揭秘 AI 绘画的加速引擎：PLMS 采样方法详解
在当今的人工智能世界里，文字生成图片（Text-to-Image）的技术就像魔法一样。你输入“一只在太空中骑自行车的猫”，几秒钟后，一张精美的画作就出现了。这背后的功臣通常是扩散模型（Diffusion Models）。
但是，扩散模型有一个著名的缺点：慢。为了解决这个问题，科学家们发明了各种“加速器”，其中最著名、最常用的之一就是 PLMS。
今天，我们就用通俗易懂的方式，来拆解一下 PLMS 到底是什么。
1. 基础概念：什么是“采样” (Sampling)？
在理解 PLMS 之前，我们先要明白 AI 是怎么画画的。
扩散模型的画画过程，其实是一个“去噪”的过程。
- 起步： AI 拿到一张全是雪花点（随机噪声）的图，就像老式电视机没有信号时的画面。
- 过程： AI 一步一步地把雪花点擦除，慢慢显露出原本的轮廓、颜色，最后变成清晰的图像。
这个“一步一步去噪”的过程，在术语里就叫做采样（Sampling）。
形象比喻：雕刻大师
想象一下，你面前有一块长得像正方体的粗糙石头（噪声图）。你的任务是把它雕刻成一座精美的雕像（最终图像）。
- 采样就是你每一刀刻下去的动作。
- 传统的采样方法需要刻几百甚至上千刀，每一刀都要极其小心，所以速度很慢。
2. 什么是 PLMS？
PLMS 的全称是 Pseudo Linear Multi-Step（伪线性多步法）。听起来虽然很吓人，但它的核心目的非常简单：用更少的步骤，画出一样好的画。
它是专门为加速扩散模型而设计的算法。在 Stable Diffusion 等流行的 AI 绘画软件中，PLMS 经常作为默认的选项之一。
核心原理：预判与惯性
传统的简单采样方法（比如 DDIM）往往只看“眼下这一步”该怎么走。它小心翼翼，走一步看一步。
而 PLMS 是一种高阶方法。它不仅看“眼下”，还会参考“过去几步”的经验，来更准地预判“下一步”该怎么走。
3. 生活中的类比：老司机开车
为了让你彻底明白 PLMS 和传统方法的区别，我们来做一个“开车过弯道”的比喻。
场景：
你要在这个弯弯曲曲的山路上（图像生成的数学路径），把车开到终点（清晰的成图）。
🚗 方法 A：新手司机 (传统采样，如 DDPM/Euler)
新手司机非常谨慎。他每开一米，都要停下来即使查看地图，计算一下方向盘该打多少度。
- 特点： 极其准确，肯定不会开出悬崖。
- 缺点： 走走停停，开完全程需要很久（需要几百步采样）。
🏎️ 方法 B：PLMS 老司机 (Pseudo Linear Multi-Step)
PLMS 是一个经验丰富的赛车手。他不需要每一米都停下来看地图。
- 利用惯性和经验： 当他过弯时，他会记住前几秒的方向盘角度和车身动态（利用历史梯度信息）。
- 预判： 他心想：“刚才那两段路都是向左急转，根据趋势，下一段路大概率还是要左转，所以我不需要重新计算，顺着势头打方向盘就行。”
- 结果： 他动作连贯，大步流星地就把车开到了终点。新手司机要修正 100 次方向，PLMS 老司机可能只需要操作 50 次，甚至 20 次。
4. 图解 PLMS 的工作流程
由于我们无法展示动态视频，请参考下面的图表来理解不同方法在去噪过程中的步长差异。
传统的采样 (Standard Sampling):
1 | 噪声 [XXXXX] -> 计算 -> [X4XXX] -> 计算 -> [XX3XX] -> ... (需要 100 步) -> 清晰图 |
PLMS 采样:
1 | 噪声 [XXXXX] -> [历史数据1+2+3的辅助] -> 预判大跳跃 -> [XX3XX] -> ... (只需 50 步) -> 清晰图 |
PLMS 为什么能跳得准？
这就好比天气预报。
- 如果只看现在的云彩，很难预测明天的天气。
- 但如果你结合了昨天、前天、大前天的气压变化趋势（多步历史信息），你就能利用数学公式，非常准确地推算出明天的天气。
- PLMS 就是利用了过去几个去噪步骤产生的“数据梯度”，拟合出一条更直的路径，直接冲向终点。
5. PLMS 的优缺点总结
虽然 PLMS 很厉害，但它也不是完美的。在实际使用 AI 绘画时，了解它的特性很有帮助。
| 特性 | 说明 | 评价 |
|---|---|---|
| 速度 | 非常快 | ⭐⭐⭐⭐⭐ (通常只需 50 步即可达到极佳效果) |
| 画质 | 平滑，噪点少 | ⭐⭐⭐⭐ |
| 风格 | 往往生成的画面比较柔和，不像某些暴力算法那样锐利 | 因人而异 |
| 缺点 | 对于极其复杂的细节，或者步数极低（<20步）时，可能不如一些更新的算法（如 DPM++ 2M Karras） | 在现代模型中已稍显落后，但仍是经典 |
6. 结论
PLMS (Pseudo Linear Multi-Step) 是一种让 AI 绘画“提速”的聪明算法。它不仅仅是埋头苦干，而是学会了利用“过去的经验”来预判“未来的路”。
如果你在使用 Stable Diffusion 这样的工具，不想等待太久，又希望得到一张质量上乘的图片，选择 PLMS 采样器（通常设置为 40-50 步）是一个非常稳健且高效的选择。虽然现在有更多新的算法出现，但在 AI 发展的历史上，PLMS 是让大规模图像生成真正走向大众的重要功臣。
Demystifying the AI Art Speed Engine: Explaining the PLMS Sampling Method
In the world of Artificial Intelligence today, Text-to-Image technology feels like magic. You type “a cat riding a bicycle in space,” and seconds later, a beautiful artwork appears. The hero behind this is usually the Diffusion Model.
