Naari Jewelry VTON (Virtual Try-On)
A fine-tuned Stable Diffusion Inpainting model for virtual jewelry try-on. This model enables users to virtually try on necklaces and other jewelry items on person images.
Model Description
Naari Jewelry VTON is designed for the jewelry virtual try-on task. It uses inpainting techniques to seamlessly place jewelry items onto images of people, creating realistic visualizations of how jewelry would look when worn.
Key Features
- Virtual try-on for necklaces and jewelry
- Based on Stable Diffusion Inpainting architecture
- Trained on jewelry segmentation datasets
- Supports mask-guided inpainting
Intended Uses
- E-commerce: Allow customers to virtually try jewelry before purchasing
- Fashion Apps: Enable jewelry visualization in mobile applications
- Content Creation: Generate realistic jewelry wearing images
How to Use
from diffusers import StableDiffusionInpaintPipeline
import torch
from PIL import Image
# Load the model
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"GaneshGowri/naari-jewelry-vton",
torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
# Load your images
image = Image.open("person_image.png").convert("RGB")
mask = Image.open("neck_mask.png").convert("RGB")
# Generate
result = pipe(
prompt="elegant gold necklace on person",
image=image,
mask_image=mask,
num_inference_steps=50,
guidance_scale=7.5
).images[0]
result.save("result.png")
Training Details
- Base Model: runwayml/stable-diffusion-inpainting
- Training Data: VITON-HD dataset + Roboflow Necklace Segmentation
- Training Platform: Kaggle (T4x2 GPU)
- Framework: Hugging Face Diffusers
Limitations
- Best results with front-facing portrait images
- Mask quality affects output quality
- May require prompt tuning for specific jewelry types
Citation
@misc{naari-jewelry-vton,
author = {GaneshGowri},
title = {Naari Jewelry VTON},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/GaneshGowri/naari-jewelry-vton}
}
License
This model is released under the Apache 2.0 license.
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runwayml/stable-diffusion-inpainting