Krea 2 공개: 창의적 탐색을 강화한 12B 오픈웨이트 이미지 모델

research Krea 2 Technical Report In this technical report we introduce Krea 2: a series of foundation models designed for both wide aesthetic diversity and user creative control. We present: (1) our principles for data curation; (2) the model architectures; (3) our multi-stage training pipeline; (4) our distributed infrastructure; and (5) future work. Sangwu Lee, Erwann Millon, Le Zhuo, Matthew Newton, Andrei Filatov, Abhinay Devarinti, Elea Zhong, Avram Djordjevic, Gabriel Menezes, Will Beddow, Titus Ebbecke, Mihai Petrescu, Owen Fahey, Gian Saß, Felix Gil, Victor Perez · June 23, 2026 · 58 min read Release page Official website Hugging Face Weights & license GitHub Code & license The model weights and inference are released under a permissive license . 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 reduce this gap, we build two systems that make Krea 2 more exploratory and steerable from both text and image inputs: a prompt expander and a style-reference system. The prompt expander maps simple or underspecified user prompts into richer visual directions without overwriting the user’s intent. It is trained through a two-stage SFT and RL pipeline on top of open-source LLMs, where the objective is not only to improve image quality, but also to encourage creative variation and controllable exploration. Complementing this textual interface, the style-reference system lets users express visual intent through images when words are insufficient. It allows users to inject the style or mood of one or more reference images with minimal content leakage, while providing fine-grained control over style strength and weighted style mixing. Together, these components define Krea 2 as a foundation model for exploratory generation. Instead of optimizing only for a single polished default, Krea 2 is designed to expose a broad visual space and give users practical ways to move through it, using both text and image-based control. Krea 2 is among the top 10 models on the the Artificial Analysis leaderboard for text-to-image, and scores 2nd place among models from independent labs. Krea 2 serves as a comprehensive baseline and enables a creative generative experience while maintaining competitive performance. Data Data Curation Principles Before detailing our data pipeline, it is important to establish what constitutes a good data mix for our purpose. A good mix does not consist solely of “high quality” images. Diversity and broad domain coverage are essential given our objective of building an expressive, stylistically diverse model. We argue that conventional model-based filtering, which uses aesthetic-score and image-quality-assessment (IQA) models, introduces implicit biases. For example, such methods may classify a blurry image as low quality, even though motion blur or softness can be a deliberate artistic choice. Furthermore, we argue that as long as a caption accurately describes its image, even an undesirable image may be helpful in downstream use cases: because the model precisely understands the undesired behavior, such samples can later be used to steer generations away from that distribution. For these reasons, we build the pretraining dataset by filtering out only: Duplicated samples and over-represented concepts. Samples for which VLMs consistently fail to capture important aspects of the image. Samples that induce undesired biases and artifacts. Samples with high visual complexity that is too difficult to model reliably at low resolution. AI-generated samples These conditions shape a pretraining dataset with broad coverage while avoiding poor text-to-image alignment and artifacts. Importantly, we use no AI-generated images in our pretraining mix. Synthetic data and distillation can be an effective shortcut for acquiring model capabilities. However we find that even a small proportion of AI-generated images introduces biases into the model’s output distribution, as synthetic images tend to be easier to learn, which effectively imposes an upper bound on model quality. We therefore designed in-house classifiers to filter such images out. Captioning We employ a multi-stage approach to produce captions. First, we run an OCR model on each target image to extract any visible text. In the second stage, we provide both the OCR results and any available metadata (camera settings, known entities, and so on) to the captioning model, which produces an enriched caption that incorporates world knowledge alongside the extracted text. General captioning pipeline Once a context-rich, long-form natural-language caption is obtained, we use a cheaper LLM to reformat it into a variety of lengths and formats, exposing the model to a range of prompt styles. Empirically, we find that training on long prompts provides dense supervision, yielding faster convergence and lower training loss. For many downstream and applied use cases, however, performance on short and medium-length prompts remains important. We therefore train predominantly on long captions while ensuring the model is exposed to short and medium-length prompts throughout training. Our overall training pipeline and data stages Pretraining Data Pretraining data spans 256px, 512px, and 1024px resolution stages. Progressively scaling the resoluti