Stable Diffusion: A Comprehensive Guide to AI Painting

Stable Diffusion (SD) has emerged as a pivotal model in the AI landscape, marking a transition from traditional deep learning to the AIGC era. Its ability to generate images from text (txt2img) and images (img2img) has spurred innovation across industries. Unlike other models, SD is fully open-source, fostering a vibrant ecosystem of AI painting communities, custom-trained models, and auxiliary tools. This openness has democratized AI painting, making it accessible to a global audience and driving the AIGC revolution. SD is akin to the 'YOLO' of AI painting, offering a blend of performance and accessibility.

At its core, Stable Diffusion leverages diffusion models, which involve forward and reverse diffusion processes. The forward process adds Gaussian noise to an image until it becomes random noise. The reverse process then denoises the image, gradually reconstructing it. This process is governed by a parameterized Markov chain, ensuring stability and generalization. From an artistic perspective, diffusion models mimic the creative process, where elements interact dynamically to form a cohesive structure. The introduction of Latent space is a key innovation, compressing data into a lower-dimensional space, significantly reducing computational costs and enabling SD to run on consumer-grade hardware.

The workflow of Stable Diffusion involves several key steps. First, text prompts are encoded into Text Embeddings using a CLIP Text Encoder. For text-to-image tasks, a Gaussian noise matrix serves as the initial Latent Feature. For image-to-image tasks, the input image is encoded into a Latent Feature using a VAE Encoder. The 'image optimization module,' comprising a U-Net network and a Schedule algorithm, iteratively refines the Latent Feature by predicting and removing noise while incorporating text semantics. Finally, the optimized Latent Feature is decoded back into a pixel-level image using a VAE Decoder. This iterative denoising process gradually transforms noise into a coherent image.

The training of Stable Diffusion can be viewed as a process of learning how to add and remove noise effectively. The training logic involves randomly selecting a training sample, sampling a timestep, adding Gaussian noise, predicting noise using a U-Net, and calculating the loss between predicted and actual noise. Time Embedding is used to simulate the addition of noise over time. The U-Net model learns to predict noise at different levels, enabling it to generate coherent images. Text information is integrated through attention mechanisms, allowing the model to understand and incorporate textual prompts into the generated images. The inputs to the training process include images, text, and noise intensity.

Stable Diffusion consists of three core components: VAE (Variational Autoencoder), U-Net, and CLIP Text Encoder. The VAE compresses images into a low-dimensional Latent space and reconstructs them. The U-Net predicts noise residuals and reconstructs images from noise. The CLIP Text Encoder encodes text prompts into a format that the model can understand. These components work together to enable the generation of high-quality images from text or other images.

The VAE in Stable Diffusion is based on an Encoder-Decoder architecture. The Encoder converts input images into low-dimensional Latent features, while the Decoder reconstructs pixel-level images from these features. The VAE plays a crucial role in image compression and reconstruction. Different VAE models can alter the details and colors of generated images. The VAE's architecture includes GSC components, Downsample components, Upsample components, ResNetBlock modules, and SelfAttention models. The training process involves L1 regression loss, perceptual loss, and a patch-based adversarial training strategy. Regularization losses, such as KL and VQ regularization, are used to prevent arbitrary scaling in the Latent space.

The U-Net model in Stable Diffusion predicts noise residuals and reconstructs input feature matrices. It iteratively removes predicted noise from the original noise matrix, gradually denoising the image Latent Feature. The U-Net's architecture includes ResNetBlock modules, Spatial Transformer modules, and CrossAttnDownBlock, CrossAttnUpBlock, and CrossAttnMidBlock modules. These modules enable the model to understand and incorporate both image and text information. The U-Net's structure is based on the traditional Encoder-Decoder architecture, with added components for improved performance.

Text prompts influence image generation through attention mechanisms. Each training sample corresponds to a text description, which is encoded into Text Embeddings using the CLIP Text Encoder. These Text Embeddings are coupled with the U-Net structure in the form of Cross Attention, enabling the model to fuse image and text information. This process allows the model to generate images that align with the given text prompts.

While Stable Diffusion has become a core generative model, other models like GANs, VAEs, and Flow-based models continue to play a role in the AIGC era. GANs, for example, are used in AI painting workflows for tasks like image super-resolution, face restoration, and style transfer. These models complement Stable Diffusion, enhancing its capabilities and expanding its applications.

Stable Diffusion has revolutionized the AI painting landscape, democratizing access to AI-generated art and driving innovation across industries. Its open-source nature, combined with its powerful capabilities, has fostered a vibrant ecosystem of AI painting communities and custom-trained models. As the AIGC era continues to evolve, Stable Diffusion is poised to remain a key player, shaping the future of AI-generated content and creative expression.