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: prithivMLmods/Qwen-Image-Edit-2511-Unblur-Upscale may make mistakes. Please check the Important Info section.

Qwen-Image-Edit-2511-Unblur-Upscale

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.

Quick Start with Diffusers

Compatible with versions 2509 and 2511.

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image

# Switch to "mps" for Apple devices
pipe = DiffusionPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit-2511",
    dtype=torch.bfloat16,
    device_map="cuda"
)

pipe.load_lora_weights("prithivMLmods/Qwen-Image-Edit-2511-Unblur-Upscale")

prompt = "unblur and upscale"

input_image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
)

image = pipe(image=input_image, prompt=prompt).images[0]

Trained on ModelScope.cn

Recommended: Qwen-Image-Edit-Unblur-Upscale_20.safetensors / Qwen-Image-Edit-Unblur-Upscale_15.safetensors

Trigger Prompt

Use the following prompt to apply the unblur and upscale effect:

unblur and upscale

Download Model

You can download the model files from the Files & versions tab: Download