Introducing 4o Image Generation

Addendum to GPT-4o System Card: 4o image generation

4o image generation is a new, significantly more capable image generation approach than our earlier DALL·E 3 series of models. It can create photorealistic output.

Introducing our latest image generation model in the API

Our latest image generation model is now available in the API via ‘gpt-image-1’—enabling developers and businesses to build professional-grade, customizable visuals directly into their own tools and platforms.

Introducing ChatGPT Images 2.0

ChatGPT Images 2. 0 introduces a state-of-the-art image generation model with improved text rendering, multilingual support, and advanced visual reasoning.

GPT-4

We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

arXiv:2607. 13125v2 Announce Type: replace-cross Abstract: We introduce Boogu-Image-0.

Introducing vision to the fine-tuning API

Developers can now fine-tune GPT-4o with images and text to improve vision capabilities

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.

Welcome aMUSEd: Efficient Text-to-Image Generation

Open Preference Dataset for Text-to-Image Generation by the 🤗 Community

The new ChatGPT Images is here

The new ChatGPT Images is powered by our flagship image generation model, delivering more precise edits, consistent details, and image generation up to 4× faster. The upgraded model is rolling out to all ChatGPT users today and is also available in the API as GPT-Image-1.

OpenAI o3 and o4-mini System Card

OpenAI o3 and OpenAI o4-mini combine state-of-the-art reasoning with full tool capabilities—web browsing, Python, image and file analysis, image generation, canvas, automations, file search, and memory.

How far can we go with ImageNet for Text-to-Image generation?

The paper argues that large-scale text‑to‑image models can be trained effectively on ImageNet, provided the dataset is enriched with carefully crafted text and image augmentations. Using this approach, the authors match the performance of state‑of‑the‑art models such as FLUX, surpassing SD3 on GenEval by +5 points and SDXL on DPGBench by +12, while employing only 1/1000th of the training images and significantly fewer parameters. The method requires just 500 hours of H100 GPU time, making it a more reproducible and accessible alternative to massive web‑scraped datasets.