Search for: All records

Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Sate-of-the-art models like Stable Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text depiction, resulting in misspelled or inconsistent text. We introduce a training-free method with minimal computational overhead that significantly enhances text rendering quality. Specifically, we introduce an overshooting sampler for pretrained rectified flow (RF) models, by alternating between over-simulating the learned ordinary differential equation (ODE) and reintroducing noise. Compared to the Euler sampler, the overshooting sampler effectively introduces an extra Langevin dynamics term that can help correct the compounding error from successive Euler steps and therefore improve the text rendering. However, when the overshooting strength is high, we observe over-smoothing artifacts on the generated images. To address this issue, we propose an Attention Modulated Overshooting sampler (AMO), which adaptively controls the strength of overshooting for each image patch according to their attention score with the text content. AMO demonstrates a 32.3% and 35.9% improvement in text rendering accuracy on SD3 and Flux without compromising overall image quality or increasing inference cost. 

Knowledge distillation leverages a teacher model to improve the training of a student model. A persistent challenge is that a better teacher does not always yield a better student, to which a common mitigation is to use additional supervision from several “intermediate” teachers. One empirically validated variant of this principle is progressive distillation, where the student learns from successive intermediate checkpoints of the teacher. Using sparse parity as a sandbox, we identify an implicit curriculum as one mechanism through which progressive distillation accelerates the student’s learning. This curriculum is available only through the intermediate checkpoints but not the final converged one, and imparts both empirical acceleration and a provable sample complexity benefit to the student. We then extend our investigation to Transformers trained on probabilistic context-free grammars (PCFGs) and real-world pre-training datasets (Wikipedia and Books). Through probing the teacher model, we identify an analogous implicit curriculum where the model progressively learns features that capture longer context. Our theoretical and empirical findings on sparse parity, complemented by empirical observations on more complex tasks, highlight the benefit of progressive distillation via implicit curriculum across setups. 

Abstract In this work, we analyze data collected by an HF transmitter/receiver radio link, operating as an oblique ionosonde between the McMurdo Station (transmitter) and South Pole Station (receiver) at 4.1, 5.1, 6.0, 6.4, and 7.2 MHz between 28 February and 14 March 2019. To help contextualize the link's data we have performed numerical raytrace simulations to help understand the observations. By considering both the data and simulations, we have identified both single‐ and two‐hop E‐ and F‐region propagation modes in the data, where the multi‐hop modes were observed in the hours around sunrise and sunset in the 4.1 and 5.1 MHz channels. This is an unexpected result given the accepted wisdom that multi‐hop modes, which require a ground scatter component, cannot be supported in Antarctica because of the highly absorptive ice covering much of the continent. Our results show that multi‐hop propagation modes can be supported in the region under specific ionospheric conditions—around sunrise and sunset—if the mode's ground scatter component is collocated with the Transantarctic Mountains. The mountains are located along the great‐circle path between the link's transmitter and receiver. However, the combination of favorable ionospheric and ground scattering conditions makes the detection of the multi‐hop mode a rare occurrence in the data set analyzed here. These findings are critical to data analysis efforts of any current or future oblique ionosonde systems operating in Antarctica and other regions such as the Arctic.