fermatresearch/sdxl-outpainting-lora:a542ccf3 – Run with an API on Replicate

Version

You're looking at a specific version of this model. Jump to the model overview.

fermatresearch /sdxl-outpainting-lora:a542ccf3

Playground API

Input

prompt

string

Shift + Return to add a new line

beautiful european city with dramatic lightbeautiful european city with dramatic light

Input prompt

Default: "An astronaut riding a rainbow unicorn"

outpaint_left

integer

(minimum: 0, maximum: 512)

How many pixels the mask should grow in the left direction

Default: 0

outpaint_right

integer

(minimum: 0, maximum: 512)

How many pixels the mask should grow in the right direction

Default: 0

outpaint_down

integer

(minimum: 0, maximum: 512)

How many pixels the mask should grow in the down direction

Default: 0

outpaint_up

integer

(minimum: 0, maximum: 512)

How many pixels the mask should grow in the up direction

Default: 0

image

file

Input image to inpaint

condition_scale

number

(minimum: 0, maximum: 1)

The bigger this number is, the more ControlNet interferes

Default: 0.15

lora_weights

string

Shift + Return to add a new line

Replicate LoRA weights to use. Leave blank to use the default weights.

lora_scale

number

(minimum: 0, maximum: 1)

LoRA additive scale. Only applicable on trained models.

Default: 0.8

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output

Default: 1

scheduler

string

scheduler

Default: "K_EULER"

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

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

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

npx create-replicate --model=fermatresearch/sdxl-outpainting-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";

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

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

const output = await replicate.run(
  "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804",
  {
    input: {
      image: "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png",
      prompt: "beautiful european city with dramatic light",
      scheduler: "K_EULER",
      lora_scale: 0.8,
      num_outputs: 1,
      outpaint_up: 0,
      outpaint_down: 0,
      outpaint_left: 256,
      guidance_scale: 7.5,
      outpaint_right: 256,
      apply_watermark: false,
      condition_scale: 0.25,
      negative_prompt: ""
    }
  }
);
console.log(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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804",
    input={
        "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png",
        "prompt": "beautiful european city with dramatic light",
        "scheduler": "K_EULER",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "outpaint_up": 0,
        "outpaint_down": 0,
        "outpaint_left": 256,
        "guidance_scale": 7.5,
        "outpaint_right": 256,
        "apply_watermark": False,
        "condition_scale": 0.25,
        "negative_prompt": ""
    }
)
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 fermatresearch/sdxl-outpainting-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": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804",
    "input": {
      "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png",
      "prompt": "beautiful european city with dramatic light",
      "scheduler": "K_EULER",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "outpaint_up": 0,
      "outpaint_down": 0,
      "outpaint_left": 256,
      "guidance_scale": 7.5,
      "outpaint_right": 256,
      "apply_watermark": false,
      "condition_scale": 0.25,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/fermatresearch/sdxl-outpainting-lora@sha256:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804 \
  -i 'image="https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png"' \
  -i 'prompt="beautiful european city with dramatic light"' \
  -i 'scheduler="K_EULER"' \
  -i 'lora_scale=0.8' \
  -i 'num_outputs=1' \
  -i 'outpaint_up=0' \
  -i 'outpaint_down=0' \
  -i 'outpaint_left=256' \
  -i 'guidance_scale=7.5' \
  -i 'outpaint_right=256' \
  -i 'apply_watermark=false' \
  -i 'condition_scale=0.25' \
  -i 'negative_prompt=""'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/fermatresearch/sdxl-outpainting-lora@sha256:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png",
      "prompt": "beautiful european city with dramatic light",
      "scheduler": "K_EULER",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "outpaint_up": 0,
      "outpaint_down": 0,
      "outpaint_left": 256,
      "guidance_scale": 7.5,
      "outpaint_right": 256,
      "apply_watermark": false,
      "condition_scale": 0.25,
      "negative_prompt": ""
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2024-06-05T15:15:14.447491Z",
  "created_at": "2024-06-05T15:07:54.956000Z",
  "data_removed": false,
  "error": null,
  "id": "95r3xqswshrgp0cfx3rsr9q67m",
  "input": {
    "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png",
    "prompt": "beautiful european city with dramatic light",
    "scheduler": "K_EULER",
    "lora_scale": 0.8,
    "num_outputs": 1,
    "outpaint_up": 0,
    "outpaint_down": 0,
    "outpaint_left": 256,
    "guidance_scale": 7.5,
    "outpaint_right": 256,
    "apply_watermark": false,
    "condition_scale": 0.25,
    "negative_prompt": ""
  },
  "logs": "Using seed: 34499\nApplying smart preprocessing...\nRunning PatchMatch\nPatchMatch completed, time taken: 12332.704345703125 ms\nRunning PatchMatch\nPatchMatch completed, time taken: 21385.12060546875 ms\n  0%|          | 0/19 [00:00<?, ?it/s]\n  5%|▌         | 1/19 [00:00<00:10,  1.77it/s]\n 11%|█         | 2/19 [00:00<00:07,  2.42it/s]\n 16%|█▌        | 3/19 [00:01<00:07,  2.27it/s]\n 21%|██        | 4/19 [00:01<00:06,  2.20it/s]\n 26%|██▋       | 5/19 [00:02<00:06,  2.16it/s]\n 32%|███▏      | 6/19 [00:02<00:06,  2.14it/s]\n 37%|███▋      | 7/19 [00:03<00:05,  2.13it/s]\n 42%|████▏     | 8/19 [00:03<00:05,  2.12it/s]\n 47%|████▋     | 9/19 [00:04<00:04,  2.11it/s]\n 53%|█████▎    | 10/19 [00:04<00:04,  2.11it/s]\n 58%|█████▊    | 11/19 [00:05<00:03,  2.10it/s]\n 63%|██████▎   | 12/19 [00:05<00:03,  2.10it/s]\n 68%|██████▊   | 13/19 [00:06<00:02,  2.10it/s]\n 74%|███████▎  | 14/19 [00:06<00:02,  2.10it/s]\n 79%|███████▉  | 15/19 [00:07<00:01,  2.10it/s]\n 84%|████████▍ | 16/19 [00:07<00:01,  2.09it/s]\n 89%|████████▉ | 17/19 [00:08<00:00,  2.09it/s]\n 95%|█████████▍| 18/19 [00:08<00:00,  2.09it/s]\n100%|██████████| 19/19 [00:08<00:00,  2.09it/s]\n100%|██████████| 19/19 [00:08<00:00,  2.12it/s]",
  "metrics": {
    "predict_time": 47.387431,
    "total_time": 439.491491
  },
  "output": [
    "https://replicate.delivery/pbxt/ijEcNuS5hnIBMFstWsEkcPeINwiyvS0ZaeepJePZwZfHgZcXC/out-0.png"
  ],
  "started_at": "2024-06-05T15:14:27.060060Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/95r3xqswshrgp0cfx3rsr9q67m",
    "cancel": "https://api.replicate.com/v1/predictions/95r3xqswshrgp0cfx3rsr9q67m/cancel"
  },
  "version": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804"
}

Generated in

47.4 seconds

Tweak it ShareReport