![]() ![]() I've integrated the plat interface which makes the NPE itself independent of framework, so you should be able to run it with Blocks, TensorFlow, PyTorch, P圜affe, what have you, by modifying the IAN class provided in models.py. Remainder of the IAN experiments (including SVHN) coming soon. ![]() I'll be adding folders and cleaning things up soon. I messed around with the keywords for get_model, you'll need to deal with these if you wish to run any model other than IAN_simple through the editor.Įverything is presently just dumped into a single, unorganized directory. Since the MADE really only acts as an autoregressive randomizer I'm not too worried about this, but it does bear looking into. ![]() My MADE layer currently only accepts hidden unit sizes that are equal to the size of the latent vector, which will present itself as a BAD_PARAM error. 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: See here for instructions on downloading and preparing it. You will need Fuel along with the 64圆4 version of celebA. Note that this automatically returns you to sample mode, and may require hitting "infer" rather than "reset" to get back to photo editing. Use the scroll wheel to lighten or darken an image patch (equivalent to using a pure white or pure black paintbrush).Use the sample button to generate a random latent vector and corresponding image.Use "Infer" to return to an original ground truth image from the dataset. Press "Update" to update the ground-truth image and corresponding reconstruction with the current image.Use the reset button to return to the ground truth image.npz) by typing in a number from 0-999 in the bottom left box and hitting "infer." You can select different entries from the subset of the celebA validation set (included in this repository as an.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 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).You can make use of any model with an inference mechanism (VAE or ALI-based GAN). If you wish to use a different model, simply edit the line with "config path" in the NPE.py file. Note that I presently only have the non-cuDNN option working for IAN_simple. If you don't have cuDNN, simply change line 56 of the NPE.py file from dnn=True to dnn=False. If you're on a linux machine, you can just insert THEANO_FLAGS=floatX=float32 before the command line call. theanorc file and at least set the flag FLOATX=float32. If you're on a Windows machine, you will want to create a. 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) numpy, scipy, PIL, Tkinter and tkColorChooser, but it is likely that your python distribution already has those.īy default, the NPE runs on IAN_simple.I highly recommend cuDNN as speed is key, but it is not a dependency.You may be able to use early versions of Python2, but I'm pretty sure there's some incompatibilities with Python3 in here. To run the Neural Photo Editor, you will need: This repository contains code for the paper " Neural Photo Editing with Introspective Adversarial Networks," and the Associated Video. A simple interface for editing natural photos with generative neural networks. ![]()
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