qr2ai/diffusion_lab 🔢📝❓🖼️ → 🖼️

▶️ 64 runs 📅 Nov 2023 ⚙️ Cog 0.8.6

About

Example Output

Prompt:

"Wooden Style"

Output

Performance Metrics

6.42s Prediction Time

1110.44s Total Time

All Input Parameters

{
  "seed": -1,
  "prompt": "Wooden Style",
  "sampler": "DPM++ Karras SDE",
  "strength": 0.9,
  "batch_size": 1,
  "guidance_scale": 7.5,
  "negative_prompt": "ugly, disfigured, low quality, blurry, nsfw",
  "qr_code_content": "Hello",
  "num_inference_steps": 40,
  "controlnet_conditioning_scale": 1.5
}

Input Parameters

seed Type: integerDefault: -1: Seed
prompt (required) Type: string: The prompt to guide QR Code generation.
sampler Default: DPM++ Karras SDE: The sampling method
strength Type: numberDefault: 0.9Range: 0 - 1: Indicates how much to transform the masked portion of the reference `image`. Must be between 0 and 1.
batch_size Type: integerDefault: 1Range: 1 - 4: Batch size for this prediction
qr_code_image Type: string: The QR Code image (optional).
guidance_scale Type: numberDefault: 7.5Range: 0.1 - 30: Scale for classifier-free guidance
negative_prompt Type: stringDefault: ugly, disfigured, low quality, blurry, nsfw: The negative prompt to guide image generation.
qr_code_content Type: stringDefault:: The website/content your QR Code will point to.
num_inference_steps Type: integerDefault: 40Range: 20 - 100: Number of diffusion steps
controlnet_conditioning_scale Type: numberDefault: 1.5Range: 1 - 2: 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

Type: array • Items Type: string • Items Format: uri

Example Execution Logs

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Version Details

Version ID: c0aee3b7ea054bc30e92d20bd3631fb379e39241b6d34b58a50356a3d9ad8e20
Version Created: November 5, 2023

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