Prediction

Model

daanelson/training-2:0019e28f

vyjsz5zbrgx4qqqkcvo6htgyse

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 14s

Created

over 2 years ago

Webhook

–

Input

prompt: An <s0> riding a rainbow unicorn
width: 1024
height: 1024
num_outputs: 1
scheduler: DDIM
num_inference_steps: 50
guidance_scale: 7.5
prompt_strength: 0.8
refine: expert_ensemble_refiner
high_noise_frac: 0.8

{
  "guidance_scale": 7.5,
  "height": 1024,
  "high_noise_frac": 0.8,
  "num_inference_steps": 50,
  "num_outputs": 1,
  "prompt": "An <s0> riding a rainbow unicorn",
  "prompt_strength": 0.8,
  "refine": "expert_ensemble_refiner",
  "scheduler": "DDIM",
  "width": 1024
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_PVj**********************************

This is your API token. Keep it to yourself.

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 daanelson/training-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "daanelson/training-2:0019e28f3fd28dc2dce2e4c8f425f63285f157a83acb646e17336dcb16fc57aa",
  {
    input: {
      guidance_scale: 7.5,
      height: 1024,
      high_noise_frac: 0.8,
      num_inference_steps: 50,
      num_outputs: 1,
      prompt: "An <s0> riding a rainbow unicorn",
      prompt_strength: 0.8,
      refine: "expert_ensemble_refiner",
      scheduler: "DDIM",
      width: 1024
    }
  }
);

// 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=r8_PVj**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run daanelson/training-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "daanelson/training-2:0019e28f3fd28dc2dce2e4c8f425f63285f157a83acb646e17336dcb16fc57aa",
    input={
        "guidance_scale": 7.5,
        "height": 1024,
        "high_noise_frac": 0.8,
        "num_inference_steps": 50,
        "num_outputs": 1,
        "prompt": "An <s0> riding a rainbow unicorn",
        "prompt_strength": 0.8,
        "refine": "expert_ensemble_refiner",
        "scheduler": "DDIM",
        "width": 1024
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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=r8_PVj**********************************

This is your API token. Keep it to yourself.

