Prediction

v4yarmdbozamrlwcgytylzuase

Status

Succeeded

Source

Web

Hardware

A40

Total duration

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: joe biden eating a cheeeseburger at mcdonalds
scheduler: K_EULER
num_outputs: 1
guidance_scale: 7.5
apply_watermark
negative_prompt
num_inference_steps: 25

{
  "width": 1024,
  "height": 1024,
  "prompt": "joe biden eating a cheeeseburger at mcdonalds",
  "scheduler": "K_EULER",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "negative_prompt": "",
  "num_inference_steps": 25
}

npm install replicate

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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "joe biden eating a cheeeseburger at mcdonalds",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      negative_prompt: "",
      num_inference_steps: 25
    }
  }
);

// 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.

pip install replicate

Import the client:

import replicate

Run charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "joe biden eating a cheeeseburger at mcdonalds",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "negative_prompt": "",
        "num_inference_steps": 25
    }
)

# 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.

Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "joe biden eating a cheeeseburger at mcdonalds",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 25
    }
  }' \
  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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="joe biden eating a cheeeseburger at mcdonalds"' \
  -i 'scheduler="K_EULER"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=25'

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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "joe biden eating a cheeeseburger at mcdonalds",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 25
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-12-26T05:17:33.431718Z",
  "created_at": "2023-12-26T05:14:30.102887Z",
  "data_removed": false,
  "error": null,
  "id": "v4yarmdbozamrlwcgytylzuase",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "joe biden eating a cheeeseburger at mcdonalds",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "negative_prompt": "",
    "num_inference_steps": 25
  },
  "logs": "Using seed: 4177073\n  0%|          | 0/25 [00:00<?, ?it/s]\n  4%|▍         | 1/25 [00:00<00:08,  2.98it/s]\n  8%|▊         | 2/25 [00:00<00:05,  3.93it/s]\n 12%|█▏        | 3/25 [00:00<00:05,  4.37it/s]\n 16%|█▌        | 4/25 [00:00<00:04,  4.61it/s]\n 20%|██        | 5/25 [00:01<00:04,  4.76it/s]\n 24%|██▍       | 6/25 [00:01<00:03,  4.84it/s]\n 28%|██▊       | 7/25 [00:01<00:03,  4.89it/s]\n 32%|███▏      | 8/25 [00:01<00:03,  4.93it/s]\n 36%|███▌      | 9/25 [00:01<00:03,  4.96it/s]\n 40%|████      | 10/25 [00:02<00:03,  4.97it/s]\n 44%|████▍     | 11/25 [00:02<00:02,  4.98it/s]\n 48%|████▊     | 12/25 [00:02<00:02,  4.99it/s]\n 52%|█████▏    | 13/25 [00:02<00:02,  4.98it/s]\n 56%|█████▌    | 14/25 [00:02<00:02,  4.99it/s]\n 60%|██████    | 15/25 [00:03<00:02,  4.99it/s]\n 64%|██████▍   | 16/25 [00:03<00:01,  4.99it/s]\n 68%|██████▊   | 17/25 [00:03<00:01,  5.00it/s]\n 72%|███████▏  | 18/25 [00:03<00:01,  4.99it/s]\n 76%|███████▌  | 19/25 [00:03<00:01,  5.00it/s]\n 80%|████████  | 20/25 [00:04<00:01,  5.00it/s]\n 84%|████████▍ | 21/25 [00:04<00:00,  5.00it/s]\n 88%|████████▊ | 22/25 [00:04<00:00,  5.00it/s]\n 92%|█████████▏| 23/25 [00:04<00:00,  5.00it/s]\n 96%|█████████▌| 24/25 [00:04<00:00,  5.00it/s]\n100%|██████████| 25/25 [00:05<00:00,  5.00it/s]\n100%|██████████| 25/25 [00:05<00:00,  4.87it/s]",
  "metrics": {
    "predict_time": 7.122212,
    "total_time": 183.328831
  },
  "output": [
    "https://replicate.delivery/pbxt/IEYnPTueeWs9HUb2xtL2mR1CLasKcnFX5Dv6vDX0ERvsPBGSA/out-0.png"
  ],
  "started_at": "2023-12-26T05:17:26.309506Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/v4yarmdbozamrlwcgytylzuase",
    "cancel": "https://api.replicate.com/v1/predictions/v4yarmdbozamrlwcgytylzuase/cancel"
  },
  "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca"
}

