stability-ai / stable-diffusion

A latent text-to-image diffusion model capable of generating photo-realistic images given any text input (Updated 1 year, 11 months ago)

Warm

Public
110.5M runs
A100 (80GB)
GitHub
Paper
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Prediction

stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a

Model

stability-ai/stable-diffusion:d70beb40

qffyxjvmbvfdbao7vvv2oss2gq

Status

Succeeded

Source

Web

Hardware

–

Total duration

11.1s

Created

almost 3 years ago by @bfirsh

Input

{
  "prompt": "multicolor hyperspace",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a",
  {
    input: {
      prompt: "multicolor hyperspace",
      num_outputs: "1",
      guidance_scale: 7.5,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a",
    input={
        "prompt": "multicolor hyperspace",
        "num_outputs": "1",
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a",
    "input": {
      "prompt": "multicolor hyperspace",
      "num_outputs": "1",
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-08-22T22:48:08Z",
  "created_at": "2022-08-22T22:47:56.927485Z",
  "data_removed": false,
  "error": "",
  "id": "qffyxjvmbvfdbao7vvv2oss2gq",
  "input": {
    "prompt": "multicolor hyperspace",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 1841\r\n\r\n0it [00:00, ?it/s]\r\n2it [00:00, 10.34it/s]\r\n4it [00:00,  9.29it/s]\r\n6it [00:00,  9.19it/s]\r\n7it [00:00,  9.12it/s]\r\n8it [00:00,  9.29it/s]\r\n9it [00:00,  9.25it/s]\r\n10it [00:01,  9.12it/s]\r\n11it [00:01,  9.22it/s]\r\n12it [00:01,  9.17it/s]\r\n13it [00:01,  9.27it/s]\r\n14it [00:01,  9.35it/s]\r\n15it [00:01,  9.18it/s]\r\n16it [00:01,  9.23it/s]\r\n17it [00:01,  8.99it/s]\r\n19it [00:02,  9.45it/s]\r\n20it [00:02,  9.16it/s]\r\n21it [00:02,  9.19it/s]\r\n23it [00:02,  9.27it/s]\r\n24it [00:02,  9.23it/s]\r\n25it [00:02,  9.34it/s]\r\n26it [00:02,  9.29it/s]\r\n27it [00:02,  9.29it/s]\r\n28it [00:03,  9.20it/s]\r\n29it [00:03,  9.15it/s]\r\n30it [00:03,  9.23it/s]\r\n32it [00:03,  9.36it/s]\r\n33it [00:03,  9.26it/s]\r\n34it [00:03,  9.19it/s]\r\n35it [00:03,  9.24it/s]\r\n36it [00:03,  9.30it/s]\r\n37it [00:04,  9.13it/s]\r\n38it [00:04,  9.26it/s]\r\n39it [00:04,  9.22it/s]\r\n40it [00:04,  9.10it/s]\r\n41it [00:04,  9.06it/s]\r\n42it [00:04,  9.13it/s]\r\n43it [00:04,  9.24it/s]\r\n44it [00:04,  9.41it/s]\r\n45it [00:04,  9.31it/s]\r\n46it [00:04,  9.21it/s]\r\n47it [00:05,  8.73it/s]\r\n48it [00:05,  9.01it/s]\r\n49it [00:05,  9.25it/s]\r\n50it [00:05,  8.80it/s]\r\n51it [00:05,  8.90it/s]\r\n52it [00:05,  9.15it/s]\r\n53it [00:05,  8.95it/s]\r\n54it [00:05,  9.10it/s]\r\n55it [00:05,  9.17it/s]\r\n57it [00:06,  9.49it/s]\r\n58it [00:06,  9.48it/s]\r\n59it [00:06,  9.24it/s]\r\n60it [00:06,  9.33it/s]\r\n61it [00:06,  9.20it/s]\r\n62it [00:06,  9.26it/s]\r\n63it [00:06,  9.22it/s]\r\n64it [00:06,  9.26it/s]\r\n65it [00:07,  9.07it/s]\r\n67it [00:07,  9.31it/s]\r\n68it [00:07,  9.21it/s]\r\n69it [00:07,  9.30it/s]\r\n70it [00:07,  9.24it/s]\r\n71it [00:07,  9.21it/s]\r\n72it [00:07,  9.20it/s]\r\n73it [00:07,  9.21it/s]\r\n74it [00:08,  9.21it/s]\r\n75it [00:08,  9.24it/s]\r\n76it [00:08,  9.09it/s]\r\n78it [00:08,  9.14it/s]\r\n79it [00:08,  9.27it/s]\r\n80it [00:08,  9.17it/s]\r\n82it [00:08,  9.38it/s]\r\n83it [00:09,  9.15it/s]\r\n84it [00:09,  9.28it/s]\r\n85it [00:09,  9.22it/s]\r\n86it [00:09,  9.15it/s]\r\n87it [00:09,  9.37it/s]\r\n88it [00:09,  9.17it/s]\r\n89it [00:09,  9.28it/s]\r\n90it [00:09,  9.24it/s]\r\n91it [00:09,  9.21it/s]\r\n92it [00:09,  9.04it/s]\r\n93it [00:10,  9.28it/s]\r\n94it [00:10,  9.34it/s]\r\n95it [00:10,  9.25it/s]\r\n96it [00:10,  9.18it/s]\r\n97it [00:10,  9.13it/s]\r\n98it [00:10,  9.12it/s]\r\n99it [00:10,  9.04it/s]\r\n100it [00:10,  9.21it/s]\r\n100it [00:10,  9.22it/s]",
  "metrics": {
    "predict_time": 11,
    "total_time": 11.072515
  },
  "output": [
    "https://replicate.delivery/mgxm/980bca24-f8d8-41bb-9f52-fe84bca9c251/out-0.png"
  ],
  "started_at": "2022-08-22T22:47:57Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/qffyxjvmbvfdbao7vvv2oss2gq",
    "cancel": "https://api.replicate.com/v1/predictions/qffyxjvmbvfdbao7vvv2oss2gq/cancel"
  },
  "version": "d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a"
}

