Create your own QR code !!

3 min readNov 28, 2023

The topic I’m going to introduce today is creating your unique QR code that exists nowhere else in the world.

Let’s goooooooooooooooo ! 🔥🔥🔥🔥🔥

⭐Step 1:⭐

Let’s quietly (-q) install the required libraries first.

pip install -q qrcode diffusers transformers accelerate xformers

⭐Step 2:⭐

Import the installed libraries.

import qrcode
import torch
from PIL import Image
from diffusers import StableDiffusionPipeline, StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler, DPMSolverMultistepScheduler
from diffusers.utils import load_image
import matplotlib.pyplot as plt

⭐Step 3:⭐

Testing the generation of QR code.

qr = qrcode.QRCode(
    version=1, # 1-40

    # Low, Medium High (qr code quality)
    error_correction=qrcode.constants.ERROR_CORRECT_H, # L, M, H
    box_size=17,
    border=4,
)
qr.add_data("https://medium.com/@hichengkang")
qr.make()

img = qr.make_image(fill_color="black", back_color="white")

print(img.size)

img.save("./my_qrcode.png")
print("Finish !")

⭐Step 4:⭐

model setting.

# popular
# "runwayml/stable-diffusion-v1-5",

# animate
# "stablediffusionapi/anything-v5",

# others : 
# huggingface.com

sd_pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    safety_checker=None,
    torch_dtype=torch.float16)

# create new scheduler
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config, use_karras=True, algorithm_type="sde-dpmsolver++")

# turn off the cpu
sd_pipe.enable_model_cpu_offload()

⭐Step 5:⭐

generation the Image.

# Similarly stable diffusion

image = sd_pipe(
    prompt="cyberpunk city",
    negative_prompt="ugly, disfigured, low quality, blurry, nsfw",

    # set image size
    width=768,
    height=768,

    # set seed
    generator=torch.manual_seed(-1),
)

image.images[0].save("init.png")

image.images[0]

⭐Step 6:⭐

Fictionalize Step 1 ~ 5

import qrcode
import torch
from PIL import Image
from diffusers import StableDiffusionPipeline, StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler, DPMSolverMultistepScheduler
from diffusers.utils import load_image
import matplotlib.pyplot as plt

controlnet = ControlNetModel.from_pretrained(
    "DionTimmer/controlnet_qrcode-control_v1p_sd15",
    torch_dtype=torch.float16)

pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    controlnet=controlnet,
    safety_checker=None,
    torch_dtype=torch.float16)

# pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras=True, algorithm_type="sde-dpmsolver++")
pipe.enable_model_cpu_offload()

resize the image

def resize_for_condition_image(input_image: Image, resolution: int):
    input_image = input_image.convert("RGB")
    W, H = input_image.size
    k = float(resolution) / min(H, W)
    H *= k
    W *= k
    H = int(round(H / 64.0)) * 64
    W = int(round(W / 64.0)) * 64
    img = input_image.resize((W, H), resample=Image.LANCZOS)
    return img

Return the result

def make_qr_art(
    url,
    init_image,
    prompt,
    guidance_scale=10,
    controlnet_conditioning_scale=1.4,
    seed=-1,
    strength=0.9,
    num_inference_steps=40):

    qr = qrcode.QRCode(
        version=1, # 1 - 40
        error_correction=qrcode.constants.ERROR_CORRECT_H,
        box_size=17,
        border=4,
    )
    qr.add_data(url)
    qr.make()

    qr_image = qr.make_image(fill_color="black", back_color="white")

    qr_image_resized = resize_for_condition_image(qr_image, 768)
    init_image_resized = resize_for_condition_image(init_image, 768)

    image = pipe(
        prompt=prompt,
        negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
        image=init_image_resized,
        control_image=qr_image_resized,
        width=768,
        height=768,
        guidance_scale=guidance_scale, # 20, 7.5
        controlnet_conditioning_scale=controlnet_conditioning_scale, # 1.5. 1.3
        generator=torch.manual_seed(seed),
        strength=strength, # 0.9
        num_inference_steps=num_inference_steps,  # 150, 40
    )

    return image.images[0]

⭐Final Step:⭐

Let’s generate the QR code image!!!

image: Background image
control_image: QR code
guidance_scale: How well to follow the prompt
controlnet_conditioning_scale:How well to preserve the QR code
strength: How well to preserve the QR code
num_inference_steps: Quality of the generated image, Diffusion denoising steps”

[ =====>More detail <=====]

make_qr_art(
    url="https://medium.com/@hichengkang",
    init_image=load_image("init.png"),
    prompt="cyberpunk city",
    guidance_scale=7.5,
    controlnet_conditioning_scale=1.5,
    seed=-1,
    strength=0.9,
    num_inference_steps=50)