ControlNetとStable Diffusionを使った独自のAI生成画像の作り方

Generative AI: The Future of Image Augmentation and Dataset Population

Generative AI, a type of artificial intelligence that can create new content without explicit programming, is revolutionizing the field of image augmentation and dataset population. By using machine learning algorithms to analyze and learn from large datasets, generative AI models can generate new images, music, text, and even video that closely resemble the training data.

One popular technique in generative AI is the use of Generative Adversarial Networks (GANs). GANs consist of two neural networks – a generator and a discriminator – that work together to generate new content. The generator creates new samples, such as images or text, from a random noise signal, while the discriminator evaluates whether the generated samples are similar enough to the training data.

While GANs have shown impressive results, they can be challenging to train and may suffer from issues such as mode collapse. To address these limitations, a variant called Stable Diffusion has been developed. Stable Diffusion gradually generates an image from a noise signal by passing it through a sequence of increasingly complex convolutional neural networks (CNNs). This process produces high-quality, diverse, and realistic images that are suitable for various applications.

In a recent experiment, Stable Diffusion was used to perform background augmentation for single objects. By masking certain areas of an image and providing a textual prompt, Stable Diffusion modified only the masked areas, resulting in a realistic and visually coherent image. However, some imperfections were observed, such as borders between the object and the background not being pristine.

To overcome this, a two-stage approach was implemented. First, traditional augmentations were applied to the original image using Albumentations, a popular library for image transformations. Then, the transformed image was passed through Stable Diffusion for background augmentation. This approach resulted in crisper and more realistic images with increased variation.

To further enhance the generative power of Stable Diffusion, InstructPix2Pix, a conditional diffusion method, was utilized. InstructPix2Pix combines Stable Diffusion and GPT-3 to edit images based on human instructions. In the experiment, the color of an object was modified using InstructPix2Pix, and the resulting image was passed through Stable Diffusion for in-painting. The combination of these techniques produced impressive results.

While generative AI shows great promise for image augmentation and dataset population, there are still limitations to consider. The generation process requires prompt engineering and user intervention, and there is a lack of fine-grained control over specific augmentations. Additionally, the compute power and time required for generating images make large-scale generation impractical without dedicated hardware.

However, as methods like Stable Diffusion continue to evolve, future research developments may overcome these limitations and provide more practical and widely applicable generative AI tools. Generative AI has the potential to significantly improve the development of machine learning applications by reducing the effort required for dataset curation and annotation.

At Datature, we are actively exploring the use of generative AI techniques to enhance the training performance and model robustness of computer vision models. By simplifying the workflow for annotating, training, visualizing, and deploying computer vision models, we aim to accelerate product launches and improve overall performance.

If you’re interested in trying out generative AI for augmenting your own images or have any questions, feel free to join our Community Slack or contact us. We’re excited to see how generative AI can benefit your projects!