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Generating Stylized QR Art with Stable Diffusion & ControlNet

Β· 8 min read
Serhii Hrekov
Serhii Hrekov
software engineer, creator, artist, programmer, projects founder

Gone are the days of boring black-and-white squares! With the advent of advanced AI image generation models like Stable Diffusion, we can now create QR codes that are not only scannable but are also stunning works of art. This guide will walk you through the process of generating stylized QR codes that seamlessly blend into captivating images.

1. The Core Idea: ControlNet for Structure​

Regular Stable Diffusion struggles to maintain the intricate grid pattern of a QR code. This is where ControlNet comes in [5.1].

  • ControlNet: An add-on to Stable Diffusion that allows you to "control" the output image's composition using an input image (like our QR code) as a structural guide. It ensures the QR code's pattern remains intact and scannable while the AI stylizes the rest of the image [5.2].

2. The Workflow​

  1. Generate a High-Quality QR Code: Use qrcode (or segno) to create a standard, high-error-correction QR code.
  2. Choose a Base Image (Optional but Recommended): A simple background image or texture to guide the AI's composition.
  3. Craft Your Prompt: Describe the desired artistic style, subject, and scene for the AI.
  4. Use ControlNet: Feed the QR code as an input to ControlNet, allowing Stable Diffusion to generate an image that incorporates the QR's structure.

3. Setting Up Your Environment (Local or Cloud)​

Running Stable Diffusion and ControlNet locally requires a powerful GPU (e.g., NVIDIA with CUDA support). For many, using cloud-based platforms like Google Colab (with a GPU runtime) or specialized AI art services is more accessible.

# If running locally (requires CUDA-compatible GPU)
pip install diffusers transformers accelerate torch invisible-watermark
# For ControlNet, you'll also need the specific ControlNet models,
# often downloaded separately or managed by a UI like Automatic1111/ComfyUI.
# Example: "control_v11f1p_sd15_qrcode" model

4. Code Example: Conceptual Approach (using diffusers)​

Note: A direct, runnable diffusers script for QR ControlNet is complex due to model loading and specific ControlNet pipeline usage. This conceptual example outlines the steps you'd take, often abstracted by UI tools like Automatic1111 or ComfyUI, or specific Colab notebooks.

import qrcode
from PIL import Image
# from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
# import torch

def generate_base_qr_image(data, filename="base_qr.png"):
    """Generates a simple, high-error-correction QR code as a PNG."""
    qr = qrcode.QRCode(
        version=1,
        error_correction=qrcode.constants.ERROR_CORRECT_H, # Crucial for embedding
        box_size=10,
        border=4,
    )
    qr.add_data(data)
    qr.make(fit=True)
    img = qr.make_image(fill_color="black", back_color="white").convert("RGB")
    img.save(filename)
    return img

# --- Step 1: Generate the base QR code ---
qr_data_url = "https://yourwebsite.com/ai-art-gallery"
base_qr_image = generate_base_qr_image(qr_data_url)
print("Base QR code generated.")

# --- Step 2: (Conceptual) Load Stable Diffusion and ControlNet Model ---
# In a real setup, you'd load pre-trained models.
# controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-qrcode", torch_dtype=torch.float16)
# pipe = StableDiffusionControlNetPipeline.from_pretrained(
#     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
# )
# pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# pipe.to("cuda")

# --- Step 3: (Conceptual) Run the AI generation ---
# This part is highly dependent on the specific ControlNet pipeline you're using.
# It involves:
#   - Your text prompt (e.g., "A mystical forest at sunset, cinematic, detailed, glowing mushrooms")
#   - The generated base_qr_image as the control image
#   - Optional: A negative prompt to avoid unwanted features
#   - Guidance scale, inference steps, seed, etc.

# Example of what the call might look like conceptually:
# generated_image = pipe(
#     prompt="a beautiful fantasy forest, glowing flora, cinematic, artstation, highly detailed",
#     negative_prompt="blurry, low quality, deformed, bad anatomy, ugly",
#     image=base_qr_image, # This is the ControlNet input
#     num_inference_steps=30,
#     guidance_scale=7.5,
#     generator=torch.Generator("cuda").manual_seed(1234),
# ).images[0]

# generated_image.save("ai_art_qr_forest.png")
# print("AI-stylized QR code generated. Remember to test its scannability!")

# Since the full `diffusers` setup is complex for a simple code block,
# the best way to experiment is via online tools or specialized Colab notebooks.
print("\nTo truly generate, use a UI like Automatic1111 or ComfyUI with the 'ControlNet QR Code' model,")
print("or a dedicated Google Colab notebook for AI QR art generation.")

5. Best Practices for Scannable QR Art​

  1. High Error Correction (H): Always use ERROR_CORRECT_H when generating the base QR code. This allows for up to 30% of the code to be "damaged" (by the AI stylization) and still be scannable [5.3].
  2. Simple Base Prompt: Start with a clear and concise prompt for the AI.
  3. Contrast is Key: Even with AI art, maintain reasonable contrast between the QR "modules" and the background for optimal scannability.
  4. Test, Test, Test: Always scan the generated image with multiple phone cameras before using it in production.
  5. Small Details: The AI might try to "smooth out" the harsh lines of the QR code. You can sometimes add "crisp lines", "sharp focus" to your negative prompt or prompt itself to guide it.

πŸ“š Sources & Further Reading​

  1. Stable Diffusion & ControlNet:
  1. QR Code Basics: