anotherjesse/dogbooth:35b7f2e0 – Run with an API on Replicate

Version

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anotherjesse /dogbooth:35b7f2e0

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "a photo of an astronaut riding a horse on mars"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 768

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 768

prompt_strength

number

Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler

Default: "K_EULER"

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=anotherjesse/dogbooth

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 anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "anotherjesse/dogbooth:35b7f2e003fc1611c44ca016dbfb7e8b3489bcc81b7d7e7580c61d3e7cad803c",
  {
    input: {
      seed: 64596,
      width: 512,
      height: 512,
      prompt: "a velvet painting of dgg playing poker",
      scheduler: "DDIM",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);
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 anotherjesse/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/dogbooth:35b7f2e003fc1611c44ca016dbfb7e8b3489bcc81b7d7e7580c61d3e7cad803c",
    input={
        "seed": 64596,
        "width": 512,
        "height": 512,
        "prompt": "a velvet painting of dgg playing poker",
        "scheduler": "DDIM",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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 anotherjesse/dogbooth 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": "35b7f2e003fc1611c44ca016dbfb7e8b3489bcc81b7d7e7580c61d3e7cad803c",
    "input": {
      "seed": 64596,
      "width": 512,
      "height": 512,
      "prompt": "a velvet painting of dgg playing poker",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  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/anotherjesse/dogbooth@sha256:35b7f2e003fc1611c44ca016dbfb7e8b3489bcc81b7d7e7580c61d3e7cad803c \
  -i 'seed=64596' \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="a velvet painting of dgg playing poker"' \
  -i 'scheduler="DDIM"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=50'

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/anotherjesse/dogbooth@sha256:35b7f2e003fc1611c44ca016dbfb7e8b3489bcc81b7d7e7580c61d3e7cad803c
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 64596,
      "width": 512,
      "height": 512,
      "prompt": "a velvet painting of dgg playing poker",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2022-12-13T03:27:47.631709Z",
  "created_at": "2022-12-13T03:27:44.383106Z",
  "data_removed": false,
  "error": null,
  "id": "umfdfqshkzaxxl3eaedgnmjyea",
  "input": {
    "seed": 64596,
    "width": "512",
    "height": "512",
    "prompt": "a velvet painting of dgg playing poker",
    "scheduler": "DDIM",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 64596\nusing weights:  True\n  0%|          | 0/50 [00:00<?, ?it/s]\n  4%|▍         | 2/50 [00:00<00:02, 17.81it/s]\n  8%|▊         | 4/50 [00:00<00:02, 17.40it/s]\n 12%|█▏        | 6/50 [00:00<00:02, 17.85it/s]\n 16%|█▌        | 8/50 [00:00<00:02, 17.88it/s]\n 20%|██        | 10/50 [00:00<00:02, 17.75it/s]\n 24%|██▍       | 12/50 [00:00<00:02, 17.88it/s]\n 28%|██▊       | 14/50 [00:00<00:01, 18.10it/s]\n 32%|███▏      | 16/50 [00:00<00:01, 18.25it/s]\n 36%|███▌      | 18/50 [00:00<00:01, 18.35it/s]\n 40%|████      | 20/50 [00:01<00:01, 18.40it/s]\n 44%|████▍     | 22/50 [00:01<00:01, 18.38it/s]\n 48%|████▊     | 24/50 [00:01<00:01, 18.31it/s]\n 52%|█████▏    | 26/50 [00:01<00:01, 18.31it/s]\n 56%|█████▌    | 28/50 [00:01<00:01, 18.32it/s]\n 60%|██████    | 30/50 [00:01<00:01, 18.34it/s]\n 64%|██████▍   | 32/50 [00:01<00:00, 18.48it/s]\n 68%|██████▊   | 34/50 [00:01<00:00, 18.55it/s]\n 72%|███████▏  | 36/50 [00:01<00:00, 18.52it/s]\n 76%|███████▌  | 38/50 [00:02<00:00, 18.58it/s]\n 80%|████████  | 40/50 [00:02<00:00, 18.34it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 18.46it/s]\n 88%|████████▊ | 44/50 [00:02<00:00, 18.43it/s]\n 92%|█████████▏| 46/50 [00:02<00:00, 18.45it/s]\n 96%|█████████▌| 48/50 [00:02<00:00, 18.37it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.38it/s]\n100%|██████████| 50/50 [00:02<00:00, 18.28it/s]",
  "metrics": {
    "predict_time": 3.209735,
    "total_time": 3.248603
  },
  "output": [
    "https://replicate.delivery/pbxt/lYHXrMvQiO5pKpW4fl82oeHyScJk79EnmKf4SmpmOjCmZ0SgA/out-0.png"
  ],
  "started_at": "2022-12-13T03:27:44.421974Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/umfdfqshkzaxxl3eaedgnmjyea",
    "cancel": "https://api.replicate.com/v1/predictions/umfdfqshkzaxxl3eaedgnmjyea/cancel"
  },
  "version": "61a1f4404240790613bcb098ab5ba7b178124a6a3040739b2b6f2f2d7b8666ea"
}

Generated in

3.2 seconds

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This example was created by a different version, anotherjesse/dogbooth:61a1f440.