Efficient Diffusion Personalization
3 papers with code • 0 benchmarks • 0 datasets
This task consists of both memory and parameter efficient personalization of diffusion models
Benchmarks
These leaderboards are used to track progress in Efficient Diffusion Personalization
Most implemented papers
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
mkshing/svdiff-pytorch
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ICCV 2023
Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities.
A Closer Look at Parameter-Efficient Tuning in Diffusion Models
Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient.
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning
mkshing/DiffFit-pytorch
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ICCV 2023
This paper proposes DiffFit, a parameter-efficient strategy to fine-tune large pre-trained diffusion models that enable fast adaptation to new domains.
