Prediction

Model

bryantanjw/entropy-lol:e7ca1ebd

ID

eyuwiftbzmwvthoykk2ygo6n3a

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

13m 49s

Created

almost 2 years ago

Webhook

–

Input

checkpoint_model: Aniverse.safetensors
input_prompt: masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,
negative_prompt: (worst quality:1.4), (low quality:1.4)
steps: 25
sampler_name: dpmpp_2m
seed: 0
cfg: 6
lora: gaming/Ahri.safetensors
custom_lora: Empty string
lora_strength: 1
width: 512
height: 720
batch_size: 4

{
  "batch_size": 4,
  "cfg": 6,
  "checkpoint_model": "Aniverse.safetensors",
  "custom_lora": "",
  "height": 720,
  "input_prompt": "masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,",
  "lora": "gaming/Ahri.safetensors",
  "lora_strength": 1,
  "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
  "sampler_name": "dpmpp_2m",
  "seed": 0,
  "steps": 25,
  "width": 512
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_IqH**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run bryantanjw/entropy-lol using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bryantanjw/entropy-lol:e7ca1ebdcf9d2af7ea66291dbf9902b1eefbb7de51da8128c72fe36915d799b8",
  {
    input: {
      batch_size: 4,
      cfg: 6,
      checkpoint_model: "Aniverse.safetensors",
      custom_lora: "",
      height: 720,
      input_prompt: "masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,",
      lora: "gaming/Ahri.safetensors",
      lora_strength: 1,
      negative_prompt: "(worst quality:1.4), (low quality:1.4)",
      sampler_name: "dpmpp_2m",
      seed: 0,
      steps: 25,
      width: 512
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_IqH**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run bryantanjw/entropy-lol using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bryantanjw/entropy-lol:e7ca1ebdcf9d2af7ea66291dbf9902b1eefbb7de51da8128c72fe36915d799b8",
    input={
        "batch_size": 4,
        "cfg": 6,
        "checkpoint_model": "Aniverse.safetensors",
        "custom_lora": "",
        "height": 720,
        "input_prompt": "masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,",
        "lora": "gaming/Ahri.safetensors",
        "lora_strength": 1,
        "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
        "sampler_name": "dpmpp_2m",
        "seed": 0,
        "steps": 25,
        "width": 512
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_IqH**********************************

This is your API token. Keep it to yourself.

