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GenQR.py
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GenQR.py
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import os
import qrcode
import requests
from PIL import Image
import torch
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
# Generate a QR code for a given text or URL with the highest error correction level
def generate_qr(data, id):
filename = f"{id}-qrcode.png"
qr = qrcode.QRCode(version=1,
error_correction=qrcode.constants.ERROR_CORRECT_H,
box_size=10,
border=4)
qr.add_data(data)
qr.make(fit=True)
# Create an image from the QR code
qr_image = qr.make_image(fill_color="black", back_color="white")
# Save the image file as PNG format
qr_image.save(filename, "PNG")
# Function to resize an image to a given resolution without adding padding
def resize_image(input_image: Image, resolution: int) -> Image:
input_image = input_image.convert("RGB")
resized_image = input_image.resize((resolution, resolution), resample=Image.LANCZOS)
return resized_image
# Function to download an image from a URL and save it as a PNG file
def download_image(image_url, save_path):
try:
response = requests.get(image_url, stream=True)
response.raise_for_status()
with open(save_path, 'wb') as file:
for chunk in response.iter_content(chunk_size=8192):
file.write(chunk)
# Open the downloaded image using PIL
image = Image.open(save_path)
# Save the image as PNG format
image.save(save_path, "PNG")
return True
except requests.exceptions.RequestException as e:
print(f"Error downloading image: {e}")
return False
# Function to download the model and get the pipeline
def get_pipeline():
try:
# Load the ControlNet model from a pretrained checkpoint
controlnet = ControlNetModel.from_pretrained(
"DionTimmer/controlnet_qrcode-control_v11p_sd21",
torch_dtype=torch.float16)
# Create a StableDiffusionControlNetImg2ImgPipeline with the loaded ControlNet model
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16)
# Enable memory-efficient attention for the pipeline
pipe.enable_xformers_memory_efficient_attention()
# Set the scheduler for the pipeline to DDIMScheduler with its current configuration
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
# Enable CPU offload for the model
pipe.enable_model_cpu_offload()
return pipe
except Exception as e:
print(f"Error initializing pipeline: {e}")
return None
# Generate QR code art using the provided inputs
def generate_qr_art(id, url, reference_image_url, prompt, pipe):
try:
# Generate the QR code image
generate_qr(url, id)
# Download the reference image
reference_image_path = f"{id}_reference.png"
if not download_image(reference_image_url, reference_image_path):
return False
# Load the QR code image from local storage
source_image_path = f"{id}-qrcode.png"
source_image = Image.open(source_image_path)
source_image = resize_image(source_image, 768)
# Load the initial image
init_image = Image.open(reference_image_path)
init_image = resize_image(init_image, 768)
generator = torch.manual_seed(123121231)
# Generate the image using the pipeline
image = pipe(
prompt=prompt,
negative_prompt="ugly, disfigured, low quality, blurry",
image=source_image,
control_image=init_image,
width=768,
height=768,
guidance_scale=7.5,
controlnet_conditioning_scale=1.5,
generator=generator,
strength=0.9,
num_inference_steps=150
)
# Save the generated image
output_path = "output.png"
image.images[0].save(output_path)
# Clean up temporary files
os.remove(source_image_path)
os.remove(reference_image_path)
return True
except Exception as e:
print(f"Error generating QR art: {e}")
return False