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title: Zero-Shot Video Generation
emoji: 🎥
colorFrom: indigo
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit
short_description: Neural video studio using latent diffusion & cross attention
sdk_version: 6.6.0
Zero-Shot Video Generation
A zero-shot neural synthesis studio leveraging latent diffusion models and cross-frame attention to synthesize temporally consistent video sequences directly from unconstrained textual prompts.
[Source Code](Source Code/) · Project Report · Video Demo · Live Demo
Authors · Overview · Features · Structure · Results · Quick Start · Usage Guidelines · License · About · Acknowledgments
🤝🏻 Special Acknowledgement
Special thanks to Jithin Gijo and Ritika Agarwal for their meaningful contributions, guidance, and support that helped shape this work.
Overview
Zero-Shot Video Generation is an advanced neural synthesis framework designed to transform textual descriptions into high-fidelity, temporally consistent video sequences. By leveraging a training-free paradigm of cross-domain latent transfer (repurposing pre-trained Latent Diffusion Models (LDMs)), this project enables dynamic motion synthesis without requiring specialized video-based training datasets.
The core architecture implements a specialized inference-time logic that constrains the generative process across a discrete temporal axis. This addresses the fundamental challenge of spatial-temporal manifold consistency, ensuring that a sequence of independent latent samples converges into a coherent motion trajectory that mirrors the nuanced semantics of the input prompt.
Attribution
This project builds upon the foundational research and implementation of the Text2Video-Zero repository by Picsart AI Research (PAIR).
🎥 Defining Zero-Shot Video Synthesis
In the context of generative AI, Zero-Shot Video Synthesis refers to the production of video content where the model has not been explicitly trained on motion data. The system operates by injecting temporal structural priors, specifically cross-frame attention and latent trajectory warping, into a pre-trained image generator. This method eliminates the prohibitive computational and data costs of traditional video models while preserving the high-fidelity stylistic capabilities of large-scale diffusion backbones.
The repository serves as a comprehensive technical case study into Generative Machine Learning, Latent Space Dynamics, and Neural Attention Modulation. It bridges the gap between theoretical research and practical deployment through an optimized Gradio-based interactive studio, allowing for high-performance experimentation with zero-shot motion synthesis heuristics.
Synthesis Heuristics
The generative engine is governed by strict computational design patterns ensuring fidelity and temporal coherence:
- Temporal Consistency: Custom cross-frame attention layers and latent warping ensure background stability and smooth object motion across sequential frames.
- Zero-Shot Inference: Harnessing foundational image diffusion models (e.g., Stable Diffusion) to synthesize motion dynamically without specialized video fine-tuning.
- Architectural Flexibility: Supports multiple diffusion backbones, allowing adaptive synthesis paths tailored to diverse visual aesthetics and rendering requirements.
Spatial-Temporal Precision Integration
To maximize sequence clarity, the engine employs a multi-stage neural pipeline. Latent motion fields refine the temporal stream, strictly coupling structural dynamics with state changes. This ensures the generated scene constantly aligns with the underlying textual simulation.
Features
| Feature | Description |
|---|---|
| Core Diffusion | Integrates Stable Diffusion pipelines customized for continuous temporal frame synthesis. |
| Interactive Studio | Implements a robust standalone interface via Gradio for immediate generative video study. |
| Academic Clarity | In-depth and detailed scholarly comments integrated throughout the codebase for transparent logic study. |
| Neural Topology | Efficient hardware acceleration via PyTorch and CUDA ensuring optimal tensor computations. |
| Inference Pipeline | Modular architecture supporting multiple model checkpoints directly from the Hugging Face Hub. |
| Motion Warping | Advanced latent trajectory mapping ensuring realistic subject motion and background preservation. |
Interactive Polish: The Visual Singularity
We have engineered a premium, logic-driven interface that exposes the complex text-to-video synthesis pipeline simply and elegantly. The visual language focuses on a modern gradient aesthetic, ensuring maximum focus on generative analysis.
Tech Stack
- Languages: Python 3.8+
- Logic: Neural Pipelines (Cross-frame Attention & Latent Warping)
- Frameworks: PyTorch & Diffusers
- UI System: Modern Design (Gradio & Custom CSS)
- Execution: Local acceleration (CUDA) / CPU gracefully degraded fallback
Project Structure
ZERO-SHOT-VIDEO-GENERATION/
│
├── docs/ # Academic Documentation
│ └── SPECIFICATION.md # Technical Architecture
│
├── ML Project/ # Research Assets & Deliverables
│ ├── Zero-Shot Video Generation - Project Proposal.pdf
│ ├── Zero-Shot Video Generation Project Report.pdf
│ ├── Zero-Shot Video Generation.pdf
│ └── Zero-Shot Video Generation.mp4
│
├── Source Code/ # Primary Application Layer
│ ├── annotator/ # Auxiliary Processing Modules
│ ├── app.py # Main Gradio Studio Interface
│ ├── app_text_to_video.py # UI Components for Text2Video
│ ├── config.py # Architectural Configurations
│ ├── gradio_utils.py # UI Helper Utilities
│ ├── hf_utils.py # Hub Scraping & Model Loading
│ ├── model.py # Neural Orchestration & Inference
│ ├── text_to_video_pipeline.py # Temporal Denoising & Warping Logic
│ ├── utils.py # Processing & Attention Mechanisms
│ ├── environment.yaml # Conda Environment Config
│ ├── requirements.txt # Dependency Manifest
│ └── style.css # Component Styling
│
├── .gitattributes # Signal Normalization
├── .gitignore # Deployment Exclusions
├── SECURITY.md # Security Protocols
├── CITATION.cff # Academic Citation Manifest
├── codemeta.json # Metadata Standard
├── LICENSE # MIT License
├── README.md # Project Entrance
└── ZERO-SHOT-VIDEO-GENERATION.ipynb # Research Notebook
Results
Quick Start
1. Prerequisites
- Python 3.8+: Required for runtime execution. Download Python
- Git: For version control and cloning. Download Git
- CUDA Toolkit: (Optional but highly recommended) For GPU acceleration.
