Fast Qwen Image Edit 2509 with diffusers: using Qwen Rapid AIO for 4-step inference, and Qwen next scene LoRA for cinematic image sequences with natural visual progression from frame to frame

fast_qwen_edit.py

import torch
from diffusers.models import QwenImageTransformer2DModel
from diffusers import QwenImageEditPlusPipeline
from diffusers.utils import load_image

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# load pipeline with Rapid AIO transfromer 
pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", 
                                                transformer= QwenImageTransformer2DModel.from_pretrained("linoyts/Qwen-Image-Edit-Rapid-AIO", 
                                                                                                         subfolder='transformer',
                                                                                                         torch_dtype=dtype,
                                                                                                         device_map='cuda'),torch_dtype=dtype).to(device)
# load next scene LoRA
pipe.load_lora_weights(
        "lovis93/next-scene-qwen-image-lora-2509", 
        weight_name="next-scene_lora-v2-3000.safetensors", adapter_name="next-scene"
    )
pipe.set_adapters(["next-scene"], adapter_weights=[1.])
pipe.fuse_lora(adapter_names=["next-scene"], lora_scale=1.)
pipe.unload_lora_weights()

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).images[0]