bryantanjw/entropy-lol | Run with an API on Replicate

Cold

LoRA + Iterative 4x Upscale ComfyUI Workflow

Public

3.1K runs

A100 (80GB)

Run with an API

Pricing

GitHub

Playground API Examples README Versions

Input

checkpoint_model

string

Checkpoint Model

Default: "Aniverse.safetensors"

input_prompt

*string

Shift + Return to add a new line

masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,masterpiece, (detailed, highres, best quality), 1girl, IncrsAhri, braid, fox tail, multiple tails, korean clothes, skirt, blurry, blurry background, arms behind back, seductive smile,

Prompt

negative_prompt

string

Shift + Return to add a new line

Negative Prompt

Default: "(worst quality:1.4), (low quality:1.4), simple background, bad anatomy"

steps

integer

(minimum: 1, maximum: 100)

Inference Steps

Default: 20

sampler_name

string

Sampler Name

Default: "dpmpp_2m"

seed

integer

Sampling seed, leave Empty for Random

cfg

number

(minimum: 1, maximum: 20)

CFG Scale

Default: 7

lora

string

LoRA Model

Default: "gaming/Ahri.safetensors"

custom_lora

string

Shift + Return to add a new line

Link to LoRA file (.safetensors). Replicate doesn't allow .safetensors file uploads :(, but you can upload it on app.entropy.so.

lora_strength

number

(minimum: 0, maximum: 1)

LoRA Strength

Default: 1

width

integer

Image Width

Default: 340

height

integer

Image Height

Default: 512

batch_size

integer

(minimum: 1, maximum: 4)

Batch Size

Default: 1

upscale_factor

number

(minimum: 0, maximum: 4)

Upscale Factor

Default: 3

Run this model in Node.js with one line of code:

npx create-replicate --model=bryantanjw/entropy-lol

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

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:0861c317ed4a82b364f81f554879b6c2e72e4a4e7d197dc32284295374c52665",
  {
    input: {
      cfg: 6,
      lora: "gaming/Ahri.safetensors",
      seed: 0,
      steps: 25,
      width: 512,
      height: 720,
      batch_size: 4,
      custom_lora: "",
      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,",
      sampler_name: "dpmpp_2m",
      lora_strength: 1,
      upscale_factor: 3,
      negative_prompt: "(worst quality:1.4), (low quality:1.4)",
      checkpoint_model: "Aniverse.safetensors"
    }
  }
);

// 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=<paste-your-token-here>

Find your API token in your account settings.

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:0861c317ed4a82b364f81f554879b6c2e72e4a4e7d197dc32284295374c52665",
    input={
        "cfg": 6,
        "lora": "gaming/Ahri.safetensors",
        "seed": 0,
        "steps": 25,
        "width": 512,
        "height": 720,
        "batch_size": 4,
        "custom_lora": "",
        "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,",
        "sampler_name": "dpmpp_2m",
        "lora_strength": 1,
        "upscale_factor": 3,
        "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
        "checkpoint_model": "Aniverse.safetensors"
    }
)

# 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=<paste-your-token-here>

Find your API token in your account settings.

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:0861c317ed4a82b364f81f554879b6c2e72e4a4e7d197dc32284295374c52665",
    "input": {
      "cfg": 6,
      "lora": "gaming/Ahri.safetensors",
      "seed": 0,
      "steps": 25,
      "width": 512,
      "height": 720,
      "batch_size": 4,
      "custom_lora": "",
      "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,",
      "sampler_name": "dpmpp_2m",
      "lora_strength": 1,
      "upscale_factor": 3,
      "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
      "checkpoint_model": "Aniverse.safetensors"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-01-28T08:33:16.101056Z",
  "created_at": "2024-01-28T08:19:27.031954Z",
  "data_removed": false,
  "error": null,
  "id": "eyuwiftbzmwvthoykk2ygo6n3a",
  "input": {
    "cfg": 6,
    "lora": "gaming/Ahri.safetensors",
    "seed": 0,
    "steps": 25,
    "width": 512,
    "height": 720,
    "batch_size": 4,
    "custom_lora": "",
    "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,",
    "sampler_name": "dpmpp_2m",
    "lora_strength": 1,
    "negative_prompt": "(worst quality:1.4), (low quality:1.4)",
    "checkpoint_model": "Aniverse.safetensors"
  },
  "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",
  "metrics": {
    "predict_time": 243.86861,
    "total_time": 829.069102
  },
  "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"
  ],
  "started_at": "2024-01-28T08:29:12.232446Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/eyuwiftbzmwvthoykk2ygo6n3a",
    "cancel": "https://api.replicate.com/v1/predictions/eyuwiftbzmwvthoykk2ygo6n3a/cancel"
  },
  "version": "e7ca1ebdcf9d2af7ea66291dbf9902b1eefbb7de51da8128c72fe36915d799b8"
}

Generated in

4 minutes

Tweak it Iterate in playground Report View full prediction

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
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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

This output was created using a different version of the model, bryantanjw/entropy-lol:e7ca1ebd.

Examples

View more examples

Run time and cost

This model costs approximately $0.30 to run on Replicate, or 3 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 4 minutes. The predict time for this model varies significantly based on the inputs.

Readme

If you just want to use the base checkpoint models, set LoRA strength to 0.

Send character requests on Twitter @bryantanjw or on app.entropy.so.