Install required packages:

pip install torch torchvision diffusers transformers accelerate
Install the latest diffusers from GitHub:

pip install git+https://github.com/huggingface/diffusers

🧪 Usage

🔧 Qwen-Image-Edit Initialization

python

from diffusers import QwenImageEditPipeline
import torch
from PIL import Image

# Load the pipeline
pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509")
pipeline.to(torch.bfloat16)
pipeline.to("cuda")

🔌 Load LoRA Weights

python

# Load trained LoRA weights for in-scene editing
pipeline.load_lora_weights("flymy-ai/qwen-image-edit-2509-inscene-lora", weight_name="pytorch_lora_weights.safetensors")

🎨 Edit Image with Qwen-Image-Edit Inscene LoRA

python

# Load input image
image = Image.open("./assets/qie2_input.jpg").convert("RGB")

# Define in-scene editing prompt
prompt = "Make a shot in the same scene of the left hand securing the edge of the cutting board while the right hand tilts it, causing the chopped tomatoes to slide off into the pan, camera angle shifts slightly to the left to center more on the pan."

# Generate edited image with enhanced scene understanding
inputs = {
    "image": image,
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "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")

🖼️ Sample Output - Qwen-Image-Edit Inscene

Input Image:

Prompt: "Make a shot in the same scene of the bottle being tilted further, zoom in slightly to capture the wine pouring into the glass as the level rises and its color becomes more pronounced, shift focus to the man's facial expression as he concentrates on the pouring, keeping the candle steady in the background"

Output with Inscene LoRA:

Workflow Features

✅ Pre-configured for Qwen-Image-Edit + Inscene LoRA inference
✅ Optimized settings for in-scene editing quality
✅ Enhanced spatial understanding and scene coherence
✅ Easy prompt and parameter adjustment
✅ Compatible with various input image types

🎯 What is Inscene LoRA?

This LoRA model is specifically trained to enhance Qwen-Image-Edit's ability to perform in-scene image editing. It focuses on:

Scene Coherence: Maintaining logical spatial relationships within the scene
Object Positioning: Better understanding of object placement and movement
Camera Perspective: Improved handling of viewpoint changes and camera movements
Action Sequences: Enhanced ability to depict sequential actions within the same scene
Contextual Editing: Preserving scene context while making targeted modifications

🔧 Training Information

This LoRA model was trained using the FlyMy.AI LoRA Trainer with:

Base Model: Qwen/Qwen-Image-Edit
Training Focus: In-scene image editing and spatial understanding
Dataset: Curated collection of scene-based editing examples (InScene dataset)
Optimization: Low-rank adaptation for efficient fine-tuning

📊 Model Specifications

Model Type: LoRA (Low-Rank Adaptation)
Base Model: Qwen/Qwen-Image-Edit
File Format: SafeTensors (.safetensors)
Specialization: In-scene image editing
Training Framework: Diffusers + Accelerate
Memory Efficient: Optimized for consumer GPUs

🤝 Support

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📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.