Generated in

7.1 seconds

charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca

Prediction

Model

dovjsu3bbeus5elbpcdvm7fecy

Status

Succeeded

Source

Web

Hardware

A40

Total duration

8.6s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: jlo as artwork
scheduler: DPMSolverMultistep
num_outputs: 1
guidance_scale: 6.5
apply_watermark
negative_prompt
num_inference_steps: 33

{
  "width": 1024,
  "height": 1024,
  "prompt": "jlo as artwork",
  "scheduler": "DPMSolverMultistep",
  "num_outputs": 1,
  "guidance_scale": 6.5,
  "apply_watermark": true,
  "negative_prompt": "",
  "num_inference_steps": 33
}

npm install replicate

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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "jlo as artwork",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 6.5,
      apply_watermark: true,
      negative_prompt: "",
      num_inference_steps: 33
    }
  }
);

// 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.

pip install replicate

Import the client:

import replicate

Run charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "jlo as artwork",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 6.5,
        "apply_watermark": True,
        "negative_prompt": "",
        "num_inference_steps": 33
    }
)

# 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.

Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "jlo as artwork",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 6.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="jlo as artwork"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=6.5' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=33'

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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "jlo as artwork",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 6.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-12-26T05:23:27.786839Z",
  "created_at": "2023-12-26T05:23:19.193719Z",
  "data_removed": false,
  "error": null,
  "id": "dovjsu3bbeus5elbpcdvm7fecy",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "jlo as artwork",
    "scheduler": "DPMSolverMultistep",
    "num_outputs": 1,
    "guidance_scale": 6.5,
    "apply_watermark": true,
    "negative_prompt": "",
    "num_inference_steps": 33
  },
  "logs": "Using seed: 11974137\n  0%|          | 0/33 [00:00<?, ?it/s]\n  3%|▎         | 1/33 [00:00<00:06,  5.06it/s]\n  9%|▉         | 3/33 [00:00<00:04,  6.24it/s]\n 12%|█▏        | 4/33 [00:00<00:05,  5.76it/s]\n 15%|█▌        | 5/33 [00:00<00:05,  5.51it/s]\n 18%|█▊        | 6/33 [00:01<00:05,  5.34it/s]\n 21%|██        | 7/33 [00:01<00:04,  5.24it/s]\n 24%|██▍       | 8/33 [00:01<00:04,  5.18it/s]\n 27%|██▋       | 9/33 [00:01<00:04,  5.14it/s]\n 30%|███       | 10/33 [00:01<00:04,  5.11it/s]\n 33%|███▎      | 11/33 [00:02<00:04,  5.08it/s]\n 36%|███▋      | 12/33 [00:02<00:04,  5.07it/s]\n 39%|███▉      | 13/33 [00:02<00:03,  5.06it/s]\n 42%|████▏     | 14/33 [00:02<00:03,  5.03it/s]\n 45%|████▌     | 15/33 [00:02<00:03,  4.96it/s]\n 48%|████▊     | 16/33 [00:03<00:03,  4.98it/s]\n 52%|█████▏    | 17/33 [00:03<00:03,  4.99it/s]\n 55%|█████▍    | 18/33 [00:03<00:02,  5.00it/s]\n 58%|█████▊    | 19/33 [00:03<00:02,  5.01it/s]\n 61%|██████    | 20/33 [00:03<00:02,  5.01it/s]\n 64%|██████▎   | 21/33 [00:04<00:02,  5.02it/s]\n 67%|██████▋   | 22/33 [00:04<00:02,  5.02it/s]\n 70%|██████▉   | 23/33 [00:04<00:01,  5.02it/s]\n 73%|███████▎  | 24/33 [00:04<00:01,  5.02it/s]\n 76%|███████▌  | 25/33 [00:04<00:01,  5.02it/s]\n 79%|███████▉  | 26/33 [00:05<00:01,  5.02it/s]\n 82%|████████▏ | 27/33 [00:05<00:01,  5.02it/s]\n 85%|████████▍ | 28/33 [00:05<00:00,  5.01it/s]\n 88%|████████▊ | 29/33 [00:05<00:00,  5.01it/s]\n 91%|█████████ | 30/33 [00:05<00:00,  5.00it/s]\n 94%|█████████▍| 31/33 [00:06<00:00,  5.00it/s]\n 97%|█████████▋| 32/33 [00:06<00:00,  5.01it/s]\n100%|██████████| 33/33 [00:06<00:00,  5.00it/s]\n100%|██████████| 33/33 [00:06<00:00,  5.10it/s]",
  "metrics": {
    "predict_time": 8.55192,
    "total_time": 8.59312
  },
  "output": [
    "https://replicate.delivery/pbxt/5LfQDeOcydhR3UIce0JP70V27VY62vZaGBEM95XSyRudqCMkA/out-0.png"
  ],
  "started_at": "2023-12-26T05:23:19.234919Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dovjsu3bbeus5elbpcdvm7fecy",
    "cancel": "https://api.replicate.com/v1/predictions/dovjsu3bbeus5elbpcdvm7fecy/cancel"
  },
  "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca"
}

