lucataco / sdxl-lightning-multi-controlnet

SDXL lightning mult-controlnet, img2img & inpainting

Cold

Public
9.5K runs
L40S
GitHub
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Prediction

lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee

Model

lucataco/sdxl-lightning-multi-controlnet:d5116b11

7pvmfwtbgfqbzivckkuekdqama

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

1.9s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: A monkey making latte art
refine: no_refiner
scheduler: K_EULER
num_outputs: 1
controlnet_1: none
controlnet_2: none
controlnet_3: none
guidance_scale: 0
apply_watermark
negative_prompt: worst quality, low quality
prompt_strength: 0.8
sizing_strategy: width_height
controlnet_1_end: 1
controlnet_2_end: 1
controlnet_3_end: 1
controlnet_1_start: 0
controlnet_2_start: 0
controlnet_3_start: 0
num_inference_steps: 4
controlnet_1_conditioning_scale: 0.75
controlnet_2_conditioning_scale: 0.75
controlnet_3_conditioning_scale: 0.75

{
  "width": 1024,
  "height": 1024,
  "prompt": "A monkey making latte art",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "num_outputs": 1,
  "controlnet_1": "none",
  "controlnet_2": "none",
  "controlnet_3": "none",
  "guidance_scale": 0,
  "apply_watermark": true,
  "negative_prompt": "worst quality, low quality",
  "prompt_strength": 0.8,
  "sizing_strategy": "width_height",
  "controlnet_1_end": 1,
  "controlnet_2_end": 1,
  "controlnet_3_end": 1,
  "controlnet_1_start": 0,
  "controlnet_2_start": 0,
  "controlnet_3_start": 0,
  "num_inference_steps": 4,
  "controlnet_1_conditioning_scale": 0.75,
  "controlnet_2_conditioning_scale": 0.75,
  "controlnet_3_conditioning_scale": 0.75
}

Install Replicate’s Node.js client library:

npm install replicate

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 lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A monkey making latte art",
      refine: "no_refiner",
      scheduler: "K_EULER",
      num_outputs: 1,
      controlnet_1: "none",
      controlnet_2: "none",
      controlnet_3: "none",
      guidance_scale: 0,
      apply_watermark: true,
      negative_prompt: "worst quality, low quality",
      prompt_strength: 0.8,
      sizing_strategy: "width_height",
      controlnet_1_end: 1,
      controlnet_2_end: 1,
      controlnet_3_end: 1,
      controlnet_1_start: 0,
      controlnet_2_start: 0,
      controlnet_3_start: 0,
      num_inference_steps: 4,
      controlnet_1_conditioning_scale: 0.75,
      controlnet_2_conditioning_scale: 0.75,
      controlnet_3_conditioning_scale: 0.75
    }
  }
);

// 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

Import the client:

import replicate

Run lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A monkey making latte art",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "controlnet_1": "none",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "guidance_scale": 0,
        "apply_watermark": True,
        "negative_prompt": "worst quality, low quality",
        "prompt_strength": 0.8,
        "sizing_strategy": "width_height",
        "controlnet_1_end": 1,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "num_inference_steps": 4,
        "controlnet_1_conditioning_scale": 0.75,
        "controlnet_2_conditioning_scale": 0.75,
        "controlnet_3_conditioning_scale": 0.75
    }
)
print(output)

