tstramer / tron-legacy-diffusion

Cold

Public
1.9K runs
A100 (80GB)

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Run with an API

Playground API Examples README Versions

Prediction

tstramer/tron-legacy-diffusion:2aedef524f727afd0ee52a2bf928d5a7f704e29b276d2834d54ba8fc6dcedaa7

Model

tstramer/tron-legacy-diffusion:2aedef52

yijpsvjyz5fprdgtnslfhg4kt4

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4.3s

Created

over 2 years ago

Input

width: 512
height: 512
prompt: city landscape in the style of trnlgcy
scheduler: K-LMS
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "city landscape in the style of trnlgcy",
  "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/tron-legacy-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tstramer/tron-legacy-diffusion:2aedef524f727afd0ee52a2bf928d5a7f704e29b276d2834d54ba8fc6dcedaa7",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "city landscape in the style of trnlgcy",
      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/tron-legacy-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tstramer/tron-legacy-diffusion:2aedef524f727afd0ee52a2bf928d5a7f704e29b276d2834d54ba8fc6dcedaa7",
    input={
        "width": 512,
        "height": 512,
        "prompt": "city landscape in the style of trnlgcy",
        "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/tron-legacy-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/tron-legacy-diffusion:2aedef524f727afd0ee52a2bf928d5a7f704e29b276d2834d54ba8fc6dcedaa7",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "city landscape in the style of trnlgcy",
      "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": "2022-11-08T01:40:45.636182Z",
  "created_at": "2022-11-08T01:40:41.359586Z",
  "data_removed": false,
  "error": null,
  "id": "yijpsvjyz5fprdgtnslfhg4kt4",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "city landscape in the style of trnlgcy",
    "scheduler": "K-LMS",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 42585\n\n  0%|          | 0/50 [00:00<?, ?it/s]\n  4%|▍         | 2/50 [00:00<00:04, 11.51it/s]\n  8%|▊         | 4/50 [00:00<00:03, 12.48it/s]\n 12%|█▏        | 6/50 [00:00<00:03, 12.88it/s]\n 16%|█▌        | 8/50 [00:00<00:03, 13.06it/s]\n 20%|██        | 10/50 [00:00<00:03, 13.22it/s]\n 24%|██▍       | 12/50 [00:00<00:02, 13.43it/s]\n 28%|██▊       | 14/50 [00:01<00:02, 13.37it/s]\n 32%|███▏      | 16/50 [00:01<00:02, 13.36it/s]\n 36%|███▌      | 18/50 [00:01<00:02, 13.31it/s]\n 40%|████      | 20/50 [00:01<00:02, 13.19it/s]\n 44%|████▍     | 22/50 [00:01<00:02, 12.91it/s]\n 48%|████▊     | 24/50 [00:01<00:01, 13.02it/s]\n 52%|█████▏    | 26/50 [00:01<00:01, 13.12it/s]\n 56%|█████▌    | 28/50 [00:02<00:01, 13.17it/s]\n 60%|██████    | 30/50 [00:02<00:01, 13.21it/s]\n 64%|██████▍   | 32/50 [00:02<00:01, 13.22it/s]\n 68%|██████▊   | 34/50 [00:02<00:01, 13.27it/s]\n 72%|███████▏  | 36/50 [00:02<00:01, 13.11it/s]\n 76%|███████▌  | 38/50 [00:02<00:00, 13.18it/s]\n 80%|████████  | 40/50 [00:03<00:00, 13.28it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 13.32it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 13.31it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 13.15it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 13.24it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.29it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.17it/s]",
  "metrics": {
    "predict_time": 4.242559,
    "total_time": 4.276596
  },
  "output": [
    "https://replicate.delivery/pbxt/BIgc37Myv6K2Mxmfg79McDvfFY7B2UdB67HbRhsr4tqdW29PA/out-0.png"
  ],
  "started_at": "2022-11-08T01:40:41.393623Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/yijpsvjyz5fprdgtnslfhg4kt4",
    "cancel": "https://api.replicate.com/v1/predictions/yijpsvjyz5fprdgtnslfhg4kt4/cancel"
  },
  "version": "2aedef524f727afd0ee52a2bf928d5a7f704e29b276d2834d54ba8fc6dcedaa7"
}

Generated in

4.3 seconds

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