Generated in

11.0 seconds

Tweak it Share Report View full prediction

Using seed: 1841

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Prediction

stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a

Model

stability-ai/stable-diffusion:d70beb40

rktab6ur7ve3dfuvh7efbo3j54

Status

Succeeded

Source

Web

Hardware

–

Total duration

12.1s

Created

almost 3 years ago by @bfirsh

Input

{
  "prompt": "a gentleman otter in a 19th century portrait",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "num_inference_steps": 100
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a",
  {
    input: {
      prompt: "a gentleman otter in a 19th century portrait",
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 100
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a",
    input={
        "prompt": "a gentleman otter in a 19th century portrait",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 100
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a",
    "input": {
      "prompt": "a gentleman otter in a 19th century portrait",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 100
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-08-22T22:53:49.771482Z",
  "created_at": "2022-08-22T22:53:37.724733Z",
  "data_removed": false,
  "error": null,
  "id": "rktab6ur7ve3dfuvh7efbo3j54",
  "input": {
    "prompt": "a gentleman otter in a 19th century portrait",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "num_inference_steps": 100
  },
  "logs": "Using seed: 45586\n\n0it [00:00, ?it/s]\n2it [00:00,  9.55it/s]\n3it [00:00,  8.54it/s]\n5it [00:00,  9.26it/s]\n6it [00:00,  9.27it/s]\n7it [00:00,  9.27it/s]\n8it [00:00,  9.26it/s]\n9it [00:00,  9.23it/s]\n10it [00:01,  9.12it/s]\n11it [00:01,  9.32it/s]\n12it [00:01,  9.31it/s]\n13it [00:01,  9.32it/s]\n14it [00:01,  9.30it/s]\n15it [00:01,  9.13it/s]\n16it [00:01,  9.24it/s]\n17it [00:01,  9.34it/s]\n18it [00:01,  9.29it/s]\n19it [00:02,  9.30it/s]\n20it [00:02,  9.23it/s]\n21it [00:02,  9.26it/s]\n22it [00:02,  9.29it/s]\n23it [00:02,  9.28it/s]\n24it [00:02,  9.13it/s]\n25it [00:02,  9.33it/s]\n26it [00:02,  9.30it/s]\n27it [00:02,  9.32it/s]\n28it [00:03,  9.29it/s]\n29it [00:03,  9.19it/s]\n30it [00:03,  9.27it/s]\n31it [00:03,  9.30it/s]\n32it [00:03,  9.31it/s]\n33it [00:03,  9.20it/s]\n34it [00:03,  9.33it/s]\n35it [00:03,  9.25it/s]\n36it [00:03,  9.34it/s]\n37it [00:03,  9.30it/s]\n38it [00:04,  9.16it/s]\n39it [00:04,  9.31it/s]\n40it [00:04,  9.32it/s]\n41it [00:04,  9.29it/s]\n42it [00:04,  9.32it/s]\n43it [00:04,  9.12it/s]\n44it [00:04,  9.31it/s]\n45it [00:04,  9.35it/s]\n46it [00:04,  9.33it/s]\n47it [00:05,  9.29it/s]\n48it [00:05,  9.33it/s]\n49it [00:05,  9.33it/s]\n50it [00:05,  9.26it/s]\n51it [00:05,  9.27it/s]\n52it [00:05,  9.19it/s]\n53it [00:05,  9.30it/s]\n54it [00:05,  9.20it/s]\n55it [00:05,  9.27it/s]\n56it [00:06,  9.31it/s]\n57it [00:06,  9.30it/s]\n58it [00:06,  9.23it/s]\n59it [00:06,  9.30it/s]\n60it [00:06,  9.28it/s]\n61it [00:06,  9.26it/s]\n62it [00:06,  9.34it/s]\n63it [00:06,  9.13it/s]\n64it [00:06,  9.36it/s]\n65it [00:07,  9.33it/s]\n66it [00:07,  9.32it/s]\n67it [00:07,  9.31it/s]\n68it [00:07,  9.29it/s]\n69it [00:07,  9.32it/s]\n70it [00:07,  9.22it/s]\n71it [00:07,  9.30it/s]\n72it [00:07,  9.31it/s]\n73it [00:07,  9.30it/s]\n74it [00:07,  9.31it/s]\n75it [00:08,  9.31it/s]\n76it [00:08,  9.29it/s]\n77it [00:08,  9.20it/s]\n78it [00:08,  9.30it/s]\n79it [00:08,  9.31it/s]\n80it [00:08,  9.18it/s]\n81it [00:08,  9.27it/s]\n82it [00:08,  9.32it/s]\n83it [00:08,  9.29it/s]\n84it [00:09,  9.24it/s]\n85it [00:09,  9.26it/s]\n86it [00:09,  9.28it/s]\n87it [00:09,  9.26it/s]\n88it [00:09,  9.28it/s]\n89it [00:09,  9.12it/s]\n90it [00:09,  9.32it/s]\n91it [00:09,  9.32it/s]\n92it [00:09,  8.92it/s]\n94it [00:10,  9.32it/s]\n95it [00:10,  9.37it/s]\n96it [00:10,  9.33it/s]\n97it [00:10,  9.34it/s]\n98it [00:10,  9.33it/s]\n99it [00:10,  9.33it/s]\n100it [00:10,  9.31it/s]\n100it [00:10,  9.27it/s]",
  "metrics": {
    "predict_time": 11.81052,
    "total_time": 12.046749
  },
  "output": [
    "https://replicate.delivery/mgxm/8d6a8069-b91f-4e61-8136-fa7c0775532c/out-0.png"
  ],
  "started_at": "2022-08-22T22:53:37.960962Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/rktab6ur7ve3dfuvh7efbo3j54",
    "cancel": "https://api.replicate.com/v1/predictions/rktab6ur7ve3dfuvh7efbo3j54/cancel"
  },
  "version": "d70beb400d223e6432425a5299910329c6050c6abcf97b8c70537d6a1fcb269a"
}

