zhouhaojiang/dreamshaper_8_with_lora | Run with an API on Replicate

zhouhaojiang / dreamshaper_8_with_lora

Dreamshaper V8 support civitai lora,text2image,image2image

Cold

Public
271 runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

1girl,face,curly hair,red hair,white background1girl,face,curly hair,red hair,white background

Input prompt. The model will generate an image based on this prompt.

Default: "1girl,face,curly hair,red hair,white background"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

Default: "nsfw"

width

integer

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

Default: 512

height

integer

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

Default: 512

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 50)

Number of denoising steps.Recommend 25~35

Default: 30

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

image

string

Shift + Return to add a new line

(Img2Img) Inital image to generate variations of. If this is not none, Img2Img will be invoked.Just One

mask

string

Shift + Return to add a new line

Black and white image to use as mask for inpainting over the image provided. White pixels are inpainted and black pixels are preserved

prompt_strength

number

(Img2Img) Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

lora_urls

string

Shift + Return to add a new line

List of urls for safetensors of lora models, seperated with | .

Default: ""

lora_scales

string

Shift + Return to add a new line

List of scales for safetensors of lora models, seperated with |

Default: "0.5"

seed

integer

Random seed. Leave blank to randomize the seed

disable_safety_checker

boolean

Disable safety checker for generated images. This feature is only available through the API. See [https://replicate.com/docs/how-does-replicate-work#safety](https://replicate.com/docs/how-does-replicate-work#safety)

Default: false

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

npx create-replicate --model=zhouhaojiang/dreamshaper_8_with_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 zhouhaojiang/dreamshaper_8_with_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zhouhaojiang/dreamshaper_8_with_lora:ddc7446a51479009b48a90f3603de1d89b8c41a9606deb5fff436c3942a07921",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "1girl,face,curly hair,red hair,white background",
      lora_urls: "",
      scheduler: "DPMSolverMultistep",
      lora_scales: "0.5",
      num_outputs: 1,
      guidance_scale: 7.5,
      negative_prompt: "nsfw",
      prompt_strength: 0.8,
      num_inference_steps: 30
    }
  }
);

// 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 zhouhaojiang/dreamshaper_8_with_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zhouhaojiang/dreamshaper_8_with_lora:ddc7446a51479009b48a90f3603de1d89b8c41a9606deb5fff436c3942a07921",
    input={
        "width": 512,
        "height": 512,
        "prompt": "1girl,face,curly hair,red hair,white background",
        "lora_urls": "",
        "scheduler": "DPMSolverMultistep",
        "lora_scales": "0.5",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "negative_prompt": "nsfw",
        "prompt_strength": 0.8,
        "num_inference_steps": 30
    }
)
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 zhouhaojiang/dreamshaper_8_with_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": "zhouhaojiang/dreamshaper_8_with_lora:ddc7446a51479009b48a90f3603de1d89b8c41a9606deb5fff436c3942a07921",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "1girl,face,curly hair,red hair,white background",
      "lora_urls": "",
      "scheduler": "DPMSolverMultistep",
      "lora_scales": "0.5",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "nsfw",
      "prompt_strength": 0.8,
      "num_inference_steps": 30
    }
  }' \
  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 costs approximately $0.0048 to run on Replicate, or 208 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 L40S GPU hardware. Predictions typically complete within 5 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.