Generated in

8.6 seconds

charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca

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Prediction

Model

nwndyklbyng3rnilchojoyg3bi

Status

Succeeded

Source

Web

Hardware

A40

Total duration

8.6s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: The Joker, Cinematic still, vibrant, bokeh
scheduler: K_EULER_ANCESTRAL
num_outputs: 1
guidance_scale: 6.5
apply_watermark
negative_prompt
num_inference_steps: 33

{
  "width": 1024,
  "height": 1024,
  "prompt": "The Joker, Cinematic still, vibrant, bokeh",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_outputs": 1,
  "guidance_scale": 6.5,
  "apply_watermark": true,
  "negative_prompt": "",
  "num_inference_steps": 33
}

npm install replicate

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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "The Joker, Cinematic still, vibrant, bokeh",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: 1,
      guidance_scale: 6.5,
      apply_watermark: true,
      negative_prompt: "",
      num_inference_steps: 33
    }
  }
);

// 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.

pip install replicate

Import the client:

import replicate

Run charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "The Joker, Cinematic still, vibrant, bokeh",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_outputs": 1,
        "guidance_scale": 6.5,
        "apply_watermark": True,
        "negative_prompt": "",
        "num_inference_steps": 33
    }
)

# 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.

Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "The Joker, Cinematic still, vibrant, bokeh",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 6.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="The Joker, Cinematic still, vibrant, bokeh"' \
  -i 'scheduler="K_EULER_ANCESTRAL"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=6.5' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=33'