Generated in

11.8 seconds

Tweak it Share Report View full prediction

Using seed: 45586

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Prediction

stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125

Model

stability-ai/stable-diffusion:714ee02b

bhyzpqdgnbhsljpnxp45hbv5ay

Status

Succeeded

Source

Web

Hardware

–

Total duration

11.9s

Created

almost 3 years ago by @bfirsh

Input

{
  "prompt": "pencil sketch of robots playing poker",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "num_inference_steps": 100
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125",
  {
    input: {
      prompt: "pencil sketch of robots playing poker",
      num_outputs: "1",
      guidance_scale: 7.5,
      num_inference_steps: 100
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125",
    input={
        "prompt": "pencil sketch of robots playing poker",
        "num_outputs": "1",
        "guidance_scale": 7.5,
        "num_inference_steps": 100
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125",
    "input": {
      "prompt": "pencil sketch of robots playing poker",
      "num_outputs": "1",
      "guidance_scale": 7.5,
      "num_inference_steps": 100
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-08-23T00:17:13.340058Z",
  "created_at": "2022-08-23T00:17:01.433071Z",
  "data_removed": false,
  "error": null,
  "id": "bhyzpqdgnbhsljpnxp45hbv5ay",
  "input": {
    "prompt": "pencil sketch of robots playing poker",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "num_inference_steps": 100
  },
  "logs": "Using seed: 52932\n\n0it [00:00, ?it/s]\n2it [00:00,  9.74it/s]\n3it [00:00,  9.69it/s]\n4it [00:00,  9.13it/s]\n5it [00:00,  9.39it/s]\n6it [00:00,  9.45it/s]\n7it [00:00,  9.32it/s]\n8it [00:00,  8.85it/s]\n10it [00:01,  9.34it/s]\n11it [00:01,  9.37it/s]\n12it [00:01,  9.22it/s]\n13it [00:01,  9.39it/s]\n14it [00:01,  9.19it/s]\n15it [00:01,  9.37it/s]\n16it [00:01,  9.13it/s]\n18it [00:01,  9.37it/s]\n19it [00:02,  9.36it/s]\n20it [00:02,  8.97it/s]\n22it [00:02,  9.38it/s]\n23it [00:02,  9.27it/s]\n24it [00:02,  9.34it/s]\n25it [00:02,  9.14it/s]\n26it [00:02,  9.28it/s]\n27it [00:02,  9.33it/s]\n28it [00:03,  9.21it/s]\n29it [00:03,  9.13it/s]\n31it [00:03,  9.32it/s]\n32it [00:03,  9.32it/s]\n33it [00:03,  9.10it/s]\n34it [00:03,  9.24it/s]\n35it [00:03,  9.36it/s]\n36it [00:03,  9.33it/s]\n37it [00:03,  9.12it/s]\n38it [00:04,  9.35it/s]\n39it [00:04,  9.33it/s]\n40it [00:04,  9.22it/s]\n41it [00:04,  9.07it/s]\n42it [00:04,  9.30it/s]\n43it [00:04,  9.26it/s]\n44it [00:04,  9.36it/s]\n45it [00:04,  9.31it/s]\n46it [00:04,  9.08it/s]\n47it [00:05,  9.33it/s]\n48it [00:05,  9.33it/s]\n49it [00:05,  9.32it/s]\n50it [00:05,  9.28it/s]\n51it [00:05,  9.27it/s]\n52it [00:05,  9.01it/s]\n54it [00:05,  9.26it/s]\n55it [00:05,  9.28it/s]\n56it [00:06,  9.20it/s]\n57it [00:06,  9.27it/s]\n58it [00:06,  9.26it/s]\n59it [00:06,  9.14it/s]\n60it [00:06,  9.36it/s]\n61it [00:06,  9.19it/s]\n62it [00:06,  9.25it/s]\n63it [00:06,  9.32it/s]\n64it [00:06,  9.31it/s]\n65it [00:07,  9.25it/s]\n66it [00:07,  9.07it/s]\n68it [00:07,  9.34it/s]\n69it [00:07,  9.28it/s]\n70it [00:07,  9.18it/s]\n71it [00:07,  9.24it/s]\n72it [00:07,  9.24it/s]\n73it [00:07,  9.29it/s]\n74it [00:07,  9.22it/s]\n75it [00:08,  9.07it/s]\n76it [00:08,  9.31it/s]\n77it [00:08,  9.33it/s]\n78it [00:08,  9.04it/s]\n79it [00:08,  9.11it/s]\n80it [00:08,  9.22it/s]\n81it [00:08,  9.32it/s]\n82it [00:08,  9.34it/s]\n83it [00:08,  9.21it/s]\n84it [00:09,  9.23it/s]\n85it [00:09,  9.29it/s]\n86it [00:09,  9.27it/s]\n87it [00:09,  9.30it/s]\n88it [00:09,  9.15it/s]\n89it [00:09,  9.36it/s]\n90it [00:09,  9.29it/s]\n91it [00:09,  9.29it/s]\n92it [00:09,  8.94it/s]\n94it [00:10,  9.35it/s]\n95it [00:10,  9.31it/s]\n96it [00:10,  9.29it/s]\n97it [00:10,  9.29it/s]\n98it [00:10,  9.01it/s]\n100it [00:10,  9.34it/s]\n100it [00:10,  9.26it/s]",
  "metrics": {
    "predict_time": 11.67104,
    "total_time": 11.906987
  },
  "output": [
    "https://replicate.delivery/mgxm/891fd1f9-302e-4d1f-a462-c0c2624ffbca/out-0.png"
  ],
  "started_at": "2022-08-23T00:17:01.669018Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/bhyzpqdgnbhsljpnxp45hbv5ay",
    "cancel": "https://api.replicate.com/v1/predictions/bhyzpqdgnbhsljpnxp45hbv5ay/cancel"
  },
  "version": "714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125"
}

