High-Resolution Image Synthesis with Latent Diffusion Models

AI image generation has become the latest sensation, propelled by groundbreaking models like DALL·E, Stability AI, and Mid-Journey. These models have captured global attention for their ability to effortlessly create captivating visuals from textual prompts. The allure lies in their seamless process: users provide a description, and the AI model produces stunning images.

In the world of machine learning, generative AI has been stealing the spotlight. From text generators like ChatGPT to image creators like DALL-E, these models are pushing the boundaries of what AI can do.

In the realm of image generation, diffusion models have emerged as a powerful tool for producing high-quality samples. Among these, "Latent Diffusion Models" are gaining popularity, thanks to their unique blend of mathematical sophistication and practical effectiveness.

But what exactly are Latent Diffusion Models, and how do they differ from traditional diffusion models? In this blog, we offer a concise summary of the paper that introduced Latent Diffusion in 2022.

Before diving into the details of Latent Diffusion Models, let's first review some of the earlier techniques used in image generation. Understanding these methods will provide valuable context and help explain how Latent Diffusion Models work.

Variational Autoencoders (VAEs):

VAEs are a type of autoencoder. An autoencoder has two components: the encoder, which is responsible for compressing the input into a lower dimensional space, and the decoder, which is responsible for reconstructing the input from the latent space.

VAEs work similarly to autoencoders, but with a key difference: instead of the latent space being a set of variables, it is a set of latent distributions. This means that the encoder maps the input to a distribution in the latent space, and the decoder maps the latent space distribution back to the input space.

In the context of image generation, this means that after training the VAE, the decoder can accept noise or random latent variables as input and generate new images based on these distributions. This innovative approach has significantly advanced the field of AI image generation, paving the way for more complex and diverse outputs.

Generative Adversarial Networks (GANs):

Generative Adversarial Networks (GANs) is a deep learning architecture consisting of two neural networks trained adversarially: Generator and Discriminator.

• The Generator is a neural network responsible for creating realistic images from random noise input.
• Discriminator learns to differentiate between real images from the training set and fake images produced by the generator.

During training, these networks engage in a constant game of one-upmanship: the generator aims to produce images so convincing that they deceive the discriminator, while the discriminator strives to accurately distinguish between real and fake images. This adversarial training dynamic is governed by a loss function that incentivizes the generator to produce images closely resembling those in the training set, while pushing the discriminator to improve its discrimination abilities. Through this adversarial interplay, GANs have revolutionized image generation, enabling the creation of remarkably realistic and diverse visual outputs. However, a major drawback of GANs are mode collapse and training instability.

Diffusion Models:

Diffusion Models are a class of latent variable generative models. They include renowned examples such as DALL-E , Midjourney, and the open-source Stable Diffusion. These diffusion based models excel in generating realistic images from textual input provided by users.

A diffusion model consists of three major components:

• Diffusion Process: We start with a real image and gradually add gaussian noise to it over a fixed number of steps. This is the diffusion process, and it can be thought of as a Markov chain that transforms a real image into a noise image.
• Reverse process: A model is trained to reverse this process, i.e., to denoise the image at each step. The network is given the noisy image and the current step, and it predicts the denoised image.
• Sampling: Once the network is trained, the model can be used generate new images.

The key advantage of diffusion models is that they can generate high-quality images, often with more fine-grained details and fewer artifacts than other generative models like GANs. They're also conceptually simple and easy to train, as they avoid the adversarial training and mode collapse issues of GANs.

Latent Diffusion Model:

Diffusion models typically operate directly in pixel space, making model optimization and inference time-consuming and computationally expensive. To address this, the authors in "High-Resolution Image Synthesis with Latent Diffusion Models" introduce a novel approach: training diffusion models in the latent space of powerful pretrained autoencoders.

Power of latent diffusion models is in using an encoder to compress high-dimensional input images into a lower-dimensional latent space. This latent space retains the essential information of the original images but in a more compact form. This compression helps in reducing the computational requirements during the generation process while preserving the quality of the outputs.

• Autoencoder Training:

To obtain the latent representation, the VAE is trained by minimizing three types of losses. The first is the reconstruction loss, which minimizes the difference between the original image and the reconstructed image produced by the decoder. The second is the adversarial loss, where a patch-based discriminator learns to distinguish between original and reconstructed images. Finally, a regularization loss is added to ensure the latent space is well-scaled and has a small variance, keeping the latent vectors centered around zero.

• Latent Diffusion Model

Now let's go over the different steps of the complete process:

1. The input image is fed into a pre-trained encoder network to extract its latent representation.
2. The latent representation is then subjected to a forward diffusion process, where Gaussian noise is sequentially added to it over a fixed number of time steps (T).
3. The noisy latent representation is then passed through a denoising U-Net network, which gradually refines it over a series of reverse diffusion steps. This process ultimately results in a denoised latent representation.
4. The denoised latent representation is finally passed through a decoder network to generate the output image.
5. To create more versatile conditional image generators, the U-Net backbone is augmented with a cross-attention mechanism. This approach enables the learning of attention-based models that can accommodate various input modalities for conditioning.

The above summary offers a high-level overview of the latent diffusion model for image generation. To gain a more comprehensive understanding of the model and its underlying approach, I highly recommend reading the original paper . The paper provides a detailed explanation of the model architecture, training procedures, and experimental results, as well as insights into potential applications and future research directions.