daanelson/speedy-sdxl-test 🔢📝❓✓ → 🖼️
About
SDXL, but faster

Example Output
Prompt:
"An astronaut riding a rainbow unicorn"
Output

Performance Metrics
9.42s
Prediction Time
9.42s
Total Time
All Input Parameters
{ "width": 1024, "height": 1024, "prompt": "An astronaut riding a rainbow unicorn", "refine": "no_refiner", "scheduler": "K_EULER", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Input Parameters
- seed
- Random seed. Leave blank to randomize the seed
- width
- Width of output image
- height
- Height of output image
- prompt
- Input prompt
- refine
- Which refine style to use
- scheduler
- scheduler
- num_outputs
- Number of images to output.
- refine_steps
- For base_image_refiner, the number of steps to refine, defaults to num_inference_steps
- guidance_scale
- Scale for classifier-free guidance
- apply_watermark
- Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.
- high_noise_frac
- For expert_ensemble_refiner, the fraction of noise to use
- negative_prompt
- Input Negative Prompt
- prompt_strength
- Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
- num_inference_steps
- Number of denoising steps
Output Schema
Output
Example Execution Logs
Using seed: 31413 Prompt: An astronaut riding a rainbow unicorn txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:07, 6.79it/s] 4%|▍ | 2/50 [00:00<00:07, 6.74it/s] 6%|▌ | 3/50 [00:00<00:06, 6.73it/s] 8%|▊ | 4/50 [00:00<00:06, 6.72it/s] 10%|█ | 5/50 [00:00<00:06, 6.72it/s] 12%|█▏ | 6/50 [00:00<00:06, 6.70it/s] 14%|█▍ | 7/50 [00:01<00:06, 6.69it/s] 16%|█▌ | 8/50 [00:01<00:06, 6.70it/s] 18%|█▊ | 9/50 [00:01<00:06, 6.70it/s] 20%|██ | 10/50 [00:01<00:05, 6.71it/s] 22%|██▏ | 11/50 [00:01<00:05, 6.72it/s] 24%|██▍ | 12/50 [00:01<00:05, 6.72it/s] 26%|██▌ | 13/50 [00:01<00:05, 6.70it/s] 28%|██▊ | 14/50 [00:02<00:05, 6.69it/s] 30%|███ | 15/50 [00:02<00:05, 6.70it/s] 32%|███▏ | 16/50 [00:02<00:05, 6.69it/s] 34%|███▍ | 17/50 [00:02<00:04, 6.71it/s] 36%|███▌ | 18/50 [00:02<00:04, 6.70it/s] 38%|███▊ | 19/50 [00:02<00:04, 6.70it/s] 40%|████ | 20/50 [00:02<00:04, 6.70it/s] 42%|████▏ | 21/50 [00:03<00:04, 6.70it/s] 44%|████▍ | 22/50 [00:03<00:04, 6.70it/s] 46%|████▌ | 23/50 [00:03<00:04, 6.70it/s] 48%|████▊ | 24/50 [00:03<00:03, 6.70it/s] 50%|█████ | 25/50 [00:03<00:03, 6.69it/s] 52%|█████▏ | 26/50 [00:03<00:03, 6.70it/s] 54%|█████▍ | 27/50 [00:04<00:03, 6.69it/s] 56%|█████▌ | 28/50 [00:04<00:03, 6.70it/s] 58%|█████▊ | 29/50 [00:04<00:03, 6.70it/s] 60%|██████ | 30/50 [00:04<00:02, 6.70it/s] 62%|██████▏ | 31/50 [00:04<00:02, 6.70it/s] 64%|██████▍ | 32/50 [00:04<00:02, 6.69it/s] 66%|██████▌ | 33/50 [00:04<00:02, 6.68it/s] 68%|██████▊ | 34/50 [00:05<00:02, 6.67it/s] 70%|███████ | 35/50 [00:05<00:02, 6.66it/s] 72%|███████▏ | 36/50 [00:05<00:02, 6.67it/s] 74%|███████▍ | 37/50 [00:05<00:01, 6.67it/s] 76%|███████▌ | 38/50 [00:05<00:01, 6.68it/s] 78%|███████▊ | 39/50 [00:05<00:01, 6.68it/s] 80%|████████ | 40/50 [00:05<00:01, 6.68it/s] 82%|████████▏ | 41/50 [00:06<00:01, 6.68it/s] 84%|████████▍ | 42/50 [00:06<00:01, 6.68it/s] 86%|████████▌ | 43/50 [00:06<00:01, 6.68it/s] 88%|████████▊ | 44/50 [00:06<00:00, 6.69it/s] 90%|█████████ | 45/50 [00:06<00:00, 6.69it/s] 92%|█████████▏| 46/50 [00:06<00:00, 6.68it/s] 94%|█████████▍| 47/50 [00:07<00:00, 6.67it/s] 96%|█████████▌| 48/50 [00:07<00:00, 6.66it/s] 98%|█████████▊| 49/50 [00:07<00:00, 6.67it/s] 100%|██████████| 50/50 [00:07<00:00, 6.67it/s] 100%|██████████| 50/50 [00:07<00:00, 6.69it/s]
Version Details
- Version ID
c3368e8507c19578b460a8f2f42224f7712c108b0eb0edd1e7b71f9f4f3b2ef9
- Version Created
- September 24, 2023