Generated in

11.7 seconds

Tweak it Share Report View full prediction

Using seed: 52932

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Prediction

stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125

Model

stability-ai/stable-diffusion:714ee02b

rjcl54wakbbrfcajzllmidreya

Status

Succeeded

Source

Web

Hardware

–

Total duration

32.9s

Created

almost 3 years ago by @bfirsh

Input

{
  "prompt": "phase shift into an era of human+AI art collab",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "num_inference_steps": "301"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125",
  {
    input: {
      prompt: "phase shift into an era of human+AI art collab",
      num_outputs: "1",
      guidance_scale: 7.5,
      num_inference_steps: "301"
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125",
    input={
        "prompt": "phase shift into an era of human+AI art collab",
        "num_outputs": "1",
        "guidance_scale": 7.5,
        "num_inference_steps": "301"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125",
    "input": {
      "prompt": "phase shift into an era of human+AI art collab",
      "num_outputs": "1",
      "guidance_scale": 7.5,
      "num_inference_steps": "301"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-08-23T00:26:28Z",
  "created_at": "2022-08-23T00:25:55.134698Z",
  "data_removed": false,
  "error": "",
  "id": "rjcl54wakbbrfcajzllmidreya",
  "input": {
    "prompt": "phase shift into an era of human+AI art collab",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "num_inference_steps": "301"
  },
  "logs": "Using seed: 48356\r\n\r\n0it [00:00, ?it/s]\r\n2it [00:00,  9.30it/s]\r\n3it [00:00,  8.75it/s]\r\n4it [00:00,  9.08it/s]\r\n5it [00:00,  8.94it/s]\r\n6it [00:00,  8.71it/s]\r\n8it [00:00,  9.05it/s]\r\n9it [00:00,  9.06it/s]\r\n11it [00:01,  9.47it/s]\r\n12it [00:01,  9.07it/s]\r\n14it [00:01,  9.23it/s]\r\n15it [00:01,  9.13it/s]\r\n16it [00:01,  8.91it/s]\r\n18it [00:01,  9.37it/s]\r\n19it [00:02,  9.02it/s]\r\n20it [00:02,  9.01it/s]\r\n21it [00:02,  9.23it/s]\r\n22it [00:02,  8.90it/s]\r\n24it [00:02,  9.47it/s]\r\n25it [00:02,  9.42it/s]\r\n26it [00:02,  9.30it/s]\r\n27it [00:02,  9.07it/s]\r\n28it [00:03,  9.25it/s]\r\n29it [00:03,  9.30it/s]\r\n30it [00:03,  9.05it/s]\r\n31it [00:03,  9.10it/s]\r\n32it [00:03,  9.06it/s]\r\n33it [00:03,  9.31it/s]\r\n34it [00:03,  9.34it/s]\r\n35it [00:03,  9.25it/s]\r\n36it [00:03,  9.09it/s]\r\n37it [00:04,  9.16it/s]\r\n38it [00:04,  9.39it/s]\r\n39it [00:04,  9.09it/s]\r\n40it [00:04,  8.47it/s]\r\n42it [00:04,  8.98it/s]\r\n44it 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  "metrics": {
    "predict_time": 33,
    "total_time": 32.865302
  },
  "output": [
    "https://replicate.delivery/mgxm/129d24dc-e39e-4240-8978-88420cc1f910/out-0.png"
  ],
  "started_at": "2022-08-23T00:25:55Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/rjcl54wakbbrfcajzllmidreya",
    "cancel": "https://api.replicate.com/v1/predictions/rjcl54wakbbrfcajzllmidreya/cancel"
  },
  "version": "714ee02be40e197b6d107bcf1fa0533e876ccc727122e7dc15a62a428dcda125"
}

