Allegro-TI2V
A
Allegro TI2V
Overview :
Allegro-TI2V is a text-to-image-to-video generation model that creates video content based on user-provided prompts and images. The model is recognized for its open-source nature, diverse content creation capabilities, high-quality outputs, compact efficient model parameters, and support for various precision and GPU memory optimizations. It represents cutting-edge advancements in AI technology for video generation, holding significant technical value and commercial application potential. The Allegro-TI2V model is available on the Hugging Face platform under the Apache 2.0 open-source license, allowing users to download and use it for free.
Target Users :
The target audience includes video content creators, visual effects artists, game developers, researchers, and any professionals who need to generate video content. Allegro-TI2V is particularly suitable for users who require quick production of high-quality video content, whether for entertainment, education, or commercial purposes, thanks to its powerful video generation capabilities and efficient model design.
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Use Cases
Example 1: Using Allegro-TI2V to generate a video of a car driving based on a text prompt and an image.
Example 2: Creating an animated video of animals running in a forest using Allegro-TI2V.
Example 3: Combining Allegro-TI2V with EMA-VFI technology to interpolate a 15FPS video to 30FPS for improved video smoothness.
Features
- Open source: Model weights and code fully accessible to the community under the Apache 2.0 license.
- Diverse content creation: Capable of generating a wide range of content from close-ups of people and animals to diverse dynamic scenes.
- Text-to-image-to-video generation: Supports generating videos from user-provided prompts and images, including generating subsequent video content based on the first frame image and prompt, as well as generating intermediate video content based on the first and last frame images.
- High-quality output: Capable of generating six seconds of detailed video at 720x1280 resolution and 15FPS, which can be interpolated to 30FPS using EMA-VFI.
- Compact and efficient: Includes a VideoVAE model with 175M parameters and a VideoDiT model with 2.8B parameters, supporting various precisions (FP32, BF16, FP16), with GPU memory usage of 9.3GB when using CPU offloading in BF16 mode.
- Multi-precision support: The model supports various precision options, including FP32, BF16, and FP16, to meet different hardware and performance needs.
- Fast inference: Inference time is 20 minutes on a single GPU (H100) or 3 minutes on 8x H100.
How to Use
1. Download the Allegro code from GitHub.
2. Install the necessary dependencies, ensuring that Python version is 3.10 or higher, PyTorch version is 2.4 or higher, and CUDA version is 12.4 or higher.
3. Download the Allegro-TI2V model weights from Hugging Face.
4. Use the provided command-line tools to run inferences, inputting the necessary parameters such as user prompts and the path to the first frame image.
5. If needed, use EMA-VFI to interpolate the generated video from 15FPS to 30FPS to enhance video quality.
6. Save the generated video using tools like imageio.
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