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Loading...Introduction to Efficient Image Generation
Last quarter, our team discovered that generating high-quality images with diffusion models and neural architecture search (NAS) could be a game-changer for our application. However, we soon realized that most docs skip the hard part - implementing and comparing these models in real-world scenarios. Here's what I learned when diving deep into diffusion models and NAS for efficient image generation.
The Problem with Current Image Generation Models
When we started exploring image generation, we tried using traditional generative adversarial networks (GANs). However, we discovered that GANs are difficult to train and often produce low-quality images. This led us to investigate diffusion models and NAS as alternative approaches.
Diffusion Models: A Deep Dive
Diffusion models, such as Stable Diffusion 2.1, have shown great promise in generating high-quality images.
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- Real-world examples and metrics
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