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A simple interface for editing natural photos with generative neural networks.
| Date | Stars |
|---|---|
| 2026-07-24 | 2074 |
| 2026-07-25 | 2073 |
| 2026-07-28 | 2073 |
| 2026-07-30 | 2073 |
| 2026-07-31 | 2074 |
| 2026-08-06 | 2074 |
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# Neural Photo Editor A simple interface for editing natural photos with generative neural networks.    This repository contains code for the paper "[Neural Photo Editing with Introspective Adversarial Networks](http://arxiv.org/abs/1609.07093)," and the [Associated Video](https://www.youtube.com/watch?v=FDELBFSeqQs). ## Installation To run the Neural Photo Editor, you will need: - Python, likely version 2.7. You may be able to use early versions of Python2, but I'm pretty sure there's some incompatibilities with Python3 in here. - [Theano](http://deeplearning.net/software/theano/), development version. - [lasagne](http://lasagne.readthedocs.io/en/latest/user/installation.html), development version. - I highly recommend [cuDNN](https://developer.nvidia.com/cudnn) as speed is key, but it is not a dependency. - numpy, scipy, PIL, Tkinter and tkColorChooser, but it is likely that your python distribution already has those. ## Running the NPE By default, the NPE runs on IAN_simple. This is a slimmed-down version of the IAN without MDC or RGB-Beta blocks, which runs without lag on a laptop GPU with ~1GB of memory (GT730M) If you're on a Windows machine, you will want to create a .theanorc file and at least set the flag FLOATX=float32. If you're on a linux machine, you can just insert THEANO_FLAGS=floatX=float32 before the command line call. If you don't have cuDNN, simply change line 56 of the NPE.py file from dnn=True to dnn=False. Note that I presently only have the non-cuDNN option working for IAN_simple. Then, run the command: ```sh python NPE.py ``` If you wish to use a different model, simply edit the line with "config path" in the NPE.py file. You can make use of any model with an inference mechanism (VAE or ALI-based GAN). ## Commands - You can paint the image by picking a color and painting on the image, or paint in the latent space canvas (the red and blue tiles below the image). - The long horizontal slider controls the magnitude of the latent brush, and the smaller horizontal slider controls the size of both the latent and the main image brush. - You can select different entries from the subset of the celebA validation set (included in this repository as an .npz) by typing in a number from 0-999 in the bottom left box and hitting "infer." - Use the reset button to return to the ground truth image. - Press "Update" to update the ground-truth image and corresponding reconstruction with the current image. Use "Infer" to return to an original ground truth image from the dataset. - Use the sample button to generate a random latent vector and corresponding image. - Use the scroll wheel to lighten or darken an image patch (equivalent to using a pure white or pure black paintbrush). Note that this automatically returns you to sample mode, and may require hitting "infer" rather than "reset" to get back to photo editing. ## Training an IAN on celebA You will need [Fuel](https://github.com/mila-udem/fuel) along with the 64x64 version of celebA. See [here](https://github.com/vdumoulin/discgen) for instructions on downloading and preparing it. If you wish to train a model, the IAN.py file contains the model configuration, and the train_IAN.py file contains the training code, which can be run like this: ```sh python train_IAN.py IAN.py ``` By default, this code will save (and overwrite!) the weights to a .npz file with the same name as the config.py file (i.e. "IAN.py -> IAN.npz"), and will output a jsonl log of the training with metrics recorded after every chunk. Use the --resume=True flag when calling to resume training a model--it will automatically pick up from the most recent epoch. ## Sampling the IAN # You can generate a sample and reconstruction+interpolation grid with: ```sh python sample_IAN.py IAN.py ``` Note that you will need [matplotlib](http://matplotlib.org/). to do so. ## Kno
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d364bfb5dd738e7f, topic:deep-learning
matched fp:d364bfb5dd738e7f, topic:computer-vision
