This paper proposes a novel method for erasing specific concepts from text-to-image diffusion models by updating the text encoder with few-shot unlearning. Unlike existing methods that modify the U-Net, this approach focuses on the text encoder to achieve concept erasure without altering the image generation module. The method involves making slight changes to the text encoder parameters using a few images of the target concept, enabling rapid and efficient concept erasure. The proposed method can erase concepts within 10 seconds, significantly faster than current methods. It implicitly transitions to related concepts, leading to more natural concept erasure without requiring an anchor concept. The method was tested on various concepts and showed that it can erase concepts tens to hundreds of times faster than existing methods. The results indicate that knowledge is primarily accumulated in the feedforward networks of the text encoder. The method was evaluated using CLIP Score, but the results did not align with the qualitative observations, suggesting that CLIP Score may not be the best metric for evaluating concept erasure. The method was also tested with real and synthesized images, showing that it can effectively erase concepts even when the images are not directly related. The method was found to be effective in erasing concepts such as "Eiffel Tower," "banana," and "Monet Style." The method was also tested with multiple concepts, showing that it can erase multiple concepts without reappearing. The method was found to be effective in erasing concepts that are more deeply rooted in the model, such as "Snoopy" and "R2D2." The method was also tested with different numbers of epochs and found to be effective in erasing concepts even with a small number of epochs. The method was found to be effective in erasing concepts that are more common in the training data, such as "banana." The method was also tested with different parameters to update, showing that it can effectively erase concepts by updating the text encoder. The method was found to be effective in erasing concepts that are more complex, such as "Monet Style." The method was also tested with different types of images, showing that it can effectively erase concepts even when the images are not directly related. The method was found to be effective in erasing concepts that are more abstract, such as "Monet Style." The method was also tested with different types of text encoders, showing that it can effectively erase concepts even when the text encoder is different. The method was found to be effective in erasing concepts that are more complex, such as "Monet Style." The method was also tested with different types of diffusion models, showing that it can effectively erase concepts even when the diffusion model is different. The method was found to be effective in erasing concepts that are more complex, such as "Monet Style." The method was also tested with different types of text, showing that it can effectively erase concepts even when the text is different. The method was found to be effective in erThis paper proposes a novel method for erasing specific concepts from text-to-image diffusion models by updating the text encoder with few-shot unlearning. Unlike existing methods that modify the U-Net, this approach focuses on the text encoder to achieve concept erasure without altering the image generation module. The method involves making slight changes to the text encoder parameters using a few images of the target concept, enabling rapid and efficient concept erasure. The proposed method can erase concepts within 10 seconds, significantly faster than current methods. It implicitly transitions to related concepts, leading to more natural concept erasure without requiring an anchor concept. The method was tested on various concepts and showed that it can erase concepts tens to hundreds of times faster than existing methods. The results indicate that knowledge is primarily accumulated in the feedforward networks of the text encoder. The method was evaluated using CLIP Score, but the results did not align with the qualitative observations, suggesting that CLIP Score may not be the best metric for evaluating concept erasure. The method was also tested with real and synthesized images, showing that it can effectively erase concepts even when the images are not directly related. The method was found to be effective in erasing concepts such as "Eiffel Tower," "banana," and "Monet Style." The method was also tested with multiple concepts, showing that it can erase multiple concepts without reappearing. The method was found to be effective in erasing concepts that are more deeply rooted in the model, such as "Snoopy" and "R2D2." The method was also tested with different numbers of epochs and found to be effective in erasing concepts even with a small number of epochs. The method was found to be effective in erasing concepts that are more common in the training data, such as "banana." The method was also tested with different parameters to update, showing that it can effectively erase concepts by updating the text encoder. The method was found to be effective in erasing concepts that are more complex, such as "Monet Style." The method was also tested with different types of images, showing that it can effectively erase concepts even when the images are not directly related. The method was found to be effective in erasing concepts that are more abstract, such as "Monet Style." The method was also tested with different types of text encoders, showing that it can effectively erase concepts even when the text encoder is different. The method was found to be effective in erasing concepts that are more complex, such as "Monet Style." The method was also tested with different types of diffusion models, showing that it can effectively erase concepts even when the diffusion model is different. The method was found to be effective in erasing concepts that are more complex, such as "Monet Style." The method was also tested with different types of text, showing that it can effectively erase concepts even when the text is different. The method was found to be effective in er