In the context of Stable Diffusion, PLMS (Pseudo-Linear Multistep) is a sampling method used to generate images from latent representations. It is designed to balance the trade-off between image quality and computational efficiency.
Key Features of PLMS:
- Sampling Process: PLMS operates by iteratively refining the latent representation through multiple steps, each adjusting the image to better match the desired output. This multistep approach allows for more detailed and accurate image generation compared to single-step methods.
- Efficiency: While PLMS involves multiple steps, it is optimized to perform these iterations efficiently, making it suitable for generating high-quality images without excessive computational demands.
- Comparison with Other Samplers: PLMS is often compared to other sampling methods like DDIM and DPM++ in terms of image quality and performance. Users may choose PLMS when seeking a balance between detail and computational efficiency.