jbilcke/sdxl-starfield 🖼️🔢📝❓✓ → 🖼️

▶️ 275 runs 📅 Oct 2023 ⚙️ Cog v0.8.1+dev
image-inpainting image-to-image text-to-image

About

Example Output

Prompt:

"japanese garden, beautiful, zen garden, skyline, neotokyo, sci-fi, in the style of TOK"

Output

Example output

Performance Metrics

18.74s Prediction Time
18.71s Total Time
All Input Parameters 
{
  "width": 1024,
  "height": 768,
  "prompt": "japanese garden, beautiful, zen garden, skyline, neotokyo, sci-fi, in the style of TOK",
  "refine": "expert_ensemble_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.9,
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "photo, drawing, cropped",
  "prompt_strength": 0.8,
  "num_inference_steps": 90
}
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: 38681
Prompt: japanese garden, beautiful, zen garden, skyline, neotokyo, sci-fi, in the style of <s0><s1>
txt2img mode
  0%|          | 0/71 [00:00<?, ?it/s]
  1%|▏         | 1/71 [00:00<00:14,  4.97it/s]
  3%|▎         | 2/71 [00:00<00:13,  4.95it/s]
  4%|▍         | 3/71 [00:00<00:13,  4.94it/s]
  6%|▌         | 4/71 [00:00<00:13,  4.94it/s]
  7%|▋         | 5/71 [00:01<00:13,  4.93it/s]
  8%|▊         | 6/71 [00:01<00:13,  4.93it/s]
 10%|▉         | 7/71 [00:01<00:12,  4.93it/s]
 11%|█▏        | 8/71 [00:01<00:12,  4.93it/s]
 13%|█▎        | 9/71 [00:01<00:12,  4.92it/s]
 14%|█▍        | 10/71 [00:02<00:12,  4.91it/s]
 15%|█▌        | 11/71 [00:02<00:12,  4.91it/s]
 17%|█▋        | 12/71 [00:02<00:12,  4.91it/s]
 18%|█▊        | 13/71 [00:02<00:11,  4.91it/s]
 20%|█▉        | 14/71 [00:02<00:11,  4.91it/s]
 21%|██        | 15/71 [00:03<00:11,  4.91it/s]
 23%|██▎       | 16/71 [00:03<00:11,  4.91it/s]
 24%|██▍       | 17/71 [00:03<00:10,  4.91it/s]
 25%|██▌       | 18/71 [00:03<00:10,  4.90it/s]
 27%|██▋       | 19/71 [00:03<00:10,  4.90it/s]
 28%|██▊       | 20/71 [00:04<00:10,  4.90it/s]
 30%|██▉       | 21/71 [00:04<00:10,  4.90it/s]
 31%|███       | 22/71 [00:04<00:10,  4.90it/s]
 32%|███▏      | 23/71 [00:04<00:09,  4.90it/s]
 34%|███▍      | 24/71 [00:04<00:09,  4.90it/s]
 35%|███▌      | 25/71 [00:05<00:09,  4.90it/s]
 37%|███▋      | 26/71 [00:05<00:09,  4.90it/s]
 38%|███▊      | 27/71 [00:05<00:08,  4.90it/s]
 39%|███▉      | 28/71 [00:05<00:08,  4.90it/s]
 41%|████      | 29/71 [00:05<00:08,  4.90it/s]
 42%|████▏     | 30/71 [00:06<00:08,  4.90it/s]
 44%|████▎     | 31/71 [00:06<00:08,  4.90it/s]
 45%|████▌     | 32/71 [00:06<00:07,  4.90it/s]
 46%|████▋     | 33/71 [00:06<00:07,  4.90it/s]
 48%|████▊     | 34/71 [00:06<00:07,  4.90it/s]
 49%|████▉     | 35/71 [00:07<00:07,  4.90it/s]
 51%|█████     | 36/71 [00:07<00:07,  4.90it/s]
 52%|█████▏    | 37/71 [00:07<00:06,  4.91it/s]
 54%|█████▎    | 38/71 [00:07<00:06,  4.92it/s]