Generated in

33.0 seconds

Tweak it Share Report View full prediction

Using seed: 48356

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Prediction

stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c

Model

stability-ai/stable-diffusion:3080f37e

sqyie5bdmrewdoxi6ehhxlesfe

Status

Succeeded

Source

Web

Hardware

–

Total duration

4.7s

Created

almost 3 years ago by @andreasjansson

Input

{
  "width": 512,
  "height": 512,
  "prompt": "eye",
  "init_image": "https://replicate.delivery/mgxm/7e0ffe6c-04fd-4440-92b2-e4376b4620a5/out-0_1.png",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "eye",
      init_image: "https://replicate.delivery/mgxm/7e0ffe6c-04fd-4440-92b2-e4376b4620a5/out-0_1.png",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c",
    input={
        "width": 512,
        "height": 512,
        "prompt": "eye",
        "init_image": "https://replicate.delivery/mgxm/7e0ffe6c-04fd-4440-92b2-e4376b4620a5/out-0_1.png",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "eye",
      "init_image": "https://replicate.delivery/mgxm/7e0ffe6c-04fd-4440-92b2-e4376b4620a5/out-0_1.png",
      "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.

Output

Generated in

4.5 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c

Model

stability-ai/stable-diffusion:3080f37e

ljnv74ftfbfvtlqrvsz5zarakm

Status

Succeeded

Source

Web

Hardware

–

Total duration

9.1s

Created

almost 3 years ago by @andreasjansson

Input

{
  "mask": "https://replicate.delivery/mgxm/24034da1-d461-4c60-ba7b-1e9e7184a3f1/time-surrealism-watch-oil-wallpaper-preview-mask.jpeg",
  "width": 512,
  "height": 512,
  "prompt": "modern apple watches with colorful hd displays in a surrealist style",
  "init_image": "https://replicate.delivery/mgxm/dff721db-bf78-4e04-a3a1-c412fe6e76b0/time-surrealism-watch-oil-wallpaper-preview.jpeg",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.5,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c",
  {
    input: {
      mask: "https://replicate.delivery/mgxm/24034da1-d461-4c60-ba7b-1e9e7184a3f1/time-surrealism-watch-oil-wallpaper-preview-mask.jpeg",
      width: 512,
      height: 512,
      prompt: "modern apple watches with colorful hd displays in a surrealist style",
      init_image: "https://replicate.delivery/mgxm/dff721db-bf78-4e04-a3a1-c412fe6e76b0/time-surrealism-watch-oil-wallpaper-preview.jpeg",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.5,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c",
    input={
        "mask": "https://replicate.delivery/mgxm/24034da1-d461-4c60-ba7b-1e9e7184a3f1/time-surrealism-watch-oil-wallpaper-preview-mask.jpeg",
        "width": 512,
        "height": 512,
        "prompt": "modern apple watches with colorful hd displays in a surrealist style",
        "init_image": "https://replicate.delivery/mgxm/dff721db-bf78-4e04-a3a1-c412fe6e76b0/time-surrealism-watch-oil-wallpaper-preview.jpeg",
        "num_outputs": "1",
        "guidance_scale": 7.5,
        "prompt_strength": 0.5,
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:3080f37ef32771c9984d65033cbe71caa96c69680008bae64cf691724a6df04c",
    "input": {
      "mask": "https://replicate.delivery/mgxm/24034da1-d461-4c60-ba7b-1e9e7184a3f1/time-surrealism-watch-oil-wallpaper-preview-mask.jpeg",
      "width": 512,
      "height": 512,
      "prompt": "modern apple watches with colorful hd displays in a surrealist style",
      "init_image": "https://replicate.delivery/mgxm/dff721db-bf78-4e04-a3a1-c412fe6e76b0/time-surrealism-watch-oil-wallpaper-preview.jpeg",
      "num_outputs": "1",
      "guidance_scale": 7.5,
      "prompt_strength": 0.5,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Generated in

