Simply Explained: High-Resolution Image Synthesis with Latent Diffusion Models
This article is to explain the paper High-Resolution Image Synthesis with Latent Diffusion Models to people who are not familiar with machine learning.
Here’s the original abstract.
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve new state-of-the-art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including text-to-image synthesis, unconditional image generation and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.
This research is about improving a type of machine learning model known as diffusion models (DMs). DMs are great at creating new images from scratch or modifying existing ones. They work by breaking down how images are made into a step-by-step process. This approach also allows for a control mechanism that can guide the creation of the image without needing to retrain the model.
But there’s a problem. DMs usually work directly with pixel data, which is very resource-intensive. Training these models can take hundreds of days using powerful graphics processing units (GPUs), and using the models can also be slow because it needs to do many calculations in a sequence.
The researchers propose a solution: instead of working directly with pixel data, they use DMs in the ‘latent space’ of another type of model known as an autoencoder. Think of ‘latent space’ as a kind of compressed representation of the data that retains the important information but is much simpler to work with.
This approach allows them to strike a balance between reducing complexity and preserving detail in the images, which greatly improves the visual quality. They also add a component called ‘cross-attention layers’ to their model, which allows it to take into account different types of input, such as text or shapes, to guide the image creation process. This makes the model very versatile and able to create high-quality, high-resolution images.
The researchers call their new model ‘latent diffusion models’ (LDMs), and they find that it performs exceptionally well on various tasks, such as repairing images (inpainting), generating images based on a class or category, creating images from text, generating images from scratch, and improving the resolution of images. Importantly, LDMs achieve these results while being much less demanding on computational resources compared to traditional DMs.
