage.jpg" width="50%"/>
|
45 |
+
<br>
|
46 |
+
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").
|
47 |
+
</p>
|
48 |
+
|
49 |
+
|
50 |
+
## Pretrained Models and Datasets
|
51 |
+
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.
|
52 |
+
|
53 |
+
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").
|
54 |
+
All datasets used from Textual Inversion can be found under "datasets".
|
55 |
+
|
56 |
+
## Generate your tree
|
57 |
+
The logic for generating the tree is under "main_multiseed.py", which runs the framwork for a <b>single node</b>.
|
58 |
+
You can generate the tree by passing your own parameters to main_multiseed.py. An example is given in "run_decompose.sh":
|
59 |
+
```
|
60 |
+
python main_multiseed.py --parent_data_dir "cat_sculpture/" --node v0 --test_name "v0" --GPU_ID "${GPU_ID}" --multiprocess 0
|
61 |
+
```
|
62 |
+
Notes:
|
63 |
+
- The "test_name" should be identical to the chosen node
|
64 |
+
Results:
|
65 |
+
- The results will be saved to "outputs/<parent_data_dir>/<node>"
|
66 |
+
- In "final_samples.jpg" you can see a batch of random samples of the learned nodes
|
67 |
+
- Under "consistency_test" we save the results of the seed selection procedure
|
68 |
+
- Once training is finished, you can continue with splitting the generated nodes as well
|
69 |
+
|
70 |
+
## Inference
|
71 |
+
We made a notebook ("inspiration_tree_playground.ipynb") to play with the results, and this should be the most convenient option.
|
72 |
+
Notes:
|
73 |
+
- The notebook shows the concepts learned in each node
|
74 |
+
- You can also load checkpoints from early iterations
|
75 |
+
- You can generate new concepts using natural language sentences
|
76 |
+
|
77 |
+
# Acknowledgements
|
78 |
+
Our code builds on the [diffusers implementation of textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
|
79 |
+
|
80 |
+
## Citation
|
81 |
+
If you find this useful for your research, please cite the following:
|
82 |
+
```bibtex
|
83 |
+
@article{vinker2023concept,
|
84 |
+
title={Concept Decomposition for Visual Exploration and Inspiration},
|
85 |
+
author={Yael Vinker and Andrey Voynov and Daniel Cohen-Or and Ariel Shamir},
|
86 |
+
journal={arXiv preprint arXiv:2305.18203},
|
87 |
+
year={2023}
|
88 |
+
}
|
89 |
+
```
|
90 |
+
|
91 |
+
## Disclaimer
|
92 |
+
|
93 |
+
This is not an officially supported Google product.
|
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