Prediction

Model

ai-forever/kandinsky-2:65a15f6e

u225e7n6kvfy7dee4j4rwcx4aq

Status

Succeeded

Source

Web

Hardware

–

Total duration

29.6s

Created

over 2 years ago by fofr

Webhook

–

Input

prompt: An illustration of a quaint garden, watercolor, green and blue, white background
num_inference_steps: 100
guidance_scale: 4
scheduler: p_sampler
prior_cf_scale: 4
prior_steps: 5

{
  "guidance_scale": 4,
  "num_inference_steps": 100,
  "prior_cf_scale": 4,
  "prior_steps": "5",
  "prompt": "An illustration of a quaint garden, watercolor, green and blue, white background",
  "scheduler": "p_sampler"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_ZLb**********************************

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

const output = await replicate.run(
  "ai-forever/kandinsky-2:65a15f6e3c538ee4adf5142411455308926714f7d3f5c940d9f7bc519e0e5c1a",
  {
    input: {
      guidance_scale: 4,
      num_inference_steps: 100,
      prior_cf_scale: 4,
      prior_steps: "5",
      prompt: "An illustration of a quaint garden, watercolor, green and blue, white background",
      scheduler: "p_sampler"
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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_ZLb**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

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

output = replicate.run(
    "ai-forever/kandinsky-2:65a15f6e3c538ee4adf5142411455308926714f7d3f5c940d9f7bc519e0e5c1a",
    input={
        "guidance_scale": 4,
        "num_inference_steps": 100,
        "prior_cf_scale": 4,
        "prior_steps": "5",
        "prompt": "An illustration of a quaint garden, watercolor, green and blue, white background",
        "scheduler": "p_sampler"
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.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_ZLb**********************************

This is your API token. Keep it to yourself.

