What is Stable Diffusion? (Latent Diffusion Models Explained)

What's AI by Louis-François Bouchard
27 Aug 202206:40

TLDRThe video explores the concept of Stable Diffusion and Latent Diffusion Models, which have revolutionized image generation tasks like text-to-image and style transfer. Despite their computationally expensive nature, recent advancements have made these models more efficient by working within a compressed image representation, or 'latent space', rather than pixel space. This allows for faster generation and the ability to handle different input modalities. The video also introduces Quack, a platform that simplifies ML model deployment, and invites viewers to check out the open-source Stable Diffusion model for various image synthesis tasks.


  • 🌟 Diffusion models like DALL-E and mid-journey are powerful for image generation but require high computing power and long training times.
  • 🔄 These models work by iteratively learning to remove noise from random inputs, conditioned by text or images, to produce a final image.
  • 🚀 The basic diffusion process involves adding noise to real images during training and then learning to reverse this process for generation.
  • 💡 The main challenge with diffusion models is their direct work with pixel data, leading to large input sizes and high computational costs.
  • 🛠️ Latent diffusion models address this by working within a compressed image representation, or latent space, instead of pixel space.
  • 🔧 By using a latent space, models can be more computationally efficient and work with different input modalities like text or images.
  • 🔗 The process involves encoding the initial image and condition inputs into the latent space, merging them, and then using a diffusion model for generation.
  • 🔄 The model includes an attention mechanism to learn how to best combine the input and conditioning data within the latent space.
  • 🔍 A decoder is used to reconstruct the final high-resolution image from the denoised latent space input, essentially upsampling the results.
  • 📚 The Stable Diffusion model is an example of an open-source latent diffusion model that can be run on personal GPUs, making it more accessible.
  • 🔗 For those interested, the code and pre-trained models for Stable Diffusion are available, allowing developers to experiment with their own setups.

Q & A

  • What is the common mechanism behind recent powerful image models like DALL-E and Mid Journey?

    -The common mechanism is diffusion models, which have achieved state-of-the-art results for various image tasks including text to image and image generation.

  • Why are diffusion models computationally expensive during both training and inference?

    -Diffusion models work sequentially on the whole image, which makes the training and inference times expensive, requiring hundreds of GPUs for training and causing a delay in results.

  • What are the downsides of working directly with pixels in image generation models?

    -Working directly with pixels involves large data input like images, which is computationally expensive and time-consuming.

  • What is a latent diffusion model and how does it improve computational efficiency?

    -A latent diffusion model is a transformation of diffusion models into a compressed image representation, allowing for more efficient and faster image generation with smaller data size.

  • How does the encoding process in latent diffusion models work?

    -The encoding process involves using an encoder model to take the image and extract the most relevant information in a subspace, reducing its size while keeping as much information as possible.

  • What role does the attention mechanism play in latent diffusion models?

    -The attention mechanism learns the best way to combine the input and conditioning inputs in the latent space, adding a transformer feature to diffusion models.

  • How does the decoder in latent diffusion models contribute to the final image generation?

    -The decoder acts as the reverse step of the initial encoder, taking the modified and denoised input in the latent space to construct a final high-resolution image through upsampling.

  • What is the significance of the Stable Diffusion model being open-sourced?

    -The open-sourcing of the Stable Diffusion model allows developers to have their own text-to-image and image synthesis models running on their own GPUs, without requiring hundreds of them.

  • How can businesses benefit from the Quack platform mentioned in the video?

    -Quack provides a fully managed platform that unifies ML engineering and data operations, enabling the continuous productization of ML models at scale and reducing the complexity of model deployment.

  • What are some of the tasks that diffusion models can be used for, as mentioned in the video?

    -Diffusion models can be used for a wide variety of tasks such as super resolution, painting, and even text to image generation.

  • What does the video suggest for those interested in testing the Stable Diffusion model?

    -The video encourages developers to use the available code and pre-trained models, and to share their test IDs, results, or feedback with the creator for further discussion.



🧠 Introduction to Diffusion Models in AI Image Generation

This paragraph introduces the concept of diffusion models, which are foundational to recent advancements in AI-driven image generation tasks. It discusses the commonalities among powerful image models like DALL-E and Mid Journey, such as their high computational cost, extensive training times, and the shared mechanism of diffusion. The paragraph also touches on the downside of these models, which is their sequential operation on images leading to expensive training and inference times. This results in the requirement for substantial computational resources, making it accessible mainly to large companies. The explanation of diffusion models as iterative processes that transform random noise into images through learning is provided, highlighting the training process where models learn to apply noise to real images and then reverse the process for generation. The paragraph concludes with an introduction to the sponsor, Quack, which offers a platform for ML model deployment, and hints at a solution to the computational inefficiency of diffusion models.


