llsean/cog-a1111-webui | Run with an API on Replicate

llsean / cog-a1111-webui

A1111 webui api

Cold

Public
3.1K runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Prompt

negative_prompt

string

Shift + Return to add a new line

Negative Prompt

Default: ""

width

integer

(minimum: 1, maximum: 1024)

Width of output image

Default: 512

height

integer

(minimum: 1, maximum: 1024)

Height of output image

Default: 512

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output

Default: 1

scheduler

string

scheduler

Default: "DPM++ SDE Karras"

num_inference_steps

integer

(minimum: 1, maximum: 100)

Number of denoising steps

Default: 20

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 7.5

seed

integer

Random seed. Leave blank to randomize the seed

Default: -1

enable_hr

boolean

Hires. fix

Default: false

hr_upscaler

string

Upscaler for Hires. fix

Default: "Latent"

hr_steps

integer

(minimum: 0, maximum: 100)

Inference steps for Hires. fix

Default: 20

denoising_strength

number

(minimum: 0, maximum: 1)

Denoising strength. 1.0 corresponds to full destruction of information in init image

Default: 0.5

hr_scale

number

(minimum: 1, maximum: 4)

Factor to scale image by

Default: 2

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

npx create-replicate --model=llsean/cog-a1111-webui

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 llsean/cog-a1111-webui using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "llsean/cog-a1111-webui:f254d34e1d79fa724fd80b5649ddd99eb128b9fdab16c988bf59f4a27c45f656",
  {
    input: {
      seed: -1,
      width: 512,
      height: 512,
      prompt: " a man",
      hr_scale: 2,
      hr_steps: 20,
      enable_hr: false,
      scheduler: "DPM++ SDE Karras",
      hr_upscaler: "Latent",
      num_outputs: 1,
      guidance_scale: 7.5,
      negative_prompt: "",
      denoising_strength: 0.5,
      num_inference_steps: 20
    }
  }
);

// 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 llsean/cog-a1111-webui using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "llsean/cog-a1111-webui:f254d34e1d79fa724fd80b5649ddd99eb128b9fdab16c988bf59f4a27c45f656",
    input={
        "seed": -1,
        "width": 512,
        "height": 512,
        "prompt": " a man",
        "hr_scale": 2,
        "hr_steps": 20,
        "enable_hr": False,
        "scheduler": "DPM++ SDE Karras",
        "hr_upscaler": "Latent",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "denoising_strength": 0.5,
        "num_inference_steps": 20
    }
)

