tstramer / material-diffusion

width: 512
height: 512
prompt: Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k
scheduler: K-LMS
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
  "scheduler": "K-LMS",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run tstramer/material-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tstramer/material-diffusion:a42692c54c0f407f803a0a8a9066160976baedb77c91171a01730f9b0d7beeff",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
      scheduler: "K-LMS",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      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

Import the client:

import replicate

Run tstramer/material-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tstramer/material-diffusion:a42692c54c0f407f803a0a8a9066160976baedb77c91171a01730f9b0d7beeff",
    input={
        "width": 512,
        "height": 512,
        "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
        "scheduler": "K-LMS",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run tstramer/material-diffusion 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": "tstramer/material-diffusion:a42692c54c0f407f803a0a8a9066160976baedb77c91171a01730f9b0d7beeff",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
      "scheduler": "K-LMS",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-11-13T03:11:17.948447Z",
  "created_at": "2022-11-13T03:07:03.790192Z",
  "data_removed": false,
  "error": null,
  "id": "itzymcxcyrddxelbfae6jkpb7i",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k",
    "scheduler": "K-LMS",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 52336\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:03<02:57,  3.63s/it]\n  6%|▌         | 3/50 [00:03<00:47,  1.00s/it]\n 10%|█         | 5/50 [00:03<00:23,  1.90it/s]\n 14%|█▍        | 7/50 [00:04<00:14,  2.98it/s]\n 18%|█▊        | 9/50 [00:04<00:09,  4.22it/s]\n 22%|██▏       | 11/50 [00:04<00:07,  5.54it/s]\n 26%|██▌       | 13/50 [00:04<00:05,  6.88it/s]\n 30%|███       | 15/50 [00:04<00:04,  8.07it/s]\n 34%|███▍      | 17/50 [00:04<00:03,  9.02it/s]\n 38%|███▊      | 19/50 [00:05<00:03, 10.04it/s]\n 42%|████▏     | 21/50 [00:05<00:02, 10.78it/s]\n 46%|████▌     | 23/50 [00:05<00:02, 11.48it/s]\n 50%|█████     | 25/50 [00:05<00:02, 12.04it/s]\n 54%|█████▍    | 27/50 [00:05<00:01, 12.43it/s]\n 58%|█████▊    | 29/50 [00:05<00:01, 12.67it/s]\n 62%|██████▏   | 31/50 [00:05<00:01, 12.72it/s]\n 66%|██████▌   | 33/50 [00:06<00:01, 12.94it/s]\n 70%|███████   | 35/50 [00:06<00:01, 13.11it/s]\n 74%|███████▍  | 37/50 [00:06<00:00, 13.23it/s]\n 78%|███████▊  | 39/50 [00:06<00:00, 13.30it/s]\n 82%|████████▏ | 41/50 [00:06<00:00, 13.38it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 13.29it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 13.07it/s]\n 94%|█████████▍| 47/50 [00:07<00:00, 13.14it/s]\n 98%|█████████▊| 49/50 [00:07<00:00, 13.14it/s]\n100%|██████████| 50/50 [00:07<00:00,  6.79it/s]",
  "metrics": {
    "predict_time": 9.265848,
    "total_time": 254.158255
  },
  "output": [
    "https://replicate.delivery/pbxt/XcklpSF1o7Z1I91xQQQHFvJfltWEa3HuQpoeVVTvN7GVJhffA/out-0.png"
  ],
  "started_at": "2022-11-13T03:11:08.682599Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/itzymcxcyrddxelbfae6jkpb7i",
    "cancel": "https://api.replicate.com/v1/predictions/itzymcxcyrddxelbfae6jkpb7i/cancel"
  },
  "version": "a42692c54c0f407f803a0a8a9066160976baedb77c91171a01730f9b0d7beeff"
}

Generated in

9.3 seconds

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