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

Run lucataco/sdxl-lightning-multi-controlnet 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": "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A monkey making latte art",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "controlnet_1": "none",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "guidance_scale": 0,
      "apply_watermark": true,
      "negative_prompt": "worst quality, low quality",
      "prompt_strength": 0.8,
      "sizing_strategy": "width_height",
      "controlnet_1_end": 1,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "num_inference_steps": 4,
      "controlnet_1_conditioning_scale": 0.75,
      "controlnet_2_conditioning_scale": 0.75,
      "controlnet_3_conditioning_scale": 0.75
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-02-27T06:45:53.271773Z",
  "created_at": "2024-02-27T06:45:51.331746Z",
  "data_removed": false,
  "error": null,
  "id": "7pvmfwtbgfqbzivckkuekdqama",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "A monkey making latte art",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "controlnet_1": "none",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "guidance_scale": 0,
    "apply_watermark": true,
    "negative_prompt": "worst quality, low quality",
    "prompt_strength": 0.8,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 1,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 4,
    "controlnet_1_conditioning_scale": 0.75,
    "controlnet_2_conditioning_scale": 0.75,
    "controlnet_3_conditioning_scale": 0.75
  },
  "logs": "Using seed: 3660614901\nUsing given dimensions\nresize took: 0.00s\nPrompt: A monkey making latte art\nUsing txt2img pipeline\n  0%|          | 0/4 [00:00<?, ?it/s]\n 25%|██▌       | 1/4 [00:00<00:00,  9.16it/s]\n 50%|█████     | 2/4 [00:00<00:00,  9.06it/s]\n 75%|███████▌  | 3/4 [00:00<00:00,  9.02it/s]\n100%|██████████| 4/4 [00:00<00:00,  9.02it/s]\n100%|██████████| 4/4 [00:00<00:00,  9.04it/s]\ninference took: 1.01s\nprediction took: 1.41s",
  "metrics": {
    "predict_time": 1.924191,
    "total_time": 1.940027
  },
  "output": [
    "https://replicate.delivery/pbxt/LyjoffeuFgTjTp9Mj464ensrCWzL3LJw2Zz5fWmwf4sKI3smE/out-0.png"
  ],
  "started_at": "2024-02-27T06:45:51.347582Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7pvmfwtbgfqbzivckkuekdqama",
    "cancel": "https://api.replicate.com/v1/predictions/7pvmfwtbgfqbzivckkuekdqama/cancel"
  },
  "version": "d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee"
}

Generated in

1.9 seconds

Tweak it ShareReport

Prediction

lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee

Model

lucataco/sdxl-lightning-multi-controlnet:d5116b11

56rxbylbcjgp6fkozzwcp75dx4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

3.3s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm
refine: no_refiner
scheduler: K_EULER
num_outputs: 1
controlnet_1: soft_edge_hed
controlnet_2: none
controlnet_3: none
guidance_scale: 0
apply_watermark
negative_prompt: worst quality, low quality
prompt_strength: 0.8
sizing_strategy: width_height
controlnet_1_end: 1
controlnet_2_end: 1
controlnet_3_end: 1
controlnet_1_image
controlnet_1_start: 0
controlnet_2_start: 0
controlnet_3_start: 0
num_inference_steps: 4
controlnet_1_conditioning_scale: 0.75
controlnet_2_conditioning_scale: 0.75
controlnet_3_conditioning_scale: 0.75

{
  "width": 1024,
  "height": 1024,
  "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "num_outputs": 1,
  "controlnet_1": "soft_edge_hed",
  "controlnet_2": "none",
  "controlnet_3": "none",
  "guidance_scale": 0,
  "apply_watermark": true,
  "negative_prompt": "worst quality, low quality",
  "prompt_strength": 0.8,
  "sizing_strategy": "width_height",
  "controlnet_1_end": 1,
  "controlnet_2_end": 1,
  "controlnet_3_end": 1,
  "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
  "controlnet_1_start": 0,
  "controlnet_2_start": 0,
  "controlnet_3_start": 0,
  "num_inference_steps": 4,
  "controlnet_1_conditioning_scale": 0.75,
  "controlnet_2_conditioning_scale": 0.75,
  "controlnet_3_conditioning_scale": 0.75
}

Install Replicate’s Node.js client library:

npm install replicate

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 lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
      refine: "no_refiner",
      scheduler: "K_EULER",
      num_outputs: 1,
      controlnet_1: "soft_edge_hed",
      controlnet_2: "none",
      controlnet_3: "none",
      guidance_scale: 0,
      apply_watermark: true,
      negative_prompt: "worst quality, low quality",
      prompt_strength: 0.8,
      sizing_strategy: "width_height",
      controlnet_1_end: 1,
      controlnet_2_end: 1,
      controlnet_3_end: 1,
      controlnet_1_image: "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
      controlnet_1_start: 0,
      controlnet_2_start: 0,
      controlnet_3_start: 0,
      num_inference_steps: 4,
      controlnet_1_conditioning_scale: 0.75,
      controlnet_2_conditioning_scale: 0.75,
      controlnet_3_conditioning_scale: 0.75
    }
  }
);

