cjwbw/tcs-sdxl-lora:b84384a9 | Run with an API on Replicate

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cjwbw /tcs-sdxl-lora:b84384a9

Input

prompt

string

Shift + Return to add a new line

closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.

Input prompt

Default: "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor."

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

num_inference_steps

integer

(minimum: 1)

Number of denoising steps

Default: 4

eta

number

(minimum: 0, maximum: 1)

Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502.

Default: 0.3

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=cjwbw/tcs-sdxl-lora

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 cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d",
  {
    input: {
      eta: 0.3,
      prompt: "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.",
      negative_prompt: "",
      num_inference_steps: 16
    }
  }
);

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

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

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 cjwbw/tcs-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d",
    input={
        "eta": 0.3,
        "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.",
        "negative_prompt": "",
        "num_inference_steps": 16
    }
)
print(output)

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 cjwbw/tcs-sdxl-lora 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": "cjwbw/tcs-sdxl-lora:b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d",
    "input": {
      "eta": 0.3,
      "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.",
      "negative_prompt": "",
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-03-04T00:46:08.955857Z",
  "created_at": "2024-03-04T00:46:04.880508Z",
  "data_removed": false,
  "error": null,
  "id": "vnyeu3lbnammj4sqjoxjlo35yq",
  "input": {
    "eta": 0.3,
    "prompt": "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.",
    "negative_prompt": "",
    "num_inference_steps": 16
  },
  "logs": "Using seed: 39033\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:01,  9.01it/s]\n 12%|█▎        | 2/16 [00:00<00:01,  8.93it/s]\n 19%|█▉        | 3/16 [00:00<00:01,  8.88it/s]\n 25%|██▌       | 4/16 [00:00<00:01,  8.88it/s]\n 31%|███▏      | 5/16 [00:00<00:01,  8.88it/s]\n 38%|███▊      | 6/16 [00:00<00:01,  8.86it/s]\n 44%|████▍     | 7/16 [00:00<00:01,  8.84it/s]\n 50%|█████     | 8/16 [00:00<00:00,  8.84it/s]\n 56%|█████▋    | 9/16 [00:01<00:00,  8.84it/s]\n 62%|██████▎   | 10/16 [00:01<00:00,  8.82it/s]\n 69%|██████▉   | 11/16 [00:01<00:00,  8.81it/s]\n 75%|███████▌  | 12/16 [00:01<00:00,  8.81it/s]\n 81%|████████▏ | 13/16 [00:01<00:00,  8.81it/s]\n 88%|████████▊ | 14/16 [00:01<00:00,  8.82it/s]\n 94%|█████████▍| 15/16 [00:01<00:00,  8.82it/s]\n100%|██████████| 16/16 [00:01<00:00,  8.83it/s]\n100%|██████████| 16/16 [00:01<00:00,  8.84it/s]",
  "metrics": {
    "predict_time": 4.032461,
    "total_time": 4.075349
  },
  "output": "https://replicate.delivery/pbxt/XxcqFfQLJCwXekEZH9Ef4hoSMxmXM1nR9d7cMxvqE8se8yyJB/out.png",
  "started_at": "2024-03-04T00:46:04.923396Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vnyeu3lbnammj4sqjoxjlo35yq",
    "cancel": "https://api.replicate.com/v1/predictions/vnyeu3lbnammj4sqjoxjlo35yq/cancel"
  },
  "version": "b84384a91c526c4d0e7aeb8ced7de4132775cbe2a7be1b64231b6e143207e95d"
}

Generated in

4.0 seconds

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