playgroundai/playground-v2.5-1024px-aesthetic:a45f82a1

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playgroundai /playground-v2.5-1024px-aesthetic:a45f82a1

Input

prompt

string

Shift + Return to add a new line

Astronaut in a jungle, cold color palette, muted colors, detailed, 8kAstronaut in a jungle, cold color palette, muted colors, detailed, 8k

Input prompt

Default: "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"

negative_prompt

string

Shift + Return to add a new line

ugly, deformed, noisy, blurry, distortedugly, deformed, noisy, blurry, distorted

Negative Input prompt

Default: "ugly, deformed, noisy, blurry, distorted"

image

file

Input image for img2img or inpaint mode

mask

file

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

width

integer

(minimum: 256, maximum: 1536)

Width of output image

Default: 1024

height

integer

(minimum: 256, maximum: 1536)

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

Scheduler. DPMSolver++ or DPM++2MKarras is recommended for most cases

Default: "DPMSolver++"

num_inference_steps

integer

(minimum: 1, maximum: 60)

Number of denoising steps

Default: 25

guidance_scale

number

(minimum: 0.1, maximum: 20)

Scale for classifier-free guidance

Default: 3

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

Default: 0.8

seed

integer

Random seed. Leave blank to randomize the seed

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

disable_safety_checker

boolean

Disable safety checker for generated images. This feature is only available through the API. See https://replicate.com/docs/how-does-replicate-work#safety

Default: false

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

npx create-replicate --model=playgroundai/playground-v2.5-1024px-aesthetic

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 playgroundai/playground-v2.5-1024px-aesthetic using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "playgroundai/playground-v2.5-1024px-aesthetic:a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
      scheduler: "DPMSolver++",
      num_outputs: 1,
      guidance_scale: 3,
      apply_watermark: true,
      negative_prompt: "ugly, deformed, noisy, blurry, distorted",
      prompt_strength: 0.8,
      num_inference_steps: 25
    }
  }
);
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 playgroundai/playground-v2.5-1024px-aesthetic using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "playgroundai/playground-v2.5-1024px-aesthetic:a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
        "scheduler": "DPMSolver++",
        "num_outputs": 1,
        "guidance_scale": 3,
        "apply_watermark": True,
        "negative_prompt": "ugly, deformed, noisy, blurry, distorted",
        "prompt_strength": 0.8,
        "num_inference_steps": 25
    }
)
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 playgroundai/playground-v2.5-1024px-aesthetic 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": "a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
      "scheduler": "DPMSolver++",
      "num_outputs": 1,
      "guidance_scale": 3,
      "apply_watermark": true,
      "negative_prompt": "ugly, deformed, noisy, blurry, distorted",
      "prompt_strength": 0.8,
      "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/playgroundai/playground-v2.5-1024px-aesthetic@sha256:a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"' \
  -i 'scheduler="DPMSolver++"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=3' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt="ugly, deformed, noisy, blurry, distorted"' \
  -i 'prompt_strength=0.8' \
  -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/playgroundai/playground-v2.5-1024px-aesthetic@sha256:a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
      "scheduler": "DPMSolver++",
      "num_outputs": 1,
      "guidance_scale": 3,
      "apply_watermark": true,
      "negative_prompt": "ugly, deformed, noisy, blurry, distorted",
      "prompt_strength": 0.8,
      "num_inference_steps": 25
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2024-03-15T21:00:51.212675Z",
  "created_at": "2024-03-15T20:59:27.831539Z",
  "data_removed": false,
  "error": null,
  "id": "hhow2slbfyrxyaxtbucbhzwcti",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "scheduler": "DPMSolver++",
    "num_outputs": 1,
    "guidance_scale": 3,
    "apply_watermark": true,
    "negative_prompt": "ugly, deformed, noisy, blurry, distorted",
    "prompt_strength": 0.8,
    "num_inference_steps": 25
  },
  "logs": "Using seed: 2478151516\nPrompt: Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\ntxt2img mode\n  0%|          | 0/25 [00:00<?, ?it/s]\n  4%|▍         | 1/25 [00:00<00:07,  3.06it/s]\n 12%|█▏        | 3/25 [00:00<00:04,  5.13it/s]\n 16%|█▌        | 4/25 [00:00<00:04,  5.08it/s]\n 20%|██        | 5/25 [00:01<00:03,  5.05it/s]\n 24%|██▍       | 6/25 [00:01<00:03,  5.03it/s]\n 28%|██▊       | 7/25 [00:01<00:03,  5.00it/s]\n 32%|███▏      | 8/25 [00:01<00:03,  4.99it/s]\n 36%|███▌      | 9/25 [00:01<00:03,  4.98it/s]\n 40%|████      | 10/25 [00:02<00:03,  4.98it/s]\n 44%|████▍     | 11/25 [00:02<00:02,  4.98it/s]\n 48%|████▊     | 12/25 [00:02<00:02,  4.97it/s]\n 52%|█████▏    | 13/25 [00:02<00:02,  4.96it/s]\n 56%|█████▌    | 14/25 [00:02<00:02,  4.96it/s]\n 60%|██████    | 15/25 [00:03<00:02,  4.97it/s]\n 64%|██████▍   | 16/25 [00:03<00:01,  4.97it/s]\n 68%|██████▊   | 17/25 [00:03<00:01,  4.96it/s]\n 72%|███████▏  | 18/25 [00:03<00:01,  4.96it/s]\n 76%|███████▌  | 19/25 [00:03<00:01,  4.96it/s]\n 80%|████████  | 20/25 [00:04<00:01,  4.96it/s]\n 84%|████████▍ | 21/25 [00:04<00:00,  4.96it/s]\n 88%|████████▊ | 22/25 [00:04<00:00,  4.95it/s]\n 92%|█████████▏| 23/25 [00:04<00:00,  4.95it/s]\n 96%|█████████▌| 24/25 [00:04<00:00,  4.95it/s]\n100%|██████████| 25/25 [00:05<00:00,  4.95it/s]\n100%|██████████| 25/25 [00:05<00:00,  4.94it/s]",
  "metrics": {
    "predict_time": 8.01971,
    "total_time": 83.381136
  },
  "output": [
    "https://replicate.delivery/pbxt/XAK4XRgpjYaCGRrm9yxzO2bacj4XTE1Nl6bwaXKOHKYApJoE/out-0.png"
  ],
  "started_at": "2024-03-15T21:00:43.192965Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/hhow2slbfyrxyaxtbucbhzwcti",
    "cancel": "https://api.replicate.com/v1/predictions/hhow2slbfyrxyaxtbucbhzwcti/cancel"
  },
  "version": "a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24"
}

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