// 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

Import the client:

import replicate

Run lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "controlnet_1": "soft_edge_hed",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "guidance_scale": 0,
        "apply_watermark": True,
        "negative_prompt": "worst quality, low quality",
        "prompt_strength": 0.8,
        "sizing_strategy": "width_height",
        "controlnet_1_end": 1,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "num_inference_steps": 4,
        "controlnet_1_conditioning_scale": 0.75,
        "controlnet_2_conditioning_scale": 0.75,
        "controlnet_3_conditioning_scale": 0.75
    }
)
print(output)

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

Run lucataco/sdxl-lightning-multi-controlnet 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": "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "controlnet_1": "soft_edge_hed",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "guidance_scale": 0,
      "apply_watermark": true,
      "negative_prompt": "worst quality, low quality",
      "prompt_strength": 0.8,
      "sizing_strategy": "width_height",
      "controlnet_1_end": 1,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "num_inference_steps": 4,
      "controlnet_1_conditioning_scale": 0.75,
      "controlnet_2_conditioning_scale": 0.75,
      "controlnet_3_conditioning_scale": 0.75
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-02-27T06:47:01.532833Z",
  "created_at": "2024-02-27T06:46:58.234118Z",
  "data_removed": false,
  "error": null,
  "id": "56rxbylbcjgp6fkozzwcp75dx4",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "controlnet_1": "soft_edge_hed",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "guidance_scale": 0,
    "apply_watermark": true,
    "negative_prompt": "worst quality, low quality",
    "prompt_strength": 0.8,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 1,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 4,
    "controlnet_1_conditioning_scale": 0.75,
    "controlnet_2_conditioning_scale": 0.75,
    "controlnet_3_conditioning_scale": 0.75
  },
  "logs": "Using seed: 3924493178\nUsing given dimensions\nresize took: 0.03s\nPrompt: extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm\nProcessing image with soft_edge_hed\ncontrolnet preprocess took: 0.58s\nUsing txt2img + controlnet pipeline\nLoading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 15134.09it/s]\nYou have 1 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n  0%|          | 0/4 [00:00<?, ?it/s]\n 25%|██▌       | 1/4 [00:00<00:00,  6.19it/s]\n 50%|█████     | 2/4 [00:00<00:00,  6.16it/s]\n 75%|███████▌  | 3/4 [00:00<00:00,  6.14it/s]\n100%|██████████| 4/4 [00:00<00:00,  6.14it/s]\n100%|██████████| 4/4 [00:00<00:00,  6.14it/s]\ninference took: 1.25s\nprediction took: 2.34s",
  "metrics": {
    "predict_time": 3.286964,
    "total_time": 3.298715
  },
  "output": [
    "https://replicate.delivery/pbxt/uWqiRRbTlezCGyfu5ziSaIpsiuzmLGqukIJQeWGztkRJ7m1kA/control-0.png",
    "https://replicate.delivery/pbxt/X0rtJUsEeXyOekN1yAEqeJLvSQegmKQoeroNC9Hzef6myuZNJA/out-0.png"
  ],
  "started_at": "2024-02-27T06:46:58.245869Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/56rxbylbcjgp6fkozzwcp75dx4",
    "cancel": "https://api.replicate.com/v1/predictions/56rxbylbcjgp6fkozzwcp75dx4/cancel"
  },
  "version": "d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee"
}

Generated in

3.3 seconds

Tweak it ShareReport

Prediction

lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee

Model

lucataco/sdxl-lightning-multi-controlnet:d5116b11

einq5ntbkgrvquihdvi6vyzdoe

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

2.0s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire
refine: no_refiner
scheduler: K_EULER
num_outputs: 1
controlnet_1: none
controlnet_2: none
controlnet_3: none
guidance_scale: 0
apply_watermark
negative_prompt: worst quality, low quality
prompt_strength: 0.8
sizing_strategy: width_height
controlnet_1_end: 1
controlnet_2_end: 1
controlnet_3_end: 1
controlnet_1_start: 0
controlnet_2_start: 0
controlnet_3_start: 0
num_inference_steps: 4
controlnet_1_conditioning_scale: 0.75
controlnet_2_conditioning_scale: 0.75
controlnet_3_conditioning_scale: 0.75

