abdullahmakhdoom/diffusers-txtnimg2img – Run with an API on Replicate

Input

prompt

string

Shift + Return to add a new line

A Vincent Van Gogh's starry nights landascape of plant marsA Vincent Van Gogh's starry nights landascape of plant mars

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=abdullahmakhdoom/diffusers-txtnimg2img

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 abdullahmakhdoom/diffusers-txtnimg2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "abdullahmakhdoom/diffusers-txtnimg2img:d8019f36ecde068f82adb2337c187219f4997f26606c38f7f4002115bdc3991d",
  {
    input: {
      image: "https://replicate.delivery/pbxt/JHT88iNscz4MMEmzE5FE5Il5cFOjlVKKgNJ752VdONnDwoE6/mars_landscape.jpeg",
      width: 512,
      height: 512,
      prompt: "A Vincent Van Gogh's starry nights landascape of plant mars",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      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 abdullahmakhdoom/diffusers-txtnimg2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "abdullahmakhdoom/diffusers-txtnimg2img:d8019f36ecde068f82adb2337c187219f4997f26606c38f7f4002115bdc3991d",
    input={
        "image": "https://replicate.delivery/pbxt/JHT88iNscz4MMEmzE5FE5Il5cFOjlVKKgNJ752VdONnDwoE6/mars_landscape.jpeg",
        "width": 512,
        "height": 512,
        "prompt": "A Vincent Van Gogh's starry nights landascape of plant mars",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "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 abdullahmakhdoom/diffusers-txtnimg2img 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": "abdullahmakhdoom/diffusers-txtnimg2img:d8019f36ecde068f82adb2337c187219f4997f26606c38f7f4002115bdc3991d",
    "input": {
      "image": "https://replicate.delivery/pbxt/JHT88iNscz4MMEmzE5FE5Il5cFOjlVKKgNJ752VdONnDwoE6/mars_landscape.jpeg",
      "width": 512,
      "height": 512,
      "prompt": "A Vincent Van Gogh\'s starry nights landascape of plant mars",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "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": "2023-08-02T15:26:56.255685Z",
  "created_at": "2023-08-02T15:19:54.939618Z",
  "data_removed": false,
  "error": null,
  "id": "skxh2lbbc4yyexf43btnihh5oi",
  "input": {
    "image": "https://replicate.delivery/pbxt/JHT88iNscz4MMEmzE5FE5Il5cFOjlVKKgNJ752VdONnDwoE6/mars_landscape.jpeg",
    "width": 512,
    "height": 512,
    "prompt": "A Vincent Van Gogh's starry nights landascape of plant mars",
    "scheduler": "DPMSolverMultistep",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 25
  },
  "logs": "Using seed: 62186\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:02<00:38,  2.04s/it]\n 10%|█         | 2/20 [00:04<00:35,  2.00s/it]\n 15%|█▌        | 3/20 [00:05<00:33,  1.95s/it]\n 20%|██        | 4/20 [00:07<00:31,  1.97s/it]\n 25%|██▌       | 5/20 [00:09<00:29,  1.95s/it]\n 30%|███       | 6/20 [00:12<00:28,  2.06s/it]\n 35%|███▌      | 7/20 [00:14<00:28,  2.22s/it]\n 40%|████      | 8/20 [00:17<00:29,  2.47s/it]\n 45%|████▌     | 9/20 [00:20<00:28,  2.63s/it]\n 50%|█████     | 10/20 [00:23<00:27,  2.74s/it]\n 55%|█████▌    | 11/20 [00:26<00:24,  2.76s/it]\n 60%|██████    | 12/20 [00:29<00:22,  2.79s/it]\n 65%|██████▌   | 13/20 [00:32<00:19,  2.81s/it]\n 70%|███████   | 14/20 [00:34<00:16,  2.82s/it]\n 75%|███████▌  | 15/20 [00:37<00:14,  2.81s/it]\n 80%|████████  | 16/20 [00:40<00:11,  2.79s/it]\n 85%|████████▌ | 17/20 [00:42<00:08,  2.69s/it]\n 90%|█████████ | 18/20 [00:45<00:05,  2.74s/it]\n 95%|█████████▌| 19/20 [00:48<00:02,  2.79s/it]\n100%|██████████| 20/20 [00:51<00:00,  2.83s/it]\n100%|██████████| 20/20 [00:51<00:00,  2.58s/it]",
  "metrics": {
    "predict_time": 57.496948,
    "total_time": 421.316067
  },
  "output": [
    "https://replicate.delivery/pbxt/mD1kKoRpPz55LF39jW7iOVYMk9XYxiMPHFMcEBgAdtAwngVE/out-0.png"
  ],
  "started_at": "2023-08-02T15:25:58.758737Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/skxh2lbbc4yyexf43btnihh5oi",
    "cancel": "https://api.replicate.com/v1/predictions/skxh2lbbc4yyexf43btnihh5oi/cancel"
  },
  "version": "d8019f36ecde068f82adb2337c187219f4997f26606c38f7f4002115bdc3991d"
}

Generated in

57.5 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.32 to run on Replicate, or 3 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 4 minutes. The predict time for this model varies significantly based on the inputs.