Viggle/Viggle-Image-Swap

image-to-image model by Viggle

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Model card & documentation

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Built with Qwen. A LoRA adapter for Qwen/Qwen-Image-2.1. It replaces people in a picture with characters from character sheets. Each person is replaced whole: face, hair, body and clothes. The pose, expression, framing, background and lighting of the picture stay as they were. It samples in 6 steps without classifier-free guidance, or, with --turbo, in 2 steps handed over to Qwen-Image-2.1-Turbo for 6 more: sharper detail. Each row is: the character sheet(s), the source picture, the output. All are made with inference/swap.py at its defaults (seed 0, tone fix on); the only inputs are the sheets, the source and the short WHO text shown. The top image replaces two people at once: --char queen.png woman --char king.png man --left 1 2. One person, letterboxed source: --char astronaut.png dancer Two people in close contact, both replaced: --char flavio.png man --char fleur.png woman --left 1 2 A close-up selfie: --char odessa.png woman…

Viggle/Viggle-Image-Swap on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-11-12T10:25:15.815Z

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Model overview

Viggle/Viggle-Image-Swap is a image-to-image model repository published by Viggle on Hugging Face. The source reports the diffusers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: image-to-image
Library: diffusers
Recent downloads (30 days): 2,584
Cumulative likes: 54
Hugging Face trending score: 48
Reported safetensors parameters: Not reported
Architecture: Not reported
License: other
Access: Not gated by Hugging Face
Created: 2026-10-27T05:10:33.000Z
Last modified: 2026-11-03T22:07:57.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: Qwen/Qwen-Image-2.1

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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