FitDiT
F
Fitdit
Overview :
FitDiT aims to address issues of low fidelity and robustness in image-based virtual try-ons. By introducing a clothing texture extractor and employing frequency domain learning alongside an expanded relaxed mask strategy, it significantly enhances the fit and detail representation of virtual try-ons. Its main advantages include the ability to generate realistic and detail-rich clothing images, applicable in various scenarios with high practical value and competitiveness. Specific pricing and market positioning have not been clearly established yet.
Target Users :
The target audience primarily consists of fashion designers, e-commerce platforms, fashion bloggers, and consumers interested in virtual try-ons. FitDiT offers realistic fitting experiences that help users better visualize how clothing fits, enhancing both shopping experiences and design efficiency.
Total Visits: 204
Top Region: US(100.00%)
Website Views : 67.6K
Use Cases
Fashion designers utilize FitDiT for rapid previews of different designs on models, accelerating the design iteration process.
E-commerce platforms integrate FitDiT to allow consumers to virtually try on clothing before making purchases, reducing return rates.
Fashion bloggers use FitDiT to create virtual try-on videos, showcasing more possibilities for clothing combinations.
Features
Utilizes the Diffusion Transformers (DiT) architecture, allocating more parameters and attention to high-resolution features to improve image quality.
Introduces a clothing texture extractor that fine-tunes garment features by evolving clothing priors, allowing for better capture of rich details like stripes, patterns, and texts.
Customizes frequency distance loss for frequency domain learning to enhance high-frequency clothing details.
Employs an expanded relaxed mask strategy to ensure garments fit correctly in length, preventing the generation of clothes that fill the entire masked area during cross-category try-ons.
Surpasses all baseline models in both qualitative and quantitative evaluations, with an inference time of just 4.57 seconds for a single 1024x768 image.
How to Use
1. Visit the online demo site or use Huggingface Space.
2. Upload images of clothing and the target person.
3. Select appropriate settings, such as clothing type and fitting area.
4. Click to begin the try-on process and wait for the model to generate results.
5. Review the generated virtual fitting images for assessment and adjustments.
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