{
  "completed_at": "2023-10-13T20:14:06.422064Z",
  "created_at": "2023-10-13T20:13:35.897670Z",
  "data_removed": false,
  "error": null,
  "id": "4yl7hedbgfxmv5mna3rcqjzrqa",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "In the style of KimJungGi hundreds of anthropomorphic animals living in a vintage version of Paris city. black and white. wide view. ",
    "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
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  "logs": "Using seed: 31846\nEnsuring enough disk space...\nFree disk space: 1647949238272\nDownloading weights: https://pbxt.replicate.delivery/FsW0QkwpomLKGh9suvAJweZXkMn4VaacNu5PDgvfaXoqM1tRA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.378s (492 MB/s)\\nExtracted 186 MB in 0.065s (2.9 GB/s)\\n'\nDownloaded weights in 0.6025729179382324 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of KimJungGi hundreds of anthropomorphic animals living in a vintage version of Paris city. black and white. wide view.\ntxt2img mode\n  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.)\nreturn F.conv2d(input, weight, bias, self.stride,\n  2%|▏         | 1/50 [00:00<00:44,  1.10it/s]\n  4%|▍         | 2/50 [00:01<00:25,  1.87it/s]\n  6%|▌         | 3/50 [00:01<00:19,  2.40it/s]\n  8%|▊         | 4/50 [00:01<00:16,  2.78it/s]\n 10%|█         | 5/50 [00:02<00:14,  3.04it/s]\n 12%|█▏        | 6/50 [00:02<00:13,  3.22it/s]\n 14%|█▍        | 7/50 [00:02<00:12,  3.35it/s]\n 16%|█▌        | 8/50 [00:02<00:12,  3.44it/s]\n 18%|█▊        | 9/50 [00:03<00:11,  3.50it/s]\n 20%|██        | 10/50 [00:03<00:11,  3.55it/s]\n 22%|██▏       | 11/50 [00:03<00:10,  3.58it/s]\n 24%|██▍       | 12/50 [00:03<00:10,  3.60it/s]\n 26%|██▌       | 13/50 [00:04<00:10,  3.61it/s]\n 28%|██▊       | 14/50 [00:04<00:09,  3.62it/s]\n 30%|███       | 15/50 [00:04<00:09,  3.63it/s]\n 32%|███▏      | 16/50 [00:05<00:09,  3.63it/s]\n 34%|███▍      | 17/50 [00:05<00:09,  3.64it/s]\n 36%|███▌      | 18/50 [00:05<00:08,  3.64it/s]\n 38%|███▊      | 19/50 [00:05<00:08,  3.64it/s]\n 40%|████      | 20/50 [00:06<00:08,  3.64it/s]\n 42%|████▏     | 21/50 [00:06<00:07,  3.64it/s]\n 44%|████▍     | 22/50 [00:06<00:07,  3.64it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.64it/s]\n 48%|████▊     | 24/50 [00:07<00:07,  3.64it/s]\n 50%|█████     | 25/50 [00:07<00:06,  3.64it/s]\n 52%|█████▏    | 26/50 [00:07<00:06,  3.65it/s]\n 54%|█████▍    | 27/50 [00:08<00:06,  3.65it/s]\n 56%|█████▌    | 28/50 [00:08<00:06,  3.65it/s]\n 58%|█████▊    | 29/50 [00:08<00:05,  3.65it/s]\n 60%|██████    | 30/50 [00:08<00:05,  3.65it/s]\n 62%|██████▏   | 31/50 [00:09<00:05,  3.65it/s]\n 64%|██████▍   | 32/50 [00:09<00:04,  3.65it/s]\n 66%|██████▌   | 33/50 [00:09<00:04,  3.65it/s]\n 68%|██████▊   | 34/50 [00:09<00:04,  3.65it/s]\n 70%|███████   | 35/50 [00:10<00:04,  3.65it/s]\n 72%|███████▏  | 36/50 [00:10<00:03,  3.65it/s]\n 74%|███████▍  | 37/50 [00:10<00:03,  3.65it/s]\n 76%|███████▌  | 38/50 [00:11<00:03,  3.65it/s]\n 78%|███████▊  | 39/50 [00:11<00:03,  3.65it/s]\n 80%|████████  | 40/50 [00:11<00:02,  3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02,  3.65it/s]\n 84%|████████▍ | 42/50 [00:12<00:02,  3.65it/s]\n 86%|████████▌ | 43/50 [00:12<00:01,  3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01,  3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01,  3.65it/s]\n 92%|█████████▏| 46/50 [00:13<00:01,  3.65it/s]\n 94%|█████████▍| 47/50 [00:13<00:00,  3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00,  3.65it/s]\n 98%|█████████▊| 49/50 [00:14<00:00,  3.65it/s]\n100%|██████████| 50/50 [00:14<00:00,  3.64it/s]\n100%|██████████| 50/50 [00:14<00:00,  3.49it/s]",
  "metrics": {
    "predict_time": 18.286902,
    "total_time": 30.524394
  },
  "output": [
    "https://pbxt.replicate.delivery/0dsj80GWIpp4KplVdZT1oXkgyMJnt7hAI0smVxNim4TDXdbE/out-0.png"
  ],
  "started_at": "2023-10-13T20:13:48.135162Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4yl7hedbgfxmv5mna3rcqjzrqa",
    "cancel": "https://api.replicate.com/v1/predictions/4yl7hedbgfxmv5mna3rcqjzrqa/cancel"
  },
  "version": "4cced661e10fb03775b2637678a49698d36594bb6c644f099783783efd5b88b3"
}