🎥 Prompt2Clip

Prompt2Clip generates high-quality videos from text prompts using YOLOv10 models. Designed for efficiency and flexibility, it fine-tunes object detection models on custom datasets and enables real-time inference for a seamless text-to-video experience.

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🚀 Key Features

• Text-to-Video Conversion: Converts natural language prompts into video clips.
• Custom Dataset Training: Fine-tunes YOLOv10 models with bird and bee datasets for enhanced detection.
• Real-Time Inference: Supports single-image and streaming video detection.
• Cloud-Based Workflow: Uses Google Colab for GPU-accelerated training and processing.
• Customizable Parameters: Flexible settings for model size, inference steps, and detection thresholds.

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🛠️ How It Works

Prompt2Clip combines advanced AI models and custom workflows:

1. Dataset Integration: Downloads datasets from Roboflow for custom object detection tasks.
2. Model Training: Fine-tunes YOLOv10 on labeled datasets for accurate detection.
3. Video Generation: Combines frames generated through detection into cohesive video clips.
4. Inference Pipelines: Enables real-time detection on single images or streaming video.

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📂 Project Structure

Prompt2Clip/
│
├── datasets/           # Custom datasets for birds and bees
├── models/             # Pre-trained and fine-tuned YOLOv10 models
├── scripts/            # Scripts for training, inference, and video generation
├── examples/           # Example outputs of text-to-video generation
└── README.md           # Project documentation

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🖥️ Usage

1. Clone the Repository

git clone https://github.com/MansurPro/Prompt2Clip.git
cd Prompt2Clip

2. Set Up the Environment

Run Prompt2Clip in Google Colab for GPU-accelerated operations. Install the required Python packages:

pip install -r requirements.txt

3. Train the Model

python train.py --dataset datasets/birds --model yolov10m.pt --epochs 10

4. Run Inference

python inference.py --image_path path/to/image.jpg --model_path models/yolov10_best.pt

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🎨 Examples

Prompt	Generated Video
"A bird flying over a forest"	View
"A bee hovering near a flower"	View

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📊 Performance

• Efficiency: Fine-tuned for fast and accurate text-to-video generation.
• Customizability: Supports flexible detection thresholds and model configurations.
• Scalability: Leverages GPU resources for high-throughput operations.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🙌 Acknowledgments

Prompt2Clip builds on the following open-source tools and datasets:

• YOLOv10 for object detection.
• Roboflow for dataset integration.
• Google Colab for cloud-based GPU acceleration.

Thank you to the open-source community for enabling innovative solutions like this!