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  2. Wikipedia:Non-free content/Image size calculator - Wikipedia

    en.wikipedia.org/.../Image_size_calculator

    Upload file; Search. Search. Appearance. ... Non-free content/Image size calculator. Add languages. ... Non-free content § Image resolution;

  3. Wikipedia:Free image resources - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Free_image_resources

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  4. Wikipedia:Random - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Random

    On Wikipedia and other sites running on MediaWiki, Special:Random can be used to access a random article in the main namespace; this feature is useful as a tool to generate a random article. Depending on your browser, it's also possible to load a random page using a keyboard shortcut (in Firefox , Edge , and Chrome Alt-Shift + X ).

  5. List of image-sharing websites - Wikipedia

    en.wikipedia.org/wiki/List_of_image-sharing_websites

    With a free account, the user can use up to 10GB of bandwidth per month and 2GB storage. Unlimited free storage, 1MB per photo and 10 minutes per video (with image size restrictions). No size restrictions with Pro account. Pinterest: United States Photo sharing/social networking 11,700,000 [22] Unknown Pixabay: Germany [23]

  6. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]

  7. Artificial intelligence art - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_art

    The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [32] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images.