Run daanelson/training-2 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": "daanelson/training-2:0019e28f3fd28dc2dce2e4c8f425f63285f157a83acb646e17336dcb16fc57aa",
    "input": {
      "guidance_scale": 7.5,
      "height": 1024,
      "high_noise_frac": 0.8,
      "num_inference_steps": 50,
      "num_outputs": 1,
      "prompt": "An <s0> riding a rainbow unicorn",
      "prompt_strength": 0.8,
      "refine": "expert_ensemble_refiner",
      "scheduler": "DDIM",
      "width": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "vyjsz5zbrgx4qqqkcvo6htgyse",
  "model": "daanelson/training-2",
  "version": "0019e28f3fd28dc2dce2e4c8f425f63285f157a83acb646e17336dcb16fc57aa",
  "input": {
    "guidance_scale": 7.5,
    "height": 1024,
    "high_noise_frac": 0.8,
    "num_inference_steps": 50,
    "num_outputs": 1,
    "prompt": "An <s0> riding a rainbow unicorn",
    "prompt_strength": 0.8,
    "refine": "expert_ensemble_refiner",
    "scheduler": "DDIM",
    "width": 1024
  },
  "logs": "Using seed: 53975\ntxt2img mode\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:01<00:58,  1.50s/it]\n  5%|▌         | 2/40 [00:01<00:26,  1.44it/s]\n  8%|▊         | 3/40 [00:01<00:15,  2.33it/s]\n 10%|█         | 4/40 [00:01<00:11,  3.27it/s]\n 12%|█▎        | 5/40 [00:01<00:08,  4.20it/s]\n 15%|█▌        | 6/40 [00:02<00:06,  4.99it/s]\n 18%|█▊        | 7/40 [00:02<00:05,  5.76it/s]\n 20%|██        | 8/40 [00:02<00:05,  6.40it/s]\n 22%|██▎       | 9/40 [00:02<00:04,  6.92it/s]\n 25%|██▌       | 10/40 [00:02<00:04,  7.34it/s]\n 28%|██▊       | 11/40 [00:02<00:03,  7.67it/s]\n 30%|███       | 12/40 [00:02<00:03,  7.90it/s]\n 32%|███▎      | 13/40 [00:02<00:03,  8.10it/s]\n 35%|███▌      | 14/40 [00:03<00:03,  8.22it/s]\n 38%|███▊      | 15/40 [00:03<00:03,  8.33it/s]\n 40%|████      | 16/40 [00:03<00:02,  8.39it/s]\n 42%|████▎     | 17/40 [00:03<00:02,  8.25it/s]\n 45%|████▌     | 18/40 [00:03<00:02,  8.35it/s]\n 48%|████▊     | 19/40 [00:03<00:02,  8.41it/s]\n 50%|█████     | 20/40 [00:03<00:02,  8.45it/s]\n 52%|█████▎    | 21/40 [00:03<00:02,  8.48it/s]\n 55%|█████▌    | 22/40 [00:03<00:02,  8.51it/s]\n 57%|█████▊    | 23/40 [00:04<00:02,  8.37it/s]\n 60%|██████    | 24/40 [00:04<00:01,  8.36it/s]\n 62%|██████▎   | 25/40 [00:04<00:01,  8.30it/s]\n 65%|██████▌   | 26/40 [00:04<00:01,  8.38it/s]\n 68%|██████▊   | 27/40 [00:04<00:01,  8.45it/s]\n 70%|███████   | 28/40 [00:04<00:01,  8.50it/s]\n 72%|███████▎  | 29/40 [00:04<00:01,  8.50it/s]\n 75%|███████▌  | 30/40 [00:04<00:01,  8.45it/s]\n 78%|███████▊  | 31/40 [00:05<00:01,  8.47it/s]\n 80%|████████  | 32/40 [00:05<00:00,  8.23it/s]\n 82%|████████▎ | 33/40 [00:05<00:00,  8.33it/s]\n 85%|████████▌ | 34/40 [00:05<00:00,  8.39it/s]\n 88%|████████▊ | 35/40 [00:05<00:00,  8.37it/s]\n 90%|█████████ | 36/40 [00:05<00:00,  8.39it/s]\n 92%|█████████▎| 37/40 [00:05<00:00,  8.31it/s]\n 95%|█████████▌| 38/40 [00:05<00:00,  8.39it/s]\n 98%|█████████▊| 39/40 [00:06<00:00,  8.45it/s]\n100%|██████████| 40/40 [00:06<00:00,  8.33it/s]\n100%|██████████| 40/40 [00:06<00:00,  6.51it/s]\n  0%|          | 0/10 [00:00<?, ?it/s]\n 10%|█         | 1/10 [00:00<00:02,  4.12it/s]\n 20%|██        | 2/10 [00:00<00:01,  5.68it/s]\n 30%|███       | 3/10 [00:00<00:01,  6.46it/s]\n 40%|████      | 4/10 [00:00<00:00,  6.91it/s]\n 50%|█████     | 5/10 [00:00<00:00,  7.18it/s]\n 60%|██████    | 6/10 [00:00<00:00,  7.34it/s]\n 70%|███████   | 7/10 [00:01<00:00,  7.23it/s]\n 80%|████████  | 8/10 [00:01<00:00,  7.38it/s]\n 90%|█████████ | 9/10 [00:01<00:00,  7.48it/s]\n100%|██████████| 10/10 [00:01<00:00,  7.55it/s]\n100%|██████████| 10/10 [00:01<00:00,  7.03it/s]",
  "output": [
    "https://replicate.delivery/pbxt/8Eap6aw4nYKiAxTLSfoSEHuwrU0VC80pCdSSwqECKqtakeTRA/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-07-27T07:41:36.123862Z",
  "started_at": "2023-07-27T07:43:40.100582Z",
  "completed_at": "2023-07-27T07:43:49.76431Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/vyjsz5zbrgx4qqqkcvo6htgyse/cancel",
    "get": "https://api.replicate.com/v1/predictions/vyjsz5zbrgx4qqqkcvo6htgyse"
  },
  "metrics": {
    "predict_time": 9.663728,
    "total_time": 133.640448
  }
}

