andreasjansson/stable-diffusion-wip – Run with an API on Replicate

andreasjansson / stable-diffusion-wip

(development branch) Inpainting for Stable Diffusion

Cold

Public
13.4K runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: ""

width

integer

Width of output image

Default: 512

height

integer

Height of output image

Default: 512

init_image

file

Preview

Inital image to generate variations of. Will be resized to the specified width and height

mask

file

Preview

Black and white image to use as mask for inpainting over init_image. Black pixels are inpainted and white pixels are preserved. Experimental feature, tends to work better with prompt strength of 0.5-0.7

prompt_strength

number

Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

num_outputs

integer

Number of images to output

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

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=andreasjansson/stable-diffusion-wip

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 andreasjansson/stable-diffusion-wip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "andreasjansson/stable-diffusion-wip:5c17c98e8b490e89dc2d799ad4c6a45f580a8e0d7a2af80e5162dfff1ced6aea",
  {
    input: {
      mask: "https://replicate.delivery/mgxm/dc5488e0-d589-480b-825f-c02596700731/nyc-mask.png",
      width: 512,
      height: 512,
      prompt: "angry godzilla",
      init_image: "https://replicate.delivery/mgxm/d8f4574e-5c9a-4291-88ac-4eb53095f8d9/nyc-4854718_1280.png",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.7,
      num_inference_steps: 100
    }
  }
);
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 andreasjansson/stable-diffusion-wip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "andreasjansson/stable-diffusion-wip:5c17c98e8b490e89dc2d799ad4c6a45f580a8e0d7a2af80e5162dfff1ced6aea",
    input={
        "mask": "https://replicate.delivery/mgxm/dc5488e0-d589-480b-825f-c02596700731/nyc-mask.png",
        "width": 512,
        "height": 512,
        "prompt": "angry godzilla",
        "init_image": "https://replicate.delivery/mgxm/d8f4574e-5c9a-4291-88ac-4eb53095f8d9/nyc-4854718_1280.png",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.7,
        "num_inference_steps": 100
    }
)
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 andreasjansson/stable-diffusion-wip 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": "5c17c98e8b490e89dc2d799ad4c6a45f580a8e0d7a2af80e5162dfff1ced6aea",
    "input": {
      "mask": "https://replicate.delivery/mgxm/dc5488e0-d589-480b-825f-c02596700731/nyc-mask.png",
      "width": 512,
      "height": 512,
      "prompt": "angry godzilla",
      "init_image": "https://replicate.delivery/mgxm/d8f4574e-5c9a-4291-88ac-4eb53095f8d9/nyc-4854718_1280.png",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.7,
      "num_inference_steps": 100
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-08-25T17:06:58.554703Z",
  "created_at": "2022-08-25T17:06:50.609076Z",
  "data_removed": false,
  "error": null,
  "id": "pavovg3zgvforlqdnqebkor2fu",
  "input": {
    "mask": "https://replicate.delivery/mgxm/dc5488e0-d589-480b-825f-c02596700731/nyc-mask.png",
    "width": 512,
    "height": 512,
    "prompt": "angry godzilla",
    "init_image": "https://replicate.delivery/mgxm/d8f4574e-5c9a-4291-88ac-4eb53095f8d9/nyc-4854718_1280.png",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "prompt_strength": 0.7,
    "num_inference_steps": "100"
  },
  "logs": "Using seed: 7913\n\n0it [00:00, ?it/s]\n1it [00:00,  9.70it/s]\n3it [00:00, 11.43it/s]\n5it [00:00, 11.63it/s]\n7it [00:00, 11.74it/s]\n9it [00:00, 12.19it/s]\n11it [00:00, 12.42it/s]\n13it [00:01, 12.46it/s]\n15it [00:01, 12.71it/s]\n17it [00:01, 12.79it/s]\n19it [00:01, 12.43it/s]\n21it [00:01, 12.03it/s]\n23it [00:01, 12.17it/s]\n25it [00:02, 12.17it/s]\n27it [00:02, 12.34it/s]\n29it [00:02, 12.41it/s]\n31it [00:02, 12.58it/s]\n33it [00:02, 12.28it/s]\n35it [00:02, 12.09it/s]\n37it [00:03, 12.08it/s]\n39it [00:03, 12.16it/s]\n41it [00:03, 11.80it/s]\n43it [00:03, 11.78it/s]\n45it [00:03, 12.09it/s]\n47it [00:03, 12.46it/s]\n49it [00:04, 12.67it/s]\n51it [00:04, 12.24it/s]\n53it [00:04, 12.01it/s]\n55it [00:04, 11.80it/s]\n57it [00:04, 11.93it/s]\n59it [00:04, 11.81it/s]\n61it [00:05, 11.77it/s]\n63it [00:05, 11.70it/s]\n65it [00:05, 11.96it/s]\n67it [00:05, 11.72it/s]\n69it [00:05, 11.95it/s]\n71it [00:05, 12.40it/s]\n73it [00:06, 12.20it/s]\n75it [00:06, 11.99it/s]\n77it [00:06, 11.79it/s]\n79it [00:06, 11.72it/s]\n79it [00:06, 12.07it/s]",
  "metrics": {
    "predict_time": 7.737198,
    "total_time": 7.945627
  },
  "output": [
    "https://replicate.delivery/mgxm/23f303ac-3324-499f-9053-c11243d9ea8e/out-0.png"
  ],
  "started_at": "2022-08-25T17:06:50.817505Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/pavovg3zgvforlqdnqebkor2fu",
    "cancel": "https://api.replicate.com/v1/predictions/pavovg3zgvforlqdnqebkor2fu/cancel"
  },
  "version": "b605bc2c60c7419dd3fdac55a9f3667e9cf802518cd9261c8c3dc61cdd2a64f7"
}

Generated in

7.7 seconds

Tweak itReport

This example was created by a different version, andreasjansson/stable-diffusion-wip:b605bc2c.

Examples

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Run time and cost

This model costs approximately $0.015 to run on Replicate, or 66 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 11 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Work-in-progress in-painting branch of Stable Diffusion.

Based on this PR: https://github.com/huggingface/diffusers/pull/241

Code: https://github.com/andreasjansson/cog-stable-diffusion/tree/init-image-inpainting