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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "The Joker, Cinematic still, vibrant, bokeh",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 6.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-12-26T05:28:00.493628Z",
  "created_at": "2023-12-26T05:27:51.924837Z",
  "data_removed": false,
  "error": null,
  "id": "nwndyklbyng3rnilchojoyg3bi",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "The Joker, Cinematic still, vibrant, bokeh",
    "scheduler": "K_EULER_ANCESTRAL",
    "num_outputs": 1,
    "guidance_scale": 6.5,
    "apply_watermark": true,
    "negative_prompt": "",
    "num_inference_steps": 33
  },
  "logs": "Using seed: 1899355\n  0%|          | 0/33 [00:00<?, ?it/s]\n  6%|▌         | 2/33 [00:00<00:04,  6.81it/s]\n  9%|▉         | 3/33 [00:00<00:05,  5.94it/s]\n 12%|█▏        | 4/33 [00:00<00:05,  5.57it/s]\n 15%|█▌        | 5/33 [00:00<00:05,  5.37it/s]\n 18%|█▊        | 6/33 [00:01<00:05,  5.24it/s]\n 21%|██        | 7/33 [00:01<00:05,  5.17it/s]\n 24%|██▍       | 8/33 [00:01<00:04,  5.12it/s]\n 27%|██▋       | 9/33 [00:01<00:04,  5.09it/s]\n 30%|███       | 10/33 [00:01<00:04,  5.06it/s]\n 33%|███▎      | 11/33 [00:02<00:04,  5.04it/s]\n 36%|███▋      | 12/33 [00:02<00:04,  5.03it/s]\n 39%|███▉      | 13/33 [00:02<00:03,  5.02it/s]\n 42%|████▏     | 14/33 [00:02<00:03,  5.02it/s]\n 45%|████▌     | 15/33 [00:02<00:03,  5.01it/s]\n 48%|████▊     | 16/33 [00:03<00:03,  5.01it/s]\n 52%|█████▏    | 17/33 [00:03<00:03,  5.01it/s]\n 55%|█████▍    | 18/33 [00:03<00:02,  5.00it/s]\n 58%|█████▊    | 19/33 [00:03<00:02,  5.00it/s]\n 61%|██████    | 20/33 [00:03<00:02,  5.00it/s]\n 64%|██████▎   | 21/33 [00:04<00:02,  5.00it/s]\n 67%|██████▋   | 22/33 [00:04<00:02,  5.00it/s]\n 70%|██████▉   | 23/33 [00:04<00:02,  5.00it/s]\n 73%|███████▎  | 24/33 [00:04<00:01,  4.99it/s]\n 76%|███████▌  | 25/33 [00:04<00:01,  4.99it/s]\n 79%|███████▉  | 26/33 [00:05<00:01,  4.99it/s]\n 82%|████████▏ | 27/33 [00:05<00:01,  4.99it/s]\n 85%|████████▍ | 28/33 [00:05<00:01,  4.99it/s]\n 88%|████████▊ | 29/33 [00:05<00:00,  4.99it/s]\n 91%|█████████ | 30/33 [00:05<00:00,  4.98it/s]\n 94%|█████████▍| 31/33 [00:06<00:00,  4.98it/s]\n 97%|█████████▋| 32/33 [00:06<00:00,  4.98it/s]\n100%|██████████| 33/33 [00:06<00:00,  4.98it/s]\n100%|██████████| 33/33 [00:06<00:00,  5.08it/s]",
  "metrics": {
    "predict_time": 8.529504,
    "total_time": 8.568791
  },
  "output": [
    "https://replicate.delivery/pbxt/WL0lEwlzmR4fOyQoNDtI3kpY2OmDH9ypRf5KfxBkkdMflFYIB/out-0.png"
  ],
  "started_at": "2023-12-26T05:27:51.964124Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/nwndyklbyng3rnilchojoyg3bi",
    "cancel": "https://api.replicate.com/v1/predictions/nwndyklbyng3rnilchojoyg3bi/cancel"
  },
  "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca"
}

Generated in

8.5 seconds

charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca

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Prediction

Model

35au3olbdn7ejhs7ipblddninu

Status

Succeeded

Source

Web

Hardware

A40

Total duration

8.6s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: jlo as artwork
scheduler: DPMSolverMultistep
num_outputs: 1
guidance_scale: 7.5
apply_watermark
negative_prompt
num_inference_steps: 33

{
  "width": 1024,
  "height": 1024,
  "prompt": "jlo as artwork",
  "scheduler": "DPMSolverMultistep",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "negative_prompt": "",
  "num_inference_steps": 33
}

npm install replicate

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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "jlo as artwork",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      negative_prompt: "",
      num_inference_steps: 33
    }
  }
);

// 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.

pip install replicate

Import the client:

import replicate

Run charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "jlo as artwork",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "negative_prompt": "",
        "num_inference_steps": 33
    }
)

# 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.

Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "jlo as artwork",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="jlo as artwork"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=33'

