abdullahmakhdoom / diffusers-txtnimg2img

{
  "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
}

Install Replicate’s Node.js client library:

npm install replicate

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

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.

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

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