import os
os.environ['HF_HUB_DISABLE_SYMLINKS_WARNING'] = '1'

from diffusers import QwenImageEditPipeline
import torch
from PIL import Image
import glob

def find_image():
    script_dir = os.path.dirname(os.path.abspath(__file__))
    extensions = ['*.png', '*.jpg', '*.jpeg', '*.webp', '*.bmp']
    for ext in extensions:
        files = glob.glob(os.path.join(script_dir, ext))
        if files:
            return files[0]
    return None

input_image_path = find_image()
if not input_image_path:
    print("Error: No image found in script directory")
    exit(1)

print(f"Loading image: {os.path.basename(input_image_path)}")
image = Image.open(input_image_path).convert("RGB")

max_size = 768
if max(image.size) > max_size:
    image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)

print("Loading model...")
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32

pipeline = QwenImageEditPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit",
    torch_dtype=dtype
)

if torch.cuda.is_available():
    pipeline.enable_attention_slicing()
    pipeline.enable_vae_slicing()

pipeline.to(device)

print("Loading LoRA...")
pipeline.load_lora_weights(
    "Jonny001/Qwen-Image-Edit-Remove-Clothes",
    weight_name="qwen-edit-remove-clothes.safetensors"
)

print("Generating...")

inputs = {
    "image": image,
    "prompt": "remove all the clothes of the figure in the picture",
    "generator": torch.manual_seed(12345),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 50,
}

with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_path = "result.png"
    output_image.save(output_path)
    print(f"Saved: {output_path}")