laion-ai/laionide-v3:153af311 – Run with an API on Replicate

Version

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laion-ai /laionide-v3:153af311

Input

prompt

*string

Shift + Return to add a new line

The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch cardThe Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card

Text prompt to use.

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

string

Upsample temperature. 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: "40"

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-v3

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-v3 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-v3:153af311cc39000fee1effcee0acbd5d954967193a5ab56368ffcd4766781de2",
  {
    input: {
      seed: 0,
      prompt: "The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card",
      side_x: 64,
      side_y: 64,
      batch_size: 3,
      upsample_temp: 0.998,
      guidance_scale: 3,
      upsample_stage: true,
      timestep_respacing: "25",
      sr_timestep_respacing: "27"
    }
  }
);
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-v3 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "laion-ai/laionide-v3:153af311cc39000fee1effcee0acbd5d954967193a5ab56368ffcd4766781de2",
    input={
        "seed": 0,
        "prompt": "The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card",
        "side_x": 64,
        "side_y": 64,
        "batch_size": 3,
        "upsample_temp": 0.998,
        "guidance_scale": 3,
        "upsample_stage": True,
        "timestep_respacing": "25",
        "sr_timestep_respacing": "27"
    }
)

# The laion-ai/laionide-v3 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-v3/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-v3 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": "153af311cc39000fee1effcee0acbd5d954967193a5ab56368ffcd4766781de2",
    "input": {
      "seed": 0,
      "prompt": "The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card",
      "side_x": 64,
      "side_y": 64,
      "batch_size": 3,
      "upsample_temp": 0.998,
      "guidance_scale": 3,
      "upsample_stage": true,
      "timestep_respacing": "25",
      "sr_timestep_respacing": "27"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/laion-ai/laionide-v3@sha256:153af311cc39000fee1effcee0acbd5d954967193a5ab56368ffcd4766781de2 \
  -i 'seed=0' \
  -i 'prompt="The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card"' \
  -i 'side_x=64' \
  -i 'side_y=64' \
  -i 'batch_size=3' \
  -i 'upsample_temp=0.998' \
  -i 'guidance_scale=3' \
  -i 'upsample_stage=true' \
  -i 'timestep_respacing="25"' \
  -i 'sr_timestep_respacing="27"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/laion-ai/laionide-v3@sha256:153af311cc39000fee1effcee0acbd5d954967193a5ab56368ffcd4766781de2
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 0,
      "prompt": "The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card",
      "side_x": 64,
      "side_y": 64,
      "batch_size": 3,
      "upsample_temp": 0.998,
      "guidance_scale": 3,
      "upsample_stage": true,
      "timestep_respacing": "25",
      "sr_timestep_respacing": "27"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2022-02-25T16:20:55.080250Z",
  "created_at": "2022-02-25T16:20:18.982110Z",
  "data_removed": false,
  "error": null,
  "id": "zheef7k26fhytoxwz7ceri2744",
  "input": {
    "prompt": "The Lovers demon skull werewolf tentacle tarot card | Tarot card on artstation | eldritch card",
    "side_x": 64,
    "side_y": 64,
    "batch_size": 3,
    "upsample_temp": 0.998,
    "guidance_scale": 3,
