Input

prompt

string

Shift + Return to add a new line

lvngvncnt, beautiful woman, highly detailed lvngvncnt, beautiful woman, highly detailed 

Input prompt

Default: "A fantasy landscape, trending on artstation"

negative_prompt

string

Shift + Return to add a new line

The prompt NOT to guide the image generation. Ignored when not using guidance

image

*file

Inital image to generate variations of.

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

prompt_strength

number

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

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 8)

Number of images to output. Higher number of outputs may OOM.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 25

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

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

npx create-replicate --model=hazxone/img2img-vg

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 hazxone/img2img-vg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "hazxone/img2img-vg:3edc1fecc293d8c88657d5442ece7857f907bc50b451e0603c7981c240f6a94d",
  {
    input: {
      image: "https://replicate.delivery/pbxt/JDjlS9i196GEZuR9lSzZJHRVSUcVmTl9NXdEPb5D6Lxs1VF2/vg-2.jpg",
      width: 512,
      height: 512,
      prompt: "lvngvncnt, beautiful woman, highly detailed ",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 6.07,
      prompt_strength: 0.8,
      num_inference_steps: 25
    }
  }
);

// 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 hazxone/img2img-vg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "hazxone/img2img-vg:3edc1fecc293d8c88657d5442ece7857f907bc50b451e0603c7981c240f6a94d",
    input={
        "image": "https://replicate.delivery/pbxt/JDjlS9i196GEZuR9lSzZJHRVSUcVmTl9NXdEPb5D6Lxs1VF2/vg-2.jpg",
        "width": 512,
        "height": 512,
        "prompt": "lvngvncnt, beautiful woman, highly detailed ",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 6.07,
        "prompt_strength": 0.8,
        "num_inference_steps": 25
    }
)
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 hazxone/img2img-vg 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": "hazxone/img2img-vg:3edc1fecc293d8c88657d5442ece7857f907bc50b451e0603c7981c240f6a94d",
    "input": {
      "image": "https://replicate.delivery/pbxt/JDjlS9i196GEZuR9lSzZJHRVSUcVmTl9NXdEPb5D6Lxs1VF2/vg-2.jpg",
      "width": 512,
      "height": 512,
      "prompt": "lvngvncnt, beautiful woman, highly detailed ",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 6.07,
      "prompt_strength": 0.8,
      "num_inference_steps": 25
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-07-23T03:01:11.711540Z",
  "created_at": "2023-07-23T03:00:59.548985Z",
  "data_removed": false,
  "error": null,
  "id": "xu4h2gbb7kig4qtovszczfm4ia",
  "input": {
    "image": "https://replicate.delivery/pbxt/JDjlS9i196GEZuR9lSzZJHRVSUcVmTl9NXdEPb5D6Lxs1VF2/vg-2.jpg",
    "width": 512,
    "height": 512,
    "prompt": "lvngvncnt, beautiful woman, highly detailed ",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "guidance_scale": 6.07,
    "prompt_strength": 0.8,
    "num_inference_steps": 25
  },
  "logs": "Using seed: 48979\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:09,  2.07it/s]\n 10%|█         | 2/20 [00:00<00:09,  2.00it/s]\n 15%|█▌        | 3/20 [00:01<00:08,  2.02it/s]\n 20%|██        | 4/20 [00:01<00:08,  2.00it/s]\n 25%|██▌       | 5/20 [00:02<00:07,  2.01it/s]\n 30%|███       | 6/20 [00:03<00:07,  1.99it/s]\n 35%|███▌      | 7/20 [00:03<00:06,  2.01it/s]\n 40%|████      | 8/20 [00:04<00:06,  1.98it/s]\n 45%|████▌     | 9/20 [00:04<00:05,  1.99it/s]\n 50%|█████     | 10/20 [00:05<00:05,  1.98it/s]\n 55%|█████▌    | 11/20 [00:05<00:04,  1.99it/s]\n 60%|██████    | 12/20 [00:06<00:03,  2.00it/s]\n 65%|██████▌   | 13/20 [00:06<00:03,  2.00it/s]\n 70%|███████   | 14/20 [00:07<00:03,  2.00it/s]\n 75%|███████▌  | 15/20 [00:07<00:02,  2.00it/s]\n 80%|████████  | 16/20 [00:08<00:02,  2.00it/s]\n 85%|████████▌ | 17/20 [00:08<00:01,  2.00it/s]\n 90%|█████████ | 18/20 [00:09<00:01,  1.99it/s]\n 95%|█████████▌| 19/20 [00:09<00:00,  1.99it/s]\n100%|██████████| 20/20 [00:10<00:00,  1.98it/s]\n100%|██████████| 20/20 [00:10<00:00,  1.99it/s]",
  "metrics": {
    "predict_time": 12.190099,
    "total_time": 12.162555
  },
  "output": [
    "https://replicate.delivery/pbxt/sx061Hd4cVozNVzgw5bOdzedJy0WhKx5PNtphm2Nlqa7TSpIA/out-0.png"
  ],
  "started_at": "2023-07-23T03:00:59.521441Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xu4h2gbb7kig4qtovszczfm4ia",
    "cancel": "https://api.replicate.com/v1/predictions/xu4h2gbb7kig4qtovszczfm4ia/cancel"
  },
  "version": "76b7599585142940646d43cdaa259aa530d95fc270dbc1374d987ecab71469fb"
}

Generated in

12.2 seconds

Tweak itReport View full prediction

This output was created using a different version of the model, hazxone/img2img-vg:76b75995.

Examples

View more examples

Run time and cost

This model costs approximately $0.040 to run on Replicate, or 25 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 T4 GPU hardware. Predictions typically complete within 3 minutes. The predict time for this model varies significantly based on the inputs.

Readme

Replicate img2img model in the style of Van Gogh

This is an img2img in the style of Van Gogh. It was written in cog format to be push to replicate.com

[Replicate version 3edc1fe] This diffusion model was made from training 58 images of Van Gogh paintings (https://huggingface.co/hazwan/vg-session). It is a mix of portrait and landscape.

This model does not look visually appealing because the original painting is more abstract compared to the Loving Vincent model below which trained on a movie that carefully made in the style of Van Gogh

[Replicate version 76b7599] The diffusion model is from https://huggingface.co/dallinmackay/Van-Gogh-diffusion

cog.yaml specify the dependencies and python libraries

predict.py is to consume input (image and prompt) and run the input through the diffusion model

Cog process

To initialize the directory

cog init

To build locally and do inference to the image balloon.jpg

cog predict -i prompt="vangh, man" -i image=@imgs/man_1.jpg"

Next add credential and push the build to replicate.com

cog login
cog push r8.im/hazxone/img2img-vg

Inference process

Head over to https://replicate.com/hazxone/img2img-vg to try the web demo

For the 3edc1fe model, use ‘vangh’ as the trigger word

For the 76b7599 model, use ‘lvngvncnt’ in the beginning of the prompt.

hazxone / img2img-vg

Input

Output

Examples

Run time and cost

Readme

Replicate img2img model in the style of Van Gogh

Cog process

Inference process

Logs (xu4h2gbb7kig4qtovszczfm4ia)