Stable Diffusion 3: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

The video begins with an introduction to Stable Diffusion 3, highlighting its positive reception based on samples and demonstrations available on the developers' website. The speaker notes that this version introduces a new capability for Stable Diffusion, which is the ability to spell, marking a significant improvement. The video aims to delve into the workings of diffusion models, with a focus on the use of Transformers and the sequence-to-sequence model. The speaker expresses enthusiasm for the potential of this open-source model and provides a brief overview of the previous versions, emphasizing the improvements made in Stable Diffusion 3.

This paragraph delves into the specifics of how diffusion models operate, particularly the forward and backward processes. The forward process involves adding noise to an image, while the backward process involves training a model to predict and remove this noise. The speaker explains the use of a neural network to predict the noise in an image and the subsequent subtraction of this noise to retrieve the original image. The concept of a chain is introduced to account for the imperfections in prediction, allowing for a refinement process over multiple steps. The paragraph also touches on the idea of using a deterministic process to remove the signal from the image, highlighting the role of the model in this process.

The speaker discusses the evolution of diffusion models, transitioning from DDPM to the use of ODEs and SDEs. The paragraph explains how these mathematical models are used to represent the data distribution and noise distribution, guiding the model towards a specific point on the noise distribution. The concept of an SDE is used to model the forward process, with a stochastic element adding randomness to the trajectory. The speaker then describes the use of an ODE to reverse this process, moving back towards the original data point. The paragraph also introduces the idea of a score, which is the gradient of the probability of an image with respect to the input parameters, and how it can be used to maximize the quality of the generated image.

In this paragraph, the speaker elaborates on the need for multiple steps in the diffusion process due to the curvature of the trajectory in high-dimensional space. The speaker uses a visual analogy to explain how the model's prediction may not align perfectly with the original trajectory, necessitating incremental steps to correct the path. The process is likened to the射手 (solver) method, where the model iteratively improves its estimate until it reaches the desired output. The speaker emphasizes the importance of this refinement process in achieving accurate results, as opposed to a single-step approach that may not account for the complexity of the trajectory.

The speaker introduces the concept of rectified flows, which are used in Stable Diffusion 3 to model the ordinary differential equation (ODE) for the backward process in diffusion. The paragraph explains how the model learns the velocity, or the derivative of the state with respect to time, to create a trajectory from the data to the noise. The speaker describes the objective function used to train the model, which involves a noise-matching objective and additional terms to account for the difficulty of predicting intermediate values. The paragraph also discusses the use of a weighing term to focus the model's learning on the middle of the trajectory, where the signal and noise are both present.

The speaker explains that Stable Diffusion 3 operates in the latent space rather than pixel space, facilitated by the use of a variational autoencoder. The image is first encoded into a latent representation, which is a compressed version of the image's features. The diffusion process is then applied to this latent representation, which is noisy at the outset. The model is trained to reverse this process, removing the noise in multiple steps until the original image is reconstructed. The speaker notes that the autoencoder and the diffusion model are trained independently, with the autoencoder being trained on a large dataset of images to create an effective encoding of the image into the latent space.

The paragraph discusses the integration of text into the modeling process, using CLIP and T5 to encode text information. CLIP is used to generate intermediate representations of text, while T5 provides a more detailed, fine-grained representation. The speaker describes how these representations are concatenated to form a comprehensive text vector, which is then combined with the time step information and latent image information. The paragraph highlights the use of sinusoidal embeddings to represent the time step in the diffusion process, allowing for a unique positional encoding. The speaker also notes the addition of pulled information from the T5 model, which contains more detailed information about the text.

The speaker shares insights on the training strategies used for Stable Diffusion 3. The model is initially pre-trained on low-resolution images and then fine-tuned on higher resolutions and different aspect ratios. The speaker emphasizes the importance of re-captioning the training data using state-of-the-art vision-language models to generate synthetic annotations, which improves the quality of the training data and, consequently, the model's performance. The paragraph also discusses the use of half-precision training with RMS normalization to stabilize attention entropy and prevent training divergences, a technique derived from an Apple paper. The speaker concludes by noting the effectiveness of rectified flows in the diffusion process and the model's strong correlation with human preference, indicating its potential for high-quality image generation.