⚡️Pyramid Flow⚡️

What is the project about?

Pyramid Flow is an autoregressive video generation method based on Flow Matching. It focuses on training efficiency and generates high-quality videos. It's designed to be trained on publicly available datasets.

What problem does it solve?

Existing video diffusion models are computationally expensive, especially when dealing with noisy latents at full resolution. Pyramid Flow addresses this by using flow matching to interpolate between latents of different resolutions and noise levels. This allows for simultaneous generation and decompression of visual content, leading to better computational efficiency. It also aims to provide a high-quality, open-source alternative to commercial video generation models, trained solely on publicly available data.

What are the features of the project?

High-Quality Video Generation: Generates 10-second videos at 768p resolution and 24 FPS. Also supports 5-second videos at 384p and 24 FPS, and 1024p image generation.
Autoregressive Generation: Allows for image-to-video generation in addition to text-to-video.
Flow Matching: Uses flow matching for efficient interpolation between latents of different resolutions.
Training Efficiency: Optimized for training with a single DiT (Diffusion Transformer).
Open-Source Datasets: Trained exclusively on open-source datasets.
Multiple Model Variants: Offers miniFLUX (improved human structure and motion) and SD3-based models.
Multi-GPU Inference: Supports multi-GPU inference for faster generation and reduced memory usage per GPU.
CPU Offloading: Offers CPU offloading options to run with limited GPU memory (as low as 8GB).
Gradio Demo: Provides a user-friendly Gradio demo for easy interaction.
Hugging Face Integration: Models and demo are available on Hugging Face.
MPS Backend Support: Supports Apple Silicon (M-series chips) using the MPS backend.
Training Code: Includes code for training the VAE and finetuning the DiT, allowing for customization and further research.

What are the technologies used in the project?

Python: The primary programming language.
PyTorch: The deep learning framework.
Flow Matching: The core technique for efficient latent interpolation.
Diffusion Transformer (DiT): The model architecture.
VAE (Variational Autoencoder): Used for encoding and decoding video frames. Specifically, a MAGVIT-v2 like continuous 3D VAE.
Gradio: For creating the interactive demo.
Hugging Face Hub: For model hosting and distribution.
Conda: For environment management.
CUDA: For GPU acceleration.
MPS: For Apple Silicon GPU acceleration.

What are the benefits of the project?

Efficiency: Reduced computational cost compared to traditional video diffusion models.
High Quality: Generates high-resolution, smooth videos.
Open Source: Provides a fully open-source solution, including training code.
Accessibility: Can run on consumer-grade hardware with CPU offloading.
Flexibility: Supports both text-to-video and image-to-video generation.
Customizability: Training code allows users to build upon and modify the model.
Competitive Performance: Achieves results comparable to commercial models, despite using only public data.

What are the use cases of the project?

Video Content Creation: Generating short video clips for various purposes (e.g., social media, marketing, entertainment).
Image-to-Video Animation: Bringing still images to life by generating video continuations.
Research: Serving as a platform for further research in video generation and flow matching.
Prototyping: Quickly creating video prototypes for ideas and concepts.
Educational Tool: Demonstrating the capabilities of modern AI video generation techniques.
Data Augmentation: Generating synthetic video data for training other models.