Generated in

9.7 seconds

Tweak it Report

Using seed: 53975 txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:01<00:58, 1.50s/it] 5%|▌ | 2/40 [00:01<00:26, 1.44it/s] 8%|▊ | 3/40 [00:01<00:15, 2.33it/s] 10%|█ | 4/40 [00:01<00:11, 3.27it/s] 12%|█▎ | 5/40 [00:01<00:08, 4.20it/s] 15%|█▌ | 6/40 [00:02<00:06, 4.99it/s] 18%|█▊ | 7/40 [00:02<00:05, 5.76it/s] 20%|██ | 8/40 [00:02<00:05, 6.40it/s] 22%|██▎ | 9/40 [00:02<00:04, 6.92it/s] 25%|██▌ | 10/40 [00:02<00:04, 7.34it/s] 28%|██▊ | 11/40 [00:02<00:03, 7.67it/s] 30%|███ | 12/40 [00:02<00:03, 7.90it/s] 32%|███▎ | 13/40 [00:02<00:03, 8.10it/s] 35%|███▌ | 14/40 [00:03<00:03, 8.22it/s] 38%|███▊ | 15/40 [00:03<00:03, 8.33it/s] 40%|████ | 16/40 [00:03<00:02, 8.39it/s] 42%|████▎ | 17/40 [00:03<00:02, 8.25it/s] 45%|████▌ | 18/40 [00:03<00:02, 8.35it/s] 48%|████▊ | 19/40 [00:03<00:02, 8.41it/s] 50%|█████ | 20/40 [00:03<00:02, 8.45it/s] 52%|█████▎ | 21/40 [00:03<00:02, 8.48it/s] 55%|█████▌ | 22/40 [00:03<00:02, 8.51it/s] 57%|█████▊ | 23/40 [00:04<00:02, 8.37it/s] 60%|██████ | 24/40 [00:04<00:01, 8.36it/s] 62%|██████▎ | 25/40 [00:04<00:01, 8.30it/s] 65%|██████▌ | 26/40 [00:04<00:01, 8.38it/s] 68%|██████▊ | 27/40 [00:04<00:01, 8.45it/s] 70%|███████ | 28/40 [00:04<00:01, 8.50it/s] 72%|███████▎ | 29/40 [00:04<00:01, 8.50it/s] 75%|███████▌ | 30/40 [00:04<00:01, 8.45it/s] 78%|███████▊ | 31/40 [00:05<00:01, 8.47it/s] 80%|████████ | 32/40 [00:05<00:00, 8.23it/s] 82%|████████▎ | 33/40 [00:05<00:00, 8.33it/s] 85%|████████▌ | 34/40 [00:05<00:00, 8.39it/s] 88%|████████▊ | 35/40 [00:05<00:00, 8.37it/s] 90%|█████████ | 36/40 [00:05<00:00, 8.39it/s] 92%|█████████▎| 37/40 [00:05<00:00, 8.31it/s] 95%|█████████▌| 38/40 [00:05<00:00, 8.39it/s] 98%|█████████▊| 39/40 [00:06<00:00, 8.45it/s] 100%|██████████| 40/40 [00:06<00:00, 8.33it/s] 100%|██████████| 40/40 [00:06<00:00, 6.51it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.12it/s] 20%|██ | 2/10 [00:00<00:01, 5.68it/s] 30%|███ | 3/10 [00:00<00:01, 6.46it/s] 40%|████ | 4/10 [00:00<00:00, 6.91it/s] 50%|█████ | 5/10 [00:00<00:00, 7.18it/s] 60%|██████ | 6/10 [00:00<00:00, 7.34it/s] 70%|███████ | 7/10 [00:01<00:00, 7.23it/s] 80%|████████ | 8/10 [00:01<00:00, 7.38it/s] 90%|█████████ | 9/10 [00:01<00:00, 7.48it/s] 100%|██████████| 10/10 [00:01<00:00, 7.55it/s] 100%|██████████| 10/10 [00:01<00:00, 7.03it/s]