DPM2

Here are the key points about the DPM2 sampler for diffusion probabilistic models:

• DPM2 is a sampling algorithm for diffusion models proposed by CompVis researchers in 2022.
• It optimizes the sampling process to generate high fidelity images from text prompts.
• Uses a recurrent state space model parameterized by MLPs to dynamically predict timestep noises during sampling.
• The noise predictor takes in timestep and sampled image as input and outputs the predicted noise for the timestep.
• This enables adaptive non-linear noise scheduling conditioned on the image itself.
• Further improves quality by encouraging lower sample variance during the sampling process.
• Additional guidance is provided by a classifier network that discriminates real vs fake images.
• All components are trained end-to-end with the diffusion model itself.
• Achieves state-of-the-art sample quality and coherence at 256x256 resolution.
• Enables creating photorealistic images from text using CompVis models like GLIDE stable diffusion.
• DPM2 improves over prior samplers by optimizing noise injection adaptive to image content.

Overall, DPM2 is an optimized sampler for diffusion models that achieves excellent sample quality through dynamic noise prediction and guidance. It advances the state-of-the-art in generative modeling.