Embedding models, which transform inputs into dense vector representations, are pivotal for capturing semantic information across various domains and modalities. Text embedding models represent words and sentences as vectors, strategically positioning semantically similar texts in close proximity within the embedding space (Gao et al., 2021; Le and Mikolov, 2014; Reimers and Gurevych, 2019). Recent research has focused on developing general-purpose embedding models capable of excelling in diverse downstream tasks, including information retrieval, clustering, and classification (Cer et al., 2018; Muennighoff et al., 2023). Leveraging their vast pre-training knowledge, large language models (LLMs) have emerged as a promising avenue for constructing such general-purpose embedding models, with the potential to significantly enhance performance across a broad spectrum of applications (Anil et al., 2023a,b; Brown et al., 2020). The integration of LLMs has revolutionized the development of high-quality embedding models through two primary approaches. Firstly, LLMs have been employed to refine training datasets by generating higher quality examples. Techniques such as hard negative mining (Lee et al., 2024) and synthetic data generation (Dai et al., 2022; Wang et al., 2023) enable the distillation of LLM knowledge into smaller, more efficient embedding models, leading to substantial performance gains. Secondly, recognizing that the embedding model parameters are frequently initialized from language models (Devlin et al., 2019; Karpukhin et al., 2020), researchers have explored leveraging LLM parameters directly for initialization (Ni et al., 2021).

We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment {\it vs.} high-resolution rendering. We first demonstrate the benefits of scaling a {\it Shallow UNet}, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes. Vermeer, our full pipeline model trained with internal datasets to produce 1024x1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.