UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
Developed by a team of researchers, including Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu, the UniPC project is a groundbreaking framework designed to improve the efficiency of sampling in diffusion models. With an implementation in PyTorch, it demonstrates significant advancements in model-agnostic sampling techniques.
What is UniPC?
UniPC stands for "Unified Predictor-Corrector" and represents a training-free framework specially crafted to facilitate rapid sampling in diffusion models. Central to its functionality are two components: UniC (the Corrector) and UniP (the Predictor). These components share a unified analytical form and are capable of supporting multiple arbitrary orders. This flexibility makes UniPC highly adaptable to various model types.
Key Features of UniPC
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Model-Agnostic Framework: UniPC can be effortlessly integrated with both pixel-space and latent-space diffusion probability models (DPMs). This means it is versatile enough to handle unconditional as well as conditional sampling tasks.
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Applicability: It is efficient with both noise prediction models and data prediction models, making it broadly applicable in various scenarios.
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Enhanced Convergence Speed: Compared to previous methods, UniPC enhances convergence speed by increasing the order of accuracy. This results in markedly improved sampling quality, particularly when operating under constraints of just a few steps (around 5 to 10).
Application and Integration
UniPC finds its applications prominently in Score-based SDE models and the popular Stable-Diffusion framework. Thorough examples of its application can be found in the project’s code repository, providing users a hands-on experience.
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ScoreSDE with UniPC: A detailed PyTorch example is available, illustrating how UniPC can be utilized to sample from a diffusion probability model that is pre-trained on the CIFAR10 dataset.
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Stable-Diffusion with UniPC: Another example shows its application in stable-diffusion, where it efficiently accelerates both conditional and unconditional sampling processes.
Integration with Hugging Face's Diffusers Library
UniPC is seamlessly integrated into Hugging Face's Diffusers library. This convenient integration allows for the use of UniPC with minimal setup, as shown in the provided example where it can be tried with just a few lines of code in PyTorch:
pip install diffusers accelerate transformers
# Code snippet to apply UniPC
from diffusers import StableDiffusionPipeline, UniPCMultistepScheduler
import torch
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "a highly realistic photo of green turtle"
generator = torch.manual_seed(0)
image = pipe(prompt, generator=generator, num_inference_steps=15).images[0]
image.save("turtle.png")
Why Choose UniPC?
UniPC not only enables faster sampling without the need for model retraining but also significantly improves the quality of results in diffusion model applications. Its integration into popular software tools and libraries ensures that both researchers and practitioners can readily benefit from its advantages.
For more in-depth information, users can explore the project page and access the framework through Hugging Face here.