4 个仓库
Awesome GitHub RepositoriesMulti-GPU Video Inference Accelerators
Systems that distribute video generation workloads across multiple GPUs to reduce inference time for large-scale outputs.
Distinct from GPU-Accelerated Inference: Distinct from GPU-Accelerated Inference: specifically distributes video generation workloads across multiple GPUs, not general inference acceleration.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Multi-GPU Video Inference Accelerators. Refine with filters or upvote what's useful.
Awesome Multi-GPU Video Inference Accelerators GitHub Repositories
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- magic-research/magic-animate
magic-research/magic-animate
10,908在 GitHub 上查看Magic Animate is a diffusion model video generator designed for human image animation. It transforms a static human photo into a temporally consistent video by mapping movements from a reference motion clip, acting as a tool to create realistic animations from a single image. The system ensures visual stability and minimizes flicker through temporal attention injection and motion-controlled noise scheduling. To accelerate the generation of high-resolution video, it includes a distributed GPU inference engine that splits model workloads across multiple graphics cards. The project covers a com
Distributes video generation workloads across multiple GPUs to reduce inference time for high-resolution output.
Python - skyworkai/skyreels-v2
SkyworkAI/SkyReels-V2
6,356在 GitHub 上查看SkyReels-V2 is a video generation system that creates, extends, and refines video clips from text descriptions, images, or both. It operates as a diffusion-based video generation model that can produce videos of any duration by denoising frames sequentially, with each new frame conditioned on the ones that came before it. The system supports generating videos from scratch using text prompts, starting from a single image and producing subsequent frames, or constraining both the first and last frames to match user-provided images. What distinguishes SkyReels-V2 is its combination of infinite-le
Distributes video generation workloads across multiple GPUs to reduce inference time for large-scale outputs.
Python - tencent-hunyuan/hunyuanvideo-1.5
Tencent-Hunyuan/HunyuanVideo-1.5
4,440在 GitHub 上查看HunyuanVideo-1.5 is a video generation foundation model and text-to-video diffusion framework. It utilizes a latent video diffusion model and a spatio-temporal transformer architecture to generate high-definition video sequences from text descriptions and images. The project enables cinematic camera control for directing pans and tilts and provides image-to-video animation capabilities. It supports visual style adaptation through low-rank adaptation tuning and uses a language model for prompt refinement to improve visual alignment. The model covers high-resolution video upscaling via a super
Accelerates video generation by using step distillation, cache inference, and sparse attention to reduce creation time.
Pythonimage-to-videotext-to-videovideo-generation - hao-ai-lab/fastvideo
hao-ai-lab/FastVideo
3,743在 GitHub 上查看FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine, a video diffusion training framework, and a modular pipeline orchestrator. It provides a distributed transformer optimizer and a distillation toolkit designed to reduce denoising steps and model complexity to increase frame rates. The project distinguishes itself through specialized acceleration techniques, including joint distillation and sparse attention training. It implements low-step video generation and weight quantization to FP8 or FP4 precision to increase throughput a
Provides an accelerated runtime for producing AI videos via optimized attention and distributed GPU processing.
Pythondiffusersdiffusion-modelsdistillation