8.9 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:a9758cbfbd5f3c2094457d996681af52552901775aa2d6dd0b17fd15df959bef

Model

stability-ai/stable-diffusion:a9758cbf

kgpqs7h2ffcfrouxeoua6tpkjy

Status

Succeeded

Source

Web

Hardware

–

Total duration

4.9s

Created

over 2 years ago by @bfirsh

Input

{
  "width": 512,
  "height": 512,
  "prompt": "Beautiful digital matte pastel paint sunflowers poppies chillwave greg rutkowski artstation",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:a9758cbfbd5f3c2094457d996681af52552901775aa2d6dd0b17fd15df959bef",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "Beautiful digital matte pastel paint sunflowers poppies chillwave greg rutkowski artstation",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:a9758cbfbd5f3c2094457d996681af52552901775aa2d6dd0b17fd15df959bef",
    input={
        "width": 512,
        "height": 512,
        "prompt": "Beautiful digital matte pastel paint sunflowers poppies chillwave greg rutkowski artstation",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:a9758cbfbd5f3c2094457d996681af52552901775aa2d6dd0b17fd15df959bef",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Beautiful digital matte pastel paint sunflowers poppies chillwave greg rutkowski artstation",
      "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.

Output

Generated in

4.7 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:5b703f0fa41880f918ab1b12c88a25b468c18639be17515259fb66a83f4ad0a4

Model

stability-ai/stable-diffusion:5b703f0f

kmp3c6s2kjdqtboq22pyokrksi

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.9s

Created

over 2 years ago by @andreasjansson

Input

{
  "width": 512,
  "height": 512,
  "prompt": "multicolor hyperspace",
  "scheduler": "K-LMS",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:5b703f0fa41880f918ab1b12c88a25b468c18639be17515259fb66a83f4ad0a4",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "multicolor hyperspace",
      scheduler: "K-LMS",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:5b703f0fa41880f918ab1b12c88a25b468c18639be17515259fb66a83f4ad0a4",
    input={
        "width": 512,
        "height": 512,
        "prompt": "multicolor hyperspace",
        "scheduler": "K-LMS",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:5b703f0fa41880f918ab1b12c88a25b468c18639be17515259fb66a83f4ad0a4",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "multicolor hyperspace",
      "scheduler": "K-LMS",
      "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.

Output

Generated in

3.8 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:8abccf52e7cba9f6e82317253f4a3549082e966db5584e92c808ece132037776

Model

stability-ai/stable-diffusion:8abccf52

rsytjp72cbghhh7gr4ax4rnwly

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

4.2s

Created

over 2 years ago by @bfirsh

Input

{
  "width": 512,
  "height": 512,
  "prompt": "multicolor hyperspace",
  "scheduler": "K-LMS",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:8abccf52e7cba9f6e82317253f4a3549082e966db5584e92c808ece132037776",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "multicolor hyperspace",
      scheduler: "K-LMS",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:8abccf52e7cba9f6e82317253f4a3549082e966db5584e92c808ece132037776",
    input={
        "width": 512,
        "height": 512,
        "prompt": "multicolor hyperspace",
        "scheduler": "K-LMS",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:8abccf52e7cba9f6e82317253f4a3549082e966db5584e92c808ece132037776",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "multicolor hyperspace",
      "scheduler": "K-LMS",
      "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.

Output

{
  "completed_at": "2022-11-03T01:17:52.861177Z",
  "created_at": "2022-11-03T01:17:48.718920Z",
  "data_removed": false,
  "error": null,
  "id": "rsytjp72cbghhh7gr4ax4rnwly",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "multicolor hyperspace",
    "scheduler": "K-LMS",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 45688\n\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:05,  9.47it/s]\n  6%|▌         | 3/50 [00:00<00:03, 12.33it/s]\n 10%|█         | 5/50 [00:00<00:03, 12.32it/s]\n 14%|█▍        | 7/50 [00:00<00:03, 12.99it/s]\n 18%|█▊        | 9/50 [00:00<00:03, 13.40it/s]\n 22%|██▏       | 11/50 [00:00<00:02, 13.62it/s]\n 26%|██▌       | 13/50 [00:00<00:02, 13.30it/s]\n 30%|███       | 15/50 [00:01<00:02, 13.48it/s]\n 34%|███▍      | 17/50 [00:01<00:02, 13.55it/s]\n 38%|███▊      | 19/50 [00:01<00:02, 13.66it/s]\n 42%|████▏     | 21/50 [00:01<00:02, 13.75it/s]\n 46%|████▌     | 23/50 [00:01<00:01, 13.76it/s]\n 50%|█████     | 25/50 [00:01<00:01, 13.35it/s]\n 54%|█████▍    | 27/50 [00:02<00:01, 13.50it/s]\n 58%|█████▊    | 29/50 [00:02<00:01, 13.60it/s]\n 62%|██████▏   | 31/50 [00:02<00:01, 13.69it/s]\n 66%|██████▌   | 33/50 [00:02<00:01, 13.74it/s]\n 70%|███████   | 35/50 [00:02<00:01, 13.78it/s]\n 74%|███████▍  | 37/50 [00:02<00:00, 13.46it/s]\n 78%|███████▊  | 39/50 [00:02<00:00, 13.57it/s]\n 82%|████████▏ | 41/50 [00:03<00:00, 13.60it/s]\n 86%|████████▌ | 43/50 [00:03<00:00, 13.68it/s]\n 90%|█████████ | 45/50 [00:03<00:00, 13.63it/s]\n 94%|█████████▍| 47/50 [00:03<00:00, 13.73it/s]\n 98%|█████████▊| 49/50 [00:03<00:00, 13.65it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.49it/s]",
  "metrics": {
    "predict_time": 4.108314,
    "total_time": 4.142257
  },
  "output": [
    "https://replicate.delivery/pbxt/Uy5Ma1EruBKkOReB5TwrX9P1ekJjnSftkujvBqkDneSCMywfB/out-0.png"
  ],
  "started_at": "2022-11-03T01:17:48.752863Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/rsytjp72cbghhh7gr4ax4rnwly",
    "cancel": "https://api.replicate.com/v1/predictions/rsytjp72cbghhh7gr4ax4rnwly/cancel"
  },
  "version": "8abccf52e7cba9f6e82317253f4a3549082e966db5584e92c808ece132037776"
}

