U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912984459116544 |
|---|---|
| author | Mitsouras, Ilias Tsonis, Eleftherios Tzouveli, Paraskevi Voulodimos, Athanasios |
| author_facet | Mitsouras, Ilias Tsonis, Eleftherios Tzouveli, Paraskevi Voulodimos, Athanasios |
| contents | Diffusion models have demonstrated remarkable performance in text-to-image synthesis, producing realistic and high resolution images that faithfully adhere to the corresponding text-prompts. Despite their great success, they still fall behind in sketch-to-image synthesis tasks, where in addition to text-prompts, the spatial layout of the generated images has to closely follow the outlines of certain reference sketches. Employing an MLP latent edge predictor to guide the spatial layout of the synthesized image by predicting edge maps at each denoising step has been recently proposed. Despite yielding promising results, the pixel-wise operation of the MLP does not take into account the spatial layout as a whole, and demands numerous denoising iterations to produce satisfactory images, leading to time inefficiency. To this end, we introduce U-Sketch, a framework featuring a U-Net type latent edge predictor, which is capable of efficiently capturing both local and global features, as well as spatial correlations between pixels. Moreover, we propose the addition of a sketch simplification network that offers the user the choice of preprocessing and simplifying input sketches for enhanced outputs. The experimental results, corroborated by user feedback, demonstrate that our proposed U-Net latent edge predictor leads to more realistic results, that are better aligned with the spatial outlines of the reference sketches, while drastically reducing the number of required denoising steps and, consequently, the overall execution time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models Mitsouras, Ilias Tsonis, Eleftherios Tzouveli, Paraskevi Voulodimos, Athanasios Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Diffusion models have demonstrated remarkable performance in text-to-image synthesis, producing realistic and high resolution images that faithfully adhere to the corresponding text-prompts. Despite their great success, they still fall behind in sketch-to-image synthesis tasks, where in addition to text-prompts, the spatial layout of the generated images has to closely follow the outlines of certain reference sketches. Employing an MLP latent edge predictor to guide the spatial layout of the synthesized image by predicting edge maps at each denoising step has been recently proposed. Despite yielding promising results, the pixel-wise operation of the MLP does not take into account the spatial layout as a whole, and demands numerous denoising iterations to produce satisfactory images, leading to time inefficiency. To this end, we introduce U-Sketch, a framework featuring a U-Net type latent edge predictor, which is capable of efficiently capturing both local and global features, as well as spatial correlations between pixels. Moreover, we propose the addition of a sketch simplification network that offers the user the choice of preprocessing and simplifying input sketches for enhanced outputs. The experimental results, corroborated by user feedback, demonstrate that our proposed U-Net latent edge predictor leads to more realistic results, that are better aligned with the spatial outlines of the reference sketches, while drastically reducing the number of required denoising steps and, consequently, the overall execution time. |
| title | U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.18425 |