bytonylee/stable-cascade 🔢📝 → 🖼️

▶️ 83 runs 📅 Sep 2024 ⚙️ Cog 0.13.2 🔗 GitHub 📄 Paper ⚖️ License

Performance

14.0sTypical run time

~140sCold start (first call)

83Total runs

About

Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Example Output

Prompt:

"A man with hoodie on, illustration"

Output

Performance Metrics

14.02s Prediction Time

139.58s Total Time

All Input Parameters

{
  "width": 1024,
  "height": 1024,
  "prompt": "A man with hoodie on, illustration",
  "num_images": 1,
  "steps_prior": 20,
  "steps_decoder": 10,
  "guidance_scale_prior": 4,
  "guidance_scale_decoder": 0
}

Input Parameters

seed Type: integer: Random seed. Leave blank to randomize the seed.
width Type: integerDefault: 1024Range: 1 - 2048: Width of the output image.
height Type: integerDefault: 1024Range: 1 - 2048: Height of the output image.
prompt Type: string: Input prompt, text of what you want to generate.
num_images Type: integerDefault: 1Range: 1 - 4: Number of output images.
steps_prior Type: integerDefault: 20Range: 1 - 50: Number of denoising steps in prior.
steps_decoder Type: integerDefault: 10Range: 1 - 50: Number of denoising steps in decoder.
negative_prompt Type: string: Input negative prompt, text of what you don't want to generate.
guidance_scale_prior Type: numberDefault: 4Range: 0 - 20: Scale for classifier-free guidance in prior.
guidance_scale_decoder Type: numberDefault: 0Range: 0 - 20: Scale for classifier-free guidance in decoder.

Output Schema

Output

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

Example Execution Logs

DEVICE: cuda
DTYPE: torch.float16
Using seed: 50236
Finish setup in 0.00011014938354492188 secs.
[Debug] Prompt: A man with hoodie on, illustration, best quality, high detail, sharp focus
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Finish generation in 12.288098096847534 secs.

Version Details

Version ID: 36c40b9cc271abb8bc4a0f8cbb59c68d0739ad076648533eac1ca7a56d268b0d
Version Created: November 22, 2024

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