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Pyramid Flow Miniflux
Overview :
Pyramid Flow miniFLUX is an autoregressive video generation method based on flow matching, focusing on training efficiency and the usage of open-source datasets. The model can generate high-quality 10-second videos at 768p resolution and 24 frames per second, naturally supporting image-to-video generation. It is a valuable tool in the fields of video content creation and research, especially in scenarios that require coherent dynamic images.
Target Users :
The target audience includes video content creators, visual effects artists, researchers, and developers. Pyramid Flow miniFLUX is particularly suitable for them as it provides an efficient and high-quality video generation method, especially for scenarios requiring the transformation of images or text into video content.
Use Cases
Generate a movie trailer showcasing scenes of astronauts on an adventure.
Transform static landscape images into dynamic videos to enhance visual impact.
Use in the education sector to visually demonstrate complex scientific processes through videos.
Features
- Supports high-quality video generation at 10 seconds, 768p resolution, and 24 frames per second.
- Enables image-to-video generation, transforming static images into dynamic video content.
- Utilizes flow-matching autoregressive video generation technology to enhance the coherence and naturalness of videos.
- Highly efficient training, capable of generating high-quality videos using only open-source datasets.
- Supports video generation at various resolutions, including 1024p images, as well as 384p and 768p videos.
- Provides a simplified two-step usage process for users to get started quickly.
- Supports CPU offloading and other VRAM-efficient features to optimize resource usage.
How to Use
1. Clone the Pyramid-Flow repository locally.
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2. Create a Python environment using conda and install dependencies.
3. Download the model from Hugging Face and set the model path.
4. Load the model and adjust the parameters as needed.
5. Use the model to generate videos, which can be text-to-video or image-to-video.
6. Export the generated video frames as a video file.
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