cjwbw/stable-diffusion-v2:867f87d0 – Run with an API on Replicate

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cjwbw /stable-diffusion-v2:867f87d0

Input

prompt

string

Shift + Return to add a new line

a photo of an astronaut riding a horse on marsa photo of an astronaut riding a horse on mars

Input prompt

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

negative_prompt

string

Shift + Return to add a new line

The prompt NOT to guide the image generation. Ignored when not using guidance

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

init_image

file

Inital image to generate variations of. Will be resized to the specified width and height

mask

file

Black and white image to use as mask for inpainting over init_image. Black pixels are inpainted and white pixels are preserved. Tends to work better with prompt strength of 0.5-0.7. Consider using https://replicate.com/andreasjansson/stable-diffusion-inpainting instead.

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: 10)

Number of images to output. If the NSFW filter is triggered, you may get fewer outputs than this.

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. If you use an init image, PNDM will be used

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=cjwbw/stable-diffusion-v2

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 cjwbw/stable-diffusion-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/stable-diffusion-v2:867f87d0d9a9bbef873e37bdf36c4835a01411c3bb18e43653d7de63fb536096",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "a photo of an astronaut riding a horse on mars",
      scheduler: "K_EULER",
      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 cjwbw/stable-diffusion-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/stable-diffusion-v2:867f87d0d9a9bbef873e37bdf36c4835a01411c3bb18e43653d7de63fb536096",
    input={
        "width": 768,
        "height": 768,
        "prompt": "a photo of an astronaut riding a horse on mars",
        "scheduler": "K_EULER",
        "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 cjwbw/stable-diffusion-v2 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": "867f87d0d9a9bbef873e37bdf36c4835a01411c3bb18e43653d7de63fb536096",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "a photo of an astronaut riding a horse on mars",
      "scheduler": "K_EULER",
      "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/chenxwh/stable-diffusion-v2@sha256:867f87d0d9a9bbef873e37bdf36c4835a01411c3bb18e43653d7de63fb536096 \
  -i 'width=768' \
  -i 'height=768' \
  -i 'prompt="a photo of an astronaut riding a horse on mars"' \
  -i 'scheduler="K_EULER"' \
  -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/chenxwh/stable-diffusion-v2@sha256:867f87d0d9a9bbef873e37bdf36c4835a01411c3bb18e43653d7de63fb536096
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "a photo of an astronaut riding a horse on mars",
      "scheduler": "K_EULER",
      "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

No output yet! Press "Submit" to start a prediction.