Q-Diffusion: Quantizing Diffusion Models (2302.04304v3)

Abstract: Diffusion models have achieved great success in image synthesis through iterative noise estimation using deep neural networks. However, the slow inference, high memory consumption, and computation intensity of the noise estimation model hinder the efficient adoption of diffusion models. Although post-training quantization (PTQ) is considered a go-to compression method for other tasks, it does not work out-of-the-box on diffusion models. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture of the diffusion models, which compresses the noise estimation network to accelerate the generation process. We identify the key difficulty of diffusion model quantization as the changing output distributions of noise estimation networks over multiple time steps and the bimodal activation distribution of the shortcut layers within the noise estimation network. We tackle these challenges with timestep-aware calibration and split shortcut quantization in this work. Experimental results show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance (small FID change of at most 2.34 compared to >100 for traditional PTQ) in a training-free manner. Our approach can also be applied to text-guided image generation, where we can run stable diffusion in 4-bit weights with high generation quality for the first time.

• The paper presents Q-Diffusion, a novel method that quantizes diffusion models to enhance efficiency while preserving generation quality.
• It introduces a tailored quantization technique that optimizes the precision-performance trade-off in generative tasks.
• Experimental results show the approach achieves competitive performance with reduced memory footprint and faster inference compared to full-precision models.

Overview of Author Guidelines for ICCV Proceedings

The document titled "LaTeX Author Guidelines for ICCV Proceedings" serves as a comprehensive guide for authors intending to submit manuscripts to the International Conference on Computer Vision (ICCV). It meticulously outlines the formatting and submission processes to ensure consistent and professional presentation of papers.

The guidelines begin with the general instructions for manuscript preparation, emphasizing the adherence to the style template provided by the ICCV. This includes an eight-page limit for the main content, excluding references, with no allowance for extra page charges—a critical requirement that ensures uniformity and fairness in the review process. Papers exceeding this limit, or those manipulating formatting to falsely conform to guidelines, are unequivocally excluded from review.

A notable feature of these guidelines is the instruction to include a "ruler" in initial submissions. This element, unique to the ICCV template, allows reviewers to annotate specific lines efficiently during the blind review process. Authors using non-LaTeX document preparation systems must replicate this ruler function.

The document reflects the strict policy on maintaining anonymity during the review process, providing explicit instructions on how to cite previous work, especially one's own, without breaching the confidentiality requirement. The guidelines caution authors against typical pitfalls, such as using possessive language that might inadvertently reveal identities.

Attention to detail extends to formatting aspects like margin specifications, column width, and font selection, favoring Times Roman for textual consistency. The guidelines specify proper sectioning, heading styles, the inclusion of figures and tables, and emphasize the need for high-quality graphics. The text must be fully justified, and authors are advised against using footnotes extensively.

Finally, for the submission to be complete, authors need to include a signed IEEE copyright release form. This requirement underscores the importance of intellectual property rights adherence in the publication process.

The provided bibliography style further enforces a standardized citation method, a critical aspect of academic writing that contributes to the paper's credibility and allows for traceability of underlying research.

Implications and Speculations

From a practical perspective, these guidelines serve as a model for ensuring that technical papers meet professional and academic standards necessary for international dissemination. Adherence to such comprehensive standards potentially influences the quality and clarity of research communication at conferences as prominent as ICCV.

Theoretically, this process contributes to the broader discourse on best practices in scientific communication, as it balances the need for detailed disclosure with efficient peer review—a cornerstone of advancing the field of computer vision and artificial intelligence.

In the future, we can anticipate more advanced tools and automated systems to aid compliance with such rigorous standards. For instance, AI-driven document processing could automatically adjust document styles and verify compliance, reducing the manual burden on authors and reviewers alike. However, while automation might streamline the technical aspects, the cognitive and creative rigor in crafting novel research narratives remains a distinctly human endeavor.