Qwen Image Edit 2511 4bit EU AI Act Compliance Profile

This is an NF4 quantized model of Qwen-image-edit-2511 so it can run on GPUs using less than 20GB VRAM. You can run it on lower VRAM like 16GB. There were other NF4 models but they made the mistake of blindly quantizing all layers in the transformer. This one does not. We retain some layers at full precision in order to ensure that we get quality output.

You can use the original Qwen-Image-Edit parameters.

Model tested: Working perfectly even with 10 steps. Contact: support@JustLab.ai for commercial support, modifications and licensing.

sample code

import os
from PIL import Image
import torch

from diffusers import QwenImageEditPlusPipeline

model_path = "ovedrive/Qwen-Image-Edit-2511-4bit"
pipeline = QwenImageEditPlusPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16)
print("pipeline loaded") # not true but whatever. do not move to cuda

pipeline.set_progress_bar_config(disable=None)
pipeline.enable_model_cpu_offload() #if you have enough VRAM replace this line with `pipeline.to("cuda")` which is 20GB VRAM
image = Image.open("./example.png").convert("RGB")
prompt = "Remove the lady head with white hair"
inputs = {
    "image": image,
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 20, # even 10 steps should be enough in many cases
}

with torch.inference_mode():
  output = pipeline(**inputs)

output_image = output.images[0]
output_image.save("output_image_edit.png")
print("image saved at", os.path.abspath("output_image_edit.png"))

FAQ

1. Does it support comfyui? Someone else has done an integration here https://github.com/mengqin/ComfyUI-UnetBnbModelLoader
2. Is there a GGUF etc ? Not here. The motivation for this is to use Diffusers and get the best performance without relying on 3rd party toolkits and transformation wich break the pipeline.
3. How to use speed up lora? See 1 above. I believe the same lor

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