omerbt/multidiffusion:24dd8961 – Run with an API on Replicate

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omerbt /multidiffusion:24dd8961

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "a photo of the dolomites"

negative_prompt

string

Shift + Return to add a new line

The prompt or prompts not to guide the image generation (what you do not want to see in the generation). Ignored when not using guidance.

width

integer

Width of output image. Lower the setting if out of memory.

Default: 4096

height

integer

Height of output image. Lower the setting if out of memory.

Default: 512

num_outputs

integer

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=omerbt/multidiffusion

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

const output = await replicate.run(
  "omerbt/multidiffusion:24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca",
  {
    input: {
      width: 4096,
      height: 512,
      prompt: "a photo of a city skyline at night",
      scheduler: "DDIM",
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 20
    }
  }
);
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 omerbt/multidiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "omerbt/multidiffusion:24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca",
    input={
        "width": 4096,
        "height": 512,
        "prompt": "a photo of a city skyline at night",
        "scheduler": "DDIM",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 20
    }
)
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 omerbt/multidiffusion 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": "24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca",
    "input": {
      "width": 4096,
      "height": 512,
      "prompt": "a photo of a city skyline at night",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 20
    }
  }' \
  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/omerbt/multidiffusion@sha256:24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca \
  -i 'width=4096' \
  -i 'height=512' \
  -i 'prompt="a photo of a city skyline at night"' \
  -i 'scheduler="DDIM"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'num_inference_steps=20'

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/omerbt/multidiffusion@sha256:24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 4096,
      "height": 512,
      "prompt": "a photo of a city skyline at night",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 20
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-02-24T23:06:53.309387Z",
  "created_at": "2023-02-24T23:05:44.172018Z",
  "data_removed": false,
  "error": null,
  "id": "qyiwfho7n5aafjr7oni3zgalwi",
  "input": {
    "width": 4096,
    "height": 512,
    "prompt": "a photo of a city skyline at night",
    "scheduler": "DDIM",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "num_inference_steps": 20
  },
  "logs": "Using seed: 57162\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:03<01:03,  3.33s/it]\n 10%|█         | 2/20 [00:06<00:59,  3.30s/it]\n 15%|█▌        | 3/20 [00:09<00:55,  3.28s/it]\n 20%|██        | 4/20 [00:13<00:53,  3.35s/it]\n 25%|██▌       | 5/20 [00:16<00:49,  3.30s/it]\n 30%|███       | 6/20 [00:19<00:44,  3.21s/it]\n 35%|███▌      | 7/20 [00:22<00:41,  3.17s/it]\n 40%|████      | 8/20 [00:25<00:37,  3.14s/it]\n 45%|████▌     | 9/20 [00:28<00:34,  3.14s/it]\n 50%|█████     | 10/20 [00:32<00:32,  3.27s/it]\n 55%|█████▌    | 11/20 [00:35<00:30,  3.35s/it]\n 60%|██████    | 12/20 [00:39<00:27,  3.41s/it]\n 65%|██████▌   | 13/20 [00:43<00:24,  3.44s/it]\n 70%|███████   | 14/20 [00:46<00:20,  3.47s/it]\n 75%|███████▌  | 15/20 [00:50<00:17,  3.48s/it]\n 80%|████████  | 16/20 [00:53<00:13,  3.43s/it]\n 85%|████████▌ | 17/20 [00:56<00:10,  3.43s/it]\n 90%|█████████ | 18/20 [01:00<00:06,  3.42s/it]\n 95%|█████████▌| 19/20 [01:03<00:03,  3.43s/it]\n100%|██████████| 20/20 [01:07<00:00,  3.41s/it]\n100%|██████████| 20/20 [01:07<00:00,  3.35s/it]",
  "metrics": {
    "predict_time": 69.066816,
    "total_time": 69.137369
  },
  "output": [
    "https://replicate.delivery/pbxt/BmhVHMk1Lx5iDh7Ak0Y3EjmP6OeFotuDtOwbFw8pjf0MUvhQA/out-0.png"
  ],
  "started_at": "2023-02-24T23:05:44.242571Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/qyiwfho7n5aafjr7oni3zgalwi",
    "cancel": "https://api.replicate.com/v1/predictions/qyiwfho7n5aafjr7oni3zgalwi/cancel"
  },
  "version": "24dd8961771359f16dfd1238566483fbfa2a90092ed9eeac0337041f45387aca"
}

Generated in

69.1 seconds

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