prompthunt/base_inference_swap_cn_embeds_long_prompt:698c3131

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prompthunt /base_inference_swap_cn_embeds_long_prompt:698c3131

Playground API

Input

weights

string

Shift + Return to add a new line

Weights url

control_image

file

Optional Image to use for guidance based on posenet

pose_image

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

image

file

Optional Image to use for img2img guidance

mask

file

Optional Mask to use for legacy inpainting

prompt

string

Shift + Return to add a new line

Input prompt

Default: "photo of cjw person"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output. Supported embeddings: realisticvision-negative-embedding, EasyNegative, FastNegativeV2, BadDream, ng_deepnegative_v1_75t, UnrealisticDream, negative_hand-neg

Default: ""

width

integer

Width of output image

Default: 512

height

integer

Height of output image

Default: 512

num_outputs

integer

(minimum: 1, maximum: 40)

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: 50)

Scale for classifier-free guidance

Default: 7.5

prompt_strength

number

(minimum: 0, maximum: 1)

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

Default: 0.8

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

seed

integer

Random seed. Leave blank to randomize the seed

should_swap_face

boolean

Should swap face

Default: false

source_image

file

Source image for face swap

show_debug_images

boolean

Show debug images

Default: false

prompt_2

string

Shift + Return to add a new line

Input prompt

prompt_3

string

Shift + Return to add a new line

Input prompt

prompt_4

string

Shift + Return to add a new line

Input prompt

pose_image_2

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

pose_image_3

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

pose_image_4

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

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

npx create-replicate --model=prompthunt/base_inference_swap_cn_embeds_long_prompt

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

const output = await replicate.run(
  "prompthunt/base_inference_swap_cn_embeds_long_prompt:698c3131ff5ca2c09ad732e17e518f1cf5c6effc7b92d8e33ba974c29d0cf26d",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "photo of cjw person",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      negative_prompt: "",
      prompt_strength: 0.8,
      should_swap_face: false,
      show_debug_images: false,
      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 prompthunt/base_inference_swap_cn_embeds_long_prompt using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prompthunt/base_inference_swap_cn_embeds_long_prompt:698c3131ff5ca2c09ad732e17e518f1cf5c6effc7b92d8e33ba974c29d0cf26d",
    input={
        "width": 512,
        "height": 512,
        "prompt": "photo of cjw person",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "should_swap_face": False,
        "show_debug_images": False,
        "num_inference_steps": 50
    }
)
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 prompthunt/base_inference_swap_cn_embeds_long_prompt 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": "prompthunt/base_inference_swap_cn_embeds_long_prompt:698c3131ff5ca2c09ad732e17e518f1cf5c6effc7b92d8e33ba974c29d0cf26d",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "photo of cjw person",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "should_swap_face": false,
      "show_debug_images": false,
      "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.