Home Community Huawei Research Introduces DiffFit For Efficiently Positive-Tuning Large Diffusion Models

Huawei Research Introduces DiffFit For Efficiently Positive-Tuning Large Diffusion Models

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Huawei Research Introduces DiffFit For Efficiently Positive-Tuning Large Diffusion Models

One of the crucial vital challenges in machine learning is modeling intricate probability distributions. Diffusion probabilistic models DPMs aim to learn the inverse of a well-defined stochastic process that progressively destroys information. 

Image synthesis, video production, and 3D editing are some areas where denoising diffusion probabilistic models (DDPMs) have shown their value. In consequence of their large parameter sizes and frequent inference steps per image, current state-of-the-art DDPMs incur high computational costs. In point of fact, not all users have access to sufficient financial means to cover the fee of computation and storage. Subsequently, it’s crucial to research strategies for effectively customizing publically available, big, pre-trained diffusion models for individual applications.

A brand new study by Huawei Noah’s Ark Lab researchers uses the Diffusion Transformer as a foundation and offers DiffFit, a simple and effective fine-tuning technique for big diffusion models. Recent NLP (BitFit) research has shown that adjusting the bias term can fine-tune a pre-trained model for downstream tasks. The researchers desired to adapt these effective tuning strategies for image generation. They first immediately apply BitFi, and to enhance feature scaling and generalizability, they incorporate learnable scaling aspects to particular layers of the model, with a default value of 1.0 and dataset-specific tweaks. The empirical results indicate that including strategic places throughout the model is crucial for improving the Frechet Inception Distance (FID) rating.

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BitFit, AdaptFormer, LoRA, and VPT are only a few of the parameter-efficient fine-tuning strategies the team used and compared over 8 downstream datasets. Regarding the variety of trainable parameters and the FID trade-off, the findings show that DiffFit performs higher than these other techniques. As well as, the researchers also found that their DiffFit strategy may very well be easily employed to fine-tune a low-resolution diffusion model, allowing it to adapt to high-resolution picture production at an inexpensive cost just by treating high-resolution images as a definite domain from low-resolution ones. 

DiffFit outperformed the prior state-of-the-art diffusion models on ImageNet 512×512 by starting with a pretrained ImageNet 256×256 checkpoint and fine-tuning DIT for less than 25 epochs. DiffFit outperforms the unique DiT-XL/2-512 model (which has 640M trainable parameters and 3M iterations) by way of FID while having only roughly 0.9 million trainable parameters. It also requires 30% less time to coach. 

Overall, DiffFit seeks to offer insight into the efficient fine-tuning of larger diffusion models by establishing an easy and powerful baseline for parameter-efficient fine-tuning in picture production.


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Tanushree

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Tanushree Shenwai is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Bhubaneswar. She is a Data Science enthusiast and has a keen interest within the scope of application of artificial intelligence in various fields. She is keen about exploring the brand new advancements in technologies and their real-life application.


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