In this article, we will delve into the fascinating realm of Few-shot Image Generation using Cross-domain Correspondence. Developed by some brilliant minds, including Utkarsh Ojha and others, this technique enables the adaptation of a Generative Adversarial Network (GAN) to new domains with remarkably few images. If you’re ready to transform the way you create and manipulate images, let’s get started!

What You Need

Before we jump into the implementation, ensure you have the following prerequisites:

• Linux Operating System
• NVIDIA GPU with CUDA CuDNN 10.2
• Python 3.6.9
• PyTorch 1.7.0
• All required libraries which can be installed via running: pip install -r requirements.txt

Understanding the Code Conceptually

The functionality of this method revolves around adapting a source GAN to generate images in a target domain using minimal images. To visualize this analogy, imagine a chef who has mastered making traditional Italian pasta (source domain). Now, the chef wants to use their skills to create a unique Japanese noodle dish (target domain) using just a few new ingredients. By understanding the foundational aspects of the original dish, they can mix and match these ingredients to create something entirely new!

Testing the Pre-trained Models

To get your feet wet, you can run tests using pre-trained models for various source and target domains. Once you have downloaded the models, store them in the .checkpoints directory.

Generate Images from a Pre-trained GAN

To generate images, execute the following command:

bash
CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_target pathtomodel

Here, model_name follows the notation of source_target, for example, ffhq_sketches.

Visualizing Correspondence Results

To observe the same noise values in both the source and target models, run:

bash
CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source pathtosource --ckpt_target pathtotarget --load_noise noise.pt

To visualize interpolations, you can run:

bash
CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source pathtosource --ckpt_target pathtosource --load_noise noise.pt --mode interpolate

You’ll be able to generate interesting transitions between source and target images!

Hand Gesture Experimentation

You can adapt your model to two different domains by running:

bash
CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source pathtosource --ckpt_target pathtomaps --load_noise noise.pt --mode interpolate

Training Your Own GAN

If you’re looking to train your own GAN, follow these steps:

Choose Your Source and Target Domain

Select a domain model from the pre-trained model table. You won’t need actual source data—only the pre-trained model is necessary.

Data Preparation

If you’re downloading raw images, unzip them into the .raw_data folder. Then, use:

python prepare_data.py --out processed_datadataset_name --size 256 .raw_datadataset_name

This prepares your images for appropriate processing.

Training Execution

After setting the parameters in train.py, you can execute your training by running:

bash
CUDA_VISIBLE_DEVICES=0 python train.py --ckpt_source pathtosource_model --data_path pathtotarget_data --exp exp_name

Running this command with default configs utilizes around 20 GB of GPU memory.

Troubleshooting Potential Issues

If you’re facing issues during installation or running the code, consider the following:

• Ensure your CUDA and cuDNN versions are compatible with your PyTorch version.
• Check that all required packages are installed correctly.
• If you encounter memory errors, try decreasing your batch size or using a GPU with more memory.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.