shefa/turbo-enigma 🔢🖼️📝✓ → 🖼️
About
SDXL based text-to-image model applying Distribution Matching Distillation, supporting zero-shot identity generation in 2-5s. https://ai-visionboard.com

Example Output
Prompt:
" stunning white female loving and caring, me fighting fires"
Output

Performance Metrics
1.95s
Prediction Time
1.97s
Total Time
All Input Parameters
{ "image": "https://firebasestorage.googleapis.com/v0/b/a-i-vision-board-1-29aznr.appspot.com/o/users%2FbFzbdQ78N4MdW9GRXJ0uO21dqK33%2Fuploads%2F1705813258118000.png?alt=media&token=69c820a5-3b67-497a-b16c-bfcfb668840e", "width": 448, "height": 896, "prompt": " stunning white female loving and caring, me fighting fires", "embeddings": "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", "faceswap_fast": true, "faceswap_slow": false, "save_embeddings": false, "num_refine_steps": 3, "num_inference_steps": 7, "disable_safety_checker": false }
Input Parameters
- seed
- Random seed. Leave blank to randomize the seed
- image
- Input image
- width
- Width of output image
- height
- Height of output image
- prompt
- Input prompt
- embeddings
- Optimization experiment
- faceswap_fast
- Experiment
- faceswap_slow
- IPadapterFaceID
- guidance_scale
- Scale for classifier-free guidance
- save_embeddings
- Optimization experiment
- num_refine_steps
- Number of refine steps
- num_inference_steps
- Number of denoising steps
- disable_safety_checker
- Disable safety checker for generated images. This feature is only available through the API. See [https://replicate.com/docs/how-does-replicate-work#safety](https://replicate.com/docs/how-does-replicate-work#safety)
Output Schema
Output
Example Execution Logs
Using seed: 13563374 loading embeddings Faceswap source overhead: 0.00s 0%| | 0/7 [00:00<?, ?it/s] 14%|█▍ | 1/7 [00:00<00:00, 8.63it/s] 43%|████▎ | 3/7 [00:00<00:00, 13.02it/s] 71%|███████▏ | 5/7 [00:00<00:00, 12.42it/s] 100%|██████████| 7/7 [00:00<00:00, 12.17it/s] 100%|██████████| 7/7 [00:00<00:00, 12.08it/s] Inference took: 0.77s 0%| | 0/3 [00:00<?, ?it/s] 100%|██████████| 3/3 [00:00<00:00, 26.34it/s] 100%|██████████| 3/3 [00:00<00:00, 26.31it/s] Refinement took: 0.31s Faceswap target overhead: 0.02s Faceswap main: 0.04s Faceswap enhance: 0.22s Faceswap total: 0.27s Total time: 1.49s
Version Details
- Version ID
d782a412563ca745fddce97c26ff3e72c551deba88a835188374a8a3ab9b43cc
- Version Created
- May 26, 2024