  Conventional Text-guided single-image editing approaches require a two-step
process, including fine-tuning the target text embedding for over 1K iterations
and the generative model for another 1.5K iterations. Although it ensures that
the resulting image closely aligns with both the input image and the target
text, this process often requires 7 minutes per image, posing a challenge for
practical application due to its time-intensive nature. To address this
bottleneck, we introduce FastEdit, a fast text-guided single-image editing
method with semantic-aware diffusion fine-tuning, dramatically accelerating the
editing process to only 17 seconds. FastEdit streamlines the generative model's
fine-tuning phase, reducing it from 1.5K to a mere 50 iterations. For diffusion
fine-tuning, we adopt certain time step values based on the semantic
discrepancy between the input image and target text. Furthermore, FastEdit
circumvents the initial fine-tuning step by utilizing an image-to-image model
that conditions on the feature space, rather than the text embedding space. It
can effectively align the target text prompt and input image within the same
feature space and save substantial processing time. Additionally, we apply the
parameter-efficient fine-tuning technique LoRA to U-net. With LoRA, FastEdit
minimizes the model's trainable parameters to only 0.37\% of the original size.
At the same time, we can achieve comparable editing outcomes with significantly
reduced computational overhead. We conduct extensive experiments to validate
the editing performance of our approach and show promising editing
capabilities, including content addition, style transfer, background
replacement, and posture manipulation, etc.
