29 мая 2024 г. · The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the ...

A general-purpose frame-work for controlling pre-trained diffusion models at inference-time via optimizing initial noise latents.

22 янв. 2024 г. · We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion ...

This tutorial will show you how to progressively apply the optimizations found in PyTorch 2 to reduce inference latency.

25 сент. 2024 г. · In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives ...

This project focuses on improving diffusion model inference time with quantization and pruning techniques.

9 нояб. 2023 г. · You can use fewer timesteps in your schedule when doing the inference after the model is trained. You can use a different schedule when doing ...

Diffusion-based language models are emerging as a promising alternative to autoregressive LMs: they approach the competence of autoregressive LMs while offering ...

A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. Diffusion process · Latent variable model · U-Net

The Stable Diffusion model uses the PNDMScheduler by default which usually requires ~50 inference steps, but more performant schedulers like ...