# No Training Required

Story-Adapter is an iterative framework designed specifically for visualizing long-form stories without the need for training. It optimizes the image generation process through an iterative paradigm and a global reference cross-attention module, maintaining semantic coherence in the story while reducing computational costs. The significance of this technology lies in its ability to generate high-quality, detail-rich images within long narratives, addressing the challenges faced by traditional text-to-image models in visualizing extended stories, such as semantic consistency and computational feasibility.

Enhance-A-Video is a project focused on improving video generation quality by adjusting the temporal attention parameters within video models to enhance consistency and visual quality between frames. This project is a collaborative effort among researchers from the National University of Singapore, Shanghai AI Laboratory, and the University of Texas at Austin. The primary advantage of Enhance-A-Video is its ability to enhance the performance of existing video models at zero cost, without the need for retraining. It introduces a temperature parameter to control inter-frame correlations, improving the temporal attention output and thereby enhancing video quality.

MagicFace represents a technology for personalized portrait synthesis that operates without training, capable of generating high-fidelity portrait images based on multiple provided concepts. This technology integrates reference concept features at the pixel level into the generated images for personalized customization. It introduces a coarse-to-fine generation process consisting of two phases: semantic layout construction and concept feature injection, achieved through Reference-aware Self-Attention (RSA) and Region-grouped Blend Attention (RBA) mechanisms. This technology excels not only in portrait synthesis and multi-concept portrait customization but also extends to texture transfer, enhancing its versatility and practicality.

AsyncDiff is a method for accelerating diffusion models through asynchronous denoising parallelization. It divides the noise prediction model into multiple components and distributes them across different devices, enabling parallel processing. This approach significantly reduces inference latency while having a minimal impact on generation quality. AsyncDiff supports a variety of diffusion models, including Stable Diffusion 2.1, Stable Diffusion 1.5, Stable Diffusion x4 Upscaler, Stable Diffusion XL 1.0, ControlNet, Stable Video Diffusion, and AnimateDiff.

SketchDeco is an innovative online tool that can convert black-and-white sketches, masks, and color palettes into realistic color images without requiring users to define text prompts. By combining ControlNet and a staged generation approach, using Stable Diffusion v1.5 and BLIP-2 text prompts, it delivers faithful image generation and user-driven coloration. It is not only fast and requires no training but also compatible with consumer-grade Nvidia RTX 4090 Super GPUs, providing a valuable resource for creative professionals and hobbyists.

FIFO-Diffusion is a novel inference technique based on pre-trained diffusion models for text-conditioned video generation. It enables the generation of videos of unlimited length without training, by iteratively executing diagonal denoising while handling an increasing level of noise across a series of consecutive frames within a queue. The methodDequeues a fully denoised frame from the head, while enqueueing a new random noise frame at the tail. Additionally, latent disentanglement is introduced to reduce the training-inference gap, and future denoising is utilized to leverage the benefits of forward references.

AnyV2V is an innovative video-to-video editing framework that enables users to edit the first frame of a video using any off-the-shelf image editing tool and then reconstruct the image-to-video using existing image-to-video generation models. This approach simplifies a variety of editing tasks, including prompt-based editing, style transfer, theme-driven editing, and identity manipulation.

SegMoE is a powerful framework that can dynamically combine Stable Diffusion models into expert mixtures within minutes, without requiring any training. It enables the instant creation of larger models, providing more knowledge, better adherence, and improved image quality. Inspired by mergekit's mixtral branch, SegMoE is specifically designed for Stable Diffusion models. It is easy to install and use, making it ideal for image generation and synthesis tasks.