Stable Diffusion vs Traditional Generative AI
Published: 2023-06-28 17:58:10

Stable diffusion is a new paradigm in Generative AI and they both are two different approaches to generating realistic and high-quality content. A class of cutting-edge machine learning techniques has become incredibly adept at generating new images from scratch or from existing ones. This is called image synthesis and has become one of the most popular use cases for generative AI. Traditional generative models such as Generative Adversarial Networks (GANs) have been at the forefront of this progress. However, a new approach called Stable Diffusion (SD) has emerged as a promising alternative. 

 

 

## ALGORITHM 

Traditional generative AI models, such as GANs are based on deep learning architectures. Generative AI modelling is a type of unsupervised machine learning where the goal is to learn the underlying pattern of the image data, so that we can generate new images with new and varied properties. 

Stable Diffusion is a recent approach that utilizes diffusion models. Diffusion models generate images by iteratively adding noise to the initial image to gradually generate a new image. The process of adding noise is controlled by a special diffusion process, hence the name ‘diffusion model’. 

 

 

## TRAINING 

GAN training proceeds in alternating periods: 

1. The discriminator trains for one or more epochs. 
 
2. The generator trains for one or more epochs. 
 
3. Repeat steps 1 and 2 to continue to train the generator and discriminator networks.
 

A generator network takes a random normal distribution (z), and outputs a generated sample that’s close to the original distribution whereas A discriminator tries to evaluate the output generated by the generator with the original sample, and outputs a value between 0 and 1. We keep the generator constant during the discriminator training phase. As discriminator training tries to figure out how to distinguish real data from fake, it must learn how to recognize the generator's flaws. 

Training a Stable Diffusion model involves three stages: 

- Create the token embeddings from the prompt. 
- Condition the UNet (noise predictor) with the embeddings. The latent space is generated using the encoder part of the autoencoder model. This is called text conditioned latent space where the UNet entirely works. 
- From the above step, the UNet predicts the noise added to the latent space and tries to denoise it. 

 

Google has pointed out, GANs do suffer a variety of issues during training such as: 

1. Failure to converge 
 
2. Mode collapse  
 
3. Vanishing gradients 
 
 

However, Stable diffusion overcomes these issues in the following manner: - 

- **Failure to converge **

This issue can lead to unstable results. Stable Diffusion mitigates this problem by employing a stable training procedure based on the principles of diffusion processes. It gradually refines an initial noise vector to generate high-quality samples. The stability of the training process ensures that the model converges to a meaningful solution, avoiding the problem of failure to converge. 

- **Mode collapse **

 Mode collapse is a common problem in traditional generative models where the model tends to generate limited or repetitive samples, failing to capture the full diversity of the target distribution. Stable Diffusion addresses this issue by iteratively refining the noise vector through diffusion steps. This diffusion process allows the model to explore a wide range of latent space, preserving the diversity of the data distribution. As a result, Stable Diffusion can generate more diverse and realistic samples, mitigating the problem of mode collapse. 

- **Vanishing Gradients **

Vanishing gradients occur when the gradients during training become very small, making it difficult for the model to learn and update its parameters effectively. This issue can hamper the training of deep generative models. Stable Diffusion tackles vanishing gradients by using a diffusion process that operates at multiple time steps. Each diffusion step involves a series of gradient computations, allowing the model to learn and propagate information over time. By leveraging these diffusion steps, Stable Diffusion enables more effective parameter updates, preventing the gradients from vanishing and improving the overall training stability. 

 

 

## HANDLING UNCERTAINTY 

Stable Diffusion is a relatively new approach in generative AI that leverages diffusion models to handle uncertainty in the generation process. 

Stable Diffusion models undergo multiple iterations of the diffusion process. At each iteration, the model refines its estimates of the probability distribution to generate more realistic samples. This iterative nature of Stable Diffusion enables the model to capture uncertainty in the data and produce samples with increasing quality and fidelity over time. 

Like other generative models, Stable Diffusion models often employ a temperature parameter to control the level of uncertainty in the generated samples. A higher temperature value encourages more exploration of the distribution, resulting in more diverse but potentially less realistic outputs. Lower temperature values bias the diffusion process towards more likely and less diverse samples. 

Stable Diffusion models are particularly effective in handling multimodal distributions as well where the data may exhibit multiple modes or clusters. The diffusion process can explore and reveal different modes in the distribution, enabling the generation of diverse samples and capturing the inherent uncertainty associated with multiple plausible outcomes. 

 

 

## APPLICATIONS 

- **Video Generation **

GANs have been successful in generating static images, and their architecture can also be adapted for video generation. Video GANs typically operate on both the spatial and temporal dimensions of the video frames. By optimizing a generator and a discriminator network, GANs can generate visually appealing and realistic videos 

In video generation using Stable Diffusion, the diffusion process is extended to incorporate the temporal dimension. Instead of applying diffusion only on individual frames, the process is extended to model the evolution of frames over time. This allows for the controlled exploration of the video distribution, generating coherent and realistic video sequences. 

- **Latent Diffusion **

It is another version of OG text to image the Stable Diffusion model. For the starter, you need to write a descriptive prompt for it to generate high-quality images.   

 

 

## CONCLUSION 

Overall, while traditional generative AI models have been widely used and successful, Stable Diffusion represents a newer approach that addresses some of the limitations of traditional methods, particularly in terms of stability and control over the output. However, it's important to note that the field of AI research is evolving rapidly, and new advancements are constantly being made in both areas. 

Even though Stable Diffusion and Traditional Generative AI have distinct characteristics, they both contribute to the advancement of generative modeling. By understanding the strengths and weaknesses of each approach, researchers and practitioners can choose the most suitable method for their specific requirements. Continued research and development in generative AI will further enhance the field and unlock new possibilities for creative and realistic data synthesis. 

Stable Diffusion addresses the limitations of Traditional Generative AI models by providing a stable training procedure, mitigating mode collapse, and overcoming vanishing gradients. While Traditional Generative AI models have the advantage of being well-established and versatile, Stable Diffusion offers improvements in training stability and mode diversity, making it a promising alternative for generative modeling tasks.