#ML #Diffusion

Intro

Have you ever wondered how AI can create stunning images from simple text descriptions? It's like magic – you type in "a panda eating tacos at the beach," and seconds later, a brand-new image appears, perfectly matching your imagination.

In this post, I'll break down Stable Diffusion in a way that's easy to understand, even if you're new to the world of machine learning. We'll use helpful diagrams and simple explanations to guide you through the process, from your text prompt to the final image. By the end, you'll have a basic understanding of the magic behind AI-generated images and a glimpse into the future of creative technology.

So, let's dive in and explore the fascinating world of AI image generation!

Big Picture

Have you ever used an AI image generator? It's as simple as typing a prompt like "a panda eating tacos at the beach," waiting a few seconds, and voila! A brand new image appears. But how does this magic happen? Let's dive into the behind-the-scenes process using the diagram.

![[Pasted image 20240703151334.jpg]]

Let's start by looking at the entire process of stable diffusion, as shown in our model. The process of creating an image from text involves several key steps:

  1. Input: We start with a text prompt, which enters through the "conditioning" area of our diagram. These can be thought of as a "guide" that the computer follows for image generation.
  2. Encoding: The computer process starts in the "pixel space" with a random computer generated image as input. This random image is transformed by the encoder into a format that computers can work with, entering the "latent space".
  3. Diffusion: Inside the "latent space", we add noise to data, basically creating a canvas of random blots of colors and dots.
  4. Denoising: At the heart of the process is "Denoising U-Net". This complex network "denoises" or removes noise from your image, guided by your text prompt. At the end of this process, we remove all the noise added in our diffusion process.
  5. Decoding: Finally, the refined data moves back into the "pixel space" through the decoder, which turns the computer's numbers and data into a visible image.

To recap, we enter a text prompt, which acts as a blueprint for the image we want to create. The computer starts with a randomly generated image and adds noise to it, making it unrecognizable. Guided by our text prompt, the AI then gradually removes this noise, revealing a new image that matches our description. The result is our final, AI-generated image that brings our words to life.

Break Down

We've gotten the overview of how AI image generation works, so let's zoom in on each component. We'll examine the specific parts of the process, understanding their unique roles in turning text into images.

![[Pasted image 20240703150627.jpg]]

  1. Conditioning: This is where your text prompt enters the system. Throughout the entire process, especially during the Denoising U-Net stage, your description acts as a "guide" for image generation. Think of it as the blueprint or recipe for your image. The more detailed and specific your prompt, the more accurately the model can interpret your vision.
  2. Text/Img Transformer: Computers don't understand human language directly. This component translates your text prompt into a numerical representation or "computer language." It converts words and concepts into vectors of numbers that the model can process. This translation allows the text to influence the image generation process at various stages.
  3. Image Input: The computer process starts here, typically with structured random noise generated by the computer. This initial noise isn't completely random but has certain statistical properties that make it suitable for the diffusion process. However, in image-to-image models, an actual image can be input here. This initial input provides the raw material that will be shaped into your final image.
  4. Encoder (E): The encoder takes the initial input (random noise or an image) and converts it into a structured numerical representation. This step compresses the information into a format that's more efficient for the model to process, capturing essential features in a compact form.
  5. Latent Vector: This is the "storage" of the whole program. The vector holds and processes the encoded data so that the computer can understand it.
  6. Forward Diffusion: In this step, noise is progressively added to the latent representation in a controlled manner. It's like slowly obscuring a clear image, making it increasingly fuzzy and indistinct. This process creates a path from clear images to pure noise, which the model will later learn to reverse.
  7. Noisy Latent Vector: This is the end result of the forward diffusion process - a completely noised representation. It's important to note that this isn't a visual image, but rather a set of numbers representing a maximally noisy state of the image.
  8. Reverse Diffusion (U-Net): The U-Net architecture is crucial for the denoising process. It's responsible for image detail retrieval and reconstruction. Imagine it as a funnel that first breaks down the noisy input, extracting relevant features, and then builds it back up, reconstructing a cleaner version guided by the conditioning information (text prompt).
  9. Denoising Network: Working hand-in-hand with the U-Net, this network predicts and removes the noise added during forward diffusion. It does this iteratively, gradually clarifying the image. With each pass, it refines its prediction, guided by your text prompt, until a clean representation emerges.
  10. Decoder (D): Once the denoising process is complete, we're left with a clean representation in the latent space. The decoder's job is to transform this abstract, numerical representation back into a visual image. It's like translating the computer's internal language back into a format our eyes can understand.
  11. Final Image: This is the culmination of the entire process - a generated image that matches your text prompt. It's a visual representation born from random noise, shaped by mathematical processes, and guided by your description.

Putting it All Together

Now that we've gone through every part, let's see how they work together. We'll walk through the entire process, step by step, using our newfound knowledge to see how a simple text prompt becomes a complex image.

![[Pasted image 20240703150659.jpg]]

  1. You type your prompt into the Conditioning component: "a panda eating tacos at the beach". Simultaneously, the computer (image input) generates structured random noise - imagine a canvas of static.
  2. The Text/Img Transformer converts "panda," "tacos," "beach" into numerical vectors. These will guide the image creation throughout the process.
  3. The Encoder (E) transforms the noise into a compact representation in Latent Space. Think of this as compressing the static into a dense packet of information.
  4. In Latent Space, Forward Diffusion adds noise gradually. Our "panda-taco-beach" concept is now hidden within layers of mathematical noise.
  5. The Denoising U-Net begins its work on this Noisy Latent Vector. Guided by the numerical representation of our prompt, it starts to extract meaningful patterns from the noise. For example, it might begin to discern the roundness of a panda, the triangular shape of a taco, and the horizontal lines of a beach.
  6. Within the U-Net, the Denoising Network plays a crucial role. In each iteration (typically 50-100 passes), it predicts and removes a layer of noise. With each pass, the "panda-taco-beach" concept becomes more defined in the latent space. The taco starts to look less grainy, more detailed, and more taco-like. Throughout this process, the original text prompt continues to influence the denoising, ensuring the image stays true to your description.
  7. As denoising progresses, the network might first identify broad concepts like "animal," "food," and "outdoor scene." Gradually, it refines these into more specific elements: the panda's shape, the taco's texture, the beach's colors.
  8. Once denoising is complete, we're left with a cleaned latent representation. It's a precise mathematical description of our scene, but not yet a visible image.
  9. The Decoder (D) takes this cleaned latent representation and transforms it back into the Pixel Space. It's like translating a complex mathematical equation into a painting.
  10. Finally, the Final Image emerges - a unique visual interpretation of "a panda eating tacos at the beach," born from random noise and shaped by AI.

Conclusion

From random noise to stunning images, we've uncovered the magic behind AI image generation. While the process is complex, the idea is simple: guiding a computer to transform chaos into art using words.

As AI reshapes our world, understanding these technologies becomes crucial. Whether you're an artist, a tech enthusiast, or simply curious, the ability to grasp and interact with AI will be invaluable.

This is just the beginning. AI has made progress by leaps and bounds in the past couple years, and we're just getting started. If you don't want to fall behind, start learning now!

Resources

[2112.10752] High-Resolution Image Synthesis with Latent Diffusion Models (arxiv.org) (Original Stable Diffusion Paper)

Claude.ai (Arguably the best AI model available right now - use to learn and understand complex topics)

https://youtube.com/playlist?list=PLOspHqNVtKAC-FUNMq8qjYVw6_semZHw0&si=iBueHaiYgf5iQf9c (IBM playlist on AI models)