🛠 Enhancing Computational Efficiency with Latent Diffusion Models

The second paragraph delves into the concept of latent diffusion models, which aim to address the computational challenges of traditional diffusion models. It describes how Robin Rumback and colleagues implemented the diffusion approach within a compressed image representation, moving away from pixel space to a more efficient latent space. This transformation allows for faster and more efficient image generation due to the reduced data size and the flexibility to work with various input modalities. The paragraph outlines the process of encoding inputs into the latent space, merging them with condition inputs using attention mechanisms, and then employing a diffusion model in this subspace. Finally, it discusses the reconstruction of the image using a decoder, resulting in a more efficient model capable of handling tasks like super-resolution, inpainting, and text-to-image generation. The paragraph concludes with an invitation for developers to explore the open-sourced Stable Diffusion model and an acknowledgment of the sponsor, Quack, and the audience.



💡Stable Diffusion

Stable Diffusion refers to a type of Latent Diffusion Model, which is a class of generative models used for creating images from textual descriptions or other forms of input. It is highlighted in the video for its efficiency and ability to generate high-quality images. The script mentions Stable Diffusion in the context of an open-source model that can be run on personal GPUs, indicating its accessibility and practical use for developers.

💡Diffusion Models

Diffusion models are a mechanism used in AI for generating data, such as images, by iteratively learning to remove noise from random inputs until a coherent image is produced. The video script explains that these models are foundational to powerful image generation tasks and have achieved state-of-the-art results, despite their computationally expensive nature.


Text-to-Image is a task where a model generates an image based on a textual description. The script discusses how diffusion models, including Stable Diffusion, have been successful in this task, creating images that correspond to the textual prompts provided to them.

💡Image Super-Resolution

Image Super-Resolution is the process of enhancing the resolution of an image, making it appear clearer and more detailed. The video mentions that diffusion models can be used for this purpose, indicating their versatility in image processing tasks beyond mere generation.

💡Latent Space

Latent Space is a reduced-dimensionality representation of data, where the original information is encoded into a more compact form. In the context of the video, diffusion models work within this latent space to generate images more efficiently, as it involves handling smaller data sizes.

💡Encoder and Decoder

The terms Encoder and Decoder refer to the processes of transforming data into a compressed form (encoding) and then reconstructing it from this form (decoding). The script explains that in Latent Diffusion Models, an image is first encoded into the latent space and then decoded back to its original form after the diffusion process.

💡Attention Mechanism

The Attention Mechanism is a feature in neural networks that allows the model to focus on certain parts of the input data, which is crucial for tasks like combining different types of inputs in diffusion models. The video script describes how this mechanism helps in merging the image representation with condition inputs in the latent space.


A Transformer is a type of neural network architecture that utilizes the attention mechanism and is known for its effectiveness in handling sequential data. The script mentions adding a Transformer feature to diffusion models, enhancing their ability to process and generate images.

💡ML Model Deployment

ML Model Deployment refers to the process of putting a trained machine learning model into a production environment where it can be used to make predictions or perform tasks. The video script discusses the complexities of this process and how it is streamlined by platforms like Quack.


Quack is a platform introduced in the script that provides a managed service for machine learning model deployment, aiming to simplify the process and enable continuous productization of ML models at scale. It is mentioned as a sponsor of the video.

💡Conditional Generation

Conditional Generation is the process of generating data based on certain conditions or inputs, such as text descriptions for image generation. The video script explains that Stable Diffusion and other diffusion models use this process to create images based on textual prompts or other conditioning inputs.


Recent powerful image models like DALL-E and Mid Journey are based on diffusion mechanisms.

Diffusion models achieve state-of-the-art results for various image tasks, including text-to-image generation.

Diffusion models work sequentially on the whole image, leading to high training and inference times.

Large companies like Google or OpenAI are the primary developers of these computationally expensive models.

Diffusion models take random noise as input and condition it with text or an image to iteratively remove noise and generate images.

The model learns to apply noise to real images during training to generate recognizable images from noise.

Once trained, the model can generate images from similar noise distribution in reverse order.

Working directly with pixels and large data inputs like images is computationally expensive.

Quack provides a platform for ML model deployment, simplifying the process for data scientists.

Latent diffusion models transform diffusion approaches into a compressed image representation for efficiency.

The model works with different modalities by encoding inputs into the same latent space.

The model uses an encoder to extract relevant information from the image and a decoder to reconstruct the final image.

Attention mechanisms are added to diffusion models to combine input and conditioning inputs effectively.

Stable Diffusion is an open-source model that allows for efficient text-to-image and image synthesis tasks.

The Stable Diffusion model can be run on personal GPUs, making it accessible for developers.

The video invites viewers to share their experiences and feedback with the Stable Diffusion model.

The video provides a link to the paper for those interested in learning more about the latent diffusion model.