Everything You Need To Know About Stable Diffusion

Stable Diffusion is a deep learning, text-to-image model released in 2022 by Stability AI based on diffusion techniques. It is primarily used to generate detailed images conditioned on text descriptions. However, it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. It is a type of deep generative artificial intelligence model. Its code and model weights have been open-sourced, and it can run on most consumer hardware.

With more open access, Stable Diffusion allows you to explore prompting the system to render imaginative concepts and combine ideas. Its image generation capabilities continue to progress as researchers fine-tune the technique to produce increasingly realistic and intricate images from text across a growing range of applications. In this article, we will provide an overview of how stable diffusion works, its capabilities, some example use cases, its limitations, and possible solutions.

Importance of Stable Diffusion 

Stable Diffusion matters because it democratises AI image generation. Unlike previous proprietary text-to-image models like DALL-E and Midjourney which were accessible only via cloud services, Stable Diffusion is open for public access. This lets you download and run this powerful generative model locally on your consumer hardware. 

By openly publishing the model code and weights rather than restricting access through paid APIs, Stable Diffusion places state-of-the-art image synthesis capabilities directly into people's hands. You no longer need to rely on intermediary big tech platforms to produce AI art on your behalf.

The reasonable system requirements also increase the reach of this technology. Stable Diffusion can smoothly run on a gaming GPU, enabling advanced text-to-image generation on mainstream personal devices. This accessibility allows everyone to experiment with prompting unique images from their machines.

Stable Diffusion Architecture

Stable Diffusion uses a latent diffusion model (LDM) developed by the CompVis research group. Diffusion models are trained to iteratively add noise to and then remove noise from images, functioning as a sequence of denoising autoencoders. The key components of Stable Diffusion's architecture are a variational autoencoder (VAE), a U-Net decoder, and an optional text encoder. 

1. The VAE compresses images into a lower-dimensional latent space that captures semantic meaning. 

2. Gaussian noise is applied to this latent representation in the forward diffusion process. The U-Net then denoises the latent vectors, reversing the diffusion. 

3. Finally, the VAE decoder reconstructs the image from the cleaned latent representation.

This denoising process can be conditioned on text prompts, images or other modalities via cross-attention layers. For text conditioning, Stable Diffusion employs a pre-trained CLIP ViT-L/14 text encoder to encode prompts into an embedding space. The modular architecture provides computational efficiency benefits for training and inference.

How Does Stable Diffusion Work?

Stable Diffusion uses a convolutional autoencoder network with attached transformer-based text encoders. The autoencoder is trained using Denoising Diffusion Probabilistic Models (DDPM) to manipulate latent image vectors by iteratively adding and removing Gaussian noise.

The diffusion process involves an encoder that takes an image x and encodes it into a latent vector. Gaussian noise is then added to corrupt this latent vector, with a parameterised variance schedule that increases noise over time. This noise injection creates the noisy encoded inputs that traverse through the architecture.

The decoder acts in reverse - trying to recreate the original image x from the noised vectors by removing noise gradually. This denoising trains the model to render images from noise by learning stable intermediate representations across diffusion steps.

The text encoders (TE) ingest textual prompts to output latent descriptions. These get concatenated and projected to the correct dimensionality before fusing with the decoder input. This conditions image generation on text relevance, enabling control over the rendering process.

During sampling, noise vectors seed the decoder which denoises outputs at each timestep based on text encoding guidance. Images thus become clearer from lower resolution to up to 1024x1024 resolution, lending global coherence.

Capabilities of Stable Diffusion

The Stable Diffusion model can generate new images from scratch through a text prompt describing elements to be included or omitted from the output. Even existing images can be re-drawn by the model to incorporate new elements described by a text prompt. This process is known as "guided image synthesis". The model also allows the use of prompts to partially change existing images via inpainting and outpainting, when used with an appropriate user interface that supports such features. It is recommended to be run with 10 GB or more VRAM, however, users with less VRAM can opt for float16 precision instead of the default float32 to carry out model performance with lower VRAM usage.

