GPT4o Image GEN. How does it work?

Overview

GPT-4o’s image generation is a cutting-edge feature that blends advanced AI techniques to create images from text prompts. Here’s a simple breakdown of how it works and the techniques involved, keeping things clear and approachable.

The Technology Behind It

GPT-4o, developed by OpenAI, is a multimodal model that can handle text, images, and more. For image generation, it uses a combination of technologies:

• A transformer processes your text prompt, turning it into visual tokens, like a blueprint for the image.
• Then, a diffusion-like decoder takes these tokens and builds the image, piece by piece, in a way that ensures it matches your description.

This hybrid approach means the system understands both what you say and how to visualize it, making the images more accurate and detailed.

The Technique in Detail

The technique involves:

• Autoregressive Transformer: This part generates visual tokens, which are like intermediate steps, ordered in a way that makes sense (top-to-bottom, left-to-right).
• Rolling Diffusion-like Decoder: This part turns those tokens into the final image, working in groups (like patches) and refining them step by step, guided by the text.

This process is slower than some competitors, taking about 30 seconds per image, but it aims for high quality and coherence with your prompt.  

Why It Matters?

This integration allows GPT-4o to create images that are not just pretty but also contextually relevant, like adding specific details or styles you ask for. It’s a big step forward from older models, which often handled text and images separately.

For more details, check out OpenAI’s official announcement here or a technical guide here.

A Comprehensive Survey Note on GPT-4o Image Generation Techniques

This section provides an in-depth exploration of the technology behind GPT-4o’s image generation capabilities, expanding on the direct answer with detailed insights for those interested in the technical underpinnings. The analysis is based on recent findings from official sources and technical guides, reflecting the state of knowledge as of April 13, 2025.

Background and Context

GPT-4o, introduced by OpenAI, is a multimodal AI model designed to process and generate content across text, images, and other modalities. Its image generation feature, rolled out in March 2025, marks a significant advancement in natively integrating visual synthesis within a language model framework. This capability was initially teased in May 2024, with full availability for paying ChatGPT users by March 2025, and later extended to free tiers with limits (e.g., three images per day for free users, as noted in an X post by CEO Sam Altman here).

The demand for this feature was high, with OpenAI reporting GPU strain due to usage, and it quickly gained attention for creating stylized images, such as those mimicking Studio Ghibli aesthetics, raising discussions around copyright and training data (as reported by TechCrunch).

Technical Architecture: A Hybrid Approach

Research suggests that GPT-4o’s image generation relies on a hybrid architecture combining autoregressive transformers and diffusion models, a departure from previous standalone image generators like DALL-E 3. The process can be broken down into several stages, as detailed below:

Text Encoding: The multimodal transformer encoder processes the text prompt, outputting dense text embeddings that capture semantic meaning.

Visual Token Generation:  An autoregressive transformer decoder generates visual tokens in a latent space, ordered top-to-bottom, left-to-right, serving as an intermediate representation.

Image Decoding: A rolling diffusion-like decoder translates these tokens into pixels, using a group-wise diffusion process. It divides the image into N groups (patches or bands), denoising each group progressively for K steps, guided by cross-attention to both visual tokens and text embeddings.  

Final Output:The decoded groups are stitched together in pixel space to form the final image. 

This pipeline is supported by a diagram from OpenAI’s official announcement Introducing 4o Image Generation | OpenAI, which illustrates “tokens -> [transformer] -> [diffusion] -> pixels,” confirming the integration of transformers and diffusion models.

Diffusion Model Specifics: Rolling Group-Wise Process

The diffusion-like decoder is particularly noteworthy. Unlike traditional diffusion models that denoise the entire image at once, GPT-4o employs a rolling group-wise approach. This means the image is divided into patches or bands, and each group is denoised progressively, allowing for finer control and potentially better alignment with the text prompt. This technique is detailed in related research, such as Rolling Diffusion Models, which discusses group-wise denoising strategies.

A technical guide from LearnOpenCV Introduction to GPT-4o Image Generation - A Complete Guide further clarifies that this process is not a simple diffusion model like DALL-E 3 but involves “aggressive post-training” to enhance visual fluency, suggesting fine-tuning for better integration with the multimodal framework.

Multimodal Integration and Performance

The evidence leans toward GPT-4o’s strength lying in its multimodal nature, where the transformer and diffusion components are tightly coupled. This integration allows for iterative refinement, where users can request edits in natural language, and the model adjusts the image accordingly, as noted in discussions on Hacker News. For example, users can ask for changes like “change day to night” or “put a hat on him,” and the model implements these with high fidelity.

However, this integration comes with trade-offs. The process is slower, taking about 30 seconds per image, compared to competitors like Gemini, which can generate images in seconds, as mentioned in a blog post Introducing 4o Image Generation. This slowness is likely due to the complex interplay between the transformer and diffusion stages, requiring additional computational resources.

Comparison with Previous Models

Prior to GPT-4o, OpenAI’s image generation relied on external models like DALL-E 3, which used a classic diffusion transformer trained to reconstruct images by removing noise from pixels, as highlighted in a VentureBeat article ‘Insane’: OpenAI introduces GPT-4o native image generation and it’s already wowing users | VentureBeat. GPT-4o’s approach is more integrated, with the image generator being part of the same model that handles text and code, trained to understand all forms of media simultaneously. This shift enhances prompt interpretation and image detail, as evidenced by user experiences shared on platforms like DataCamp GPT-4o Image Generation: A Guide With 8 Examples | DataCamp.

Practical Implications and User Experience

The technique’s effectiveness is reflected in its capabilities, such as accurate text rendering within images (e.g., clear signage, complex infographics) and the ability to mimic various styles, from photorealism to Studio Ghibli, as noted in a Medium article Inside GPT-4o’s Image Generation: Is It Really That Impressive? | by Bernard Loki “AI VISIONARY” | Apr, 2025 | Medium. Users can craft specific prompts, like “create a dynamic mural combining Byzantine iconography with cyberpunk aesthetics,” and the model delivers detailed, context-aware results, as shown in prompt examples from GPT-4o Image Generation: A Complete Guide + 12 Prompt Examples.

However, access is tiered, with Plus and Pro users ($20-$200/month) getting full access, while free users are limited, as reported by Tom’s Guide How to make AI images using ChatGPT’s new 4o model | Tom’s Guide. The API for developers is also rolling out, expected to be more expensive than DALL-E’s $0.03+/image rate, as discussed in community forums API for image generation for gpt-4o model - API - OpenAI Developer Community.

Controversies and Limitations

While the technique is impressive, there are concerns around copyright, especially with stylized outputs resembling protected works, and the use of generated images for deceptive purposes (e.g., fake receipts), as noted in TechCrunch. OpenAI addresses this by embedding metadata in generated images and enforcing guidelines, but the debate continues, particularly around training data transparency.

Additionally, the model’s reliance on a linked latent diffusion model, as revealed in user interactions on Hacker News, indicates it’s not an end-to-end token-unified architecture, which might limit certain applications compared to fully integrated systems.

Conclusion

In summary, GPT-4o’s image generation is powered by a sophisticated hybrid technique involving an autoregressive transformer for token generation and a rolling diffusion-like decoder for pixel synthesis, all within a multimodal framework. This approach enhances accuracy, detail, and contextual relevance, though it comes with trade-offs like slower generation times and ongoing debates around ethics and access. For further reading, explore the cited sources for deeper technical insights and practical applications.