glance-phase-aware-acceleration

name	glance-phase-aware-acceleration
title	Glance: Accelerating Diffusion Models with One Sample via Phase-Aware Tuning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.02899
keywords	["diffusion-models","acceleration","distillation","lora-adaptation","single-image-training"]
description	Phase-aware acceleration using two lightweight LoRA adapters (Slow-LoRA for semantic reconstruction, Fast-LoRA for texture refinement) trained on a single image in one GPU hour, achieving 5× speedup via smart per-phase acceleration rather than uniform speedup.

Summary

Glance introduces a phase-aware acceleration strategy for diffusion model distillation that applies different speedup rates to distinct denoising phases. Rather than uniformly accelerating all timesteps, the method deploys two lightweight LoRA adapters—Slow-LoRA for early semantic reconstruction and Fast-LoRA for later texture refinement. This enables efficient training on single images without massive datasets.

Core Technique

Phase Identification: Diffusion follows two conceptual phases:

• Early Phase (high noise): Reconstructs semantic structure; requires careful denoising
• Late Phase (low noise): Refines texture and detail; less critical for content

Phase-Specific LoRA Adapters: Train two lightweight adapters:

• Slow-LoRA: Low-rank matrix for early timesteps, provides conservative updates
• Fast-LoRA: Low-rank matrix for late timesteps, enables aggressive acceleration

Speedup Strategy: Slow down early phase (maintain quality), speed up late phase (minimal quality loss):

step_multiplier = {
    t > 0.7 * T: 1.0  # Early phase: no acceleration
    t <= 0.7 * T: 5.0  # Late phase: 5× acceleration
}

Implementation

LoRA architecture: For each phase, add low-rank adaptation:

# Standard linear layer: y = Wx
# LoRA: y = Wx + (A @ B)x where A, B are low-rank
# Rank r << hidden_dim (e.g., r=8, hidden_dim=768)

lora_slow = LoRA(in_dim=768, out_dim=768, rank=8)  # Early phases
lora_fast = LoRA(in_dim=768, out_dim=768, rank=8)  # Late phases

Phase-aware forward pass:

def forward(x, t):
    # Compute standard diffusion output
    out = diffusion_model(x, t)

    # Apply phase-specific LoRA
    phase = t / max_timesteps
    if phase > 0.7:  
        out = out + lora_slow(x)
    :  
        out = out + lora_fast(x)

     out