However, diffusion models have a famous drawback: they are slow. To solve this, scientists invented various “accelerators,” and one of the most famous and widely used is PLMS.
Today, let’s break down what PLMS is in plain language without getting bogged down in complex mathematics.
1. Basic Concept: What is “Sampling”?
Before understanding PLMS, we need to understand how AI draws.
The process of drawing by a diffusion model is actually a process of “denoising.”
- The Start: The AI gets a picture full of random static (noise), like an old TV screen with no signal.
- The Process: The AI wipes away the static step by step, slowly revealing the original outlines and colors until it becomes a clear image.
This process of “removing noise step by step” is technically called Sampling.
A Visual Metaphor: The Master Sculptor
Imagine you have a rough block of stone in front of you (the noise image). Your task is to carve it into a beautiful statue (the final image).
- Sampling is the action of every cut you make.
- Traditional sampling methods require carving hundreds or even thousands of times. Every cut must be extremely careful, so it is very slow.
2. What is PLMS?
PLMS stands for Pseudo Linear Multi-Step. It sounds intimidating, but its core purpose is simple: To draw an equally good picture in fewer steps.
It is an algorithm specifically designed to accelerate diffusion models. In popular AI art software like Stable Diffusion, PLMS is often one of the default options.
Core Principle: Prediction and Momentum
Traditional simple sampling methods (like DDIM) often only look at “how to take the current step.” They are cautious, taking one step and then looking around.
PLMS is a higher-order method. It doesn’t just look at “right now”; it refers to the experience of the “past few steps” to more accurately predict how to take the “next step.”
3. Real-Life Analogy: The Veteran Driver
To thoroughly understand the difference between PLMS and traditional methods, let’s use a “driving through a curve” analogy.
Scenario:
You need to drive a car to the finish line (a clear image) on a winding mountain road (the mathematical path of image generation).
🚗 Method A: The Novice Driver (Traditional Sampling, e.g., DDPM/Euler)
The novice driver is extremely cautious. Every meter he drives, he stops to check the map and calculates exactly how many degrees to turn the steering wheel.
- Feature: Extremely accurate; definitely won’t drive off a cliff.
- Drawback: Stop-and-go; it takes a long time to finish (requires hundreds of sampling steps).
🏎️ Method B: The PLMS Veteran (Pseudo Linear Multi-Step)
PLMS is an experienced race car driver. He doesn’t need to stop every meter to look at the map.
- Using Momentum and Experience: When he turns, he remembers the steering angle and car dynamics from the last few seconds (using historical gradient information).
- Prediction: He thinks, “The last two sections were sharp left turns. Based on the trend, the next section is likely still a left turn, so I don’t need to recalculate from scratch; I’ll just follow the momentum.”
- Result: His movements are fluid, and he reaches the finish line in great strides. Where the novice driver needs to correct the steering 100 times, the PLMS veteran might only need 50, or even 20 operations.
4. Visualizing the PLMS Workflow
Since we cannot show a video, please refer to the text diagram below to understand the difference in step size during the denoising process.
Standard Sampling:
1 | Noise [XXXXX] -> Calc -> [X4XXX] -> Calc -> [XX3XX] -> ... (Needs 100 steps) -> Clear Image |
PLMS Sampling:
1 | Noise [XXXXX] -> [Assisted by history 1+2+3] -> Predicted Big Jump -> [XX3XX] -> ... (Needs 50 steps) -> Clear Image |
Why can PLMS jump accurately?
This is just like weather forecasting.
- If you only look at the clouds right now, it’s hard to predict tomorrow’s weather.
- But if you combine the pressure trends from yesterday, the day before, and two days ago (Multi-Step historical information), you can use mathematical formulas to calculate tomorrow’s weather very accurately.
- PLMS uses the “data gradients” generated by the past few denoising steps to fit a straighter path, rushing directly towards the goal.
5. Summary of Pros and Cons
While PLMS is powerful, it is not perfect. Knowing its characteristics is helpful when using AI art tools.
| Feature | Description | Rating |
|---|---|---|
| Speed | Very fast | ⭐⭐⭐⭐⭐ (Usually achieves great results in just 50 steps) |
| Quality | Smooth, low noise | ⭐⭐⭐⭐ |
| Style | Often generates softer images, not as sharp as some aggressive algorithms | Subject to preference |
| Drawback | For extremely complex details, or at very low step counts (<20), it may perform worse than newer algorithms (like DPM++ 2M Karras) | Slightly dated in modern models, but remains a classic |
6. Conclusion
PLMS (Pseudo Linear Multi-Step) is a smart algorithm that puts the “speed” in AI art generation. It doesn’t just work hard; it learns to use “past experience” to predict the “road ahead.”
If you are using tools like Stable Diffusion and don’t want to wait too long but still want a high-quality image, choosing the PLMS sampler (usually set to 40-50 steps) is a very robust and efficient choice. Although many new algorithms have appeared since, PLMS remains a key contributor in the history of AI, helping bring large-scale image generation to the masses.