Generated in

4.1 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478

Model

stability-ai/stable-diffusion:27b93a24

4irm2hlalvdmlkryu4df63pwnq

Status

Succeeded

Source

Web

Hardware

–

Total duration

2.6s

Created

over 2 years ago by @chenxwh

Input

{
  "width": 512,
  "height": 512,
  "prompt": "Ragdoll cat king wearing a golden crown, intricate, elegant, highly detailed, centered, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, Tomasz Alen Kopera, Peter Mohrbacher, donato giancola, Joseph Christian Leyendecker, WLOP, Boris Vallejo",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "negative_prompt": "yellow color, nose",
  "prompt_strength": 0.8,
  "num_inference_steps": "30"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "Ragdoll cat king wearing a golden crown, intricate, elegant, highly detailed, centered, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, Tomasz Alen Kopera, Peter Mohrbacher, donato giancola, Joseph Christian Leyendecker, WLOP, Boris Vallejo",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: "1",
      guidance_scale: 7.5,
      negative_prompt: "yellow color, nose",
      prompt_strength: 0.8,
      num_inference_steps: "30"
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
    input={
        "width": 512,
        "height": 512,
        "prompt": "Ragdoll cat king wearing a golden crown, intricate, elegant, highly detailed, centered, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, Tomasz Alen Kopera, Peter Mohrbacher, donato giancola, Joseph Christian Leyendecker, WLOP, Boris Vallejo",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_outputs": "1",
        "guidance_scale": 7.5,
        "negative_prompt": "yellow color, nose",
        "prompt_strength": 0.8,
        "num_inference_steps": "30"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Ragdoll cat king wearing a golden crown, intricate, elegant, highly detailed, centered, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, Tomasz Alen Kopera, Peter Mohrbacher, donato giancola, Joseph Christian Leyendecker, WLOP, Boris Vallejo",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": "1",
      "guidance_scale": 7.5,
      "negative_prompt": "yellow color, nose",
      "prompt_strength": 0.8,
      "num_inference_steps": "30"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-11-22T23:15:24.895155Z",
  "created_at": "2022-11-22T23:15:22.302509Z",
  "data_removed": false,
  "error": null,
  "id": "4irm2hlalvdmlkryu4df63pwnq",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Ragdoll cat king wearing a golden crown, intricate, elegant, highly detailed, centered, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, Tomasz Alen Kopera, Peter Mohrbacher, donato giancola, Joseph Christian Leyendecker, WLOP, Boris Vallejo",
    "scheduler": "K_EULER_ANCESTRAL",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "negative_prompt": "yellow color, nose",
    "prompt_strength": 0.8,
    "num_inference_steps": "30"
  },
  "logs": "Using seed: 55251\n  0%|          | 0/30 [00:00<?, ?it/s]\n  7%|▋         | 2/30 [00:00<00:02, 13.41it/s]\n 13%|█▎        | 4/30 [00:00<00:01, 13.90it/s]\n 20%|██        | 6/30 [00:00<00:01, 13.63it/s]\n 27%|██▋       | 8/30 [00:00<00:01, 13.90it/s]\n 33%|███▎      | 10/30 [00:00<00:01, 14.11it/s]\n 40%|████      | 12/30 [00:00<00:01, 14.27it/s]\n 47%|████▋     | 14/30 [00:00<00:01, 14.26it/s]\n 53%|█████▎    | 16/30 [00:01<00:00, 14.44it/s]\n 60%|██████    | 18/30 [00:01<00:00, 14.55it/s]\n 67%|██████▋   | 20/30 [00:01<00:00, 14.66it/s]\n 73%|███████▎  | 22/30 [00:01<00:00, 14.68it/s]\n 80%|████████  | 24/30 [00:01<00:00, 14.74it/s]\n 87%|████████▋ | 26/30 [00:01<00:00, 14.78it/s]\n 93%|█████████▎| 28/30 [00:01<00:00, 14.74it/s]\n100%|██████████| 30/30 [00:02<00:00, 14.53it/s]\n100%|██████████| 30/30 [00:02<00:00, 14.41it/s]",
  "metrics": {
    "predict_time": 2.56023,
    "total_time": 2.592646
  },
  "output": [
    "https://replicate.delivery/pbxt/aooDunhAPcbUBh979fEblu4qqohEtlG0dPJUf1aLuUkMowCQA/out-0.png"
  ],
  "started_at": "2022-11-22T23:15:22.334925Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4irm2hlalvdmlkryu4df63pwnq",
    "cancel": "https://api.replicate.com/v1/predictions/4irm2hlalvdmlkryu4df63pwnq/cancel"
  },
  "version": "27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478"
}

