ninar12/sdxlblackmalehairstyles 🖼️🔢📝❓✓ → 🖼️
About
Trained Stable Diffusion XL Lora With Black Male Hairstyles
Example Output
Prompt:
"black male, bleached small dreads, temple fade"
Output
Performance Metrics
18.58s
Prediction Time
72.26s
Total Time
All Input Parameters
{
"width": 1024,
"height": 1024,
"prompt": "black male, bleached small dreads, temple fade",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "unrealistic",
"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: 36896 Ensuring enough disk space... Free disk space: 1521852248064 Downloading weights: https://replicate.delivery/pbxt/qSufWbK7FiWbFaXCZOODv9AAeOxqcCl5iPdiiqBu9ZVJwrYSA/trained_model.tar 2024-02-20T20:47:38Z | INFO | [ Initiating ] dest=/src/weights-cache/8326008f64344d2c minimum_chunk_size=150M url=https://replicate.delivery/pbxt/qSufWbK7FiWbFaXCZOODv9AAeOxqcCl5iPdiiqBu9ZVJwrYSA/trained_model.tar 2024-02-20T20:47:39Z | INFO | [ Complete ] dest=/src/weights-cache/8326008f64344d2c size="186 MB" total_elapsed=0.398s url=https://replicate.delivery/pbxt/qSufWbK7FiWbFaXCZOODv9AAeOxqcCl5iPdiiqBu9ZVJwrYSA/trained_model.tar b'' Downloaded weights in 0.5223355293273926 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: black male, bleached small dreads, temple fade txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:01<01:10, 1.43s/it] 4%|▍ | 2/50 [00:01<00:35, 1.34it/s] 6%|▌ | 3/50 [00:01<00:24, 1.88it/s] 8%|▊ | 4/50 [00:02<00:19, 2.33it/s] 10%|█ | 5/50 [00:02<00:16, 2.68it/s] 12%|█▏ | 6/50 [00:02<00:14, 2.95it/s] 14%|█▍ | 7/50 [00:03<00:13, 3.16it/s] 16%|█▌ | 8/50 [00:03<00:12, 3.30it/s] 18%|█▊ | 9/50 [00:03<00:12, 3.41it/s] 20%|██ | 10/50 [00:03<00:11, 3.49it/s] 22%|██▏ | 11/50 [00:04<00:11, 3.54it/s] 24%|██▍ | 12/50 [00:04<00:10, 3.58it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.60it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.62it/s] 30%|███ | 15/50 [00:05<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:05<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:06<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:06<00:08, 3.66it/s] 40%|████ | 20/50 [00:06<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:07<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:07<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.66it/s] 50%|█████ | 25/50 [00:07<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:08<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:08<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:09<00:05, 3.66it/s] 60%|██████ | 30/50 [00:09<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:10<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:10<00:04, 3.65it/s] 70%|███████ | 35/50 [00:10<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:11<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.65it/s] 80%|████████ | 40/50 [00:12<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:12<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:13<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.64it/s] 94%|█████████▍| 47/50 [00:14<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:14<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.64it/s] 100%|██████████| 50/50 [00:14<00:00, 3.37it/s]
Version Details
- Version ID
1294c57ea49537c6805c42d29eb44d54c980ba4958afd8198b3ba4137d063e12- Version Created
- February 20, 2024