Run bryantanjw/entropy-lol using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "bryantanjw/entropy-lol:e7ca1ebdcf9d2af7ea66291dbf9902b1eefbb7de51da8128c72fe36915d799b8",
    "input": {
      "batch_size": 4,
      "cfg": 6,
      "checkpoint_model": "Aniverse.safetensors",
      "custom_lora": "",
      "height": 720,
      "input_prompt": "masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,",
      "lora": "gaming/Ahri.safetensors",
      "lora_strength": 1,
      "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
      "sampler_name": "dpmpp_2m",
      "seed": 0,
      "steps": 25,
      "width": 512
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "eyuwiftbzmwvthoykk2ygo6n3a",
  "model": "bryantanjw/entropy-lol",
  "version": "e7ca1ebdcf9d2af7ea66291dbf9902b1eefbb7de51da8128c72fe36915d799b8",
  "input": {
    "batch_size": 4,
    "cfg": 6,
    "checkpoint_model": "Aniverse.safetensors",
    "custom_lora": "",
    "height": 720,
    "input_prompt": "masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,",
    "lora": "gaming/Ahri.safetensors",
    "lora_strength": 1,
    "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
    "sampler_name": "dpmpp_2m",
    "seed": 0,
    "steps": 25,
    "width": 512
  },
  "logs": "Using seed: 4693380\ngot prompt\nmodel_type EPS\nadm 0\nUsing pytorch attention in VAE\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nUsing pytorch attention in VAE\nmissing {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection'}\nleft over keys: dict_keys(['cond_stage_model.clip_l.transformer.text_model.embeddings.position_ids'])\nUsing pytorch attention in VAE\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nUsing pytorch attention in VAE\nLeftover VAE keys ['model_ema.decay', 'model_ema.num_updates']\nRequested to load SD1ClipModel\nLoading 1 new model\nRequested to load BaseModel\nLoading 1 new model\n  0%|          | 0/25 [00:00<?, ?it/s]\n  4%|▍         | 1/25 [00:00<00:14,  1.64it/s]\n  8%|▊         | 2/25 [00:00<00:08,  2.76it/s]\n 12%|█▏        | 3/25 [00:00<00:06,  3.55it/s]\n 16%|█▌        | 4/25 [00:01<00:05,  4.11it/s]\n 20%|██        | 5/25 [00:01<00:04,  4.50it/s]\n 24%|██▍       | 6/25 [00:01<00:04,  4.70it/s]\n 28%|██▊       | 7/25 [00:01<00:03,  4.90it/s]\n 32%|███▏      | 8/25 [00:01<00:03,  5.04it/s]\n 36%|███▌      | 9/25 [00:02<00:03,  5.14it/s]\n 40%|████      | 10/25 [00:02<00:02,  5.22it/s]\n 44%|████▍     | 11/25 [00:02<00:02,  5.26it/s]\n 48%|████▊     | 12/25 [00:02<00:02,  5.28it/s]\n 52%|█████▏    | 13/25 [00:02<00:02,  5.32it/s]\n 56%|█████▌    | 14/25 [00:03<00:02,  5.34it/s]\n 60%|██████    | 15/25 [00:03<00:01,  5.34it/s]\n 64%|██████▍   | 16/25 [00:03<00:01,  5.35it/s]\n 68%|██████▊   | 17/25 [00:03<00:01,  5.35it/s]\n 72%|███████▏  | 18/25 [00:03<00:01,  5.35it/s]\n 76%|███████▌  | 19/25 [00:03<00:01,  5.36it/s]\n 80%|████████  | 20/25 [00:04<00:00,  5.36it/s]\n 84%|████████▍ | 21/25 [00:04<00:00,  5.32it/s]\n 88%|████████▊ | 22/25 [00:04<00:00,  5.34it/s]\n 92%|█████████▏| 23/25 [00:05<00:00,  2.71it/s]\n 96%|█████████▌| 24/25 [00:06<00:00,  2.06it/s]\n100%|██████████| 25/25 [00:06<00:00,  2.47it/s]\n100%|██████████| 25/25 [00:06<00:00,  3.97it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\nIterativeLatentUpscale[1/3]: 853.3x1200.0 (scale:1.67)\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:14,  1.33it/s]\n 10%|█         | 2/20 [00:00<00:07,  2.38it/s]\n 15%|█▌        | 3/20 [00:01<00:09,  1.71it/s]\n 20%|██        | 4/20 [00:02<00:11,  1.35it/s]\n 25%|██▌       | 5/20 [00:03<00:12,  1.22it/s]\n 30%|███       | 6/20 [00:04<00:11,  1.21it/s]\n 35%|███▌      | 7/20 [00:05<00:11,  1.15it/s]\n 40%|████      | 8/20 [00:06<00:10,  1.11it/s]\n 45%|████▌     | 9/20 [00:07<00:10,  1.08it/s]\n 50%|█████     | 10/20 [00:08<00:08,  1.16it/s]\n 55%|█████▌    | 11/20 [00:08<00:07,  1.21it/s]\n 60%|██████    | 12/20 [00:09<00:06,  1.26it/s]\n 65%|██████▌   | 13/20 [00:10<00:06,  1.07it/s]\n 70%|███████   | 14/20 [00:11<00:05,  1.05it/s]\n 75%|███████▌  | 15/20 [00:12<00:04,  1.04it/s]\n 80%|████████  | 16/20 [00:13<00:03,  1.03it/s]\n 85%|████████▌ | 17/20 [00:14<00:02,  1.07it/s]\n 90%|█████████ | 18/20 [00:15<00:01,  1.13it/s]\n 95%|█████████▌| 19/20 [00:16<00:00,  1.09it/s]\n100%|██████████| 20/20 [00:17<00:00,  1.06it/s]\n100%|██████████| 20/20 [00:17<00:00,  1.15it/s]\nIterativeLatentUpscale[2/3]: 1194.7x1680.0 (scale:2.33)\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:11,  1.67it/s]\n 10%|█         | 2/20 [00:02<00:28,  1.59s/it]\n 15%|█▌        | 3/20 [00:05<00:32,  1.91s/it]\n 20%|██        | 4/20 [00:07<00:32,  2.06s/it]\n 25%|██▌       | 5/20 [00:09<00:32,  2.14s/it]\n 30%|███       | 6/20 [00:12<00:30,  2.19s/it]\n 35%|███▌      | 7/20 [00:14<00:28,  2.22s/it]\n 40%|████      | 8/20 [00:16<00:26,  2.24s/it]\n 45%|████▌     | 9/20 [00:18<00:24,  2.26s/it]\n 50%|█████     | 10/20 [00:21<00:22,  2.27s/it]\n 55%|█████▌    | 11/20 [00:23<00:20,  2.27s/it]\n 60%|██████    | 12/20 [00:25<00:18,  2.28s/it]\n 65%|██████▌   | 13/20 [00:28<00:15,  2.28s/it]\n 70%|███████   | 14/20 [00:30<00:13,  2.28s/it]\n 75%|███████▌  | 15/20 [00:32<00:11,  2.29s/it]\n 80%|████████  | 16/20 [00:34<00:09,  2.29s/it]\n 85%|████████▌ | 17/20 [00:37<00:06,  2.29s/it]\n 90%|█████████ | 18/20 [00:39<00:04,  2.29s/it]\n 95%|█████████▌| 19/20 [00:41<00:02,  2.33s/it]\n100%|██████████| 20/20 [00:44<00:00,  2.32s/it]\n100%|██████████| 20/20 [00:44<00:00,  