qr2ai/qr_code_ai_art_generator 🔢🖼️📝❓ → 🖼️
About
QR Code AI Art Generator

Example Output
Prompt:
"surreal concept art of a futuristic house floating on a cloud with waterfall, peaceful and modern, cosy, minimalistic, big windows, natural lighting, sci-fi, lots of details, intricate scene, correct, digital painting, fine tuned, 64k"
Output

Performance Metrics
7.52s
Prediction Time
9.41s
Total Time
All Input Parameters
{ "seed": 7649977190, "width": 768, "border": 2, "height": 768, "prompt": "surreal concept art of a futuristic house floating on a cloud with waterfall, peaceful and modern, cosy, minimalistic, big windows, natural lighting, sci-fi, lots of details, intricate scene, correct, digital painting, fine tuned, 64k", "num_outputs": 1, "guidance_scale": 5.7, "negative_prompt": "Foreboding mystical, unblended, worst quality, normal quality, low quality, low res, blurry, ugly, disfigured, nsfw, people, animal, character, anime", "qr_code_content": "qr2ai.com", "qrcode_background": "white", "num_inference_steps": 40, "controlnet_conditioning_scale": 1.11 }
Input Parameters
- seed
- Seed
- image
- Input image. If none is provided, a QR code will be generated
- width
- Width out the output image
- border
- QR code border size
- height
- Height out the output image
- prompt (required)
- The prompt to guide QR Code generation.
- num_outputs
- Number of outputs
- guidance_scale
- Scale for classifier-free guidance
- negative_prompt
- The negative prompt to guide image generation.
- qr_code_content
- The website/content your QR Code will point to.
- qrcode_background
- Background color of raw QR code
- num_inference_steps
- Number of diffusion steps
- controlnet_conditioning_scale
- The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet.
Output Schema
Output
Example Execution Logs
Seed: 7649977190 Generating QR Code from content 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:11, 3.31it/s] 5%|▌ | 2/40 [00:00<00:07, 4.78it/s] 8%|▊ | 3/40 [00:00<00:06, 5.57it/s] 10%|█ | 4/40 [00:00<00:05, 6.03it/s] 12%|█▎ | 5/40 [00:00<00:05, 6.32it/s] 15%|█▌ | 6/40 [00:01<00:05, 6.50it/s] 18%|█▊ | 7/40 [00:01<00:04, 6.61it/s] 20%|██ | 8/40 [00:01<00:04, 6.70it/s] 22%|██▎ | 9/40 [00:01<00:04, 6.75it/s] 25%|██▌ | 10/40 [00:01<00:04, 6.79it/s] 28%|██▊ | 11/40 [00:01<00:04, 6.82it/s] 30%|███ | 12/40 [00:01<00:04, 6.85it/s] 32%|███▎ | 13/40 [00:02<00:03, 6.88it/s] 35%|███▌ | 14/40 [00:02<00:03, 6.89it/s] 38%|███▊ | 15/40 [00:02<00:03, 6.91it/s] 40%|████ | 16/40 [00:02<00:03, 6.92it/s] 42%|████▎ | 17/40 [00:02<00:03, 6.92it/s] 45%|████▌ | 18/40 [00:02<00:03, 6.92it/s] 48%|████▊ | 19/40 [00:02<00:03, 6.92it/s] 50%|█████ | 20/40 [00:03<00:02, 6.93it/s] 52%|█████▎ | 21/40 [00:03<00:02, 6.93it/s] 55%|█████▌ | 22/40 [00:03<00:02, 6.93it/s] 57%|█████▊ | 23/40 [00:03<00:02, 6.93it/s] 60%|██████ | 24/40 [00:03<00:02, 6.92it/s] 62%|██████▎ | 25/40 [00:03<00:02, 6.93it/s] 65%|██████▌ | 26/40 [00:03<00:02, 6.93it/s] 68%|██████▊ | 27/40 [00:04<00:01, 6.92it/s] 70%|███████ | 28/40 [00:04<00:01, 6.92it/s] 72%|███████▎ | 29/40 [00:04<00:01, 6.92it/s] 75%|███████▌ | 30/40 [00:04<00:01, 6.93it/s] 78%|███████▊ | 31/40 [00:04<00:01, 6.92it/s] 80%|████████ | 32/40 [00:04<00:01, 6.92it/s] 82%|████████▎ | 33/40 [00:04<00:01, 6.92it/s] 85%|████████▌ | 34/40 [00:05<00:00, 6.92it/s] 88%|████████▊ | 35/40 [00:05<00:00, 6.92it/s] 90%|█████████ | 36/40 [00:05<00:00, 6.91it/s] 92%|█████████▎| 37/40 [00:05<00:00, 6.91it/s] 95%|█████████▌| 38/40 [00:05<00:00, 6.92it/s] 98%|█████████▊| 39/40 [00:05<00:00, 6.92it/s] 100%|██████████| 40/40 [00:05<00:00, 6.91it/s] 100%|██████████| 40/40 [00:05<00:00, 6.73it/s]
Version Details
- Version ID
3c11545581fedfd84313395213d8805dc23fca60c46f24cd86fb9df407ae7113
- Version Created
- November 9, 2023