Run ai-forever/kandinsky-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": "ai-forever/kandinsky-2:65a15f6e3c538ee4adf5142411455308926714f7d3f5c940d9f7bc519e0e5c1a",
    "input": {
      "guidance_scale": 4,
      "num_inference_steps": 100,
      "prior_cf_scale": 4,
      "prior_steps": "5",
      "prompt": "An illustration of a quaint garden, watercolor, green and blue, white background",
      "scheduler": "p_sampler"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "u225e7n6kvfy7dee4j4rwcx4aq",
  "model": "ai-forever/kandinsky-2",
  "version": "65a15f6e3c538ee4adf5142411455308926714f7d3f5c940d9f7bc519e0e5c1a",
  "input": {
    "guidance_scale": 4,
    "num_inference_steps": 100,
    "prior_cf_scale": 4,
    "prior_steps": "5",
    "prompt": "An illustration of a quaint garden, watercolor, green and blue, white background",
    "scheduler": "p_sampler"
  },
  "logs": "0%|          | 0/100 [00:00<?, ?it/s]\n  2%|▏         | 2/100 [00:00<00:08, 11.83it/s]\n  4%|▍         | 4/100 [00:00<00:08, 11.88it/s]\n  6%|▌         | 6/100 [00:00<00:08, 11.63it/s]\n  8%|▊         | 8/100 [00:00<00:07, 11.69it/s]\n 10%|█         | 10/100 [00:00<00:07, 11.72it/s]\n 12%|█▏        | 12/100 [00:01<00:07, 11.79it/s]\n 14%|█▍        | 14/100 [00:01<00:07, 11.86it/s]\n 16%|█▌        | 16/100 [00:01<00:07, 11.90it/s]\n 18%|█▊        | 18/100 [00:01<00:06, 11.95it/s]\n 20%|██        | 20/100 [00:01<00:06, 11.98it/s]\n 22%|██▏       | 22/100 [00:01<00:06, 11.89it/s]\n 24%|██▍       | 24/100 [00:02<00:06, 11.91it/s]\n 26%|██▌       | 26/100 [00:02<00:06, 11.92it/s]\n 28%|██▊       | 28/100 [00:02<00:06, 11.94it/s]\n 30%|███       | 30/100 [00:02<00:05, 11.98it/s]\n 32%|███▏      | 32/100 [00:02<00:05, 11.98it/s]\n 34%|███▍      | 34/100 [00:02<00:05, 11.96it/s]\n 36%|███▌      | 36/100 [00:03<00:05, 12.02it/s]\n 38%|███▊      | 38/100 [00:03<00:05, 12.05it/s]\n 40%|████      | 40/100 [00:03<00:04, 12.06it/s]\n 42%|████▏     | 42/100 [00:03<00:04, 12.05it/s]\n 44%|████▍     | 44/100 [00:03<00:04, 12.04it/s]\n 46%|████▌     | 46/100 [00:03<00:04, 11.98it/s]\n 48%|████▊     | 48/100 [00:04<00:04, 12.04it/s]\n 50%|█████     | 50/100 [00:04<00:04, 12.08it/s]\n 52%|█████▏    | 52/100 [00:04<00:03, 12.06it/s]\n 54%|█████▍    | 54/100 [00:04<00:03, 12.05it/s]\n 56%|█████▌    | 56/100 [00:04<00:03, 12.00it/s]\n 58%|█████▊    | 58/100 [00:04<00:03, 11.97it/s]\n 60%|██████    | 60/100 [00:05<00:03, 11.99it/s]\n 62%|██████▏   | 62/100 [00:05<00:03, 12.02it/s]\n 64%|██████▍   | 64/100 [00:05<00:02, 12.05it/s]\n 66%|██████▌   | 66/100 [00:05<00:02, 12.07it/s]\n 68%|██████▊   | 68/100 [00:05<00:02, 12.02it/s]\n 70%|███████   | 70/100 [00:05<00:02, 11.95it/s]\n 72%|███████▏  | 72/100 [00:06<00:02, 11.93it/s]\n 74%|███████▍  | 74/100 [00:06<00:02, 11.92it/s]\n 76%|███████▌  | 76/100 [00:06<00:02, 11.96it/s]\n 78%|███████▊  | 78/100 [00:06<00:01, 12.00it/s]\n 80%|████████  | 80/100 [00:06<00:01, 11.99it/s]\n 82%|████████▏ | 82/100 [00:06<00:01, 11.95it/s]\n 84%|████████▍ | 84/100 [00:07<00:01, 11.99it/s]\n 86%|████████▌ | 86/100 [00:07<00:01, 11.99it/s]\n 88%|████████▊ | 88/100 [00:07<00:00, 12.00it/s]\n 90%|█████████ | 90/100 [00:07<00:00, 12.03it/s]\n 92%|█████████▏| 92/100 [00:07<00:00, 12.01it/s]\n 94%|█████████▍| 94/100 [00:07<00:00, 11.89it/s]\n 96%|█████████▌| 96/100 [00:08<00:00, 11.94it/s]\n 98%|█████████▊| 98/100 [00:08<00:00, 11.94it/s]\n100%|██████████| 100/100 [00:08<00:00, 11.90it/s]\n100%|██████████| 100/100 [00:08<00:00, 11.96it/s]",
  "output": "https://replicate.delivery/pbxt/jopPv3SSXgorLlN3wuQqsQJUtKetMfiGvyQ1e43d4wi9Q8ihA/out.png",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-04-13T16:38:25.761784Z",
  "started_at": "2023-04-13T16:38:45.872018Z",
  "completed_at": "2023-04-13T16:38:55.327879Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/u225e7n6kvfy7dee4j4rwcx4aq/cancel",
    "get": "https://api.replicate.com/v1/predictions/u225e7n6kvfy7dee4j4rwcx4aq"
  },
  "metrics": {
    "predict_time": 9.455861,
    "total_time": 29.566095
  }
}

Generated in

9.5 seconds

Tweak it Report

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