MODEL PROFILE

Qwen-Image-Edit-2511-Unblur-Upscale

prithivMLmods · Qwen-Image-Edit-2511-Unblur-Upscale

> [!note] > This adapter was experimental and trained to recover facial patterns from partially blurred images. The base model produces better overall quality, but it does not preserve those patterns. > [!note] > Note: `prithivMLmods/Qwen-Image-Edit-2511-Unblur-Upscale` may make mistakes. Please check the Important Info section. > **Qwen-Image-Edit-2511-Unblur-Upscale** is an adapter LoRA developed for Qwen’s **Qwen-Image-Edit-2511** image-to-image model, specifically designed to unblur and upscale images to high resolution. The model enhances image clarity by reducing blur, restoring fine details, and improving overall sharpness while preserving natural textures and realistic colors. It maintains strong visual consistency across lighting, edges, and color tones, ensuring the upscaled output looks clean, natural, and photographically authentic rather than over-processed. This makes it well suited for restoring low-quality, blurry, or low-resolution images into sharp, high-fidelity results. Above are example outputs demonstrating the model’s ability to deliver clear, high-resolution, and visually cohesive enhancements.

Image editinglicense:apache-2.0
Context windowNot disclosed
Input → OutputImage → Image
Model weightsNot verified
Profile updated2026/10/30 06:56:28
OVERVIEW

Model overview

> [!note] > This adapter was experimental and trained to recover facial patterns from partially blurred images. The base model produces better overall quality, but it does not preserve those patterns. > [!note] > Note: `prithivMLmods/Qwen-Image-Edit-2511-Unblur-Upscale` may make mistakes. Please check the Important Info section. > **Qwen-Image-Edit-2511-Unblur-Upscale** is an adapter LoRA developed for Qwen’s **Qwen-Image-Edit-2511** image-to-image model, specifically designed to unblur and upscale images to high resolution. The model enhances image clarity by reducing blur, restoring fine details, and improving overall sharpness while preserving natural textures and realistic colors. It maintains strong visual consistency across lighting, edges, and color tones, ensuring the upscaled output looks clean, natural, and photographically authentic rather than over-processed. This makes it well suited for restoring low-quality, blurry, or low-resolution images into sharp, high-fidelity results. Above are example outputs demonstrating the model’s ability to deliver clear, high-resolution, and visually cohesive enhancements.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。