Open 5 open video models

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The obvious Lights, camera…

The introduction of Wow and SoraThe video generation has reached new heights. Creators are trying hard, and teams are incorporating these tools into their traffic flow. However, there is a catch: many closed systems collect your information and use visible or invisible watermarks to label the output as AI-ad. If you value privacy, control, and device workflow, open source models are your best bet, and several now rival VEO's results.

In this article, we will review the top five video generation models, providing technical information and a demo video to help you explore their video capabilities. All models are available Kissing the face and can run locally with Comfyui Or your favorite desktop AI programs.

The obvious 1. Wan 2.2 A14B

Wan 2.2 Improve its core of its faclusion with a mixture of mixture (MOE) that divides the decousing timesteps into certain periods of specialized experts, increasing the effective power without penalty. The team also organizes pain labels (eg lighting, composition, contrast, color tone) to make the “cinematic” look more unmanageable. Compared to WAN 2.1, a more limited training (+ 65.6% images, + 83.2% videos), which improves movement, semantics and aesthetics.

Wan 2.2 reports top-tier performance among open and closed systems. You can check the Text-to-Video and Image-to-Video Repositories for A14B on the screen: WAN-AI / Wan2.2-I2-AI /4B and Wan-I2V-A14B

The obvious 2. Huyuan video

It's a video A 13B-Parameter Open Video Foundation model trained in spatial-temporal space using a causal 3D autoencoder (VAE). Its Transformer uses “two streams to Single-Stream”: The text tokens and the tokens are first processed independently with full attention and softened, while the decoder-only multimodal llm works as a text enceder to improve the following commands and capture information.

The open source ecosystem includes code, tools, single GPU visualization (XDIT), FP8 tools, Not going well and comfyui combination, a The rising tide Demo, and penguin video benchmark.

The obvious 3. Mochi 1

Mochi 1 Is a 10b asymmetric diffion transformer (asymmdit) trained from scratch, released under Apache 2.0. Pairs with asymmetric Vae video compression 8×8 spatly and 6x temporally 12 channel 12 channel 12 channel

In the first test, the genmo Team Position Mochi 1 as an open model of the regime with high mobility and strong adhesion, which aims to close the gap with closed systems.

The obvious 4. LTX video

LTX-video Is a dit-based (defion transformer) generator built for speed: Generates 30 FPS videos at 1216×704 faster than real time, trained on a large dataset, trained to measure motion and visual quality.

The LineUP is very spilled: 13B Dev, 13B planned, 2b dincilly, and FP8 constructive, as well as spatial suppsalers and suppcalers ready to use operational comfyuis. If you're looking for fast paced and crisp motion from a single shot or a short scene sequence, the LTX is a strong choice.

The obvious 5. COGVIDEX-5B

Cogvideox-5b Is the Hile-Fidingel sibling on the 2B base, trained at Bloati16 and recommended for running at Bloatio16. It produces 6 frames per second FPS at 8 FPS with a fixed resolution of 720×4880 and supports English up to 226 tokens.

Model documentation shows random video memory (Vram) for Indon- and Multi-GPU, typical times (e.g.

The obvious Choosing a video generation model

Here are some top tips to help you choose the right video generator model for your needs.

• If you want a cinema-friendly look and 720p / 24 on one 2090: wan 2.2 (A14B basic functions;
• If you need a large base of T2v / I2v with strong motion and open source software (Huyuthaanvideo (13b, xdit parallelism, FP8 tools, comfyui)
• If you want a positive, state-of-the-art (SOTA) test for today's movement and rochi research the clear path: mochi 1 (10b asymmdit + asymmvae, Apache 2.0)
• If you care about real-time I2V and Uperalers configuration and comfyui workflow: LTX-Video (30 FPS at 1216×704, 13B / 2b and various FP8)
• If you need 6s 720×480 T2v, strong support for diffusers, and quality down to less vram: cogvideox-5b

Abid Awan Awan (@ 1Abidaliawan) is a certified trainer for a scientist with a passion for machine learning models. Currently, he specializes in content creation and technical blogging on machine learning and data science technologies. Avid holds a master's degree in technology management and a bachelor's degree in telecommunication engineering. His idea is to build an AI product using a graph neural network for students struggling with mental illness.