# data[].attributes.body
hi

# data[].attributes.attachment_link.description
Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more - no expertise or coding required.

# data[].attributes.attachment_link.readable_text
Using generative adversarial networks (GAN), we can learn how to create realistic-looking fake versions of almost anything, as shown by this collection of sites that have sprung up in the past month. Learn how it works.

# data[].attributes.attachment_link.description
Using generative adversarial networks (GAN), we can learn how to create realistic-looking fake versions of almost anything, as shown by this collection of sites that have sprung up in the past month.

# data[].attributes.attachment_link.readable_text
Interactive Image Translation with pix2pix-tensorflow

The pix2pix model works by training on pairs of images such as building facade labels to building facades, and then attempts to generate the corresponding output image from any input image you give it. The idea is straight from the pix2pix paper, which is a good read.

Trained on about 2k stock cat photos and edges automatically generated from those photos. Generates cat-colored objects, some with nightmare faces. The best one I've seen yet was a cat-beholder.

Some of the pictures look especially creepy, I think because it's easier to notice when an animal looks wrong, especially around the eyes. The auto-detected edges are not very good and in many cases didn't detect the cat's eyes, making it a bit worse for training the image translation model.

Trained on a database of building facades to labeled building facades. It doesn't seem sure about what to do with a large empty area, but if you put enough windows on there it often has reasonable results. Draw "wall" color rectangles to erase things.

I didn't have the names of the different parts of building facades so I just guessed what they were called.

Trained on a database of ~50k shoe pictures collected from Zappos along with edges generated from those pictures automatically. If you're really good at drawing the edges of shoes, you can try to produce some new designs. Keep in mind it's trained on real objects, so if you can draw more 3D things, it seems to work better.

Similar to the previous one, trained on a database of ~137k handbag pictures collected from Amazon and automatically generated edges from those pictures. If you draw a shoe here instead of a handbag, you get a very oddly textured shoe.

The models were trained and exported with the pix2pix.py script from pix2pix-tensorflow. The interactive demo is made in javascript using the Canvas API and runs the model using deeplearn.js.

The pre-trained models are available in the Datasets section on GitHub. All the ones released alongside the original pix2pix implementation should be available. The models used for the javascript implementation are available at pix2pix-tensorflow-models.

# data[].attributes.attachment_link.description
Interactive Image Translation with pix2pix-tensorflow The pix2pix model works by training on pairs of images such as building facade labels to building facades, and then attempts to generate the corresponding output image from any input image you give it. The idea is straight from the pix2pix paper, which is a good read.

# data[].attributes.attachment_link.title
starryai - Free AI Art Generator App | Create NFT Art From Text

# data[].attributes.attachment_link.description
Generate free AI artworks by transforming your words into beautiful artworks.

# data[].attributes.attachment_link.description
Create AI images from text with Craiyon. Generate, explore, and edit images online.

# data[].attributes.attachment_link.description
Uses pixray to generate an image from text prompt

# data[].attributes.attachment_link.description
The AI2 Computer Vision Explorer offers demos of a variety of popular models - try, compare, and evaluate with your own images!