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Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation
Divya Jyoti Bajpai, Arun Verma, Manjesh Kumar Hanawal
Flow Matching enables high-quality visual generation via continuous-time dynamics, but inference remains costly due to multiple sequential function evaluations. Existing accelerati…
SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal
Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands. D…
FastFlow: Accelerating The Generative Flow Matching Models with Bandit Inference
Divya Jyoti Bajpai, Dhruv Bhardwaj, Soumya Roy +4
Flow-matching models deliver state-of-the-art fidelity in image and video generation, but the inherent sequential denoising process renders them slower. Existing acceleration metho…
FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching
Divya Jyoti Bajpai, Shubham Agarwal, Apoorv Saxena +3
Flow Matching (FM) has recently emerged as a powerful approach for high-quality visual generation. However, their prohibitively slow inference due to a large number of denoising st…