Krea 2 Technical Report

Introduction

Over the past few years, image generation has seen remarkable progress. Diffusion and flow-matching models can generate high-resolution images, produce sharp photorealism and stable structure, render dense text, encode broad world knowledge, and follow user prompts in precise detail. These improvements have been driven by several interacting factors including scalable transformers architectures, improved captioning and text encoders, better latent representations, and pipelined post-training techniques. Yet as the field has optimized for reliability on these capabilities, many systems have converged toward a narrow set of default aesthetics. While effective production tools, this makes them less effective as engines for creative exploration, where users often need to search across styles, moods, compositions and visual directions rather than receive a single polished default.

To address these limitations, we present Krea 2, a series of foundation models focused on creative exploration. Krea 2’s models are built on the belief that image generation should be an exploratory medium: expressive enough to span many aesthetics, and controllable enough for creators to navigate them.

We built a large-scale data infrastructure and distributed training framework from scratch to curate a comprehensive pretraining dataset with broad world knowledge and style coverage.

Using this infrastructure, we train expressive models through a multi-stage pipeline spanning pretraining, midtraining, supervised finetuning (SFT), preference optimization, and reinforcement learning (RL), with each stage designed to progressively refine the model’s output distribution. We develop a simple yet performant diffusion transformer (DiT) architecture through thorough ablations. Our model incorporates several components that accelerate convergence , including iREPA, improved VAEs, and Qwen3-VL. We also integrate several architectural improvements, including grouped-query attention (GQA), sigmoid-gated attention, lightweight timestep modulation, and multilayer feature aggregation for text-encoder features, which together improve training stability and efficiency.

A strong base model is only useful if users can reliably reach the parts of its distribution they care about. In training, the model learns from rich, carefully constructed captions that describe images with dense visual detail. In practice, user inputs are often shorter, more ambiguous, and shaped by many different habits of expression. Some users describe a scene in natural language; others gesture toward a mood, a style, or a reference image. This creates a gap between the model’s learned conditioning space and the way creative intent is expressed at inference time.

To …