Image-to-Image
Diffusers
Safetensors
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QwenImageEditPlusPipeline
image-generation
image-editing
qwen-image
sdnq
Instructions to use OzzyGT/Qwen_Image_Edit_2511_sdnq_dynamic_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/Qwen_Image_Edit_2511_sdnq_dynamic_4bit with Diffusers:
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("OzzyGT/Qwen_Image_Edit_2511_sdnq_dynamic_4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" 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] - Notebooks
- Google Colab
- Kaggle
Qwen Image Edit 2511 SDNQ Dynamic INT4
This is an int4 quantized version of Qwen/Qwen-Image-Edit-2511 using SDNQ (SD.Next Quantization) with the dynamic option and Hadamard Rotation.
Note: You need SDNQ v0.2.0 or superior
Usage
You can find ready-to-use scripts in the diffusers-recipes repository.
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Model tree for OzzyGT/Qwen_Image_Edit_2511_sdnq_dynamic_4bit
Base model
Qwen/Qwen-Image-Edit-2511