 55%|█████▍    | 39/71 [00:07<00:06,  4.92it/s]
 56%|█████▋    | 40/71 [00:08<00:06,  4.93it/s]
 58%|█████▊    | 41/71 [00:08<00:06,  4.93it/s]
 59%|█████▉    | 42/71 [00:08<00:05,  4.93it/s]
 61%|██████    | 43/71 [00:08<00:05,  4.93it/s]
 62%|██████▏   | 44/71 [00:08<00:05,  4.93it/s]
 63%|██████▎   | 45/71 [00:09<00:05,  4.93it/s]
 65%|██████▍   | 46/71 [00:09<00:05,  4.93it/s]
 66%|██████▌   | 47/71 [00:09<00:04,  4.93it/s]
 68%|██████▊   | 48/71 [00:09<00:04,  4.93it/s]
 69%|██████▉   | 49/71 [00:09<00:04,  4.93it/s]
 70%|███████   | 50/71 [00:10<00:04,  4.93it/s]
 72%|███████▏  | 51/71 [00:10<00:04,  4.93it/s]
 73%|███████▎  | 52/71 [00:10<00:03,  4.92it/s]
 75%|███████▍  | 53/71 [00:10<00:03,  4.92it/s]
 76%|███████▌  | 54/71 [00:10<00:03,  4.92it/s]
 77%|███████▋  | 55/71 [00:11<00:03,  4.93it/s]
 79%|███████▉  | 56/71 [00:11<00:03,  4.93it/s]
 80%|████████  | 57/71 [00:11<00:02,  4.92it/s]
 82%|████████▏ | 58/71 [00:11<00:02,  4.92it/s]
 83%|████████▎ | 59/71 [00:11<00:02,  4.92it/s]
 85%|████████▍ | 60/71 [00:12<00:02,  4.92it/s]
 86%|████████▌ | 61/71 [00:12<00:02,  4.92it/s]
 87%|████████▋ | 62/71 [00:12<00:01,  4.92it/s]
 89%|████████▊ | 63/71 [00:12<00:01,  4.92it/s]
 90%|█████████ | 64/71 [00:13<00:01,  4.92it/s]
 92%|█████████▏| 65/71 [00:13<00:01,  4.92it/s]
 93%|█████████▎| 66/71 [00:13<00:01,  4.92it/s]
 94%|█████████▍| 67/71 [00:13<00:00,  4.92it/s]
 96%|█████████▌| 68/71 [00:13<00:00,  4.92it/s]
 97%|█████████▋| 69/71 [00:14<00:00,  4.92it/s]
 99%|█████████▊| 70/71 [00:14<00:00,  4.92it/s]
100%|██████████| 71/71 [00:14<00:00,  4.92it/s]
100%|██████████| 71/71 [00:14<00:00,  4.92it/s]
  0%|          | 0/19 [00:00<?, ?it/s]
  5%|▌         | 1/19 [00:00<00:02,  6.29it/s]
 11%|█         | 2/19 [00:00<00:02,  6.24it/s]
 16%|█▌        | 3/19 [00:00<00:02,  6.22it/s]
 21%|██        | 4/19 [00:00<00:02,  6.22it/s]
 26%|██▋       | 5/19 [00:00<00:02,  6.22it/s]
 32%|███▏      | 6/19 [00:00<00:02,  6.21it/s]
 37%|███▋      | 7/19 [00:01<00:01,  6.20it/s]
 42%|████▏     | 8/19 [00:01<00:01,  6.20it/s]
 47%|████▋     | 9/19 [00:01<00:01,  6.20it/s]
 53%|█████▎    | 10/19 [00:01<00:01,  6.20it/s]
 58%|█████▊    | 11/19 [00:01<00:01,  6.20it/s]
 63%|██████▎   | 12/19 [00:01<00:01,  6.19it/s]
 68%|██████▊   | 13/19 [00:02<00:00,  6.18it/s]
 74%|███████▎  | 14/19 [00:02<00:00,  6.18it/s]
 79%|███████▉  | 15/19 [00:02<00:00,  6.19it/s]
 84%|████████▍ | 16/19 [00:02<00:00,  6.19it/s]
 89%|████████▉ | 17/19 [00:02<00:00,  6.20it/s]
 95%|█████████▍| 18/19 [00:02<00:00,  6.20it/s]
100%|██████████| 19/19 [00:03<00:00,  6.20it/s]
100%|██████████| 19/19 [00:03<00:00,  6.20it/s]
Version Details
Version ID
e5083ba0840b2e515fae96f48204926e3595153df46ac7a9cda38158f5b37a52
Version Created
October 27, 2023
Run on Replicate →