    "upsample_stage": true,
    "timestep_respacing": "25",
    "sr_timestep_respacing": "27"
  },
  "logs": "Generating 64x64 samples with 25 timesteps using GLIDE-base-64px...\n\n  0%|          | 0/23 [00:00<?, ?it/s]\n  4%|▍         | 1/23 [00:00<00:17,  1.27it/s]\n  9%|▊         | 2/23 [00:01<00:15,  1.34it/s]\n 13%|█▎        | 3/23 [00:02<00:14,  1.36it/s]\n 17%|█▋        | 4/23 [00:02<00:09,  1.93it/s]\n 22%|██▏       | 5/23 [00:02<00:07,  2.52it/s]\n 26%|██▌       | 6/23 [00:02<00:05,  3.09it/s]\n 30%|███       | 7/23 [00:02<00:04,  3.61it/s]\n 35%|███▍      | 8/23 [00:03<00:03,  4.06it/s]\n 39%|███▉      | 9/23 [00:03<00:03,  4.42it/s]\n 43%|████▎     | 10/23 [00:03<00:02,  4.72it/s]\n 48%|████▊     | 11/23 [00:03<00:02,  4.94it/s]\n 52%|█████▏    | 12/23 [00:03<00:02,  5.09it/s]\n 57%|█████▋    | 13/23 [00:04<00:01,  5.22it/s]\n 61%|██████    | 14/23 [00:04<00:01,  5.31it/s]\n 65%|██████▌   | 15/23 [00:04<00:01,  5.36it/s]\n 70%|██████▉   | 16/23 [00:04<00:01,  5.39it/s]\n 74%|███████▍  | 17/23 [00:04<00:01,  5.42it/s]\n 78%|███████▊  | 18/23 [00:04<00:00,  5.43it/s]\n 83%|████████▎ | 19/23 [00:05<00:00,  5.46it/s]\n 87%|████████▋ | 20/23 [00:05<00:00,  5.46it/s]\n 91%|█████████▏| 21/23 [00:05<00:00,  5.47it/s]\n 96%|█████████▌| 22/23 [00:05<00:00,  5.47it/s]\n100%|██████████| 23/23 [00:05<00:00,  5.46it/s]\n100%|██████████| 23/23 [00:05<00:00,  3.92it/s]\nUpsampling outputs from GLIDE-base 64x64 to 256x256 using 27 timesteps...\n\n  0%|          | 0/25 [00:00<?, ?it/s]\n  4%|▍         | 1/25 [00:01<00:47,  1.97s/it]\n  8%|▊         | 2/25 [00:03<00:45,  1.96s/it]\n 12%|█▏        | 3/25 [00:05<00:43,  1.96s/it]\n 16%|█▌        | 4/25 [00:06<00:28,  1.38s/it]\n 20%|██        | 5/25 [00:06<00:21,  1.06s/it]\n 24%|██▍       | 6/25 [00:07<00:16,  1.15it/s]\n 28%|██▊       | 7/25 [00:07<00:13,  1.34it/s]\n 32%|███▏      | 8/25 [00:08<00:11,  1.50it/s]\n 36%|███▌      | 9/25 [00:08<00:09,  1.63it/s]\n 40%|████      | 10/25 [00:09<00:08,  1.73it/s]\n 44%|████▍     | 11/25 [00:09<00:07,  1.81it/s]\n 48%|████▊     | 12/25 [00:10<00:06,  1.86it/s]\n 52%|█████▏    | 13/25 [00:10<00:06,  1.90it/s]\n 56%|█████▌    | 14/25 [00:11<00:05,  1.94it/s]\n 60%|██████    | 15/25 [00:11<00:05,  1.96it/s]\n 64%|██████▍   | 16/25 [00:12<00:04,  1.97it/s]\n 68%|██████▊   | 17/25 [00:12<00:04,  1.98it/s]\n 72%|███████▏  | 18/25 [00:13<00:03,  1.99it/s]\n 76%|███████▌  | 19/25 [00:13<00:03,  1.99it/s]\n 80%|████████  | 20/25 [00:14<00:02,  1.99it/s]\n 84%|████████▍ | 21/25 [00:14<00:02,  2.00it/s]\n 88%|████████▊ | 22/25 [00:15<00:01,  1.99it/s]\n 92%|█████████▏| 23/25 [00:15<00:01,  1.99it/s]\n 96%|█████████▌| 24/25 [00:16<00:00,  2.00it/s]\n100%|██████████| 25/25 [00:16<00:00,  2.00it/s]\n100%|██████████| 25/25 [00:16<00:00,  1.49it/s]",
  "metrics": {
    "predict_time": 35.933512,
    "total_time": 36.09814
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/4b4137a8-0dab-4c71-82ed-ef42219717da/upsample_predictions.png"
    }
  ],
  "started_at": "2022-02-25T16:20:19.146738Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/zheef7k26fhytoxwz7ceri2744",
    "cancel": "https://api.replicate.com/v1/predictions/zheef7k26fhytoxwz7ceri2744/cancel"
  },
  "version": "db44d8128b86ccbf770aa1c1d6cefa2f7dc7ee02333a4a3ab004e0de89d24f76"
}

Generated in

35.9 seconds

Tweak itReport

Generating 64x64 samples with 25 timesteps using GLIDE-base-64px...

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Upsampling outputs from GLIDE-base 64x64 to 256x256 using 27 timesteps...

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This example was created by a different version, laion-ai/laionide-v3:db44d812.