Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models

• URL: http://arxiv.org/abs/2409.16535v1
• Date: Wed, 25 Sep 2024 01:02:30 GMT
• Title: Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models
• Authors: Deepak Sridhar, Nuno Vasconcelos,
• Abstract summary: Concept Sliders introduced a method for fine-grained image control and editing by learning concepts (attributes/objects) This approach adds parameters and increases inference time due to the loading and unloading of Low-Rank Adapters (LoRAs) used for learning concepts. We propose a straightforward textual inversion method to learn concepts through text embeddings, which are generalizable across models that share the same text encoder.
• Score: 53.385754347812835
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models have recently surpassed GANs in image synthesis and editing, offering superior image quality and diversity. However, achieving precise control over attributes in generated images remains a challenge. Concept Sliders introduced a method for fine-grained image control and editing by learning concepts (attributes/objects). However, this approach adds parameters and increases inference time due to the loading and unloading of Low-Rank Adapters (LoRAs) used for learning concepts. These adapters are model-specific and require retraining for different architectures, such as Stable Diffusion (SD) v1.5 and SD-XL. In this paper, we propose a straightforward textual inversion method to learn concepts through text embeddings, which are generalizable across models that share the same text encoder, including different versions of the SD model. We refer to our method as Prompt Sliders. Besides learning new concepts, we also show that Prompt Sliders can be used to erase undesirable concepts such as artistic styles or mature content. Our method is 30% faster than using LoRAs because it eliminates the need to load and unload adapters and introduces no additional parameters aside from the target concept text embedding. Each concept embedding only requires 3KB of storage compared to the 8922KB or more required for each LoRA adapter, making our approach more computationally efficient. Project Page: https://deepaksridhar.github.io/promptsliders.github.io/

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