AI Explained - OpenAI’s New ImageGen is Unexpectedly Epic … (ft. Reve, Imagen 3, Midjourney etc)
The 40 image gen from OpenAI has been tested against various models, including those not yet publicly released. It stands out for its ability to handle complex prompts with a high degree of accuracy. For example, it successfully depicted a scene with three apples on a blue elephant's trunk, capturing the essence of the location and maintaining consistent shadows, although it struggled with the unusual prompt of an elephant with three legs. This model is also notable for understanding idiomatic expressions, such as 'hold your horses,' which other models failed to interpret correctly. Additionally, it offers impressive image editing capabilities, allowing users to make modifications like adding glasses to characters seamlessly. The model's ability to generate images with accurate text and logical coherence marks a significant advancement in AI image generation, making it a valuable tool for creating detailed and contextually appropriate visuals.
Key Points:
- 40 image gen excels in handling complex prompts with high accuracy.
- It understands idiomatic expressions, unlike other models.
- Offers seamless image editing capabilities, such as adding glasses to characters.
- Generates images with accurate text and logical coherence.
- Outperforms other models in creating contextually appropriate visuals.
Details:
1. 🔍 Exploring OpenAI's New Image Generation
- OpenAI's new image generation tool, 'images in chat gbt', has been under development for over two years, with a focus on accurately following prompts to create detailed images, like six people of different ethnicities doing jazz hands.
- The model still faces challenges, such as difficulties in accurately depicting reflections or mirrors, indicating areas for further improvement.
- Sam Altman announced broad availability, making the tool accessible to all users, including those using the free tier, and integrating it into the API to enhance its applications.
2. 🖼️ Model Comparison: OpenAI vs. Competitors
2.1. OpenAI Model Performance
2.2. Handling Unconventional Prompts
2.3. Comparison with Google's Image in 3
2.4. Reev Model Evaluation
2.5. Future Model Insights
3. 🐴 The Metaphor Challenge: 'Hold Your Horses'
- OpenAI's 40 image generation tool successfully understood and conveyed the metaphor 'hold your horses' in every image, while also providing quality text.
- Other models, such as Google's Image 3 and Mid Journey, failed to grasp the metaphor, as evidenced by their outputs.
- The task tested the models' ability to interpret idiomatic expressions, not merely literal visuals.
4. 🎨 Creative Potential of Image Generation
- 40 Image Gen transforms 2D images into impressive 3D representations, showcasing strong capabilities in image enhancement.
- Despite minor inaccuracies, such as slightly imperfect logos or text, the overall quality of generated images is notable and can serve as a viable alternative to traditional methods.
- The tool's ability to create complex scenes, such as a whale emerging from water based on a thumbnail inspiration, demonstrates its advanced creative potential.
- The application of 40 Image Gen for developing AI-generated thumbnails is promising, with results that may tempt creators to shift from traditional methods.
- 40 Image Gen can generate images with captions or basic infographics, providing an efficient solution for visual storytelling.
- When tasked with illustrating a four-panel human life journey, 40 Image Gen not only delivered the requested visuals but also included additional labels, highlighting its intuitive functionality.
5. 🔧 Enhancing Images: Editing and Accuracy
- Chat GPT with images allows for direct image editing, such as adding glasses to characters, which preserves the original image while making the specified changes. This showcases its capability in maintaining the integrity of the original content while enhancing specific features.
- In the challenge of depicting the 'four stages of life', most image generators struggled, failing to capture the concept accurately. Reev came closest to achieving this, though it still missed significant age ranges, indicating a need for improvement in representing life stages comprehensively.
- Mid Journey's metaphorical and artistic approach to the 'four stages of life' resulted in an absence of human figures, highlighting a potential gap in literal representation skills.
- An unreleased model provided a unique but confusing interpretation, suggesting that experimental approaches can yield unexpected results that may not align with user expectations.
- Google AI Studio with Gemini 2 Flash was less effective in depicting the 'four stages of life', producing an image that raised questions about its representation, underscoring challenges in clear, concept-driven execution.
- Google AI Studio's ability to edit images, such as transforming a baby into an old man, demonstrates flexibility in editing but still faces challenges in ensuring accuracy and logical consistency.
6. 🛡️ Ethical Considerations and Safety Measures
6.1. Ethical Considerations in AI Image Generation
6.2. Safety Measures for Model Vulnerabilities
7. 🤔 Challenges in Image Detail and Logic
- The image generation model demonstrated a significant improvement by successfully depicting six different people with varied ethnicities performing jazz hands, which was previously a notable weakness of such models.
- The model outperformed others like Mid Journey, which struggled, and Google’s Imageen 3, which failed to meet the expectation for this prompt.
- Reeve’s model also managed to depict six different people, though it did not accurately portray the jazz hands gesture, indicating room for improvement.
- The challenge of accurately depicting complex gestures like jazz hands highlights the ongoing need for improvement in image logic and representation.
- Despite advancements, the differentiation in performance among models emphasizes the variation in capability, with some models still needing refinement to meet specific visual demands.
8. 🧐 Artistic and Logical Evaluation of AI Outputs
- AI models struggled to balance artistic appeal and logical accuracy in generating images, often missing the inclusion of specified search objects, which impacts their usability for tasks requiring precise object placement.
- Imagin 3 demonstrated a partial success by including a 'time traveler' in a medieval marketplace image, but with flawed text and obvious visibility, indicating limited logical execution.
- Reev excelled in creating visually appealing images, yet consistently failed to logically incorporate required search objects like a 'pirate' among beachgoers, highlighting a gap between aesthetics and functionality.
- The evaluation highlighted that while artistic quality was consistently high, the logical aspect of image generation was notably better in the 40 image gen model, suggesting room for improvement in logical integration across models.