arusterholz-edu/temptrain 🖼️🔢📝❓✓ → 🖼️

▶️ 175 runs 📅 Oct 2023 ⚙️ Cog 0.8.2

About

Example Output

Prompt:

"illustration of JSPR dog in cyberpunk style surrounded by city"

Output

Performance Metrics

15.98s Prediction Time

24.95s Total Time

All Input Parameters

{
  "width": 1024,
  "height": 1024,
  "prompt": "illustration of JSPR dog in cyberpunk style surrounded by city",
  "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": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Input Parameters

mask Type: string: Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.
seed Type: integer: Random seed. Leave blank to randomize the seed
image Type: string: Input image for img2img or inpaint mode
width Type: integerDefault: 1024: Width of output image
height Type: integerDefault: 1024: Height of output image
prompt Type: stringDefault: An astronaut riding a rainbow unicorn: Input prompt
refine Default: no_refiner: Which refine style to use
scheduler Default: K_EULER: scheduler
lora_scale Type: numberDefault: 0.6Range: 0 - 1: LoRA additive scale. Only applicable on trained models.
num_outputs Type: integerDefault: 1Range: 1 - 4: Number of images to output.
refine_steps Type: integer: For base_image_refiner, the number of steps to refine, defaults to num_inference_steps
guidance_scale Type: numberDefault: 7.5Range: 1 - 50: Scale for classifier-free guidance
apply_watermark Type: booleanDefault: true: 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 Type: numberDefault: 0.8Range: 0 - 1: For expert_ensemble_refiner, the fraction of noise to use
negative_prompt Type: stringDefault:: Input Negative Prompt
prompt_strength Type: numberDefault: 0.8Range: 0 - 1: Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
replicate_weights Type: string: Replicate LoRA weights to use. Leave blank to use the default weights.
num_inference_steps Type: integerDefault: 50Range: 1 - 500: Number of denoising steps
disable_safety_checker Type: booleanDefault: false: 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

Type: array • Items Type: string • Items Format: uri

Example Execution Logs

Using seed: 30638
Ensuring enough disk space...
Free disk space: 1974630436864
Downloading weights: https://pbxt.replicate.delivery/XZuDJxA3ZPYvLdGCn67QwvpTCN9Uqhu0Z3V8TY1dITkQM2cE/trained_model.tar
b'Downloaded 186 MB bytes in 0.263s (708 MB/s)\nExtracted 186 MB in 0.066s (2.8 GB/s)\n'
Downloaded weights in 0.4617924690246582 seconds
Loading fine-tuned model
Does not have Unet. assume we are using LoRA
Loading Unet LoRA
Prompt: illustration of JSPR dog in cyberpunk style surrounded by city
txt2img mode
  0%|          | 0/50 [00:00<?, ?it/s]
  2%|▏         | 1/50 [00:00<00:13,  3.68it/s]
  4%|▍         | 2/50 [00:00<00:13,  3.67it/s]
  6%|▌         | 3/50 [00:00<00:12,  3.67it/s]
  8%|▊         | 4/50 [00:01<00:12,  3.68it/s]
 10%|█         | 5/50 [00:01<00:12,  3.68it/s]
 12%|█▏        | 6/50 [00:01<00:11,  3.68it/s]
 14%|█▍        | 7/50 [00:01<00:11,  3.68it/s]
 16%|█▌        | 8/50 [00:02<00:11,  3.68it/s]
 18%|█▊        | 9/50 [00:02<00:11,  3.67it/s]
 20%|██        | 10/50 [00:02<00:10,  3.67it/s]
 22%|██▏       | 11/50 [00:02<00:10,  3.67it/s]
 24%|██▍       | 12/50 [00:03<00:10,  3.67it/s]
 26%|██▌       | 13/50 [00:03<00:10,  3.67it/s]
 28%|██▊       | 14/50 [00:03<00:09,  3.67it/s]
 30%|███       | 15/50 [00:04<00:09,  3.67it/s]
 32%|███▏      | 16/50 [00:04<00:09,  3.67it/s]
 34%|███▍      | 17/50 [00:04<00:08,  3.67it/s]
 36%|███▌      | 18/50 [00:04<00:08,  3.67it/s]
 38%|███▊      | 19/50 [00:05<00:08,  3.67it/s]
 40%|████      | 20/50 [00:05<00:08,  3.67it/s]
 42%|████▏     | 21/50 [00:05<00:07,  3.67it/s]
 44%|████▍     | 22/50 [00:05<00:07,  3.67it/s]
 46%|████▌     | 23/50 [00:06<00:07,  3.67it/s]
 48%|████▊     | 24/50 [00:06<00:07,  3.67it/s]
 50%|█████     | 25/50 [00:06<00:06,  3.67it/s]
 52%|█████▏    | 26/50 [00:07<00:06,  3.67it/s]
 54%|█████▍    | 27/50 [00:07<00:06,  3.67it/s]
 56%|█████▌    | 28/50 [00:07<00:05,  3.67it/s]
 58%|█████▊    | 29/50 [00:07<00:05,  3.67it/s]
 60%|██████    | 30/50 [00:08<00:05,  3.66it/s]
 62%|██████▏   | 31/50 [00:08<00:05,  3.66it/s]
 64%|██████▍   | 32/50 [00:08<00:04,  3.66it/s]
 66%|██████▌   | 33/50 [00:08<00:04,  3.65it/s]
 68%|██████▊   | 34/50 [00:09<00:04,  3.65it/s]
 70%|███████   | 35/50 [00:09<00:04,  3.65it/s]
 72%|███████▏  | 36/50 [00:09<00:03,  3.66it/s]
 74%|███████▍  | 37/50 [00:10<00:03,  3.66it/s]
 76%|███████▌  | 38/50 [00:10<00:03,  3.66it/s]
 78%|███████▊  | 39/50 [00:10<00:03,  3.66it/s]
 80%|████████  | 40/50 [00:10<00:02,  3.65it/s]
 82%|████████▏ | 41/50 [00:11<00:02,  3.66it/s]
 84%|████████▍ | 42/50 [00:11<00:02,  3.66it/s]
 86%|████████▌ | 43/50 [00:11<00:01,  3.66it/s]
 88%|████████▊ | 44/50 [00:12<00:01,  3.66it/s]
 90%|█████████ | 45/50 [00:12<00:01,  3.66it/s]
 92%|█████████▏| 46/50 [00:12<00:01,  3.65it/s]
 94%|█████████▍| 47/50 [00:12<00:00,  3.65it/s]
 96%|█████████▌| 48/50 [00:13<00:00,  3.65it/s]
 98%|█████████▊| 49/50 [00:13<00:00,  3.65it/s]
100%|██████████| 50/50 [00:13<00:00,  3.65it/s]
100%|██████████| 50/50 [00:13<00:00,  3.66it/s]

Version Details

Version ID: 4c62e8b2abf162b4dea15e984ab251ae92d19d11494e2789f279f1e7c79fd0ea
Version Created: October 30, 2023

Run on Replicate →