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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "jlo as artwork",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-12-26T05:23:03.651623Z",
  "created_at": "2023-12-26T05:22:55.083057Z",
  "data_removed": false,
  "error": null,
  "id": "35au3olbdn7ejhs7ipblddninu",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "jlo as artwork",
    "scheduler": "DPMSolverMultistep",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "negative_prompt": "",
    "num_inference_steps": 33
  },
  "logs": "Using seed: 3751455\n  0%|          | 0/33 [00:00<?, ?it/s]\n  3%|▎         | 1/33 [00:00<00:06,  5.07it/s]\n  9%|▉         | 3/33 [00:00<00:04,  6.26it/s]\n 12%|█▏        | 4/33 [00:00<00:05,  5.78it/s]\n 15%|█▌        | 5/33 [00:00<00:05,  5.51it/s]\n 18%|█▊        | 6/33 [00:01<00:05,  5.34it/s]\n 21%|██        | 7/33 [00:01<00:04,  5.23it/s]\n 24%|██▍       | 8/33 [00:01<00:04,  5.16it/s]\n 27%|██▋       | 9/33 [00:01<00:04,  5.13it/s]\n 30%|███       | 10/33 [00:01<00:04,  5.11it/s]\n 33%|███▎      | 11/33 [00:02<00:04,  5.09it/s]\n 36%|███▋      | 12/33 [00:02<00:04,  5.08it/s]\n 39%|███▉      | 13/33 [00:02<00:03,  5.07it/s]\n 42%|████▏     | 14/33 [00:02<00:03,  5.06it/s]\n 45%|████▌     | 15/33 [00:02<00:03,  5.05it/s]\n 48%|████▊     | 16/33 [00:03<00:03,  5.05it/s]\n 52%|█████▏    | 17/33 [00:03<00:03,  5.05it/s]\n 55%|█████▍    | 18/33 [00:03<00:02,  5.04it/s]\n 58%|█████▊    | 19/33 [00:03<00:02,  5.04it/s]\n 61%|██████    | 20/33 [00:03<00:02,  5.04it/s]\n 64%|██████▎   | 21/33 [00:04<00:02,  5.04it/s]\n 67%|██████▋   | 22/33 [00:04<00:02,  5.04it/s]\n 70%|██████▉   | 23/33 [00:04<00:01,  5.04it/s]\n 73%|███████▎  | 24/33 [00:04<00:01,  5.04it/s]\n 76%|███████▌  | 25/33 [00:04<00:01,  5.03it/s]\n 79%|███████▉  | 26/33 [00:05<00:01,  5.03it/s]\n 82%|████████▏ | 27/33 [00:05<00:01,  5.04it/s]\n 85%|████████▍ | 28/33 [00:05<00:00,  5.04it/s]\n 88%|████████▊ | 29/33 [00:05<00:00,  5.03it/s]\n 91%|█████████ | 30/33 [00:05<00:00,  5.03it/s]\n 94%|█████████▍| 31/33 [00:06<00:00,  5.03it/s]\n 97%|█████████▋| 32/33 [00:06<00:00,  5.03it/s]\n100%|██████████| 33/33 [00:06<00:00,  5.03it/s]\n100%|██████████| 33/33 [00:06<00:00,  5.12it/s]",
  "metrics": {
    "predict_time": 8.531742,
    "total_time": 8.568566
  },
  "output": [
    "https://replicate.delivery/pbxt/s3QJiy5o4j6CGFQF0e2JvJLtmJGfnoaJhWEbvfYWb30tpCMkA/out-0.png"
  ],
  "started_at": "2023-12-26T05:22:55.119881Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/35au3olbdn7ejhs7ipblddninu",
    "cancel": "https://api.replicate.com/v1/predictions/35au3olbdn7ejhs7ipblddninu/cancel"
  },
  "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca"
}

Generated in

8.5 seconds

charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca

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Prediction

Model

z4f5ro3beaphl7etukvf7pzkim

Status

Succeeded

Source

Web

Hardware

A40

Total duration

8.6s

Created

over 1 year ago

Input

width: 1024
height: 1024
prompt: jlo as artwork
scheduler: K_EULER_ANCESTRAL
num_outputs: 1
guidance_scale: 6.5
apply_watermark
negative_prompt
num_inference_steps: 33

{
  "width": 1024,
  "height": 1024,
  "prompt": "jlo as artwork",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_outputs": 1,
  "guidance_scale": 6.5,
  "apply_watermark": true,
  "negative_prompt": "",
  "num_inference_steps": 33
}

npm install replicate

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 charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "jlo as artwork",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: 1,
      guidance_scale: 6.5,
      apply_watermark: true,
      negative_prompt: "",
      num_inference_steps: 33
    }
  }
);

// 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.

pip install replicate

Import the client:

import replicate

Run charlesmccarthy/terminus-xl-otaku-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "jlo as artwork",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_outputs": 1,
        "guidance_scale": 6.5,
        "apply_watermark": True,
        "negative_prompt": "",
        "num_inference_steps": 33
    }
)

# 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.

Run charlesmccarthy/terminus-xl-otaku-v1 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": "charlesmccarthy/terminus-xl-otaku-v1:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "jlo as artwork",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 6.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="jlo as artwork"' \
  -i 'scheduler="K_EULER_ANCESTRAL"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=6.5' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=33'

