However, training these two stages separately leads to significant data bias in the inference phase, thus affecting the quality of reconstructed results.
To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution.
We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources.
Across our benchmarks and user study, we find that win-rates for DITTO outperform few-shot prompting, supervised fine-tuning, and other self-play methods by an average of 19% points.
Existing techniques aimed at improving alignment, such as refusal training, are often bypassed.
To this end, we propose single-view generalizable Human Gaussian model (HGM), a diffusion-guided framework for 3D human modeling from a single image.
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.
However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e. g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view.
Moreover, given the same computational resources, a ReNO-optimized one-step model outperforms widely-used open-source models such as SDXL and PixArt-$\alpha$, highlighting the efficiency and effectiveness of ReNO in enhancing T2I model performance at inference time.
Here we show that smaller LMs trained utilizing some of the layers of GPT2-medium (355M) and GPT-2-large (770M) can effectively match the val loss of their bigger counterparts when trained from scratch for the same number of training steps on OpenWebText dataset with 9B tokens.