One-Step Diffusion Distillation for Low-Latency Text-to-Image Generation
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Solution Overview
Problem
Conventional denoising diffusion probability models (DDPMs) require multiple iterations for image generation, leading to prohibitively long inference times, and one-step approaches using sample-matching methods produce unnatural images, especially when conditioned with text prompts.
Innovation Solution
A one-step image generation model trained using a multi-term loss derived from a gradient network, comprising a pre-trained model and a jointly-trained model, to guide the generation towards the distribution of the pre-trained model while diverging from the jointly-trained model, enabling high-quality, condition-aligned image generation in a single step.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional DDPMs use multiple iterations for image generation, then image quality is improved, but inference time becomes prohibitively long
Solution Approach 1:
The patent segments the multi-step diffusion process into a teacher model (pre-trained multi-step DDPM) and a student model (one-step generator). The teacher model performs the complex multi-iteration denoising process to generate high-quality images, while the student model learns to replicate this process in a single step through gradient matching, thereby achieving fast inference without sacrificing image quality
Solution Approach 2:
The patent creates a student model that copies the essential denoising capability from the teacher model but operates in a single step. The student model is trained to match the gradient outputs of the teacher model, enabling it to generate images in one forward pass rather than requiring multiple iterations, thus resolving the contradiction between quality and speed
2Loss of time
If one-step approaches use sample-matching methods, then inference time is reduced, but image naturalness deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the student model's gradient outputs are continuously compared with the teacher model's gradient outputs during training. This feedback loop ensures that the student model learns to produce natural-looking images by matching the teacher's denoising behavior, preventing the generation of unnatural artifacts that would otherwise occur in one-step methods
Solution Approach 2:
The patent changes the training objective from direct sample matching to gradient matching. Instead of training the student model to directly reproduce the teacher model's image outputs (which leads to unnatural results), the student model is trained to match the gradient signals that guide the denoising process, resulting in natural-looking images generated in a single step
3Adaptability or versatility
If text conditioning is added to one-step models, then task versatility is improved, but image quality deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-training the teacher model with text conditioning capabilities before creating the student model. The teacher model learns to handle text prompts and generate condition-aligned images through multiple iterations, and the student model inherits this capability through gradient matching, ensuring that text-conditioned one-step generation maintains high image quality
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, apparatus, and system for image generation include obtaining a text prompt and a noise input, and then generating a synthetic image based on the text prompt and the noise input by performing a single pass with an image generation model. The image generation model is trained based on a multi-term loss comprising a positive term based on an output of a pre-trained model, and a negative term based on an output of a jointly-trained model.