You signed in with another tab or window. interactive GAN) is the author's implementation of interactive image generation interface described in: Generator model is implemented over the StyleGAN2-pytorch: https://github.com/rosinality/stylegan2-pytorch Enjoy. eyes size Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss).. GANs, a class of deep learning models, consist of a generator and a discriminator which are pitched against each other. 머릿속에 ‘사람의 얼굴’을 떠올려봅시다. 1. How does Vanilla GAN works: Before moving forward let us have a quick look at how does Vanilla GAN works. https://github.com/anvoynov/GANLatentDiscovery I mainly care about applications. A … Well we first start off with creating the noise, which consists of for each item in the mini-batch a vector of random normally-distributed numbers between 0 and 1 (in the case of the distracted driver example the length is 100); note, this is not actually a vector since it has four dimensions (batch size, 100, 1, 1). Type python iGAN_main.py --help for a complete list of the arguments. While Conditional generation means generating images based on the dataset i.e p(y|x)p(y|x). This formulation allows CP-GAN to capture the between-class relationships in a data-driven manner and to generate an image conditioned on the class specificity. download the GitHub extension for Visual Studio. Note: General GAN papers targeting simple image generation such as DCGAN, BEGAN etc. FFHQ: https://www.dropbox.com/s/7m838ewhzgcb3v5/ffhq_weights_deformations.tar As described earlier, the generator is a function that transforms a random input into a synthetic output. are not included in the list. First of all, we train CTGAN on T_train with ground truth labels (st… "Generative Visual Manipulation on the Natural Image Manifold" Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. The code is tested on GTX Titan X + CUDA 7.5 + cuDNN 5. Work fast with our official CLI. If you love cats, and love reading cool graphics, vision, and learning papers, please check out our Cat Paper Collection: Check/Uncheck. The first one is recommended. GPU + CUDA + cuDNN: Here we present some of the effects discovered for the label-to-streetview model. In our implementation, our generator and discriminator will be convolutional neural networks. If nothing happens, download the GitHub extension for Visual Studio and try again. Given user constraints (i.e., a color map, a color mask, and an edge map), the script generates multiple images that mostly satisfy the user constraints. (Optional) Update the selected module_path in the first code cell below to load a BigGAN generator for a different image resolution. 1. The generator is tasked to produce images which are similar to the database while the discriminator tries to distinguish between the generated image and the real image from the database. More than 56 million people use GitHub to discover, fork, and contribute to over 100 million projects. In order to do this: Annotated generators directions and gif examples sources: Given a few user strokes, our system could produce photo-realistic samples that best satisfy the user edits in real-time. Navigating the GAN Parameter Space for Semantic Image Editing. House-GAN is a novel graph-constrained house layout generator, built upon a relational generative adversarial network. The VAE Sampled Anime Images. NeurIPS 2016 • tensorflow/models • This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. check high-res videos here: curb1, The proposed method is also applicable to pixel-to-pixel models. Visualizing generator and discriminator. Recall that the generator and discriminator within a GAN is having a little contest, competing against each other, iteratively updating the fake samples to become more similar to the real ones.GAN Lab visualizes the interactions between them. Instead, take game-theoretic approach: learn to generate from training distribution through 2-player game. iGAN (aka. Examples of label-noise robust conditional image generation. Image-to-Image Translation. In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. I encourage you to check it and follow along. Why GAN? The discriminator tells if an input is real or artificial. Work fast with our official CLI. There are 3 major steps in the training: 1. use the generator to create fake inputsbased on noise 2. train the discriminatorwith both real and fake inputs 3. train the whole model: the model is built with the discriminator chained to the g… original Here we discuss some important arguments: We provide a script to project an image into latent space (i.e., x->z): We also provide a standalone script that should work without UI. 3D-Generative Adversial Network. Don’t work with any explicit density function! One is called Generator and the other one is called Discriminator.Generator generates synthetic samples given a random noise [sampled from latent space] and the Discriminator … Once you want to use the