laion-ai/laionide:1f01e4fc – Run with an API on Replicate

Version

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laion-ai /laionide:1f01e4fc

Input

prompt

string

Shift + Return to add a new line

trippy acid lsd psychedelic oil painting of a man with a suittrippy acid lsd psychedelic oil painting of a man with a suit

Text prompt to use.

Default: "The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card"

batch_size

integer

(minimum: 1, maximum: 6)

Batch size. Number of generations to predict

Default: 3

side_x

integer

Must be multiple of 8. Going above 64 is not recommended. Actual image will be 4x larger.

Default: 64

side_y

integer

Must be multiple of 8. Going above 64 is not recommended. Actual image will be 4x larger.

Default: 64

upsample_stage

boolean

Performs prompt-aware upsampling by 4x base resolution

Default: true

guidance_scale

number

Classifier-free guidance scale. Higher values move further away from unconditional outputs. Lower values move closer to unconditional outputs. Negative values guide towards semantically opposite classes. 4-16 is a reasonable range.

Default: 4

upsample_temp

number

Upsample temperature. Choose from 0.997, 0.998 or 1.0. Consider lowering to ~0.997 for blurry images with fewer artifacts.

Default: 0.998

timestep_respacing

string

Number of timesteps to use for base model. Going above 50 has diminishing returns.

Default: "27"

sr_timestep_respacing

string

Number of timesteps to use for base model. Going above 40 has diminishing returns.

Default: "17"

seed

integer

Seed for reproducibility

Default: 0

Run this model in Node.js with one line of code:

npx create-replicate --model=laion-ai/laionide

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "laion-ai/laionide:1f01e4fcbf443a7e6fa9fdf4f2205abaf659eca3f597ef0db1ff08b13bae21a2",
  {
    input: {
      seed: 0,
      prompt: "trippy acid lsd psychedelic oil painting of a man with a suit",
      side_x: 80,
      side_y: 48,
      batch_size: 3,
      upsample_temp: 0.998,
      guidance_scale: 12,
      upsample_stage: true,
      timestep_respacing: "27",
      sr_timestep_respacing: "17"
    }
  }
);
console.log(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=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

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

output = replicate.run(
    "laion-ai/laionide:1f01e4fcbf443a7e6fa9fdf4f2205abaf659eca3f597ef0db1ff08b13bae21a2",
    input={
        "seed": 0,
        "prompt": "trippy acid lsd psychedelic oil painting of a man with a suit",
        "side_x": 80,
        "side_y": 48,
        "batch_size": 3,
        "upsample_temp": 0.998,
        "guidance_scale": 12,
        "upsample_stage": True,
        "timestep_respacing": "27",
        "sr_timestep_respacing": "17"
    }
)

# The laion-ai/laionide model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/laion-ai/laionide/api#output-schema
    print(item)

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=<paste-your-token-here>

Find your API token in your account settings.

