Pyramid Flow miniFLUX
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Paper | View Paper |
| Pipeline Type | Text-to-Video, Image-to-Video |
| Resolution Support | 768p (10s), 384p (5s) |
What is pyramid-flow-miniflux?
Pyramid Flow miniFLUX is a groundbreaking AI model that implements a training-efficient Autoregressive Video Generation method based on Flow Matching. It represents a significant advancement in video generation technology, capable of producing high-quality videos up to 10 seconds long at 768p resolution and 24 FPS. The model has been specifically designed to handle both text-to-video and image-to-video generation tasks with impressive results.
Implementation Details
The model utilizes a mini FLUX architecture, which has shown substantial improvements in human structure and motion stability compared to previous SD3-based implementations. It operates using a two-step process: initial frame generation followed by autoregressive video generation, with specific attention to maintaining temporal consistency and visual quality.
- Supports multiple resolution variants (768p and 384p)
- Implements bfloat16 precision for optimal performance
- Features CPU offloading capabilities for memory efficiency
- Includes VAE tiling for improved processing of high-resolution content
Core Capabilities
- Text-to-video generation with up to 10-second duration
- Image-to-video conversion with text conditioning
- High-resolution output at 768p and 24 FPS
- Adjustable guidance scales for quality and motion control
- Memory-efficient processing options
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its ability to generate high-quality, longer-duration videos (up to 10 seconds) at high resolution using a training-efficient approach. It also provides flexibility in both text-to-video and image-to-video generation tasks while maintaining stable human structures and motion.
Q: What are the recommended use cases?
The model excels in creating cinematic-style videos, movie trailers, and dynamic scene transformations. It's particularly effective for scenarios requiring high-quality video generation from either textual descriptions or static images, with specific strength in maintaining temporal consistency and visual quality.