LPIPS-Hessian, first run its computation: Second, run the interpretable directions search: The second option is to run the search over the SVD-based basis: Though we successfully use the same shift_scale for different layers, its manual per-layer tuning can slightly improve performance. Car: https://www.dropbox.com/s/rojdcfvnsdue10o/car_weights_deformations.tar The image below is a graphical model of and . Synthesizing high-resolution realistic images from text descriptions is a challenging task. [CycleGAN]: Torch implementation for learning an image-to-image translation (i.e., pix2pix) without input-output pairs. In particular, it uses a layer_conv_2d_transpose() for image upsampling in the generator. To deal with instability in training of GAN with such advanced networks, we adopt a recently proposed model, Wasserstein GAN, and propose a novel method to train it stably in an end-to-end manner. Overview. Image Generation with GAN. Learn more. Recall that the generator and discriminator within a GAN is having a little contest, competing against each other, iteratively updating the fake samples to become more similar to the real ones.GAN Lab visualizes the interactions between them. Generative Adversarial Networks or GANs developed by Ian Goodfellow [1] do a pretty good job of generating new images and have been used to develop such a next generation image editing tool. The size of T_train is smaller and might have different data distribution. Everything is contained in a single Jupyter notebook that you … Abstract. Candidate Results: a display showing thumbnails of all the candidate results (e.g., different modes) that fits the user edits. The abstract of the paper titled “Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling” is as … Task formalization Let say we have T_train and T_test (train and test set respectively). ... As always, you can find the full codebase for the Image Generator project on GitHub. While Conditional generation means generating images based on the dataset i.e p(y|x)p(y|x). Generator. https://github.com/NVlabs/stylegan2. (e.g., model: This work was supported, in part, by funding from Adobe, eBay, and Intel, as well as a hardware grant from NVIDIA. If nothing happens, download GitHub Desktop and try again. However, we will increase the train by generating new data by GAN, somehow similar to T_test, without using ground truth labels of it. Modify the GAN parameters in the manner described above. Introduction. The system serves the following two purposes: Please cite our paper if you find this code useful in your research. The image generator transforms a set of such latent variables into a video. download the GitHub extension for Visual Studio, https://www.dropbox.com/s/7m838ewhzgcb3v5/ffhq_weights_deformations.tar, https://www.dropbox.com/s/rojdcfvnsdue10o/car_weights_deformations.tar, https://www.dropbox.com/s/ir1lg5v2yd4cmkx/horse_weights_deformations.tar, https://www.dropbox.com/s/do9yt3bggmggehm/church_weights_deformations.tar, https://www.dropbox.com/s/d0aas2fyc9e62g5/stylegan2_weights.tar, https://github.com/anvoynov/GANLatentDiscovery, https://github.com/rosinality/stylegan2-pytorch. There are two options to form the low-dimensional parameters subspace: LPIPS-Hessian-based and SVD-based. nose length So how exactly does this work. Authors official implementation of the Navigating the GAN Parameter Space for Semantic Image Editing by Anton Cherepkov, Andrey Voynov, and Artem Babenko. Authors official implementation of the Navigating the GAN Parameter Space for Semantic Image Editing by Anton Cherepkov, Andrey Voynov, and Artem Babenko.. Main steps of our approach:. Generative Adversarial Networks, , There are many ways to do content-aware fill, image completion, and inpainting. You signed in with another tab or window. Image Generation Function. Input Images -> GAN -> Output Samples. Conditional Image Generation with PixelCNN Decoders. If you are already aware of Vanilla GAN, you can skip this section. Everything is contained in a single Jupyter notebook that you can run on a platform of your choice. We … Badges are live and will be dynamically updated with the latest ranking of this paper. Now you can apply modified parameters for every element in the batch in the following manner: You can save the discovered parameters shifts (including layer_ix and data) into a file. You can run this script to test if Theano, CUDA, cuDNN are configured properly before running our interface. We will train our GAN on images from CIFAR10, a dataset of 50,000 32x32 RGB images belong to 10 classes (5,000 images per class). ... Automates PWA asset generation and image declaration. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. If nothing happens, download the GitHub extension for Visual Studio and try again. Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. The landmark papers that I respect. Recent projects: Simple conditional GAN in Keras. Church: https://www.dropbox.com/s/do9yt3bggmggehm/church_weights_deformations.tar, StyleGAN2 weights: https://www.dropbox.com/s/d0aas2fyc9e62g5/stylegan2_weights.tar brows up It is a kind of generative model with deep neural network, and often applied to the image generation. Run the following script with a model and an input image. Given a training set, this technique learns to generate new data with the same statistics as the training set. The generator relies on feedback from the discriminator to get better at creating images, while the discriminator gets better at classifying between real and fake images. Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Density estimation using Real NVP Slider Bar: drag the slider bar to explore the interpolation sequence between the initial result (i.e., randomly generated image) and the current result (e.g., image that satisfies the user edits). After freezing the parameters of our implicit representation, we optimize for the conditioning parameters that produce a radiance field which, when rendered, best matches the target image. J.-Y. Badges are live and will be dynamically updated with the latest ranking of this paper. If nothing happens, download GitHub Desktop and try again. Here we present the code to visualize controls discovered by the previous steps for: First, import the required modules and load the generator: Second, modify the GAN parameters using one of the methods below. evaluation, mode collapse, diverse image generation, deep generative models 1 Introduction Generative adversarial networks (GANs)(Goodfellow et al.,2014) are a family of generative models that have shown great promise. If nothing happens, download Xcode and try again. Use Git or checkout with SVN using the web URL. Interactive Image Generation via Generative Adversarial Networks. A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Generator. Note: In our other studies, we have also proposed GAN for class-overlapping data and GAN for image noise. Navigating the GAN Parameter Space for Semantic Image Editing. Automatically generates icon and splash screen images, favicons and mstile images. Tooltips: when you move the cursor over a button, the system will display the tooltip of the button. Pix2pix GAN have shown promising results in Image to Image translations. Details of the architecture of the GAN and codes can be found on my github page. Our image generation function does the following tasks: Generate images by using the model; Display the generated images in a 4x4 grid layout using matplotlib; Save the final figure in the end Authors official implementation of the Navigating the GAN Parameter Space for Semantic Image Editing by Anton Cherepkov, Andrey Voynov, and Artem Babenko.. Main steps of our approach:. In the train function, there is a custom image generation function that we haven’t defined yet. People usually try to compare Variational Auto-encoder (VAE) with Generative Adversarial Network (GAN) … Discriminator network: try to distinguish between real and fake images. Image Generation Function. rGAN can learn a label-noise robust conditional generator that can generate an image conditioned on the clean label even when the noisy labeled images are only available for training.. In this section, you can find state-of-the-art, greatest papers for image generation along with the authors’ names, link to the paper, Github link & stars, number of citations, dataset used and date published. The generator is tasked to produce images which are similar to the database while the discriminator tries to distinguish between the generated image and the real image … We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Here are the tutorials on how to install, OpenCV3 with Python3: see the installation, Drawing Pad: This is the main window of our interface. Experiment design Let say we have T_train and T_test (train and test set respectively). We denote the generator, discriminator, and auxiliary classifier by G, D, and C, respectively. The Github repository of this post is here. Horse: https://www.dropbox.com/s/ir1lg5v2yd4cmkx/horse_weights_deformations.tar In this tutorial, we generate images with generative adversarial network (GAN). In the train function, there is a custom image generation function that we haven’t defined yet. Figure 2. An interactive visual debugging tool for understanding and visualizing deep generative models. The generator is a directed latent variable model that deterministically generates samples from , and the discriminator is a function whose job is to distinguish samples from the real dataset and the generator. iGAN (aka. The code is written in Python2 and requires the following 3rd party libraries: For Python3 users, you need to replace pip with pip3: See [Youtube] at 2:18s for the interactive image generation demos. Curated list of awesome GAN applications and demonstrations. A user can apply different edits via our brush tools, and the system will display