2.21s/it]\nIterativeLatentUpscale[Final]: 1536.0x2160.0 (scale:3.00)\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:01<00:28,  1.51s/it]\n 10%|█         | 2/20 [00:06<01:05,  3.64s/it]\n 15%|█▌        | 3/20 [00:11<01:13,  4.32s/it]\n 20%|██        | 4/20 [00:16<01:14,  4.65s/it]\n 25%|██▌       | 5/20 [00:22<01:12,  4.82s/it]\n 30%|███       | 6/20 [00:27<01:09,  4.93s/it]\n 35%|███▌      | 7/20 [00:32<01:04,  5.00s/it]\n 40%|████      | 8/20 [00:37<01:00,  5.05s/it]\n 45%|████▌     | 9/20 [00:42<00:55,  5.08s/it]\n 50%|█████     | 10/20 [00:47<00:50,  5.10s/it]\n 55%|█████▌    | 11/20 [00:52<00:45,  5.11s/it]\n 60%|██████    | 12/20 [00:58<00:40,  5.12s/it]\n 65%|██████▌   | 13/20 [01:03<00:35,  5.13s/it]\n 70%|███████   | 14/20 [01:08<00:30,  5.13s/it]\n 75%|███████▌  | 15/20 [01:13<00:25,  5.13s/it]\n 80%|████████  | 16/20 [01:18<00:20,  5.14s/it]\n 85%|████████▌ | 17/20 [01:23<00:15,  5.14s/it]\n 90%|█████████ | 18/20 [01:28<00:10,  5.14s/it]\n 95%|█████████▌| 19/20 [01:34<00:05,  5.14s/it]\n100%|██████████| 20/20 [01:39<00:00,  5.14s/it]\n100%|██████████| 20/20 [01:39<00:00,  4.96s/it]\nPrompt executed in 234.25 seconds\nnode output:  {'images': [{'filename': 'ComfyUI_temp_qbeir_00001_.png', 'subfolder': '', 'type': 'temp'}, {'filename': 'ComfyUI_temp_qbeir_00002_.png', 'subfolder': '', 'type': 'temp'}, {'filename': 'ComfyUI_temp_qbeir_00003_.png', 'subfolder': '', 'type': 'temp'}, {'filename': 'ComfyUI_temp_qbeir_00004_.png', 'subfolder': '', 'type': 'temp'}]}\ntemp\ntemp\ntemp\ntemp\nnode output:  {'images': [{'filename': 'ComfyUI_00045_.png', 'subfolder': '', 'type': 'output'}, {'filename': 'ComfyUI_00046_.png', 'subfolder': '', 'type': 'output'}, {'filename': 'ComfyUI_00047_.png', 'subfolder': '', 'type': 'output'}, {'filename': 'ComfyUI_00048_.png', 'subfolder': '', 'type': 'output'}]}\noutput\noutput\noutput\noutput\n4 images generated successfully",
  "output": [
    "https://replicate.delivery/pbxt/efWekY9BeaDOjTQOl7qv8WTmdoMGXG9E4ejvLEXpdE2IphHSC/out-0.png",
    "https://replicate.delivery/pbxt/DSTCfJXBalUGCSQnvag2iHSv9s5zAKYWamsR3mNDfFVKN8QSA/out-1.png",
    "https://replicate.delivery/pbxt/SAfHeHCOklkOPk7iTwgH5oZMSxJpoPfLSQCB9cHkqe6v0wDJB/out-2.png",
    "https://replicate.delivery/pbxt/CtJxUT3AcRbSHp6MzjOQjDOb2A9BCpDYtiQFIqd11L6SDPkE/out-3.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-01-28T08:19:27.031954Z",
  "started_at": "2024-01-28T08:29:12.232446Z",
  "completed_at": "2024-01-28T08:33:16.101056Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/eyuwiftbzmwvthoykk2ygo6n3a/cancel",
    "get": "https://api.replicate.com/v1/predictions/eyuwiftbzmwvthoykk2ygo6n3a"
  },
  "metrics": {
    "predict_time": 243.86861,
    "total_time": 829.069102
  }
}

