marlonbarrios/sdxl-ziggislide 🖼️🔢📝❓✓ → 🖼️
About
ziggislide is a model of an adventurous character of an influencer always wearing colorful fabulous outfit, goggles and helmet, he loves water sliding boards and taking selfies. To invoke his presence use the token ziggyslide inside the prompt
Example Output
Prompt:
"ziggyslide 35 years old, ginger guys selfie, slender body, flexing muscles, smile, speedo, beautiful sensual lips and perfect teeth, short beard and mustache, gay, smooth skin, tender and soft smile, freckles, dramatic light, realistic, walking on ice, steam over water ice"
Output
Performance Metrics
60.29s
Prediction Time
65.46s
Total Time
All Input Parameters
{
"width": 1024,
"height": 1024,
"prompt": "ziggyslide 35 years old, ginger guys selfie, slender body, flexing muscles, smile, speedo, beautiful sensual lips and perfect teeth, short beard and mustache, gay, smooth skin, tender and soft smile, freckles, dramatic light, realistic, walking on ice, steam over water ice",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.42,
"num_outputs": 4,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "no wrinkles in face",
"prompt_strength": 0.92,
"num_inference_steps": 50
}
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
- 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
Output Schema
Output
Example Execution Logs
Using seed: 1677 Ensuring enough disk space... Free disk space: 1849605820416 Downloading weights: https://replicate.delivery/pbxt/KbrNyHtqJJ7aB9Eez60XBVeSkwjCKTJ9fFdI4vg6YEORQCDkA/trained_model.tar 2023-12-16T14:30:20Z | INFO | [ Initiating ] dest=/src/weights-cache/243016cb0a7198cd minimum_chunk_size=500M url=https://replicate.delivery/pbxt/KbrNyHtqJJ7aB9Eez60XBVeSkwjCKTJ9fFdI4vg6YEORQCDkA/trained_model.tar 2023-12-16T14:30:21Z | INFO | [ Complete ] dest=/src/weights-cache/243016cb0a7198cd size="186 MB" total_elapsed=0.880s url=https://replicate.delivery/pbxt/KbrNyHtqJJ7aB9Eez60XBVeSkwjCKTJ9fFdI4vg6YEORQCDkA/trained_model.tar b'' Downloaded weights in 0.9831557273864746 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: ziggyslide 35 years old, ginger guys selfie, slender body, flexing muscles, smile, speedo, beautiful sensual lips and perfect teeth, short beard and mustache, gay, smooth skin, tender and soft smile, freckles, dramatic light, realistic, walking on ice, steam over water ice txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:42, 1.05s/it] 20%|██ | 10/50 [00:10<00:41, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it] 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it] 40%|████ | 20/50 [00:20<00:31, 1.05s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it] 50%|█████ | 25/50 [00:26<00:26, 1.05s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.05s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.05s/it] 60%|██████ | 30/50 [00:31<00:21, 1.05s/it] 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it] 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it] 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it] 70%|███████ | 35/50 [00:36<00:15, 1.05s/it] 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it] 74%|███████▍ | 37/50 [00:38<00:13, 1.05s/it] 76%|███████▌ | 38/50 [00:39<00:12, 1.05s/it] 78%|███████▊ | 39/50 [00:40<00:11, 1.05s/it] 80%|████████ | 40/50 [00:42<00:10, 1.05s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.05s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it]
Version Details
- Version ID
5a5401246950a98445013f4dc6372b40e23f2ff9ad4b846ce5d120b7477660bb- Version Created
- December 12, 2023