How does Stable Diffusion fundamentally work?

10 Tem 19:37

Stable Diffusion is essentially a latent diffusion model that combines diffusion (spreading) and stabilization techniques. Its core principle involves gradually reducing the noise in a noisy image (high noise level) to reach the target image. This process is called reverse diffusion: noise starting with Gaussian noise is "predicted" and partially cleaned at each step through a neural network (U-Net). Since these predictions are made in latent space (a more compact data format than RGB pixel tensors), both memory is saved and training becomes efficient. As for the differences between text-to-image and image-to-image: - **Text-to-image** directly injects text embeddings (e.g., CLIP) into the latent space and generates an image from scratch. - **Image-to-image**, on the other hand, gives the model a noisy input image, and instead of just cleaning the noise using the text embedding, it also "enhances" the image. I’ve tried both: in text-to-image, I add prompts like "8K ultra realistic," while in image-to-image, I feed a high-noise version of my photo and stabilize it, resulting in much sharper edges and details. The connection to machine learning is already at its core: the U-Net's weights come pre-trained, and the latent diffusion process is used to eliminate learned noise patterns.

10 Tem 22:15

Stable Diffusion to understand diffusion models, imagine a magazine where a photo is continuously drawn and erased. Initially, the paper is completely blank, and with each step, a clearer image emerges. Stable Diffusion follows the same logic: it starts with completely random noise ("noise") and, with each step, refines the image using fine details added by the model (the "teacher"). The model used here is a version called latent diffusion, which stores previous drawing steps in a compressed data form (latent space), increasing efficiency. This allows Stable Diffusion to generate an image in just 50-100 steps, whereas classic diffusion models would take thousands. Let’s also explain the difference between text-to-image and image-to-image using a painter’s brief. For text-to-image, you simply say, "A painter walking a dog under a streetlamp in Paris at night," and the model tries to depict it from scratch. In image-to-image, you already have a sketch—say, a black-and-white pencil drawing of a Parisian street—and the model is asked to recreate it in vibrant, photo-quality colors. As for its machine learning aspect, the model essentially solves the question, "How much noise is in this part?" at each step, and to learn this, it has been trained on millions of images. So, Stable Diffusion is nothing more than an optimized painter algorithm guided by a neural network called U-Net that directs the diffusion process.

10 Tem 23:11

Stable Diffusion essentially works by adding noise and then gradually removing it through a diffusion process, thereby transforming pure chance noise into meaningful images. In text-to-image, the model directly converts a prompt like "an eagle flying in a clear blue sky" into an image, while in image-to-image, it takes a base image you upload and modifies it according to your English description. Through machine learning, diffusion learns step by step how to clean up the learned noises, making the results from AI increasingly realistic.