Instructions to use linoyts/Qwen-Image-Edit-Rapid-AIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use linoyts/Qwen-Image-Edit-Rapid-AIO 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("linoyts/Qwen-Image-Edit-Rapid-AIO", 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
𧨠diffusers compatiable transformer weights extracted from Phr00t/Qwen-Image-Edit-Rapid-AIO - V4 For accelerated Qwen Image Edit 2509 inference in 4 steps.
use with diffusers:
import torch
from diffusers.models import QwenImageTransformer2DModel
from diffusers import QwenImageEditPlusPipeline
from diffusers.utils import load_image
transformer = QwenImageTransformer2DModel.from_pretrained("linoyts/Qwen-Image-Edit-Rapid-AIO",
subfolder="transformer",torch_dtype=torch.bfloat16)
pipeline = QwenImageEditPlusPipeline.from_pretrained(
"Qwen/Qwen-Image-Edit-2509",
transformer=transformer,
torch_dtype=torch.bfloat16
)
pipeline.to('cuda')
image1 = load_image("grumpycat.png")
prompt = "turn the cat into an orange cat"
inputs = {
"image": [image1],
"prompt": prompt,
"generator": torch.manual_seed(42),
"true_cfg_scale": 1.0,
"negative_prompt": " ",
"num_inference_steps": 4,
"guidance_scale": 1.0,
"num_images_per_prompt": 1,
}
output = pipeline(**inputs)
output_image = output.images[0]
output_image.save("output_image_edit_plus.png")
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Model tree for linoyts/Qwen-Image-Edit-Rapid-AIO
Base model
Qwen/Qwen-Image-Edit-2509