Limitations of Stable Diffusion

While stable diffusion displays exceptional image generation capabilities, it does have some limitations including:

• Image quality - The model was trained on images at various resolutions and can generate images up to 1024x1024. While 512x512 is a common resolution, the model's capabilities extend beyond this single resolution. Higher or lower resolutions may display some variation in quality, but the model is not strictly limited to a single input or output resolution.

• Inaccuracies - Insufficient and low-quality training data of human limbs results in anatomical anomalies when prompting the model to generate people. Generated limbs, hands and faces often contain unrealistic proportions or distortions betraying the lack of representative limb features in datasets.

• Accessibility Constraints - Despite democratising access to all, customising Stable Diffusion for novel use cases requires resources out of reach for most individual developers. Retraining niche datasets demands high-VRAM GPUs exceeding 30GB, which consumer cards cannot deliver. This hinders customised extensions from tailoring the model to unique needs.

• Biases-  As the model was predominantly trained on English text-image pairs mostly representing Western cultures, Stable Diffusion inherently reinforces those ingrained demographic perspectives. Generated images perpetuate biases lacking diversity while defaulting to Western types due to the absence of multicultural training data.

• Language limitations - Generative models like Stable Diffusion may have varying abilities to interpret and generate images from prompts in different languages, determined by the linguistic diversity of the training data.

Fine-Tuning Methods for Stable Diffusion

To address these limitations and biases, you can implement additional training to customise Stable Diffusion's outputs for your specific needs by fine-tuning. There are three main approaches for user-accessible fine-tuning for stable diffusion:

1. Embedding- Users provide custom image sets to train small vector representations that get appended to the model's text encoder. When embedding names are referenced in prompts, this biases images to match the visual style of the user data. Embeddings help overwrite demographic biases and mimic niche aesthetics.

2. Hypernetwork - These are tiny neural nets, originally developed to steer text generation models, that tweak key parameters inside Stable Diffusion's core architecture. By identifying and transforming important spatial regions, hypernetworks can make Stable Diffusion imitate the signature styles of specific artists absent in the original training data.

3. DreamBooth - This technique leverages user-provided image sets depicting a particular person or concept to fine-tune Stable Diffusion's generation process. After training on niche examples, prompts explicitly referring to the subject trigger precise outputs rather than defaults.

Use Cases of Stable Diffusion 

Stable diffusion’s versatile capabilities are open for practical applications across many industries including:

• Digital Media: Artists are using stable diffusion to rapidly generate sketches, storyboards, concept art and even full illustrations by describing desired subjects and styles. Media studios can also cut costs in content creation for films, video games, book covers etc.

• Product Design: Fashion Designers prompt stable diffusion to show apparel with new prints, colours and silhouette variations. Product designers describe hypothetical products to visualise and iterate 3D model CAD renderings. This accelerates early-stage ideation.

• Marketing and Advertising: Ad agencies use stable diffusion to compose product images, lifestyle scenes and social media posts. The AI-generated images cut photo shoot expenses and provide unlimited on-brand content.

• Science and Medicine: Researchers provide details of chemical compounds, genomes, molecules and diseases to visualise data and patterns. This can reveal new scientific insights for discovery. Medical images help diagnose conditions, and plan treatments for patient data.

Hyperstack's optimised infrastructure and powerful GPUs ensure smooth and seamless Stable Diffusion experiences. No more waiting for generations to render! Sign up today to access NVIDIA RTX GPUs on demand.

FAQs

What is stable diffusion in AI?

Stable Diffusion is a generative AI model that helps create original images simply from text descriptions. You just need to give this model a prompt and it will design a realistic image based on your specific needs.

Which is the best GPU for stable diffusion?

We recommend using the NVIDIA A100, H100, RTX A6000 and L40 for generative AI workloads like stable diffusion.

What are the limitations of the Stable Diffusion model?

Stable Diffusion struggles with non-512x512 images, generating anatomical inaccuracies in people, requiring high-end GPUs for retraining, perpetuating demographic biases from its Western-centric dataset, and reliably interpreting only English text prompts.