Generative AI Models For Video and Image Synthesis

AI-based Image Generation (Text-to-Image and Image-to-Image) - Generative Models for Image Synthesis

Recent advances in AI-based Image Generation spearheaded by Glide, Dalle-2, Imagen, and Stable Diffusion have taken the world of “AI Art generation” by storm.

Generating high-quality images from text descriptions is extremely challenging and requires a deep understanding of the underlying meaning of the text and the ability to generate an image consistent with that meaning.

In recent years, Diffusion Models have emerged as a powerful tool for addressing this problem. Some well-known text-to-image synthesis models include AttnGAN, StackGAN, Stable Diffusion and DALL-E.

These models have demonstrated impressive capabilities in generating photorealistic images from textual descriptions, including the generation of novel objects and scenes.

There are two type of Generative Models. One is that creates images from text prompts (text-to-image) using Transformers and then second type is that uses Diffusion Models. Here are a few popular ones listed:

a). DALL-E 2 (OpenAI): (Generative Model for text-to-images using Transformers).

b). Stable Diffusion (StabilityAI) – Diffusion Model. Text2Image is achieved when combined with additional tools like Deep Learning-based Image Generative Models such a Variational Autoencoders (VAE).

c). Midjourney

d). CLIP (OpenAI)

e). BigGAN (Google)

f). StyleGAN2 (NVIDIA)

g). IMAGEN (Google) (Imagen is not a specific model but rather a project by Google focusing on image recognition).

Here are the key questions to ask when comparing these models are:

Image Quality:

  • How realistic and visually appealing are the images generated by each model?

  • How well do they handle complex scenes, fine details, and diverse content?

Customization and Control:

  • How much control do users have over the generated content and visual elements?

  • Can users easily modify and fine-tune the generated images to suit their requirements?

Training Data and Resources:

  • What are the training data requirements for each model?

  • How much computational power and time are required to train each model effectively?

Model Size and Efficiency:

  • How large are the models in terms of memory and computational requirements?

  • How efficient are they in generating images, and how do they balance quality with computational resources?

Robustness and Generalizability:

  • How well do the models generalize to unseen data or novel content?

  • Are they robust to variations in input data and capable of handling diverse input types?

Applications and Use Cases:

  • What are the primary use cases and applications for each model?

  • Are there any limitations or specific areas where one model outperforms the others?

Last updated