cjwbw/stable-diffusion-v2:e5e1fd33 – Run with an API on Replicate

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cjwbw /stable-diffusion-v2:e5e1fd33

Playground API

Input

prompt

string

Shift + Return to add a new line

A fantasy landscape, trending on artstationA fantasy landscape, trending on artstation

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

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

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: 3)

Number of images to output. Currenly allowing 1-3, otherwise would OOM.

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. Seems only DDIM and K_EULER and DPMSolverMultistep work for sd-v2 now.

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:e5e1fd333a08c8035974a01dd42f799f1cca4625aec374643d716d9ae40cf2e4",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "A fantasy landscape, trending on artstation",
      scheduler: "DPMSolverMultistep",
      init_image: "https://replicate.delivery/pbxt/HtCXRc5QMwvXfOOqx56gzsZcmTxmOwzSzN4CpzQj8q9pzaOt/sketch-mountains-input.jpeg",
      num_outputs: 1,
      guidance_scale: 6.55,
      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:e5e1fd333a08c8035974a01dd42f799f1cca4625aec374643d716d9ae40cf2e4",
    input={
        "width": 768,
        "height": 768,
        "prompt": "A fantasy landscape, trending on artstation",
        "scheduler": "DPMSolverMultistep",
        "init_image": "https://replicate.delivery/pbxt/HtCXRc5QMwvXfOOqx56gzsZcmTxmOwzSzN4CpzQj8q9pzaOt/sketch-mountains-input.jpeg",
        "num_outputs": 1,
        "guidance_scale": 6.55,
        "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": "e5e1fd333a08c8035974a01dd42f799f1cca4625aec374643d716d9ae40cf2e4",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "A fantasy landscape, trending on artstation",
      "scheduler": "DPMSolverMultistep",
      "init_image": "https://replicate.delivery/pbxt/HtCXRc5QMwvXfOOqx56gzsZcmTxmOwzSzN4CpzQj8q9pzaOt/sketch-mountains-input.jpeg",
      "num_outputs": 1,
      "guidance_scale": 6.55,
      "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:e5e1fd333a08c8035974a01dd42f799f1cca4625aec374643d716d9ae40cf2e4 \
  -i 'width=768' \
  -i 'height=768' \
  -i 'prompt="A fantasy landscape, trending on artstation"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'init_image="https://replicate.delivery/pbxt/HtCXRc5QMwvXfOOqx56gzsZcmTxmOwzSzN4CpzQj8q9pzaOt/sketch-mountains-input.jpeg"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=6.55' \
  -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:e5e1fd333a08c8035974a01dd42f799f1cca4625aec374643d716d9ae40cf2e4
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "A fantasy landscape, trending on artstation",
      "scheduler": "DPMSolverMultistep",
      "init_image": "https://replicate.delivery/pbxt/HtCXRc5QMwvXfOOqx56gzsZcmTxmOwzSzN4CpzQj8q9pzaOt/sketch-mountains-input.jpeg",
      "num_outputs": 1,
      "guidance_scale": 6.55,
      "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": "2022-12-02T14:26:20.502898Z",
  "created_at": "2022-12-02T14:26:15.162182Z",
  "data_removed": false,
  "error": null,
  "id": "gxbijslwz5bbfffjr7y22j765e",
  "input": {
    "width": "768",
    "height": 768,
    "prompt": "A fantasy landscape, trending on artstation",
    "scheduler": "DPMSolverMultistep",
    "init_image": "https://replicate.delivery/pbxt/HtCXRc5QMwvXfOOqx56gzsZcmTxmOwzSzN4CpzQj8q9pzaOt/sketch-mountains-input.jpeg",
    "num_outputs": 1,
    "guidance_scale": "6.55",
    "prompt_strength": 0.8,
    "num_inference_steps": "50"
  },
  "logs": "Using seed: 54338\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:00<00:04,  8.46it/s]\n  8%|▊         | 3/40 [00:00<00:03,  9.95it/s]\n 12%|█▎        | 5/40 [00:00<00:03, 10.28it/s]\n 18%|█▊        | 7/40 [00:00<00:03, 10.42it/s]\n 22%|██▎       | 9/40 [00:00<00:02, 10.49it/s]\n 28%|██▊       | 11/40 [00:01<00:02, 10.53it/s]\n 32%|███▎      | 13/40 [00:01<00:02, 10.55it/s]\n 38%|███▊      | 15/40 [00:01<00:02, 10.57it/s]\n 42%|████▎     | 17/40 [00:01<00:02, 10.58it/s]\n 48%|████▊     | 19/40 [00:01<00:01, 10.59it/s]\n 52%|█████▎    | 21/40 [00:02<00:01, 10.60it/s]\n 57%|█████▊    | 23/40 [00:02<00:01, 10.59it/s]\n 62%|██████▎   | 25/40 [00:02<00:01, 10.59it/s]\n 68%|██████▊   | 27/40 [00:02<00:01, 10.60it/s]\n 72%|███████▎  | 29/40 [00:02<00:01, 10.60it/s]\n 78%|███████▊  | 31/40 [00:02<00:00, 10.60it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 10.60it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 10.60it/s]\n 92%|█████████▎| 37/40 [00:03<00:00, 10.60it/s]\n 98%|█████████▊| 39/40 [00:03<00:00, 10.60it/s]\n100%|██████████| 40/40 [00:03<00:00, 10.54it/s]",
  "metrics": {
    "predict_time": 5.304438,
    "total_time": 5.340716
  },
  "output": [
    "https://replicate.delivery/pbxt/EtsePYmau0VxLCzVZVafWf7WSYnu9NabtVrD7fJlD86uQvXAB/out-0.png"
  ],
  "started_at": "2022-12-02T14:26:15.198460Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/gxbijslwz5bbfffjr7y22j765e",
    "cancel": "https://api.replicate.com/v1/predictions/gxbijslwz5bbfffjr7y22j765e/cancel"
  },
  "version": "e5e1fd333a08c8035974a01dd42f799f1cca4625aec374643d716d9ae40cf2e4"
}

Generated in

5.3 seconds

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