OpenAI Just BROKE THE INTERNET with a New Image Model...

Okay, here's a breakdown of the video about OpenAI's new image generation capabilities in ChatGPT:

OpenAI has released native image generation directly into ChatGPT's GPT-4.0 model, bringing powerful, high-quality visual creation capabilities right into the chat interface. This marks a significant step towards truly multimodal AI, allowing seamless understanding and generation across text and images, although the current implementation is noticeably slow.

Here are the key points and details discussed:

• OpenAI launched native image generation built into the GPT-4.0 version of ChatGPT.
• This is seen as a major advancement, moving image generation beyond being just a novelty for cool art and making it truly useful for a wide variety of applications.
• Image generation isn't new, with many existing models like Midjourney, DALL-E, Stable Diffusion, etc., but this is unique because it's native within a powerful language model like GPT-4.0.
• GPT-4.0 is described as an "Omni model," meaning it's trained to understand and generate all modalities – text, images, and audio – both coming in and going out. This is exciting because it allows for seamlessly working across different types of information.
• This multimodal capability means the model can use context from both text prompts and uploaded images to generate new visuals, giving users a lot more control and allowing for cooler things like transforming an existing photo into a specific style.
• A major drawback right now is the speed; the image generation is currently very slow, often taking minutes for a single image. The presenter finds this "extremely slow" and feels it "reduces the number of viable use cases." They also speculate this might be why GPT-4.0 text responses have seemed slower recently.
• Despite the speed issue, the quality of the generated images is incredibly high and accurate.
• The model is fantastic at recreating existing images in different artistic styles, like changing a photo into anime, South Park, Simpsons, Studio Ghibli, Minecraft, Lego, voxel art, watercolor, marionette style, rubber hose animation, or Pixar style. The presenter thinks these restyled images look "phenomenal."
• It can also create completely new and complex images from text prompts, even detailed ones like a chicken riding a duck riding a dog riding a horse, doing a great job with realism (though sometimes scale might be off).
• A surprising and powerful capability is its ability to accurately render text within the generated images. Examples shown include text on whiteboards, magnetic poetry on a fridge, text in comic panels, infographics, text on trading cards, and text on a commemorative coin. The text is often perfectly spelled and looks naturally integrated, which the presenter finds "absolutely incredible" and "flawless."
• It can understand and execute specific image editing instructions, such as removing the background from a photo or adding elements like realistic glasses to a dog. The presenter notes that while background removal works, it sometimes makes the main subject look "very weird."
• It can take an uploaded image as a reference or starting point and modify it or create something similar, like using a photo of a pet to put it on a custom trading card in a specific style.
• It can generate complex visual concepts like funny infographics, product designs, menu concepts, or even recreating famous memes or scenarios with specific characters (like two witches reading a complicated parking sign). The presenter sees these as "really cool" and "absolutely gorgeous," highlighting their usefulness.
• The model can handle "in-context learning," where you provide a few example images to guide the style of a new generation based on a text prompt.
• It can turn images in older artistic styles (like paintings or drawings) into realistic-looking photographs.
• Examples showcase its ability to generate varied and creative scenes based on detailed prompts, including historical figures in unusual locations (Karl Marx at the Mall of America), fantastical scenarios (cat reflection as a tiger), specific photographic styles (Polaroid, old analog film, DSLR), and complex underwater scenes.
• The presenter notes that generating images with text used to be much harder, but this model does it "unbelievably better."
• The integration into ChatGPT makes it easier for anyone to access powerful image creation without needing separate complex software or specialized prompting techniques (though it can write more detailed prompts for itself internally).
• The presenter feels a sense of "joy and excitement" using this model, comparing it to the feeling when GPT-2 first came out, calling it a "wow moment."
• They are frustrated with OpenAI's naming conventions, questioning why image generation is tied specifically to the 4.0 model version rather than being a universal feature across the interface.
• Potential uses are vast, including simplifying tasks that previously required Photoshop expertise like removing/adding elements, making images transparent, or creating visual assets from scratch.
• This capability is seen as incredibly useful for creatives, educators, small business owners, students, or anyone needing visual content for things like websites, social media thumbnails, menus, or marketing materials.
• Limitations listed by OpenAI and observed include:
  • Cropping issues where the generated image might feel incomplete.
  • Hallucinations, similar to text models, where it can make up information or visual details, especially with low-context prompts.
  • "High binding problems," struggling to accurately combine more than 10-20 distinct concepts simultaneously, sometimes leading to misinterpretations (like spelling errors in complex scenarios).
  • Difficulty with precise graphing or complex graphical rendering.
  • Struggling with accurately rendering text in non-Latin languages (like Korean), where characters can be inaccurate or hallucinated.
  • Challenges with editing precision, particularly involving dense information or very small text within the image.
• Despite limitations, the presenter is very impressed and encourages viewers to try it out.