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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.

The launch of Civitai Green marks the platform's transformation into a clean, professional AI art creation environment, completely eliminating adult content to provide creators with safe and appropriate image and model resources, enhancing the user experience in the workplace. Through Civitai Green, the platform not only ensures the high quality and diversity of resources but also introduces a more efficient payment processor, Paddle, optimizing the user payment experience. This transformation benefits the platform's commercial development and strengthens the community ecosystem by sharing works.

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.

Researchers from MIT and Google have collaborated to develop StableRep technology, which trains detailed and efficient AI image models using AI-generated images. StableRep employs multi-positive contrastive learning methods and trains on millions of labeled synthetic images, achieving significant results in ImageNet classification. Despite its success, StableRep's image generation is relatively slow, suffers from semantic mismatch issues, and requires the underlying model to be initially trained on real data. The technology has been open-sourced on GitHub.