2. Installation & Setup
Step 1: Clone the Repository
Open your terminal and clone the repository:
git clone https://github.com/Amey-Thakur/ZERO-SHOT-VIDEO-GENERATION.git
cd ZERO-SHOT-VIDEO-GENERATION
Step 2: Configure Virtual Environment
Prepare an isolated environment to manage dependencies:
Windows (Command Prompt / PowerShell):
python -m venv venv
venv\Scripts\activate
macOS / Linux (Terminal):
python3 -m venv venv
source venv/bin/activate
Step 3: Install Core Dependencies
Navigate to the source directory and install the required libraries:
cd "Source Code"
pip install -r requirements.txt
3. Execution
A. Interactive Web Studio
Launch the primary Gradio-based studio engine from the Source Code directory:
python app.py
Studio Access: Once the engine is initialized, navigate to the local URL provided in the terminal (typically
http://127.0.0.1:7860). You can also enable public access by passing the--public_accessflag during initialization.
B. Research & Automation Studio
Execute the complete Neural Video Synthesis Research directly in the cloud. This interactive environment provides a zero-setup gateway for orchestrating high-fidelity temporal synthesis.
Zero-Shot Video Generation | Cloud Research Laboratory
Execute the complete Neural Video Synthesis Research directly in the cloud. This interactive Google Colab Notebook provides a zero-setup environment for orchestrating high-fidelity text-to-video synthesis, offering a scholarly gateway to the underlying Python-based latent diffusion and cross-frame attention architecture.
Usage Guidelines
This repository is openly shared to support learning and knowledge exchange across the academic community.
For Students
Use this project as reference material for understanding Neural Video Synthesis, Diffusion Models, and temporal latent interpolation. The Source Code is explicitly annotated to facilitate self-paced learning and exploration of Python-based generative deep learning pipelines.
For Educators
This project may serve as a practical lab example or supplementary teaching resource for Machine Learning, Computer Vision, and Generative AI courses. Attribution is appreciated when utilizing content.
For Researchers
The documentation and architectural approach may provide insights into academic project structuring, cross-frame attention mechanisms, and zero-shot temporal generation paradigms.
License
This repository and all linked academic content are made available under the MIT License. See the LICENSE file for complete terms.
Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original author.
Copyright © 2023 Amey Thakur
About This Repository
Created & Maintained by: Amey Thakur
Academic Journey: Master of Engineering in Computer Engineering (2023-2024)
Course: ELEC 8900 · Machine Learning
Institution: University of Windsor, Windsor, Ontario
Faculty: Faculty of Engineering
This project features Zero-Shot Video Generation, an advanced generative visual synthesis system designed to produce temporally consistent motion sequences from textual prompts. It represents a structured academic exploration into the frontiers of deep learning and latent space dynamics developed as part of the 3rd Semester Project at the University of Windsor.
Connect: GitHub · LinkedIn · ORCID
Acknowledgments
Grateful acknowledgment to my Major Project teammates, Jithin Gijo and Ritika Agarwal, for their collaborative excellence and shared commitment throughout the semester. Our collective efforts in synthesizing complex datasets, developing rigorous technical architectures, and authoring comprehensive engineering reports were fundamental to the successful realization of our objectives. This partnership not only strengthened the analytical depth of our shared deliverables but also provided invaluable insights into the dynamics of high-performance engineering teamwork.
Grateful acknowledgment to Jason Horn, Writing Support Desk, University of Windsor, for his distinguished mentorship and scholarly guidance. His analytical feedback and methodological rigor were instrumental in refining the intellectual depth and professional caliber of my academic work. His dedication stands as a testament to the pursuit of academic excellence and professional integrity.
Special thanks to the research team behind Text2Video-Zero (Picsart AI Research, UT Austin, U of Oregon, UIUC) for the foundational research and open-source implementation, which served as the cornerstone for this project's technical architecture.
Authors · Overview · Features · Structure · Results · Quick Start · Usage Guidelines · License · About · Acknowledgments
🧠 Machine Learning Repository · 🎥 Zero-Shot Video Generation
Presented as part of the 3rd Semester Project @ University of Windsor
🎓 MEng Computer Engineering Repository
Computer Engineering (M.Eng.) - University of Windsor
Semester-wise curriculum, laboratories, projects, and academic notes.