Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient (2411.17787v1)

Abstract: In the rapidly advancing field of image generation, Visual Auto-Regressive (VAR) modeling has garnered considerable attention for its innovative next-scale prediction approach. This paradigm offers substantial improvements in efficiency, scalability, and zero-shot generalization. Yet, the inherently coarse-to-fine nature of VAR introduces a prolonged token sequence, leading to prohibitive memory consumption and computational redundancies. To address these bottlenecks, we propose Collaborative Decoding (CoDe), a novel efficient decoding strategy tailored for the VAR framework. CoDe capitalizes on two critical observations: the substantially reduced parameter demands at larger scales and the exclusive generation patterns across different scales. Based on these insights, we partition the multi-scale inference process into a seamless collaboration between a large model and a small model. The large model serves as the 'drafter', specializing in generating low-frequency content at smaller scales, while the smaller model serves as the 'refiner', solely focusing on predicting high-frequency details at larger scales. This collaboration yields remarkable efficiency with minimal impact on quality: CoDe achieves a 1.7x speedup, slashes memory usage by around 50%, and preserves image quality with only a negligible FID increase from 1.95 to 1.98. When drafting steps are further decreased, CoDe can achieve an impressive 2.9x acceleration ratio, reaching 41 images/s at 256x256 resolution on a single NVIDIA 4090 GPU, while preserving a commendable FID of 2.27. The code is available at https://github.com/czg1225/CoDe

• The paper introduces CoDe, partitioning the VAR decoding process into a drafter for coarse content and a refiner for high-frequency detail.
• The approach reduces memory consumption by approximately 50% and accelerates inference by 1.7x to 2.9x, achieving 41 images per second at 256x256 resolution.
• Experimental results show only a slight FID increase from 1.95 to 2.27, validating the method's efficiency without significant quality loss.

Collaborative Decoding for Efficient Visual Auto-Regressive Modeling: An Examination

The paper "Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient" presents a novel approach to enhance the efficiency of Visual Auto-Regressive (VAR) modeling, specifically targeting the next-scale prediction paradigm inherent in image generation. VAR models, recognized for their scalability and zero-shot generalization capabilities, face significant challenges due to the elongated token sequences generated from their coarse-to-fine, multi-scale structure. This leads to substantial memory consumption and computational inefficiencies, particularly during the decoding phase. The authors propose Collaborative Decoding (CoDe), a strategic method that effectively mitigates these issues by leveraging the distinct computational demands at different scales and introducing an efficient collaboration between models.

Key Contributions

The primary contribution of this work is the introduction of the CoDe framework, which effectively partitions the VAR decoding process into two roles. The large model, or 'drafter', is responsible for producing initial low-frequency content, accommodating the complexities while using fewer computational resources. Conversely, the smaller 'refiner' model takes over at later stages, focusing on adding high-frequency details to produce the final image. This division not only reduces the memory footprint by approximately 50% but also accelerates the inference process by 1.7x without significant degradation in image quality, as measured by the Fréchet Inception Distance (FID).

Strong experimental results support these claims, demonstrating a 2.9x acceleration in some scenarios with a minimal FID increase from 1.95 to 2.27 using an NVIDIA 4090 GPU. These results indicate a substantial improvement over previous VAR implementations, which suffered from the quadratic growth in attention map calculations and excessive KV caching memory demands due to prolonged sequences.

Strong Numerical Results and Claims

The paper quantifies the efficiency improvements by measuring throughput (achieving 41 images per second at 256x256 resolution) and resource utilization (significant reduction in memory consumption). The balance maintained between efficiency and image quality is critical, with only a marginal FID increase being reported. These bold numerical achievements emphasize the effectiveness of decomposing the inference process between different model sizes and specialized roles.

Implications and Future Directions

Theoretically, this paper introduces a paradigm shift in the way VAR models can be optimized for efficiency, suggesting that model specialization across scales may address longstanding computational bottlenecks in hierarchical generative models. Practically, the reduction in computational load makes high-quality image generation more feasible for applications with limited hardware capabilities, potentially democratizing access to advanced image synthesis technologies.

For future research, exploring the scalability of CoDe to higher resolutions and more complex tasks could prove beneficial. Additionally, investigating the integration of newer architectural advancements or combining this approach with other model compression techniques such as pruning or knowledge distillation could further enhance efficiency gains. The paper sets a compelling foundation for future advancements in efficient visual auto-regressive modeling, promising substantial practical applications in AI-driven image generation.