plms

**PLMS** is the **Pseudo Linear Multistep diffusion sampler that reuses previous denoising predictions to extrapolate future updates** - it was an early high-impact acceleration method in latent diffusion pipelines. **What Is PLMS?** - **Definition**: Uses multistep history to approximate higher-order integration directions. - **Computation Pattern**: After startup steps, later updates leverage cached model outputs. - **Historical Role**: Common in early Stable Diffusion releases before newer solver families matured. - **Behavior**: Can generate good quality quickly but may be brittle at very low step counts. **Why PLMS Matters** - **Speed**: Reduces effective sampling cost relative to long ancestral chains. - **Practical Legacy**: Many existing workflows and presets were tuned around PLMS behavior. - **Quality Utility**: Delivers acceptable detail for moderate latency budgets. - **Migration Baseline**: Useful comparison point when adopting DPM-Solver or UniPC. - **Limitations**: May exhibit artifacts when guidance is strong or schedules are mismatched. **How It Is Used in Practice** - **Startup Handling**: Use robust initial steps before switching fully into multistep mode. - **Guidance Calibration**: Retune classifier-free guidance specifically for PLMS trajectories. - **Compatibility Check**: Validate old PLMS presets after model or VAE version changes. PLMS is **a historically important multistep sampler in latent diffusion** - PLMS remains useful in legacy stacks, but modern solvers often provide better low-step robustness.