lucataco/illusion-diffusion-hq 🔢🖼️📝❓ → 🖼️
About
Monster Labs QrCode ControlNet on top of SD Realistic Vision v5.1

Example Output
Prompt:
"(masterpiece:1.4), (best quality), (detailed), Medieval village scene with busy streets and castle in the distance"
Output

Performance Metrics
7.80s
Prediction Time
7.75s
Total Time
All Input Parameters
{ "seed": 1057727382, "image": "https://replicate.delivery/pbxt/Ja8F0jwa45Bv8AxkH0VjcyhQ4KTN0RJiLYivSskJmeCWc30n/spiral.png", "width": 768, "border": 1, "height": 768, "prompt": "(masterpiece:1.4), (best quality), (detailed), Medieval village scene with busy streets and castle in the distance", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "ugly, disfigured, low quality, blurry, nsfw", "qr_code_content": "", "qrcode_background": "gray", "num_inference_steps": 40, "controlnet_conditioning_scale": 1 }
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: 1057727382 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:05, 6.96it/s] 5%|▌ | 2/40 [00:00<00:05, 6.89it/s] 8%|▊ | 3/40 [00:00<00:05, 6.87it/s] 10%|█ | 4/40 [00:00<00:05, 6.85it/s] 12%|█▎ | 5/40 [00:00<00:05, 6.83it/s] 15%|█▌ | 6/40 [00:00<00:04, 6.83it/s] 18%|█▊ | 7/40 [00:01<00:04, 6.79it/s] 20%|██ | 8/40 [00:01<00:04, 6.79it/s] 22%|██▎ | 9/40 [00:01<00:04, 6.80it/s] 25%|██▌ | 10/40 [00:01<00:04, 6.80it/s] 28%|██▊ | 11/40 [00:01<00:04, 6.81it/s] 30%|███ | 12/40 [00:01<00:04, 6.84it/s] 32%|███▎ | 13/40 [00:01<00:03, 6.83it/s] 35%|███▌ | 14/40 [00:02<00:03, 6.83it/s] 38%|███▊ | 15/40 [00:02<00:03, 6.83it/s] 40%|████ | 16/40 [00:02<00:03, 6.84it/s] 42%|████▎ | 17/40 [00:02<00:03, 6.84it/s] 45%|████▌ | 18/40 [00:02<00:03, 6.85it/s] 48%|████▊ | 19/40 [00:02<00:03, 6.85it/s] 50%|█████ | 20/40 [00:02<00:02, 6.84it/s] 52%|█████▎ | 21/40 [00:03<00:02, 6.84it/s] 55%|█████▌ | 22/40 [00:03<00:02, 6.84it/s] 57%|█████▊ | 23/40 [00:03<00:02, 6.84it/s] 60%|██████ | 24/40 [00:03<00:02, 6.84it/s] 62%|██████▎ | 25/40 [00:03<00:02, 6.84it/s] 65%|██████▌ | 26/40 [00:03<00:02, 6.83it/s] 68%|██████▊ | 27/40 [00:03<00:01, 6.83it/s] 70%|███████ | 28/40 [00:04<00:01, 6.83it/s] 72%|███████▎ | 29/40 [00:04<00:01, 6.82it/s] 75%|███████▌ | 30/40 [00:04<00:01, 6.82it/s] 78%|███████▊ | 31/40 [00:04<00:01, 6.84it/s] 80%|████████ | 32/40 [00:04<00:01, 6.83it/s] 82%|████████▎ | 33/40 [00:04<00:01, 6.83it/s] 85%|████████▌ | 34/40 [00:04<00:00, 6.83it/s] 88%|████████▊ | 35/40 [00:05<00:00, 6.83it/s] 90%|█████████ | 36/40 [00:05<00:00, 6.83it/s] 92%|█████████▎| 37/40 [00:05<00:00, 6.83it/s] 95%|█████████▌| 38/40 [00:05<00:00, 6.83it/s] 98%|█████████▊| 39/40 [00:05<00:00, 6.83it/s] 100%|██████████| 40/40 [00:05<00:00, 6.82it/s] 100%|██████████| 40/40 [00:05<00:00, 6.83it/s]
Version Details
- Version ID
3c64e669051f9b358e748c8e2fb8a06e64122a9ece762ef133252e2c99da77c1
- Version Created
- September 24, 2023