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FASHN VTON v1.5

open-source virtual try-on model

Details

External ID
46797100
Source
HN
Company
—
Product
FASHN VTON v1.5
Website domain
github.com
Launched
Jan. 28, 2026
Cohort
—
Upvotes
6
Upvotes percentile
0.2549407114624506
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

We’re open-sourcing FASHN VTON v1.5, a production-grade virtual try-on model.Given an image of a person and an image of a garment, the model generates a photorealistic try-on result.Model specs: - It operates directly in pixel space (no VAE) - Supports maskless inference by default, and was trained from scratch. - ~972M parameters, runs on consumer GPUs - Can run in ~5 seconds on H100We built this as a focused alternative to large generalist models, with the goal of making a production-grade, specialized virtual try-on model.We’re releasing the weights, inference code, and architecture details under an Apache-2.0 license.Would love to hear your thoughts!

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Retail & commerce
Function
Content generation
Audience
B2B
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
virtual try-on for fashion
Manually corrected
False

Could you build this?

No FASHN VTON v1.5 is a state-of-the-art generative computer vision model operating in pixel space for photorealistic virtual try-on, requiring massive datasets and cluster training that AI assistance cannot synthesize.

What it would actually take: Developing this requires training a deep diffusion model on hundreds of thousands of paired high-resolution garment and human model images across diverse poses. Architecture involves specialized cross-attention conditioning, deformation warping networks, and extensive GPU cluster compute (dozens of H100s/A100s for weeks). Requires world-class generative computer vision researchers and substantial ML infrastructure funding.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 47 launches clear the similarity bar, closest 8 shown.

Attention rank: #45 of 48 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 83 days after the earliest competitor.

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