tonyllondon/goal_celebration_v1 🔢🖼️❓📝✓ → 🖼️
About
A crazy model which maybe does nothing, maybe does something, but its supposed to be about football.

Example Output
Prompt:
"A photo of the greatest footballer of all time celebrating his world cup winning goal"
Output

Performance Metrics
21.36s
Prediction Time
21.40s
Total Time
All Input Parameters
{ "width": 512, "height": 512, "prompt": "A photo of the greatest footballer of all time celebrating his world cup winning goal", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }
Input Parameters
- seed
- Random seed. Leave blank to randomize the seed
- image
- A starting image from which to generate variations (aka 'img2img'). If this input is set, the `width` and `height` inputs are ignored and the output will have the same dimensions as the input image.
- width
- Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
- height
- Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
- prompt
- Input prompt
- scheduler
- Choose a scheduler
- num_outputs
- Number of images to output.
- guidance_scale
- Scale for classifier-free guidance
- negative_prompt
- Specify things to not see in the output
- prompt_strength
- Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image
- num_inference_steps
- Number of denoising steps
- disable_safety_check
- Disable safety check. Use at your own risk!
Output Schema
Output
Example Execution Logs
Loading pipeline... Using seed: 10074 Global seed set to 10074 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:03<02:28, 3.03s/it] 4%|▍ | 2/50 [00:03<01:07, 1.41s/it] 6%|▌ | 3/50 [00:03<00:41, 1.13it/s] 8%|▊ | 4/50 [00:03<00:29, 1.55it/s] 10%|█ | 5/50 [00:04<00:23, 1.94it/s] 12%|█▏ | 6/50 [00:04<00:19, 2.31it/s] 14%|█▍ | 7/50 [00:04<00:16, 2.63it/s] 16%|█▌ | 8/50 [00:04<00:14, 2.87it/s] 18%|█▊ | 9/50 [00:05<00:13, 3.05it/s] 20%|██ | 10/50 [00:05<00:12, 3.20it/s] 22%|██▏ | 11/50 [00:05<00:11, 3.33it/s] 24%|██▍ | 12/50 [00:06<00:11, 3.41it/s] 26%|██▌ | 13/50 [00:06<00:10, 3.46it/s] 28%|██▊ | 14/50 [00:06<00:10, 3.51it/s] 30%|███ | 15/50 [00:06<00:09, 3.55it/s] 32%|███▏ | 16/50 [00:07<00:09, 3.55it/s] 34%|███▍ | 17/50 [00:07<00:09, 3.54it/s] 36%|███▌ | 18/50 [00:07<00:08, 3.57it/s] 38%|███▊ | 19/50 [00:08<00:08, 3.58it/s] 40%|████ | 20/50 [00:08<00:08, 3.57it/s] 42%|████▏ | 21/50 [00:08<00:08, 3.58it/s] 44%|████▍ | 22/50 [00:08<00:07, 3.59it/s] 46%|████▌ | 23/50 [00:09<00:07, 3.58it/s] 48%|████▊ | 24/50 [00:09<00:07, 3.57it/s] 50%|█████ | 25/50 [00:09<00:06, 3.59it/s] 52%|█████▏ | 26/50 [00:09<00:06, 3.58it/s] 54%|█████▍ | 27/50 [00:10<00:06, 3.58it/s] 56%|█████▌ | 28/50 [00:10<00:06, 3.57it/s] 58%|█████▊ | 29/50 [00:10<00:05, 3.57it/s] 60%|██████ | 30/50 [00:11<00:05, 3.56it/s] 62%|██████▏ | 31/50 [00:11<00:05, 3.56it/s] 64%|██████▍ | 32/50 [00:11<00:05, 3.56it/s] 66%|██████▌ | 33/50 [00:11<00:04, 3.55it/s] 68%|██████▊ | 34/50 [00:12<00:04, 3.55it/s] 70%|███████ | 35/50 [00:12<00:04, 3.55it/s] 72%|███████▏ | 36/50 [00:12<00:03, 3.55it/s] 74%|███████▍ | 37/50 [00:13<00:03, 3.55it/s] 76%|███████▌ | 38/50 [00:13<00:03, 3.56it/s] 78%|███████▊ | 39/50 [00:13<00:03, 3.56it/s] 80%|████████ | 40/50 [00:13<00:02, 3.56it/s] 82%|████████▏ | 41/50 [00:14<00:02, 3.57it/s] 84%|████████▍ | 42/50 [00:14<00:02, 3.57it/s] 86%|████████▌ | 43/50 [00:14<00:01, 3.57it/s] 88%|████████▊ | 44/50 [00:15<00:01, 3.57it/s] 90%|█████████ | 45/50 [00:15<00:01, 3.57it/s] 92%|█████████▏| 46/50 [00:15<00:01, 3.56it/s] 94%|█████████▍| 47/50 [00:15<00:00, 3.56it/s] 96%|█████████▌| 48/50 [00:16<00:00, 3.55it/s] 98%|█████████▊| 49/50 [00:16<00:00, 3.56it/s] 100%|██████████| 50/50 [00:16<00:00, 3.55it/s] 100%|██████████| 50/50 [00:16<00:00, 2.99it/s]
Version Details
- Version ID
2f27c20b620b50ea938a9ddbef80f801349f8575e6a3a3525df9488b501ef995
- Version Created
- February 7, 2023