Generated in

4 minutes

Tweak it Report

Using seed: 4693380 got prompt model_type EPS adm 0 Using pytorch attention in VAE Working with z of shape (1, 4, 32, 32) = 4096 dimensions. Using pytorch attention in VAE missing {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection'} left over keys: dict_keys(['cond_stage_model.clip_l.transformer.text_model.embeddings.position_ids']) Using pytorch attention in VAE Working with z of shape (1, 4, 32, 32) = 4096 dimensions. Using pytorch attention in VAE Leftover VAE keys ['model_ema.decay', 'model_ema.num_updates'] Requested to load SD1ClipModel Loading 1 new model Requested to load BaseModel Loading 1 new model 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:14, 1.64it/s] 8%|▊ | 2/25 [00:00<00:08, 2.76it/s] 12%|█▏ | 3/25 [00:00<00:06, 3.55it/s] 16%|█▌ | 4/25 [00:01<00:05, 4.11it/s] 20%|██ | 5/25 [00:01<00:04, 4.50it/s] 24%|██▍ | 6/25 [00:01<00:04, 4.70it/s] 28%|██▊ | 7/25 [00:01<00:03, 4.90it/s] 32%|███▏ | 8/25 [00:01<00:03, 5.04it/s] 36%|███▌ | 9/25 [00:02<00:03, 5.14it/s] 40%|████ | 10/25 [00:02<00:02, 5.22it/s] 44%|████▍ | 11/25 [00:02<00:02, 5.26it/s] 48%|████▊ | 12/25 [00:02<00:02, 5.28it/s] 52%|█████▏ | 13/25 [00:02<00:02, 5.32it/s] 56%|█████▌ | 14/25 [00:03<00:02, 5.34it/s] 60%|██████ | 15/25 [00:03<00:01, 5.34it/s] 64%|██████▍ | 16/25 [00:03<00:01, 5.35it/s] 68%|██████▊ | 17/25 [00:03<00:01, 5.35it/s] 72%|███████▏ | 18/25 [00:03<00:01, 5.35it/s] 76%|███████▌ | 19/25 [00:03<00:01, 5.36it/s] 80%|████████ | 20/25 [00:04<00:00, 5.36it/s] 84%|████████▍ | 21/25 [00:04<00:00, 5.32it/s] 88%|████████▊ | 22/25 [00:04<00:00, 5.34it/s] 92%|█████████▏| 23/25 [00:05<00:00, 2.71it/s] 96%|█████████▌| 24/25 [00:06<00:00, 2.06it/s] 100%|██████████| 25/25 [00:06<00:00, 2.47it/s] 100%|██████████| 25/25 [00:06<00:00, 3.97it/s] Requested to load AutoencoderKL Loading 1 new model IterativeLatentUpscale[1/3]: 853.3x1200.0 (scale:1.67) 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:14, 1.33it/s] 10%|█ | 2/20 [00:00<00:07, 2.38it/s] 15%|█▌ | 3/20 [00:01<00:09, 1.71it/s] 20%|██ | 4/20 [00:02<00:11, 1.35it/s] 25%|██▌ | 5/20 [00:03<00:12, 1.22it/s] 30%|███ | 6/20 [00:04<00:11, 1.21it/s] 35%|███▌ | 7/20 [00:05<00:11, 1.15it/s] 40%|████ | 8/20 [00:06<00:10, 1.11it/s] 45%|████▌ | 9/20 [00:07<00:10, 1.08it/s] 50%|█████ | 10/20 [00:08<00:08, 1.16it/s] 55%|█████▌ | 11/20 [00:08<00:07, 1.21it/s] 60%|██████ | 12/20 [00:09<00:06, 1.26it/s] 65%|██████▌ | 13/20 [00:10<00:06, 1.07it/s] 70%|███████ | 14/20 [00:11<00:05, 1.05it/s] 75%|███████▌ | 15/20 [00:12<00:04, 1.04it/s] 80%|████████ | 16/20 [00:13<00:03, 1.03it/s] 85%|████████▌ | 17/20 [00:14<00:02, 1.07it/s] 90%|█████████ | 18/20 [00:15<00:01, 1.13it/s] 95%|█████████▌| 19/20 [00:16<00:00, 1.09it/s] 100%|██████████| 20/20 [00:17<00:00, 1.06it/s] 100%|██████████| 20/20 [00:17<00:00, 1.15it/s] IterativeLatentUpscale[2/3]: 1194.7x1680.0 (scale:2.33) 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:11, 1.67it/s] 10%|█ | 2/20 [00:02<00:28, 1.59s/it] 15%|█▌ | 3/20 [00:05<00:32, 1.91s/it] 20%|██ | 4/20 [00:07<00:32, 2.06s/it] 25%|██▌ | 5/20 [00:09<00:32, 2.14s/it] 30%|███ | 6/20 [00:12<00:30, 2.19s/it] 35%|███▌ | 7/20 [00:14<00:28, 2.22s/it] 40%|████ | 8/20 [00:16<00:26, 2.24s/it] 45%|████▌ | 9/20 [00:18<00:24, 2.26s/it] 50%|█████ | 10/20 [00:21<00:22, 2.27s/it] 55%|█████▌ | 11/20 [00:23<00:20, 2.27s/it] 60%|██████ | 12/20 [00:25<00:18, 2.28s/it] 65%|██████▌ | 13/20 [00:28<00:15, 2.28s/it] 70%|███████ | 14/20 [00:30<00:13, 2.28s/it] 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[01:13<00:25, 5.13s/it] 80%|████████ | 16/20 [01:18<00:20, 5.14s/it] 85%|████████▌ | 17/20 [01:23<00:15, 5.14s/it] 90%|█████████ | 18/20 [01:28<00:10, 5.14s/it] 95%|█████████▌| 19/20 [01:34<00:05, 5.14s/it] 100%|██████████| 20/20 [01:39<00:00, 5.14s/it] 100%|██████████| 20/20 [01:39<00:00, 4.96s/it] Prompt executed in 234.25 seconds node output: {'images': [{'filename': 'ComfyUI_temp_qbeir_00001_.png', 'subfolder': '', 'type': 'temp'}, {'filename': 'ComfyUI_temp_qbeir_00002_.png', 'subfolder': '', 'type': 'temp'}, {'filename': 'ComfyUI_temp_qbeir_00003_.png', 'subfolder': '', 'type': 'temp'}, {'filename': 'ComfyUI_temp_qbeir_00004_.png', 'subfolder': '', 'type': 'temp'}]} temp temp temp temp node output: {'images': [{'filename': 'ComfyUI_00045_.png', 'subfolder': '', 'type': 'output'}, {'filename': 'ComfyUI_00046_.png', 'subfolder': '', 'type': 'output'}, {'filename': 'ComfyUI_00047_.png', 'subfolder': '', 'type': 'output'}, {'filename': 'ComfyUI_00048_.png', 'subfolder': '', 'type': 'output'}]} output output output output 4 images generated successfully