# 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 llsean/cog-a1111-webui 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": "llsean/cog-a1111-webui:f254d34e1d79fa724fd80b5649ddd99eb128b9fdab16c988bf59f4a27c45f656",
    "input": {
      "seed": -1,
      "width": 512,
      "height": 512,
      "prompt": " a man",
      "hr_scale": 2,
      "hr_steps": 20,
      "enable_hr": false,
      "scheduler": "DPM++ SDE Karras",
      "hr_upscaler": "Latent",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "denoising_strength": 0.5,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-28T14:16:06.398065Z",
  "created_at": "2023-11-28T14:16:02.342583Z",
  "data_removed": false,
  "error": null,
  "id": "kf4ltl3b6up5en3upoo6qhldka",
  "input": {
    "seed": -1,
    "width": 512,
    "height": 512,
    "prompt": " a man",
    "hr_scale": 2,
    "hr_steps": 20,
    "enable_hr": false,
    "scheduler": "DPM++ SDE Karras",
    "hr_upscaler": "Latent",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "negative_prompt": "",
    "denoising_strength": 0.5,
    "num_inference_steps": 20
  },
  "logs": "0%|          | 0/20 [00:00<?, ?it/s]\nTotal progress:   0%|          | 0/20 [00:00<?, ?it/s]\u001b[A\n  5%|▌         | 1/20 [00:00<00:02,  7.24it/s]\n 10%|█         | 2/20 [00:00<00:02,  7.23it/s]\nTotal progress:  10%|█         | 2/20 [00:00<00:01, 15.05it/s]\u001b[A\n 15%|█▌        | 3/20 [00:00<00:02,  7.24it/s]\n 20%|██        | 4/20 [00:00<00:02,  7.21it/s]\nTotal progress:  20%|██        | 4/20 [00:00<00:01,  9.20it/s]\u001b[A\n 25%|██▌       | 5/20 [00:00<00:02,  7.21it/s]\nTotal progress:  30%|███       | 6/20 [00:00<00:01,  8.13it/s]\u001b[A\n 30%|███       | 6/20 [00:00<00:01,  7.20it/s]\nTotal progress:  35%|███▌      | 7/20 [00:00<00:01,  7.90it/s]\u001b[A\n 35%|███▌      | 7/20 [00:00<00:01,  7.24it/s]\nTotal progress:  40%|████      | 8/20 [00:00<00:01,  7.72it/s]\u001b[A\n 40%|████      | 8/20 [00:01<00:01,  7.21it/s]\nTotal progress:  45%|████▌     | 9/20 [00:01<00:01,  7.55it/s]\u001b[A\n 45%|████▌     | 9/20 [00:01<00:01,  7.15it/s]\nTotal progress:  50%|█████     | 10/20 [00:01<00:01,  7.38it/s]\u001b[A\n 50%|█████     | 10/20 [00:01<00:01,  7.10it/s]\nTotal progress:  55%|█████▌    | 11/20 [00:01<00:01,  7.27it/s]\u001b[A\n 55%|█████▌    | 11/20 [00:01<00:01,  7.12it/s]\nTotal progress:  60%|██████    | 12/20 [00:01<00:01,  7.27it/s]\u001b[A\n 60%|██████    | 12/20 [00:01<00:01,  7.17it/s]\nTotal progress:  65%|██████▌   | 13/20 [00:01<00:00,  7.26it/s]\u001b[A\n 65%|██████▌   | 13/20 [00:01<00:00,  7.20it/s]\nTotal progress:  70%|███████   | 14/20 [00:01<00:00,  7.27it/s]\u001b[A\n 70%|███████   | 14/20 [00:01<00:00,  7.15it/s]\nTotal progress:  75%|███████▌  | 15/20 [00:01<00:00,  7.20it/s]\u001b[A\n 75%|███████▌  | 15/20 [00:02<00:00,  7.21it/s]\nTotal progress:  80%|████████  | 16/20 [00:02<00:00,  7.25it/s]\u001b[A\n 80%|████████  | 16/20 [00:02<00:00,  7.27it/s]\nTotal progress:  85%|████████▌ | 17/20 [00:02<00:00,  7.30it/s]\u001b[A\n 85%|████████▌ | 17/20 [00:02<00:00,  7.27it/s]\nTotal progress:  90%|█████████ | 18/20 [00:02<00:00,  7.26it/s]\u001b[A\n 90%|█████████ | 18/20 [00:02<00:00,  7.33it/s]\nTotal progress:  95%|█████████▌| 19/20 [00:02<00:00,  7.33it/s]\u001b[A\n 95%|█████████▌| 19/20 [00:02<00:00,  7.42it/s]\n100%|██████████| 20/20 [00:02<00:00,  7.44it/s]\nTotal progress: 100%|██████████| 20/20 [00:02<00:00,  7.44it/s]\u001b[A\nTotal progress: 100%|██████████| 20/20 [00:02<00:00,  7.44it/s]\u001b[A\nTotal progress: 100%|██████████| 20/20 [00:02<00:00,  7.44it/s]",
  "metrics": {
    "predict_time": 4.023995,
    "total_time": 4.055482
  },
  "output": [
    "https://replicate.delivery/pbxt/wQxI0jRZbX7kDNGQU7YcZz6ednDbNZVIUmyCOeSKLotlg68RA/1747788793-aad5be04-8df8-11ee-95d7-eefb0558db96.png"
  ],
  "started_at": "2023-11-28T14:16:02.374070Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/kf4ltl3b6up5en3upoo6qhldka",
    "cancel": "https://api.replicate.com/v1/predictions/kf4ltl3b6up5en3upoo6qhldka/cancel"
  },
  "version": "f254d34e1d79fa724fd80b5649ddd99eb128b9fdab16c988bf59f4a27c45f656"
}

Generated in

4.0 seconds

Tweak itReport View full prediction

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Examples

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Run time and cost

This model costs approximately $0.051 to run on Replicate, or 19 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 L40S GPU hardware. Predictions typically complete within 53 seconds. The predict time for this model varies significantly based on the inputs.

Readme

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