dmitru/sdxl-tests 🖼️🔢📝❓✓ → 🖼️
About
SDXL fine-tuned on paper collages
Example Output
Prompt:
"a paper collage in the style of TOK, showing a girl swimming in a magic underwater world with weird fishes with human eyes"
Output
Performance Metrics
16.77s
Prediction Time
18.31s
Total Time
All Input Parameters
{
"width": 1024,
"height": 1024,
"prompt": "a paper collage in the style of TOK, showing a girl swimming in a magic underwater world with weird fishes with human eyes",
"refine": "expert_ensemble_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.92,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.89,
"negative_prompt": "",
"prompt_strength": 0.8,
"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: 1405 Ensuring enough disk space... Free disk space: 1846372753408 Downloading weights: https://replicate.delivery/pbxt/QEbNqOOYZurOPVCYxB2pQ2jyTw3Uej4HakeVvD4QVydJONejA/trained_model.tar b'Downloaded 186 MB bytes in 0.408s (455 MB/s)\nExtracted 186 MB in 0.100s (1.9 GB/s)\n' Downloaded weights in 1.0749387741088867 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a paper collage in the style of <s0><s1>, showing a girl swimming in a magic underwater world with weird fishes with human eyes txt2img mode 0%| | 0/44 [00:00<?, ?it/s] 2%|▏ | 1/44 [00:00<00:11, 3.70it/s] 5%|▍ | 2/44 [00:00<00:11, 3.68it/s] 7%|▋ | 3/44 [00:00<00:11, 3.68it/s] 9%|▉ | 4/44 [00:01<00:10, 3.68it/s] 11%|█▏ | 5/44 [00:01<00:10, 3.68it/s] 14%|█▎ | 6/44 [00:01<00:10, 3.68it/s] 16%|█▌ | 7/44 [00:01<00:10, 3.67it/s] 18%|█▊ | 8/44 [00:02<00:09, 3.67it/s] 20%|██ | 9/44 [00:02<00:09, 3.67it/s] 23%|██▎ | 10/44 [00:02<00:09, 3.67it/s] 25%|██▌ | 11/44 [00:02<00:08, 3.67it/s] 27%|██▋ | 12/44 [00:03<00:08, 3.67it/s] 30%|██▉ | 13/44 [00:03<00:08, 3.67it/s] 32%|███▏ | 14/44 [00:03<00:08, 3.67it/s] 34%|███▍ | 15/44 [00:04<00:07, 3.67it/s] 36%|███▋ | 16/44 [00:04<00:07, 3.67it/s] 39%|███▊ | 17/44 [00:04<00:07, 3.67it/s] 41%|████ | 18/44 [00:04<00:07, 3.67it/s] 43%|████▎ | 19/44 [00:05<00:06, 3.67it/s] 45%|████▌ | 20/44 [00:05<00:06, 3.67it/s] 48%|████▊ | 21/44 [00:05<00:06, 3.67it/s] 50%|█████ | 22/44 [00:05<00:06, 3.67it/s] 52%|█████▏ | 23/44 [00:06<00:05, 3.66it/s] 55%|█████▍ | 24/44 [00:06<00:05, 3.66it/s] 57%|█████▋ | 25/44 [00:06<00:05, 3.66it/s] 59%|█████▉ | 26/44 [00:07<00:04, 3.66it/s] 61%|██████▏ | 27/44 [00:07<00:04, 3.66it/s] 64%|██████▎ | 28/44 [00:07<00:04, 3.66it/s] 66%|██████▌ | 29/44 [00:07<00:04, 3.66it/s] 68%|██████▊ | 30/44 [00:08<00:03, 3.66it/s] 70%|███████ | 31/44 [00:08<00:03, 3.66it/s] 73%|███████▎ | 32/44 [00:08<00:03, 3.67it/s] 75%|███████▌ | 33/44 [00:08<00:02, 3.67it/s] 77%|███████▋ | 34/44 [00:09<00:02, 3.68it/s] 80%|███████▉ | 35/44 [00:09<00:02, 3.68it/s] 82%|████████▏ | 36/44 [00:09<00:02, 3.68it/s] 84%|████████▍ | 37/44 [00:10<00:01, 3.68it/s] 86%|████████▋ | 38/44 [00:10<00:01, 3.68it/s] 89%|████████▊ | 39/44 [00:10<00:01, 3.68it/s] 91%|█████████ | 40/44 [00:10<00:01, 3.68it/s] 93%|█████████▎| 41/44 [00:11<00:00, 3.68it/s] 95%|█████████▌| 42/44 [00:11<00:00, 3.68it/s] 98%|█████████▊| 43/44 [00:11<00:00, 3.68it/s] 100%|██████████| 44/44 [00:11<00:00, 3.68it/s] 100%|██████████| 44/44 [00:11<00:00, 3.67it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.33it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.31it/s] 50%|█████ | 3/6 [00:00<00:00, 4.31it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.30it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.30it/s] 100%|██████████| 6/6 [00:01<00:00, 4.29it/s] 100%|██████████| 6/6 [00:01<00:00, 4.30it/s]
Version Details
- Version ID
b1c4743256c7db1538a013d04f7d87c788cecdb2e13a7fb2696d73a9547ad4ee- Version Created
- December 2, 2023