Try_On_Qwen_Edit_Lora_Alpha

Qwen-Edit Try-On Lora (Alpha version)

This is an alpha version Lora that is trained to perform multi-reference try-on editing with Qwen Image Edit.

• Model type: LoRA for Qwen Image Edit
• Use case: Multi-reference clothing try-on
• Input format: One top image (subject) + multiple bottom images (garments). For best results, use garment images that are high quality, and full body-shots for the subject.
• Output format: Stylized subject wearing all garments from bottom row

find us on Discord: https://discord.gg/UXN7zFuxbk

Examples:

Usage

For usage within ComfyUI using the provided workflow. Recommended settings are 30 steps, with 2.5 CFG using Euler sampler and Simple scheduler.

:warning: Warning: Both the 4-steps and 8-steps lightning lora’s did not perform well with the Try-On lora. I would recommend using it without low step lora's for now.

832x1248 is the recommended resolution used. If you would like to use a different one please use one of the reccomended resolutions below provided by Qwen:

(672, 1568),
(688, 1504),
(720, 1456),
(752, 1392),
(800, 1328),
(832, 1248),
(880, 1184),
(944, 1104),
(1024, 1024),
(1104, 944),
(1184, 880),
(1248, 832),
(1328, 800),
(1392, 752),
(1456, 720),
(1504, 688),
(1568, 672),

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Limitations

• Only trained/tested on multi-garment try-on tasks.
• May not generalize to unrelated image editing tasks.
• Requires careful prompt alignment (see below).
• Struggles with 5 or more garments (more training required)

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Example Prompts

• For Woman:
  "Style the woman in the top of the image, with every article of clothing on the bottom"

• For Man:
  "Style the man in the top of the image, with every article of clothing on the bottom"

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Training Details

• Base Model: Qwen Image Edit
• Approach: LoRA training with Ostris ai toolkit

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Donations

We’re working toward a scaled-up V2 of this project, with expanded capabilities and larger dataset/compute requirements. Your support helps make this possible:

• Ko-fi
• BTC Address: bc1qnqa7ufcw4f7cp8pzahd8jlut0ve2trq4c7t75h
• ETH Address: 0x596A4Bd554337d9D1FfbA6AA346f847CE942f945