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Context: To circumvent this, LLI-based methods nichol2021glide ; avrahami2022blendedlatent ; ramesh2022hierarchical require the user to explicitly mask a part of the image to be inpainted, and drive the edited image to change in the masked area only, while matching the background of the original image. This approach has provided appealing results, however, the masking procedure is cumbersome, hampering quick and intuitive text-driven editing. Moreover, masking the image content removes important structural information, which is completely ignored in the inpainting process. Therefore, some editing capabilities are out of the inpainting scope, such as modifying the texture of a specific object. In this paper, we introduce an intuitive and powerful textual editingmethod to semantically edit images in pre-trained text-conditioned diffusion models via Prompt-to-Prompt manipulations. To do so, we dive deep into the cross-attention layers and explore their semantic strength as a handle to control the generated image. Specifically, we consider the internal cross-attention maps, which are high-dimensional tensors that bind pixels and tokens extracted from the prompt text. We find that these maps contain rich semantic relations which critically affect the generated image. Our method, described in section 3, enables intuitive text-only editing by controlling the spatial layout corresponding to each word in the user-provided prompt. In this section, we show several applications using this technique. Text-Only Localized Editing.We first demonstrate localized editing by modifying the user-provided prompt without requiring any user-provided mask. In fig. 2, we depict an example where we generate an image using the prompt “lemon cake”. Our method allows us to retain the spatial layout, geometry, and semantics when replacing the word “lemon” with “pumpkin” (top row). Observe that the background is well-preserved, including the top-left lemons transforming into pumpkins. On the other hand, naively feeding the synthesis model with the prompt “pumpkin cake” results in a completely different geometry (3rd row), even when using the same random seed in a deterministic setting (i.e., DDIM song2020denoising ). Our method succeeds even for a challenging prompt such as “pasta cake.” (2nd row) — the generated cake consists of pasta layers with tomato sauce on top. Another example is provided in fig. 5 where we do not inject the attention of the entire prompt but only the attention of a specific word – “butterfly”. This enables the preservation of the original butterfly while changing the rest of the content. Additional results are provided in the appendix (fig. 13). In this work, we uncovered the powerful capabilities of the cross-attention layers within text-to-image diffusion models.We showed that these high-dimensional layers have an interpretable representation of spatial maps that play a key role in tying the words in the text prompt to the spatial layout of the synthesized image.With this observation, we showed how various manipulations of the prompt can directly control attributes in the synthesized image, paving the way to various applications including local and global editing.This work is a first step towards providing users with simple and intuitive means to edit images, leveraging textual semantic power. It enables users to navigate through a semantic, textual, space, which exhibits incremental changes after each step, rather than producing the desired image from scratch after each text manipulation. Our approach constitutes an intuitive image editing interface through editing only the textual prompt, therefore called Prompt-to-Prompt. This method enables various editing tasks, which are challenging otherwise, and does not requires model training, fine-tuning, extra data, or optimization. Throughout our analysis, we discover even more control over the generation process, recognizing a trade-off between the fidelity to the edited prompt and the source image. We even demonstrate that our method can be applied to real images by using an existing inversion process. Our experiments and numerous results show that our method enables seamless editing in an intuitive text-based manner over extremely diverse images. While most works that require only text (i.e., no masks) are limited to global editing crowson2022vqgan ; kwon2021clipstyler ,Bar-Tal et al. bar2022text2live  proposed a text-based localized editing technique without using any mask, showing impressive results.Yet, their techniques mainly allow changing textures, but not modifying complex structures, such as changing a bicycle to a car.Moreover, unlike our method, their approach requires training a network for each input. Unlike previous works, our method requires textual input only, by using the spatial information from the internal layers of the generative model itself. This offers the user a much more intuitive editing experience of modifying local or global details by merely modifying the text prompt.

Question: Did the method proposed in this paper perform on par with or better than the state-of-the-art methods that require users to provide spatial masks for editing?
Yes their method did perform better than mask editing methods, as authors demonstrated by examples that their method is more intuitive for users using only prompt, and doesn't require to explicitly mask parts of the image which results to remove important structural information and doesn't modify complex structures information. And their work enables local or global modifications as well and besides their method doesn't require a training network.