lucataco/sdxl 🖼️🔢📝❓✓ → 🖼️
About
SDXL v1.0 - A text-to-image generative AI model that creates beautiful images

Example Output
Prompt:
"a studio portrait photo of an iguana wearing a hat"
Output

Performance Metrics
3.61s
Prediction Time
3.62s
Total Time
All Input Parameters
{ "seed": 39287, "width": 1024, "height": 1024, "prompt": "a studio portrait photo of an iguana wearing a hat", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }
Input Parameters
- mask
- Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.
- seed
- Random seed. Leave blank to randomize the seed
- image
- Input image for img2img or inpaint mode
- width
- Width of output image
- height
- Height of output image
- prompt
- Input prompt
- refine
- Which refine style to use
- scheduler
- scheduler
- lora_scale
- LoRA additive scale. Only applicable on trained models.
- 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
- replicate_weights
- Replicate LoRA weights to use. Leave blank to use the default weights.
- num_inference_steps
- Number of denoising steps
Output Schema
Output
Example Execution Logs
Using seed: 39287 Prompt: a studio portrait photo of an iguana wearing a hat txt2img mode 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:02, 9.39it/s] 10%|█ | 2/20 [00:00<00:01, 9.18it/s] 15%|█▌ | 3/20 [00:00<00:01, 9.12it/s] 20%|██ | 4/20 [00:00<00:01, 9.09it/s] 25%|██▌ | 5/20 [00:00<00:01, 9.08it/s] 30%|███ | 6/20 [00:00<00:01, 9.05it/s] 35%|███▌ | 7/20 [00:00<00:01, 9.07it/s] 40%|████ | 8/20 [00:00<00:01, 9.03it/s] 45%|████▌ | 9/20 [00:00<00:01, 9.04it/s] 50%|█████ | 10/20 [00:01<00:01, 9.03it/s] 55%|█████▌ | 11/20 [00:01<00:00, 9.02it/s] 60%|██████ | 12/20 [00:01<00:00, 9.02it/s] 65%|██████▌ | 13/20 [00:01<00:00, 9.01it/s] 70%|███████ | 14/20 [00:01<00:00, 9.01it/s] 75%|███████▌ | 15/20 [00:01<00:00, 9.00it/s] 80%|████████ | 16/20 [00:01<00:00, 9.01it/s] 85%|████████▌ | 17/20 [00:01<00:00, 9.01it/s] 90%|█████████ | 18/20 [00:01<00:00, 9.01it/s] 95%|█████████▌| 19/20 [00:02<00:00, 9.00it/s] 100%|██████████| 20/20 [00:02<00:00, 9.00it/s] 100%|██████████| 20/20 [00:02<00:00, 9.03it/s] 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:00<00:00, 8.61it/s] 40%|████ | 2/5 [00:00<00:00, 8.40it/s] 60%|██████ | 3/5 [00:00<00:00, 8.36it/s] 80%|████████ | 4/5 [00:00<00:00, 8.33it/s] 100%|██████████| 5/5 [00:00<00:00, 8.32it/s] 100%|██████████| 5/5 [00:00<00:00, 8.35it/s]
Version Details
- Version ID
c86579ac5193bf45422f1c8b92742135aa859b1850a8e4c531bff222fc75273d
- Version Created
- November 1, 2023