

license: apache-2.0 language:
This is a model for high-definition image upscaling, trained on Qwen/Qwen-Image-Edit-2511. It is mainly used for losslessly enlarging images to approximately 2K resolution. Intended for use in ComfyUI.
This LoRA works with a modified version of Comfy's Qwen/Qwen-Image-Edit-2511 workflow. The main modification is adding a Qwen/Qwen-Image-Edit-2511 LoRA node connected to the base model.
See the Downloads section above for the modified workflow.
from diffusers import QwenImageEditPipeline
import torch
from PIL import Image
# Load the pipeline
pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511")
pipeline.to(torch.bfloat16)
pipeline.to("cuda")
# Load trained LoRA weights for in-scene editing
pipeline.load_lora_weights("valiantcat/Qwen-Image-Edit-2511-Upscale2K", weight_name="qwen_image_edit_2511_upscale.safetensors")
# Load input image
image = Image.open("./result/test.jpg").convert("RGB")
# Define in-scene editing prompt
prompt = "Upscale this picture to 4K resolution."
# Generate edited image with enhanced scene understanding
inputs = {
"image": image,
"prompt": prompt,
"generator": torch.manual_seed(12345),
"true_cfg_scale": 4.0,
"negative_prompt": " ",
"num_inference_steps": 50,
}
with torch.inference_mode():
output = pipeline(**inputs)
output_image = output.images[0]
output_image.save("edited_image.png")
Upscale this picture to 4K resolution.
There is no fixed trigger word. Specific prompts require further testing.
Weights for this model are available in Safetensors format.
This model was trained by the AI Laboratory of Chongqing Valiant Cat Technology Co., Ltd. (https://vvicat.com/). Business inquiries and collaborations are welcome.
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