SACHITRA: Scalable Application of CPU-based HTML Integrated Transformer for Regenerative Augmented Image Generation Model
Abstract
Abstract We present SACHITRA (Scalable Application of CPU-based HTML Integrated Transformer for Regenerative Augmented Image Generation Model), a generative image and video synthesis framework engineered for browser-native, CPU-bounded execution without dependence on dedicated GPU hardware. Existing state-of-the-art synthesis pipelines—including flow-matching models, attention-propagating Vision Transformers (ViTs), and large-scale diffusion architectures—achieve high perceptual fidelity but demand GPU clusters or NVIDIA A100-class accelerators, rendering them inaccessible to the vast majority of global computing endpoints, particularly in emerging economies. SACHITRA addresses this accessibility gap through three formally grounded contributions: (1) a Quantized HTML Pipeline (QHP), which orchestrates compressed generative sub-networks within an HTML5/WebAssembly execution context, exploiting browser SIMD intrinsics and SharedArrayBuffer for intra-session parallelism; (2) an Adaptive Finite-Difference Trajectory Approximation (AFDT), a training-free ODE acceleration method integrated with a UCB-1 multi-armed bandit (MAB) controller that dynamically skips redundant integration steps with formal error guarantees of order O ( |S|/T 3 ) under INT4 quantized arithmetic; and (3) a Cross-Layer Historical Attention Distillation (CHAD) mechanism that propagates compressed 4-bit integer attention matrices across encoder layers, reducing attention buffer memory by 16 × while sustaining…
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