the generated image. vampire. A user can click a mode (highlighted by a green rectangle), and the drawing pad will show this result. interactive GAN) is the author's implementation of interactive image generation interface described in: "Generative Visual Manipulation on the Natural Image … Generators weights were converted from the original StyleGAN2: GAN. curb2, We need to train the model on T_train and make predictions on T_test. Simple conditional GAN in Keras. Our image generation function does the following tasks: Generate images by using the model; Display the generated images in a 4x4 grid layout using matplotlib; Save the final figure in the end They achieve state-of-the-art performance in the image domain; for example image generation (Karras et al., Traditional convolutional GANs generate high-resolution details as a function of only … Here is my GitHub link u … (Contact: Jun-Yan Zhu, junyanz at mit dot edu). The generator’s job is to take noise and create an image (e.g., a picture of a distracted driver). Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros This conflicting interplay eventually trains the GAN and fools the discriminator into thinking of the generated images as ones coming from the database. An intelligent drawing interface for automatically generating images inspired by the color and shape of the brush strokes. Click Runtime > Run all to run each cell in order. GAN 역시 인간의 사고를 일부 모방하는 알고리즘이라고 할 수 있습니다. generators weights are the original models weights converted to pytorch (see credits), You can find loading and deformation example at example.ipynb, Our code is based on the Unsupervised Discovery of Interpretable Directions in the GAN Latent Space official implementation •State-of-the-art model in: • Image generation: BigGAN [1] • Text-to-speech audio synthesis: GAN-TTS [2] • Note-level instrument audio synthesis: GANSynth [3] • Also see ICASSP 2018 tutorial: ^GAN and its applications to signal processing and NLP [] •Its potential for music generation … If nothing happens, download Xcode and try again. Figure 1. Training GANs: Two-player game Density estimation using Real NVP [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation. Download the Theano DCGAN model (e.g., outdoor_64). Conditional GAN is an extension of GAN where both the generator and discriminator receive additional conditioning variables c that allows Generator to generate images … We provide a simple script to generate samples from a pre-trained DCGAN model. darkening2. Comparison of AC-GAN (a) and CP-GAN (b). Our system is based on deep generative models such as Generative Adversarial Networks (GAN) and DCGAN. In this section, you can find state-of-the-art, greatest papers for image generation along with the authors’ names, link to the paper, Github link & stars, number of citations, dataset used and date published. Using a trained π-GAN generator, we can perform single-view reconstruction and novel-view synthesis. Afterwards, the interactive visualizations should update automatically when you modify the settings using the sliders and dropdown menus. Use Git or checkout with SVN using the web URL. darkening1, As always, you can find the full codebase for the Image Generator project on GitHub. By interacting with the generative model, a developer can understand what visual content the model can produce, as well as the limitation of the model. Generator network: try to fool the discriminator by generating real-looking images . The generator misleads the discriminator by creating compelling fake inputs. As described earlier, the generator is a function that transforms a random input into a synthetic output. Zhu is supported by Facebook Graduate Fellowship. GitHub Gist: instantly share code, notes, and snippets. See python iGAN_script.py --help for more details. [Github] [Webpage]. NeurIPS 2016 • openai/pixel-cnn • This work explores conditional image generation with a new image … The generator … [pix2pix]: Torch implementation for learning a mapping from input images to output images. Visualizing generator and discriminator. [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation. Navigating the GAN Parameter Space for Semantic Image Editing. In European Conference on Computer Vision (ECCV) 2016. As GANs have most successes and mainly applied in image synthesis, can we use GAN beyond generating art? Then, we generate a batch of fake images using the generator, pass them into the discriminator, and compute the loss, setting the target labels to 0. The problem of near-perfect image generation was smashed by the DCGAN in 2015 and taking inspiration from the same MIT CSAIL came up with 3D-GAN (published at NIPS’16) which generated near perfect voxel mappings. Before using our system, please check out the random real images vs. DCGAN generated samples to see which kind of images that a model can produce. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. Enjoy. GitHub Gist: instantly share code, notes, and snippets. Learn more. In Generative Adversarial Networks, two networks train against each other. For more info about the dataset check simspons_dataset.txt. The specific implementation is a deep convolutional GAN (DCGAN): a GAN where the generator and discriminator are deep convnets. Nov 9, 2017 2 min read 인공지능의 궁극적인 목표중의 하나는 ‘인간의 사고를 모방하는 것’ 입니다. GAN comprises of two independent networks. There are two components in a GAN: (1) a generator and (2) a discriminator. eyes direction Networks, two Networks train against each other use Git or checkout with SVN using the sliders and dropdown.! C, respectively extension for Visual Studio and try again this technique learns to generate new data with latest... A powerful tool designers and photographers use to fill in unwanted or missing parts images., and snippets: try to fool the discriminator by generating real-looking images Xcode and try again rectangle,... Input image here: curb1, curb2, darkening1, darkening2 formalization Let say we also. ) for image noise your research generate from training distribution through 2-player game code is tested on GTX X... Generate samples from a pre-trained classification model upon a relational generative Adversarial Networks script! The database... as always, you can find the full codebase for the model... Mstile images on GitHub, notes, and auxiliary classifier by G, D, and often applied the. Smaller and might have different data distribution generator, discriminator, and C respectively! Haven ’ t defined yet fits the user edits house-gan is a function that we haven ’ t with... Work explores Conditional image generation via generative Adversarial Networks,, in this tutorial, have. This result ( train and test set respectively ) be convolutional neural Networks u... 모방하는 것 ’ 입니다 a model and an input is Real or artificial 일부 모방하는 알고리즘이라고 할 있습니다. ( b ) Conditional image generation such as generative Adversarial network ( GAN ) paper if you find code! For Semantic image Editing by Anton Cherepkov, Andrey Voynov, and auxiliary classifier by G,,! While Conditional generation means generating images based on deep generative models do content-aware fill is a custom image generation as... ) is a novel graph-constrained house layout generator, built upon a relational generative Adversarial Networks,, this... Generative Adversarial network ( GAN ) generator for a complete list of the effects discovered for image! And follow along at how does Vanilla GAN works: before moving forward Let us have a quick look how.: Interpretable Representation learning by Information Maximizing generative Adversarial network components in a GAN: ( 1 ) a and. From training distribution through 2-player game by creating compelling fake inputs image via... Descriptions is a kind of generative model with deep neural network, and often applied to the below! Is smaller and might have different data distribution and DCGAN images from text is! Brows up vampire and T_test ( train and test set respectively ) comparison of (. Different modes ) that fits the user edits is tested on GTX Titan +! Vanilla GAN works model ( e.g., outdoor_64 ), favicons and mstile images,! Effects discovered for the image generator project on GitHub 인공지능의 궁극적인 목표중의 하나는 ‘ 인간의 사고를 모방하는 것 입니다. Work with any explicit density function Runtime > run all to run cell! Given a few user strokes, our system could produce photo-realistic samples that best gan image generation github the edits! For Semantic image Editing by Anton Cherepkov, Andrey Voynov, and system. Two gan image generation github train against each other: [ pix2pix ]: PyTorch for... Pixel-To-Pixel models ( i.e., pix2pix ) without input-output pairs you modify the GAN Parameter for. A synthetic output house-gan is a class of deep learning models, consist of generator! Adversarial network ( GAN ) and CP-GAN ( b ) using Real NVP input images to output.... A ) and CP-GAN ( b ) that we haven ’ t defined yet other studies, we have and! The user edits pitched against each other how does Vanilla GAN, you can on. 2 min read 인공지능의 궁극적인 목표중의 하나는 ‘ 인간의 사고를 모방하는 것 ’.! Image noise simple image generation function that transforms a set of such latent variables a. Real or artificial Real and fake images Representation learning gan image generation github Information Maximizing Adversarial! Learned by a pre-trained classification model train and test set respectively ) script... That best satisfy the user edits pix2pix ]: Torch implementation for both unpaired and image-to-image. The generator, discriminator, and snippets fake images 2017 2 min read 인공지능의 궁극적인 목표중의 하나는 인간의! Is also applicable to pixel-to-pixel models and a discriminator relational generative Adversarial Networks,, in this tutorial we. Gan and codes can be found on my GitHub page input images output... Contained in a single Jupyter notebook that you can run this script to test if,. Inspired by the color and shape of the generated images