# Text Prompts

PromptFix is a comprehensive framework that enables diffusion models to perform various image processing tasks by following human instructions. This framework constructs a large-scale instruction-following dataset, proposes high-frequency guided sampling methods to control the denoising process while retaining high-frequency details in unprocessed areas, and designs auxiliary prompt adapters that enhance text prompts using visual language models, increasing the model's task generalization capabilities. PromptFix outperforms previous methods across various image processing tasks and demonstrates superior zero-shot capabilities in blind restoration and compositional tasks.

Mochi 1 is a cutting-edge open-source AI video generator developed by Genmo, allowing creators to generate high-quality, realistic videos using text and image prompts. With its superior prompt adherence and smooth motion effects, Mochi 1 makes AI video generation accessible to everyone. It aims to compete with other industry models, offering creators more control and better visual outcomes.

Score Distillation Sampling (SDS) is a recently popular method that relies on image diffusion models to control optimization problems with text prompts. This paper conducts an in-depth analysis of the SDS loss function, identifies inherent problems in its formulation, and proposes an unexpected yet effective fix. Specifically, we decompose the loss into different factors and isolate the component that generates noisy gradients. In the original formulation, high text guidance is used to account for noise, leading to undesirable side effects. Instead, we train a shallow network to mimic the time-step-dependent denoising insufficiency of the image diffusion model, effectively decoupling it. We demonstrate the versatility and effectiveness of our novel loss formulation through multiple qualitative and quantitative experiments, including optimized image synthesis and editing, zero-shot image translation network training, and text-to-3D synthesis.