Run laion-ai/laionide 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": "1f01e4fcbf443a7e6fa9fdf4f2205abaf659eca3f597ef0db1ff08b13bae21a2",
    "input": {
      "seed": 0,
      "prompt": "trippy acid lsd psychedelic oil painting of a man with a suit",
      "side_x": 80,
      "side_y": 48,
      "batch_size": 3,
      "upsample_temp": 0.998,
      "guidance_scale": 12,
      "upsample_stage": true,
      "timestep_respacing": "27",
      "sr_timestep_respacing": "17"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-02-16T00:19:30.609657Z",
  "created_at": "2022-02-16T00:18:59.571967Z",
  "data_removed": false,
  "error": null,
  "id": "4bcmbmh3lbapxnhjlftzomxis4",
  "input": {
    "seed": 0,
    "prompt": "trippy acid lsd psychedelic oil painting of a man with a suit",
    "side_x": "80",
    "side_y": "48",
    "batch_size": "3",
    "upsample_temp": "0.998",
    "guidance_scale": 12,
    "upsample_stage": true,
    "timestep_respacing": "27",
    "sr_timestep_respacing": "17"
  },
  "logs": "Generating 80x48 samples with 27 timesteps using GLIDE-base-64px...\n\n  0%|          | 0/25 [00:00<?, ?it/s]\n  4%|▍         | 1/25 [00:00<00:18,  1.31it/s]\n  8%|▊         | 2/25 [00:01<00:17,  1.33it/s]\n 12%|█▏        | 3/25 [00:02<00:16,  1.34it/s]\n 16%|█▌        | 4/25 [00:02<00:10,  1.91it/s]\n 20%|██        | 5/25 [00:02<00:08,  2.49it/s]\n 24%|██▍       | 6/25 [00:02<00:06,  3.06it/s]\n 28%|██▊       | 7/25 [00:02<00:05,  3.58it/s]\n 32%|███▏      | 8/25 [00:03<00:04,  4.02it/s]\n 36%|███▌      | 9/25 [00:03<00:03,  4.37it/s]\n 40%|████      | 10/25 [00:03<00:03,  4.64it/s]\n 44%|████▍     | 11/25 [00:03<00:02,  4.83it/s]\n 48%|████▊     | 12/25 [00:03<00:02,  4.99it/s]\n 52%|█████▏    | 13/25 [00:04<00:02,  5.11it/s]\n 56%|█████▌    | 14/25 [00:04<00:02,  5.21it/s]\n 60%|██████    | 15/25 [00:04<00:01,  5.28it/s]\n 64%|██████▍   | 16/25 [00:04<00:01,  5.32it/s]\n 68%|██████▊   | 17/25 [00:04<00:01,  5.35it/s]\n 72%|███████▏  | 18/25 [00:05<00:01,  5.36it/s]\n 76%|███████▌  | 19/25 [00:05<00:01,  5.36it/s]\n 80%|████████  | 20/25 [00:05<00:00,  5.36it/s]\n 84%|████████▍ | 21/25 [00:05<00:00,  5.38it/s]\n 88%|████████▊ | 22/25 [00:05<00:00,  5.38it/s]\n 92%|█████████▏| 23/25 [00:05<00:00,  5.39it/s]\n 96%|█████████▌| 24/25 [00:06<00:00,  5.37it/s]\n100%|██████████| 25/25 [00:06<00:00,  5.35it/s]\n100%|██████████| 25/25 [00:06<00:00,  3.96it/s]\nUpsampling outputs from GLIDE-base 80x48 to 320x192 using 17 timesteps...\n\n  0%|          | 0/15 [00:00<?, ?it/s]\n  7%|▋         | 1/15 [00:01<00:26,  1.89s/it]\n 13%|█▎        | 2/15 [00:03<00:24,  1.89s/it]\n 20%|██        | 3/15 [00:05<00:22,  1.89s/it]\n 27%|██▋       | 4/15 [00:06<00:14,  1.33s/it]\n 33%|███▎      | 5/15 [00:06<00:10,  1.02s/it]\n 40%|████      | 6/15 [00:07<00:07,  1.19it/s]\n 47%|████▋     | 7/15 [00:07<00:05,  1.39it/s]\n 53%|█████▎    | 8/15 [00:08<00:04,  1.55it/s]\n 60%|██████    | 9/15 [00:08<00:03,  1.68it/s]\n 67%|██████▋   | 10/15 [00:09<00:02,  1.78it/s]\n 73%|███████▎  | 11/15 [00:09<00:02,  1.86it/s]\n 80%|████████  | 12/15 [00:10<00:01,  1.91it/s]\n 87%|████████▋ | 13/15 [00:10<00:01,  1.95it/s]\n 93%|█████████▎| 14/15 [00:10<00:00,  1.98it/s]\n100%|██████████| 15/15 [00:11<00:00,  2.00it/s]\n100%|██████████| 15/15 [00:11<00:00,  1.31it/s]",
  "metrics": {
    "predict_time": 30.742278,
    "total_time": 31.03769
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/6e152613-c32a-438c-a7d6-56d8f1d9dc48/upsample_predictions.png"
    }
  ],
  "started_at": "2022-02-16T00:18:59.867379Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4bcmbmh3lbapxnhjlftzomxis4",
    "cancel": "https://api.replicate.com/v1/predictions/4bcmbmh3lbapxnhjlftzomxis4/cancel"
  },
  "version": "11a727b3c74f842f7e9d7db88df5da4d1b8389c14e186d6ceb1724abeebe4bc9"
}

Generated in

30.8 seconds

Tweak itReport

Generating 80x48 samples with 27 timesteps using GLIDE-base-64px...

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Upsampling outputs from GLIDE-base 80x48 to 320x192 using 17 timesteps...

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This example was created by a different version, laion-ai/laionide:11a727b3.