{
  "width": 1024,
  "height": 1024,
  "prompt": "The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "num_outputs": 1,
  "controlnet_1": "none",
  "controlnet_2": "none",
  "controlnet_3": "none",
  "guidance_scale": 0,
  "apply_watermark": true,
  "negative_prompt": "worst quality, low quality",
  "prompt_strength": 0.8,
  "sizing_strategy": "width_height",
  "controlnet_1_end": 1,
  "controlnet_2_end": 1,
  "controlnet_3_end": 1,
  "controlnet_1_start": 0,
  "controlnet_2_start": 0,
  "controlnet_3_start": 0,
  "num_inference_steps": 4,
  "controlnet_1_conditioning_scale": 0.75,
  "controlnet_2_conditioning_scale": 0.75,
  "controlnet_3_conditioning_scale": 0.75
}

Install Replicate’s Node.js client library:

npm install replicate

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 lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire",
      refine: "no_refiner",
      scheduler: "K_EULER",
      num_outputs: 1,
      controlnet_1: "none",
      controlnet_2: "none",
      controlnet_3: "none",
      guidance_scale: 0,
      apply_watermark: true,
      negative_prompt: "worst quality, low quality",
      prompt_strength: 0.8,
      sizing_strategy: "width_height",
      controlnet_1_end: 1,
      controlnet_2_end: 1,
      controlnet_3_end: 1,
      controlnet_1_start: 0,
      controlnet_2_start: 0,
      controlnet_3_start: 0,
      num_inference_steps: 4,
      controlnet_1_conditioning_scale: 0.75,
      controlnet_2_conditioning_scale: 0.75,
      controlnet_3_conditioning_scale: 0.75
    }
  }
);

// 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

Import the client:

import replicate

Run lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "controlnet_1": "none",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "guidance_scale": 0,
        "apply_watermark": True,
        "negative_prompt": "worst quality, low quality",
        "prompt_strength": 0.8,
        "sizing_strategy": "width_height",
        "controlnet_1_end": 1,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "num_inference_steps": 4,
        "controlnet_1_conditioning_scale": 0.75,
        "controlnet_2_conditioning_scale": 0.75,
        "controlnet_3_conditioning_scale": 0.75
    }
)
print(output)

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

Run lucataco/sdxl-lightning-multi-controlnet 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": "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "controlnet_1": "none",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "guidance_scale": 0,
      "apply_watermark": true,
      "negative_prompt": "worst quality, low quality",
      "prompt_strength": 0.8,
      "sizing_strategy": "width_height",
      "controlnet_1_end": 1,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "num_inference_steps": 4,
      "controlnet_1_conditioning_scale": 0.75,
      "controlnet_2_conditioning_scale": 0.75,
      "controlnet_3_conditioning_scale": 0.75
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-02-27T06:49:11.130282Z",
  "created_at": "2024-02-27T06:49:09.152760Z",
  "data_removed": false,
  "error": null,
  "id": "einq5ntbkgrvquihdvi6vyzdoe",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "controlnet_1": "none",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "guidance_scale": 0,
    "apply_watermark": true,
    "negative_prompt": "worst quality, low quality",
    "prompt_strength": 0.8,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 1,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 4,
    "controlnet_1_conditioning_scale": 0.75,
    "controlnet_2_conditioning_scale": 0.75,
    "controlnet_3_conditioning_scale": 0.75
  },
  "logs": "Using seed: 1114593514\nUsing given dimensions\nresize took: 0.00s\nPrompt: The 90s, a beautiful woman with a radiant smile and long hair, dressed in summer attire\nUsing txt2img pipeline\n  0%|          | 0/4 [00:00<?, ?it/s]\n 25%|██▌       | 1/4 [00:00<00:00,  9.15it/s]\n 50%|█████     | 2/4 [00:00<00:00,  9.07it/s]\n 75%|███████▌  | 3/4 [00:00<00:00,  9.03it/s]\n100%|██████████| 4/4 [00:00<00:00,  9.00it/s]\n100%|██████████| 4/4 [00:00<00:00,  9.02it/s]\ninference took: 1.02s\nprediction took: 1.44s",
  "metrics": {
    "predict_time": 1.959136,
    "total_time": 1.977522
  },
  "output": [
    "https://replicate.delivery/pbxt/P9pvO6rpXs4wDpzPkCebbQkassehZa7SGik0t6rDDMHmfm1kA/out-0.png"
  ],
  "started_at": "2024-02-27T06:49:09.171146Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/einq5ntbkgrvquihdvi6vyzdoe",
    "cancel": "https://api.replicate.com/v1/predictions/einq5ntbkgrvquihdvi6vyzdoe/cancel"
  },
  "version": "d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee"
}

Generated in

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