Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Qwen-Image-Edit LoRA Adapter
+
+This repository contains LoRA weights for **Qwen-Image-Edit**, fine-tuned for instruction-based image editing tasks.
+
+## Model Details
+- **Base Model:** [Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) (or your specific base model)
+- **Training:** Fine-tuned using PEFT/LoRA.
+
+## Usage
+
+To use this LoRA, you need to load the base `QwenImageEditPlusPipeline` and apply the adapter weights. 
+
+Since the LoRA keys trained via PEFT often differ from what Diffusers expects, the script below includes an **automatic conversion step** to ensure the weights load correctly.
+
+### Python Inference Script
+
+You can run this script to edit a single image.
+
+```python
+import os
+import torch
+from PIL import Image
+from diffusers import QwenImageEditPlusPipeline
+from safetensors.torch import load_file, save_file
+
+def load_lora_with_conversion(pipeline, lora_folder_path, weight_name="adapter_model.safetensors"):
+    """
+    Automatically converts PEFT LoRA keys to Diffusers format if needed and loads them.
+    """
+    # Define paths
+    original_weights_path = os.path.join(lora_folder_path, weight_name)
+    converted_weights_path = os.path.join(lora_folder_path, "adapter_model_converted.safetensors")
+    
+    # Check if conversion is needed
+    if not os.path.exists(converted_weights_path):
+        print(f"⚠️ Converted weights not found. Converting {original_weights_path}...")
+        
+        if not os.path.exists(original_weights_path):
+            raise FileNotFoundError(f"Cannot find LoRA weights at {original_weights_path}")
+
+        state_dict = load_file(original_weights_path)
+        new_state_dict = {}
+        
+        # Conversion logic: replace 'base_model.model' with 'transformer'
+        for key, value in state_dict.items():
+            new_key = key.replace("base_model.model", "transformer")
+            new_state_dict[new_key] = value
+            
+        save_file(new_state_dict, converted_weights_path)
+        print(f"✅ Conversion saved to {converted_weights_path}")
+    else:
+        print(f"✅ Found converted weights at {converted_weights_path}")
+
+    # Load the converted LoRA
+    pipeline.load_lora_weights(
+        lora_folder_path,
+        weight_name="adapter_model_converted.safetensors",
+        adapter_name="lora",
+    )
+    pipeline.set_adapters(["lora"], adapter_weights=[1.0])
+    print("🚀 LoRA loaded and active.")
+
+def main():
+    # --- Configuration ---
+    # 1. Path to the base model (Local path or HuggingFace ID)
+    base_model_path = "Qwen/Qwen-Image-Edit-2509" # Replace with your local path if needed
+    
+    # 2. Path to THIS LoRA folder (where you downloaded this repo)
+    lora_path = "./" # Current directory if you cloned the repo
+    
+    # 3. Input Image and Prompt
+    image_path = "test_image.jpg" # Replace with your image path
+    prompt = "remove the dog and replace it with a cat"
+    output_path = "result.png"
+    # ---------------------
+
+    # Load Pipeline
+    print(f"Loading base model from: {base_model_path}")
+    pipeline = QwenImageEditPlusPipeline.from_pretrained(
+        base_model_path,
+        torch_dtype=torch.bfloat16
+    )
+    pipeline.to("cuda")
+
+    # Load LoRA
+    try:
+        load_lora_with_conversion(pipeline, lora_path)
+    except Exception as e:
+        print(f"Error loading LoRA: {e}")
+        return
+
+    # Load Image
+    if not os.path.exists(image_path):
+        print(f"Error: Image not found at {image_path}")
+        return
+    
+    original_image = Image.open(image_path).convert("RGB")
+
+    # Inference
+    print("🎨 Generating edit...")
+    inputs = {
+        "image": original_image,
+        "prompt": prompt,
+        "generator": torch.manual_seed(42), # Fixed seed for reproducibility
+        "true_cfg_scale": 4.0,    # Recommended for Qwen-Edit
+        "guidance_scale": 1.0,
+        "negative_prompt": " ",
+        "num_inference_steps": 40,
+    }
+
+    with torch.inference_mode():
+        output = pipeline(**inputs)
+        output_image = output.images[0]
+        output_image.save(output_path)
+        print(f"✅ Image saved to {output_path}")
+
+if __name__ == "__main__":
+    main()
+