LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916505659113472 |
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| author | Smith, Ethan Seid, Rami Hojel, Alberto Mishra, Paramita Wu, Jianbo |
| author_facet | Smith, Ethan Seid, Rami Hojel, Alberto Mishra, Paramita Wu, Jianbo |
| contents | Low-Rank Adaptation (LoRA) and other parameter-efficient fine-tuning (PEFT) methods provide low-memory, storage-efficient solutions for personalizing text-to-image models. However, these methods offer little to no improvement in wall-clock training time or the number of steps needed for convergence compared to full model fine-tuning. While PEFT methods assume that shifts in generated distributions (from base to fine-tuned models) can be effectively modeled through weight changes in a low-rank subspace, they fail to leverage knowledge of common use cases, which typically focus on capturing specific styles or identities. Observing that desired outputs often comprise only a small subset of the possible domain covered by LoRA training, we propose reducing the search space by incorporating a prior over regions of interest. We demonstrate that training a hypernetwork model to generate LoRA weights can achieve competitive quality for specific domains while enabling near-instantaneous conditioning on user input, in contrast to traditional training methods that require thousands of steps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02352 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization Smith, Ethan Seid, Rami Hojel, Alberto Mishra, Paramita Wu, Jianbo Machine Learning Low-Rank Adaptation (LoRA) and other parameter-efficient fine-tuning (PEFT) methods provide low-memory, storage-efficient solutions for personalizing text-to-image models. However, these methods offer little to no improvement in wall-clock training time or the number of steps needed for convergence compared to full model fine-tuning. While PEFT methods assume that shifts in generated distributions (from base to fine-tuned models) can be effectively modeled through weight changes in a low-rank subspace, they fail to leverage knowledge of common use cases, which typically focus on capturing specific styles or identities. Observing that desired outputs often comprise only a small subset of the possible domain covered by LoRA training, we propose reducing the search space by incorporating a prior over regions of interest. We demonstrate that training a hypernetwork model to generate LoRA weights can achieve competitive quality for specific domains while enabling near-instantaneous conditioning on user input, in contrast to traditional training methods that require thousands of steps. |
| title | LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.02352 |