Mira

An experimental framework designed for high-quality long video generation, featuring extended sequence lengths and enhanced dynamic properties.

CommonProductVideoVideo GenerationText-to-Video

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Mira (Mini-Sora) is an experimental project aimed at exploring the field of high-quality, long-duration video generation, particularly in the mimicking of the Sora style. Based on existing text-to-video (T2V) generation frameworks, Mira achieves breakthroughs in several key aspects: extended sequence length, enhanced dynamic properties, and maintenance of 3D consistency. Mira is still in the experimental phase and has room for improvement compared to more advanced video generation technologies such as Sora.

Supports generation of video sequences as long as 10 seconds
20 seconds
and beyond.
Can produce videos rich in dynamics and complex movements.
Maintains 3D integrity of objects during complex dynamics and interactions
avoiding significant deformation.
Provides open-source code and checkpoints for generating videos in various resolutions and frame rates.
Includes comprehensive open-source kits for data annotation and training procedures.
Supports custom configurations to adapt to video generation requirements for different resolutions and frame rates.
Regular updates

Video Creator: Mira aids video creators in generating high-quality videos with complex dynamics and 3D effects.
Researchers: Mira provides an experimental platform for exploring and improving long video generation technology.
Developers: Open-source code and checkpoints from Mira enable secondary development and integration.

Generate a warm scene of a cute dog sniffing around on the beach.
Create a serene underwater scene showing turtles swimming among coral reefs.
Set up a video with complex dynamic interactions in a virtual environment.

Step 1: Create a conda environment and activate it.
Step 2: Install the necessary dependencies.
Step 3: Download and configure the dataset and pre-trained models.
Step 4: Run the corresponding training script based on the desired resolution.
Step 5: Run the inference script to generate videos within the activated environment.

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