An interesting example from an artist I know: If you split a canvas into several pieces, and super-resolution all of them separately, you end up with something much larger and more intricately detailed you can even do it repeatedly. This isn’t unprecedented, and in fact artists working with AI models use this technique already to create pieces that are much larger than what the AI can handle in one go. Like many artists, it started with the equivalent of a rough sketch, filled it out in a study, then really went to town on the final canvas. But at no point did the AI have to just pull 48 by whatever pixels of dog eye out of its… let’s say magic bag. Then this happens again when the eye is done again, but at 48 pixels across. Where does the detail needed for this come from? Well, the AI knows what a dog’s eye looks like, so it generates more detail as it draws. Not a lot of room for expression! But on the second image, it’s 12 pixels across.
Say for instance you have a dog on a bike and the dog’s eye is 3 pixels across in the first image. This isn’t like normal upscaling, though, as AI super-resolution creates new details in harmony with the smaller image, using the original as a basis. Imagen starts by generating a small (64×64 pixels) image and then does two “super resolution” passes on it to bring it up to 1024×1024. The other part is improved language understanding through large language models using the transformer approach, the technical aspects of which I won’t (and can’t) get into here, but it and a few other recent advances have led to convincing language models like GPT-3 and others. This was an improvement over top-to-bottom generators that could get it hilariously wrong on first guess, and others that could easily be led astray. Part of that is using diffusion techniques, which basically start with a pure noise image and slowly refine it bit by bit until the model thinks it can’t make it look any more like a dog on a bike than it already does. Text-to-image models take text inputs like “a dog on a bike” and produce a corresponding image, something that has been done for years but recently has seen huge jumps in quality and accessibility.
#GOOGLE IMAGE SEARCHY GENERATOR#
Imagen (get it?) is a text-to-image diffusion-based generator built on large transformer language models that… okay, let’s slow down and unpack that real quick. Google Research has rushed to publicize a similar model it’s been working on - which it claims is even better.
#GOOGLE IMAGE SEARCHY HOW TO#
The AI world is still figuring out how to deal with the amazing show of prowess that is DALL-E 2’s ability to draw/paint/imagine just about anything… but OpenAI isn’t the only one working on something like that. open ( my_bytes_io ) # show it in the default system photo viewer temp_img. seek ( 0 ) # create a temporary image object temp_img = Image. copy_to ( my_bytes_io ) # we go back to address 0 again so PIL can read it from start to finish my_bytes_io. copy_to ( my_bytes_io, raw_image_data ) # or without the raw data which will be automatically taken # inside the copy_to() method image. get_raw_data () # this function writes the raw image data to the object image. seek ( 0 ) # take raw image data raw_image_data = image. results (): # here we tell the BytesIO object to go back to address 0 my_bytes_io. Choose ONE option only # - This param can also be omitted from _search_params if you do not wish to define any value _search_params = ) for image in gis. # For param marked with '#': # - Multiselect is currently not feasible. > gimages search -q puppies -d /path/ -w 500 -h 500 Programmatic usage from google_images_search import GoogleImagesSearch # you can provide API key and CX using arguments, # or you can set environment variables: GCS_DEVELOPER_KEY, GCS_CX gis = GoogleImagesSearch ( 'your_dev_api_key', 'your_project_cx' ) # define search params # option for commonly used search param are shown below for easy reference. > gimages search -q puppies -d /path/on/your/drive/where/images/should/be/downloaded > export GCS_DEVELOPER_KEY =_your_dev_api_key_ > gimages -k _your_dev_api_key_ -c _your_project_cx_ search -q puppies
#GOOGLE IMAGE SEARCHY INSTALL#
Install package from : > pip install Google-Images-SearchĬLI usage # without environment variables: Visit and in the web form where you create/edit your custom search engine enable "Image search" option and for "Sites to search" option select "Search the entire web but emphasize included sites".Īfter setting up your Google developers account and project you should have been provided with developers API key and project CX. Visit and generate API key credentials for your project. Visit and enable "Custom Search API" for your project. To be able to use this library, you need to enable Google Custom Search API, generate API key credentials and set a project:
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