Cross-attention for State-based model RWKV-7 (2504.14260v1)

Abstract: We introduce CrossWKV, a novel cross-attention mechanism for the state-based RWKV-7 model, designed to enhance the expressive power of text-to-image generation. Leveraging RWKV-7's linear-complexity Weighted Key-Value (WKV) architecture, CrossWKV integrates text and image modalities in a single pass, utilizing a generalized delta rule with vector-valued gating and low-rank adaptations (LoRA) to achieve superior cross-modal alignment. Unlike Transformer-based models, CrossWKV's non-diagonal, input-dependent transition matrix enables it to represent complex functions beyond the $\mathrm{TC}^0$ complexity class, including all regular languages, as demonstrated by its ability to perform state-tracking tasks like $S_5$ permutation modeling. Evaluated within the Diffusion in RWKV-7 (DIR-7) on datasets such as LAION-5B and ImageNet, CrossWKV achieves a Frechet Inception Distance (FID) of 2.88 and a CLIP score of 0.33 on ImageNet 256x256, matching state-of-the-art performance while offering robust generalization across diverse prompts. The model's enhanced expressivity, combined with constant memory usage and linear scaling, positions it as a powerful solution for advanced cross-modal tasks, with potential applications in high-resolution generation and dynamic state manipulation.Code at https://github.com/TorchRWKV/flash-linear-attention

Cross-attention for State-based Model RWKV-7

The paper "CrossWKV: Cross-attention for State-based Model RWKV-7" presents a novel cross-attention mechanism developed to enhance the expressive power of text-to-image generation within the RWKV-7 framework. This innovative approach leverages the linear-complexity architecture of the RWKV-7 model and incorporates a Weighted Key-Value (WKV) mechanism to achieve efficient text-image integration. The core advancement in CrossWKV is its ability to perform stateful and input-dependent manipulations, which transcend the limitations of $\mathrm{TC}^0$ complexity class associated with Transformer architectures. This positions CrossWKV as a robust alternative to conventional Transformer-based models, particularly in scenarios requiring complex state tracking and regular language recognition.

Technical Summary

The RWKV-7 model, a recurrent neural network architecture, is re-imagined through attention-like mechanisms to offer linear complexity and constant memory usage, making it suitable for high-resolution image generation tasks. CrossWKV utilizes a generalized delta rule paired with vector-valued gating and low-rank adaptations (LoRA), contributing to superior cross-modal alignment through its non-diagonal, input-dependent transition matrix. This is designed to facilitate complex functions beyond $\mathrm{TC}^0$, including state-tracking tasks and permutation modeling.

Evaluations of CrossWKV within the Diffusion in RWKV-7 (DIR-7) framework reveal that the mechanism achieves a Fréchet Inception Distance (FID) of 2.88 and a CLIP score of 0.33 on ImageNet 256x256, aligning with state-of-the-art benchmarks while showcasing generalization across diverse prompts. Notably, this model maintains constant memory usage and linear computational scaling, which are key features promoting its applicability in resource-constrained environments.

Practical Implications and Future Directions

The implications of CrossWKV extend to advanced cross-modal tasks, including high-resolution generation and dynamic state manipulation. The approach promises scalability and efficiency that are advantageous in edge computing and other low-resource settings. The demonstrated ability of CrossWKV to update states dynamically while maintaining computational simplicity suggests its potential for applications requiring sophisticated reasoning and memory retention, such as strategic modeling in board games.

The theoretical underpinnings presented in this paper suggest several future research avenues. Key among them is the exploration of improved state-space models that can further balance expressivity with computational efficiency. Additionally, adapting CrossWKV to other domains beyond text-to-image generation, such as speech synthesis and complex AI-driven simulations, could unlock new capabilities in state-dependent interaction modeling.

While the paper provides comprehensive insights into the CrossWKV mechanism, continued research into optimizing low-rank adaptations and testing in varied prompt conditions might offer enhanced cross-modal results. The potential for integration with newer diffusion models could also be worthwhile to explore, as this would further bolster the robust generation qualities outlined in the evaluation section.

Conclusion

In summary, the CrossWKV mechanism represents a significant technical advancement in the field of state-based models, specifically within the RWKV architecture for text-to-image generation. By circumventing the computational constraints of Transformer models, CrossWKV offers a path towards efficient and scalable cross-modal integration, with promising applications in high-resolution and resource-constrained tasks. Future developments could augment the practical applications of this research further, reinforcing the transformative potential of cross-modal attention mechanisms in contemporary AI systems.