NVIDIA’s Autoguidance: Improving Image Quality and Variation in Diffusion Models
What is the main challenge in improving image quality and variation in diffusion models while aligning with given conditions?
The main challenge in improving image quality and variation in diffusion models while aligning with given conditions lies in the inherent entanglement of the effects of image quality and variation. This makes it difficult to control them independently. Current methods, such as classifier-free guidance (CFG), often enhance image quality at the expense of diversity, limiting their applicability in various real-world scenarios where both high quality and variability are crucial. Additionally, CFG is limited to conditional generation and suffers from task discrepancy problems, leading to skewed image compositions and oversimplified images. These limitations hinder the method’s performance and restrict its use in generating diverse and high-quality images.
How do current methods typically compromise when enhancing image quality in diffusion models?
Current methods for enhancing image quality in diffusion models often compromise by reducing image variation. This limitation arises because the effects of image quality and variation are inherently entangled, making it difficult to control them independently. As a result, improving image quality often leads to a decrease in the diversity of generated images. This trade-off is particularly evident in the classifier-free guidance (CFG) method, which uses an unconditional model to guide a conditional one. While CFG improves prompt alignment and image quality, it does so at the expense of image variation. This compromises the ability of the model to generate diverse and realistic images, which is crucial in various real-world scenarios such as medical diagnosis and autonomous driving.
What is classifier-free guidance (CFG) and how does it function in the context of diffusion models?
Classifier-free Guidance (CFG) is a technique used in diffusion models to steer the outputs of the model to better align with a given input6. It was introduced to enhance the generative quality of diffusion models by using a separate classifier to encourage desired characteristics in the output image2.
In the context of diffusion models, CFG works by combining the score estimate of the diffusion model with the gradient of an image classifier. This combination requires training an image classifier alongside the diffusion model. During the generation process, the classifier guidance is used to influence the output of the diffusion model, guiding it towards generating images that align with the given input6.
The CFG scale controls how much the input prompt steers the diffusion process5. The effect is similar to the classifier guidance. If the CFG scale is set to -1, the input prompt is ignored.
Overall, CFG is a way to improve the alignment of generated images with a given input, enhancing the quality of the generated samples6.