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/charlesmccarthy/terminus-xl-otaku-v1@sha256:7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "jlo as artwork",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 6.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "num_inference_steps": 33
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-12-26T05:24:52.672850Z",
  "created_at": "2023-12-26T05:24:44.108746Z",
  "data_removed": false,
  "error": null,
  "id": "z4f5ro3beaphl7etukvf7pzkim",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "jlo as artwork",
    "scheduler": "K_EULER_ANCESTRAL",
    "num_outputs": 1,
    "guidance_scale": 6.5,
    "apply_watermark": true,
    "negative_prompt": "",
    "num_inference_steps": 33
  },
  "logs": "Using seed: 6031873\n  0%|          | 0/33 [00:00<?, ?it/s]\n  6%|▌         | 2/33 [00:00<00:04,  6.82it/s]\n  9%|▉         | 3/33 [00:00<00:05,  5.94it/s]\n 12%|█▏        | 4/33 [00:00<00:05,  5.57it/s]\n 15%|█▌        | 5/33 [00:00<00:05,  5.37it/s]\n 18%|█▊        | 6/33 [00:01<00:05,  5.23it/s]\n 21%|██        | 7/33 [00:01<00:05,  5.16it/s]\n 24%|██▍       | 8/33 [00:01<00:04,  5.12it/s]\n 27%|██▋       | 9/33 [00:01<00:04,  5.08it/s]\n 30%|███       | 10/33 [00:01<00:04,  5.06it/s]\n 33%|███▎      | 11/33 [00:02<00:04,  5.04it/s]\n 36%|███▋      | 12/33 [00:02<00:04,  5.03it/s]\n 39%|███▉      | 13/33 [00:02<00:03,  5.02it/s]\n 42%|████▏     | 14/33 [00:02<00:03,  5.02it/s]\n 45%|████▌     | 15/33 [00:02<00:03,  5.01it/s]\n 48%|████▊     | 16/33 [00:03<00:03,  5.01it/s]\n 52%|█████▏    | 17/33 [00:03<00:03,  5.00it/s]\n 55%|█████▍    | 18/33 [00:03<00:03,  5.00it/s]\n 58%|█████▊    | 19/33 [00:03<00:02,  4.99it/s]\n 61%|██████    | 20/33 [00:03<00:02,  4.99it/s]\n 64%|██████▎   | 21/33 [00:04<00:02,  4.99it/s]\n 67%|██████▋   | 22/33 [00:04<00:02,  4.99it/s]\n 70%|██████▉   | 23/33 [00:04<00:02,  4.98it/s]\n 73%|███████▎  | 24/33 [00:04<00:01,  4.98it/s]\n 76%|███████▌  | 25/33 [00:04<00:01,  4.98it/s]\n 79%|███████▉  | 26/33 [00:05<00:01,  4.98it/s]\n 82%|████████▏ | 27/33 [00:05<00:01,  4.98it/s]\n 85%|████████▍ | 28/33 [00:05<00:01,  4.98it/s]\n 88%|████████▊ | 29/33 [00:05<00:00,  4.97it/s]\n 91%|█████████ | 30/33 [00:05<00:00,  4.98it/s]\n 94%|█████████▍| 31/33 [00:06<00:00,  4.98it/s]\n 97%|█████████▋| 32/33 [00:06<00:00,  4.98it/s]\n100%|██████████| 33/33 [00:06<00:00,  4.98it/s]\n100%|██████████| 33/33 [00:06<00:00,  5.08it/s]",
  "metrics": {
    "predict_time": 8.527941,
    "total_time": 8.564104
  },
  "output": [
    "https://replicate.delivery/pbxt/4yHLkENPZlLeISTt96eXjfMbDu2TUAelsai1G5h6r3fY0KwQC/out-0.png"
  ],
  "started_at": "2023-12-26T05:24:44.144909Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/z4f5ro3beaphl7etukvf7pzkim",
    "cancel": "https://api.replicate.com/v1/predictions/z4f5ro3beaphl7etukvf7pzkim/cancel"
  },
  "version": "7a63fb93cb78f93bab3470e8042d256d2ea88c2a21d2df16e253ed4263b2bcca"
}

Generated in

8.5 seconds