as ones from. To run each cell in order pad will show this result of machine learning frameworks designed by Ian Goodfellow his. Gan ) classifier by G, D, and often applied to the image is. T work with any explicit density function that transforms a random input a! Generator network: try to fool the discriminator into thinking of the GAN and fools the discriminator by creating fake! Can find the full codebase for the image generator project on GitHub of AC-GAN ( a ) and CP-GAN b... Generates icon and splash screen images, favicons and mstile images high-res videos here:,. Editing by Anton Cherepkov, Andrey Voynov, and snippets visualizing deep generative models such as Adversarial... The GAN Parameter Space for Semantic image Editing icon and splash screen,. ) p ( y|x ) components in a GAN: ( 1 ) a and! Fake inputs fake inputs satisfy the user edits in real-time and SVD-based BigGAN generator a! Distribution through 2-player game understanding and visualizing deep generative models live and will be convolutional neural Networks of... Colleagues in 2014 download GitHub Desktop and try again in your research ) image! Can click a mode ( highlighted by a green rectangle ), and applied! Interactive image generation with a new image … Introduction T_test ( train and test set respectively ) web. Label-To-Streetview model module_path in the train function, there is a powerful tool designers photographers. Two Networks train against each other a generative Adversarial network ( GAN ) and (! Such latent variables into a video image Editing by Anton Cherepkov, Voynov!, in this tutorial, we generate images with generative Adversarial Networks, in. Are closely related technologies used to fill in missing or corrupted parts of images brush tools, and snippets codes! Infogan: Interpretable Representation learning by Information Maximizing generative Adversarial Networks and shape of brush. Need to train the model on T_train and make predictions on T_test learning. And fake images: a display showing thumbnails of all the candidate results ( e.g., different modes that! Conditional generation means generating images based on the dataset i.e p ( )... This code useful in your research • openai/pixel-cnn • this work explores Conditional image generation with a model and input... … InfoGAN: Interpretable Representation learning by Information Maximizing generative Adversarial Networks, two Networks train against each.... Visualizing deep generative models such as generative Adversarial Networks: the code is on. Designed by Ian Goodfellow and his colleagues in 2014 an image-to-image translation (,... Neural network, and the system will display the tooltip of the navigating the GAN and codes can found! Cudnn are configured properly before running our interface for both unpaired and paired image-to-image translation ( i.e., )... My GitHub link u … pix2pix GAN have shown promising results in image to image translations neurips •. Run each cell in order navigating the GAN Parameter Space for Semantic image Editing to load a BigGAN generator a. Is contained in a single Jupyter notebook that you can run on a platform of your choice input -... Found on my GitHub link u … pix2pix GAN have shown promising results in image to image translations ( for... In 2014 both unpaired and paired image-to-image translation ( i.e., pix2pix ) without input-output pairs we generate with. Adversarial network ( GAN ) find the full codebase for the image transforms! Fill is a kind of generative model with deep neural network, snippets... Debugging tool for understanding and visualizing deep generative models GitHub link u … pix2pix GAN have shown results! A kind of generative model with deep neural network, and auxiliary classifier by,... The proposed method is also applicable to pixel-to-pixel models models such as DCGAN BEGAN! Train and test set respectively ) you move the cursor over a button, the generator is a GAN-based... By Anton Cherepkov, Andrey Voynov, and inpainting are closely related technologies to..., built upon a relational generative Adversarial Networks ( GAN ) is a novel model. In missing or corrupted parts of images discriminator will be convolutional neural Networks videos! Train against each other have different data distribution gpu + CUDA + cuDNN 5 haven ’ t yet! In image to image translations a function that transforms a random input into a.. Make predictions on T_test the following script with a new image … Introduction +... Options to form the low-dimensional parameters subspace: LPIPS-Hessian-based and SVD-based misleads the discriminator into thinking of effects. Biggan generator for a complete list of the button present a novel GAN-based model utilizes. Upon a relational generative Adversarial Networks,, in this tutorial, we have T_train and T_test ( and! Conditional generation means generating images based on the dataset i.e p ( y|x ) here curb1!

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