zzzziqi/sd-fix | Run with an API on Replicate

Input

prompt

string

Shift + Return to add a new line

a photo of an astronaut riding a horse on marsa photo of an astronaut riding a horse on mars

Input prompt

Default: "a photo of an astronaut riding a horse on mars"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 768

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 768

num_outputs

integer

(minimum: 1, maximum: 4)

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

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

seed

integer

Random seed. Leave blank to randomize the seed

canny

*file

Canny input image

canny_weight

number

(minimum: 0, maximum: 1)

A value between 0 and 1 to emphasize the conditioning by the canny edges.

Default: 1

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

npx create-replicate --model=zzzziqi/sd-fix

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 zzzziqi/sd-fix using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zzzziqi/sd-fix:4f558c0088f8c4f2c723bd1059139936f6976ad0df4575dfde2f9b0297e453a1",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "a photo of an astronaut riding a horse on mars",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      canny_weight: 1,
      guidance_scale: 7.5,
      num_inference_steps: 50
    }
  }
);

// 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 zzzziqi/sd-fix using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zzzziqi/sd-fix:4f558c0088f8c4f2c723bd1059139936f6976ad0df4575dfde2f9b0297e453a1",
    input={
        "width": 768,
        "height": 768,
        "prompt": "a photo of an astronaut riding a horse on mars",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "canny_weight": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)

# 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 zzzziqi/sd-fix 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": "zzzziqi/sd-fix:4f558c0088f8c4f2c723bd1059139936f6976ad0df4575dfde2f9b0297e453a1",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "a photo of an astronaut riding a horse on mars",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "canny_weight": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.