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Pix2pix, free and safe download. Pix2pix latest version: Image-to-image translation with conditional adversarial nets. Image-to-image translation with.
- Overview
- Setup
- Train
- Test
- Datasets
- Models
- Setup Training and Test data
- Display UI
- Citation
- Cat Paper Collection
Project | Arxiv | PyTorch
Torch implementation for learning a mapping from input images to output images, for example:
Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
CVPR, 2017.
On some tasks, decent results can be obtained fairly quickly and on small datasets. For example, to learn to generate facades (example shown above), we trained on just 400 images for about 2 hours (on a single Pascal Titan X GPU). However, for harder problems it may be important to train on far larger datasets, and for many hours or even days.
Prerequisites
•Linux or OSX •NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested)
Getting Started
•Install torch and dependencies from https://github.com/torch/distro •Install torch packages nngraph and display •Clone this repo: •Download the dataset (e.g., CMP Facades): •Train the model •(CPU only) The same training command without using a GPU or CUDNN. Setting the environment variables gpu=0 cudnn=0 forces CPU only •(Optionally) start the display server to view results as the model trains. ( See Display UI for more details): •Finally, test the model: The test results will be saved to an html file here: ./results/facades_generation/latest_net_G_val/index.html.
Switch AtoB to BtoA to train translation in opposite direction.
Models are saved to ./checkpoints/expt_name (can be changed by passing checkpoint_dir=your_dir in train.lua).
This will run the model named expt_name in direction AtoB on all images in /path/to/data/val.
Result images, and a webpage to view them, are saved to ./results/expt_name (can be changed by passing results_dir=your_dir in test.lua).
Download the datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.
•facades: 400 images from CMP Facades dataset. [Citation]
•cityscapes: 2975 images from the Cityscapes training set. [Citation]
•maps: 1096 training images scraped from Google Maps
•edges2shoes: 50k training images from UT Zappos50K dataset. Edges are computed by HED edge detector + post-processing. [Citation]
•edges2handbags: 137K Amazon Handbag images from iGAN project. Edges are computed by HED edge detector + post-processing. [Citation]
Download the pre-trained models with the following script. You need to rename the model (e.g., facades_label2image to /checkpoints/facades/latest_net_G.t7) after the download has finished.
•facades_label2image (label -> facade): trained on the CMP Facades dataset.
•cityscapes_label2image (label -> street scene): trained on the Cityscapes dataset.
•cityscapes_image2label (street scene -> label): trained on the Cityscapes dataset.
•edges2shoes (edge -> photo): trained on UT Zappos50K dataset.
•edges2handbags (edge -> photo): trained on Amazon handbags images.
Generating Pairs
We provide a python script to generate training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene. For example, these might be pairs {label map, photo} or {bw image, color image}. Then we can learn to translate A to B or B to A: Create folder /path/to/data with subfolders A and B. A and B should each have their own subfolders train, val, test, etc. In /path/to/data/A/train, put training images in style A. In /path/to/data/B/train, put the corresponding images in style B. Repeat same for other data splits (val, test, etc). Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., /path/to/data/A/train/1.jpg is considered to correspond to /path/to/data/B/train/1.jpg. Once the data is formatted this way, call: This will combine each pair of images (A,B) into a single image file, ready for training.
Notes on Colorization
No need to run combine_A_and_B.py for colorization. Instead, you need to prepare some natural images and set preprocess=colorization in the script. The program will automatically convert each RGB image into Lab color space, and create L -> ab image pair during the training. Also set input_nc=1 and output_nc=2.
Extracting Edges
We provide python and Matlab scripts to extract coarse edges from photos. Run scripts/edges/batch_hed.py to compute HED edges. Run scripts/edges/PostprocessHED.m to simplify edges with additional post-processing steps. Check the code documentation for more details.
Optionally, for displaying images during training and test, use the display package.
•Install it with: luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
•Then start the server with: th -ldisplay.start
•Open this URL in your browser: http://localhost:8000
By default, the server listens on localhost. Pass 0.0.0.0 to allow external connections on any interface:
Then open http://(hostname):(port)/ in your browser to load the remote desktop.
If you use this code for your research, please cite our paper Image-to-Image Translation Using Conditional Adversarial Networks:
If you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper Collection:
[Github] [Webpage]
19 de mar. de 2024 · This tutorial demonstrates how to build and train a conditional generative adversarial network (cGAN) called pix2pix that learns a mapping from input images to output images, as described in Image-to-image translation with conditional adversarial networks by Isola et al. (2017). pix2pix is not application specific—it can be applied ...
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would ...
Install pix2pix-tensorflow. Clone or download the above library. It is possible to do all of this with the original torch-based pix2pix (in which case you have to install torch instead of tensorflow for step 3. These instructions will assume the tensorflow version. Training pix2pix. First we need to prepare our dataset.
CycleGAN and pix2pix in PyTorch. New: Please check out img2img-turbo repo that includes both pix2pix-turbo and CycleGAN-Turbo. Our new one-step image-to-image translation methods can support both paired and unpaired training and produce better results by leveraging the pre-trained StableDiffusion-Turbo model.
