The rapid development of generative AI has raised the challenge of comprehensively evaluating its performance. A plethora of models have emerged, each showcasing increasingly impressive results. However, the pressing question remains: how do we assess the effectiveness of these text-to-image models? Traditional evaluation methods often rely on subjective visual assessments or simple metrics like CLIPScore, which frequently fail to capture the intricate details found in complex text prompts, such as relationships between objects and logical reasoning. This leads to inaccurate evaluation outcomes for many text-to-image models.

Last night, Stability AI unveiled its most powerful model - Stable Diffusion 3.5, which is not just a single model, but a package containing three versions, designed to meet the diverse needs of researchers, business enthusiasts, startups, and enterprises. The three versions are Stable Diffusion 3.5 Large, Stable Diffusion 3.5 Large Turbo, and one that will be available on October 29.

Stability AI collaborates with Amazon Web Services (AWS) to introduce three new text-to-image generation models to the Amazon Bedrock service, aiming to enhance business positioning and market competitiveness. These three models—Stable Image Ultra, Stable Diffusion3Large, and Stable Image Core—cater to different needs, offering everything from high-quality image generation to fast bulk image production.

On August 21, 2024, the Ideogram team proudly announced the launch of its latest version of the text-to-image model—Ideogram 2.0. This significant update not only provides users with free access but also introduces a series of innovative features and tools, marking a major leap in text-to-image technology.

Google's text-to-image model Imagen3's 'ImageFX' feature is now officially available to all users, offering an enhanced prompt interaction experience. Users only need to input descriptive text, and the system can generate corresponding images while optimizing the prompts, providing various options to improve the generation results. Furthermore, Imagen3 also includes a localized modification feature, allowing users to use brush tools to edit specific parts of the image and fill in related descriptive terms, with the system automatically generating the modified image, greatly improving the flexibility and convenience of image generation.

Developed jointly by researchers from the University of Hong Kong and Bytedance, LlamaGen is an image generation method based on the autoregressive model Llama, which has shown the potential to surpass traditional diffusion models in the field of image generation. The open-source release of LlamaGen quickly garnered nearly 900 stars on GitHub, acknowledging this achievement. This result not only proves the competitiveness of autoregressive models in image generation but also brings new vitality

Recently, the AI research organization Artificial Analysis launched an initiative called 'Artificial Analysis Text to Image Leaderboard & Arena' to comprehensively evaluate the performance of these models.

Stable Diffusion 3 is the most powerful text-to-image model. It adopts the MMDiT architecture, demonstrating performance that surpasses existing text-to-image generation systems. Stable Diffusion 3 excels in visual aesthetics, text adherence, and layout compared to other advanced models. The MMDiT architecture combines DiT and rectangular flow forms, processing image and language representations through independent weight sets. Stable Diffusion 3 offers flexibility.

OpenAI has announced that ChatGPT Plus and Enterprise users can now use the latest text-to-image model, DALL·E 3. DALL·E 3 has made significant advancements in image synthesis, capable of generating various types of images. This integration provides more possibilities for image generation, including realistic, anime, and creative styles. OpenAI has implemented a multi-layered safety system to restrict potentially harmful image generation. The release of DALL·E 3 offers users enhanced creativity and practicality, expanding the scope of AI-generated images.