# DiCTI: Diffusion-based Clothing Designer via Text-guided Input

arXiv: https://arxiv.org/abs/2407.03901 · HTML: https://mlsanity.com/paper/2407.03901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://mlsanity.com/paper/2403.04279/) (2024)
- [PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer](https://mlsanity.com/paper/1909.06956/) (2019)
- [PISE: Person Image Synthesis and Editing with Decoupled GAN](https://mlsanity.com/paper/2103.04023/) (2021)
- [Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2304.02051/) (2023)
- [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://mlsanity.com/paper/2004.12276/) (2020)
- [Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing](https://mlsanity.com/paper/2104.07021/) (2021)
- [C-VTON: Context-Driven Image-Based Virtual Try-On Network](https://mlsanity.com/paper/2212.04437/) (2022)
- [Compatible and Diverse Fashion Image Inpainting](https://mlsanity.com/paper/1902.01096/) (2019)
- [High Resolution Face Editing with Masked GAN Latent Code Optimization](https://mlsanity.com/paper/2103.11135/) (2021)
- [FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion](https://mlsanity.com/paper/2301.02110/) (2023)
- [Fashion++: Minimal Edits for Outfit Improvement](https://mlsanity.com/paper/1904.09261/) (2019)
- [From Design Draft to Real Attire: Unaligned Fashion Image Translation](https://mlsanity.com/paper/2008.01023/) (2020)
- [GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling](https://mlsanity.com/paper/2210.14145/) (2022)
- [Body Segmentation Using Multi-task Learning](https://mlsanity.com/paper/2212.06550/) (2022)
- [TD-GEM: Text-Driven Garment Editing Mapper](https://mlsanity.com/paper/2305.18120/) (2023)

## Built on by (most cited first, 1 of 1)

- [Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models](https://mlsanity.com/paper/2412.18421/) (2024)

## Abstract (arXiv)

Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry. While numerous methods have been proposed to benefit buyers, particularly in virtual try-on applications, there has been relatively less focus on facilitating fast prototyping for designers and customers seeking to order new designs. To address this gap, we introduce DiCTI (Diffusion-based Clothing Designer via Text-guided Input), a straightforward yet highly effective approach that allows designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of the desired garments as input, DiCTI automatically generates multiple high-resolution, photorealistic images that capture the expressed semantics. By leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI is able to synthesize convincing, high-quality images with varied clothing designs that viably follow the provided text descriptions, while being able to process very diverse and challenging inputs, captured in completely unconstrained settings. We evaluate DiCTI in comprehensive experiments on two different datasets (VITON-HD and Fashionpedia) and in comparison to the state-of-the-art (SoTa). The results of our experiments show that DiCTI convincingly outperforms the SoTA competitor in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings, generated as part of a user study.