Generated in

2.6 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478

Model

stability-ai/stable-diffusion:27b93a24

uu3pugtcofbn3cgac6k4aymef4

Status

Succeeded

Source

Web

Hardware

–

Total duration

4.7s

Created

over 2 years ago by @zeke

Input

{
  "width": 512,
  "height": 512,
  "prompt": "multicolor hyperspace",
  "scheduler": "K-LMS",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "negative_prompt": "green",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "multicolor hyperspace",
      scheduler: "K-LMS",
      num_outputs: "1",
      guidance_scale: 7.5,
      negative_prompt: "green",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
    input={
        "width": 512,
        "height": 512,
        "prompt": "multicolor hyperspace",
        "scheduler": "K-LMS",
        "num_outputs": "1",
        "guidance_scale": 7.5,
        "negative_prompt": "green",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "multicolor hyperspace",
      "scheduler": "K-LMS",
      "num_outputs": "1",
      "guidance_scale": 7.5,
      "negative_prompt": "green",
      "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.

Output

{
  "completed_at": "2022-11-22T23:34:08.104586Z",
  "created_at": "2022-11-22T23:34:03.421109Z",
  "data_removed": false,
  "error": null,
  "id": "uu3pugtcofbn3cgac6k4aymef4",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "multicolor hyperspace",
    "scheduler": "K-LMS",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "negative_prompt": "green",
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 63632\n  0%|          | 0/50 [00:00<?, ?it/s]\n  4%|▍         | 2/50 [00:00<00:03, 13.41it/s]\n  8%|▊         | 4/50 [00:00<00:03, 13.57it/s]\n 12%|█▏        | 6/50 [00:00<00:03, 13.48it/s]\n 16%|█▌        | 8/50 [00:00<00:03, 13.60it/s]\n 20%|██        | 10/50 [00:00<00:02, 13.67it/s]\n 24%|██▍       | 12/50 [00:00<00:02, 13.68it/s]\n 28%|██▊       | 14/50 [00:01<00:02, 13.71it/s]\n 32%|███▏      | 16/50 [00:01<00:02, 13.68it/s]\n 36%|███▌      | 18/50 [00:01<00:02, 13.62it/s]\n 40%|████      | 20/50 [00:01<00:02, 13.47it/s]\n 44%|████▍     | 22/50 [00:01<00:02, 13.56it/s]\n 48%|████▊     | 24/50 [00:01<00:01, 13.62it/s]\n 52%|█████▏    | 26/50 [00:01<00:01, 13.53it/s]\n 56%|█████▌    | 28/50 [00:02<00:01, 13.54it/s]\n 60%|██████    | 30/50 [00:02<00:01, 13.55it/s]\n 64%|██████▍   | 32/50 [00:02<00:01, 13.49it/s]\n 68%|██████▊   | 34/50 [00:02<00:01, 13.46it/s]\n 72%|███████▏  | 36/50 [00:02<00:01, 13.52it/s]\n 76%|███████▌  | 38/50 [00:02<00:00, 13.57it/s]\n 80%|████████  | 40/50 [00:02<00:00, 13.59it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 13.33it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 13.40it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 13.36it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 13.50it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.57it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.54it/s]",
  "metrics": {
    "predict_time": 4.647599,
    "total_time": 4.683477
  },
  "output": [
    "https://replicate.delivery/pbxt/2zVI7LtEO0aeKSruhERO0PQCVEsbFivhEyXyR0LDMSg3cYBIA/out-0.png"
  ],
  "started_at": "2022-11-22T23:34:03.456987Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/uu3pugtcofbn3cgac6k4aymef4",
    "cancel": "https://api.replicate.com/v1/predictions/uu3pugtcofbn3cgac6k4aymef4/cancel"
  },
  "version": "27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478"
}

Generated in

4.7 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478

Model

stability-ai/stable-diffusion:27b93a24

4hwtj7bbmvetvgdah5o4xln3qy

Status

Succeeded

Source

Web

Hardware

–

Total duration

4.5s

Created

over 2 years ago

Input

{
  "width": 512,
  "height": 512,
  "prompt": "multicolor hyperspace",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "multicolor hyperspace",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
    input={
        "width": 512,
        "height": 512,
        "prompt": "multicolor hyperspace",
        "scheduler": "K_EULER_ANCESTRAL",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "multicolor hyperspace",
      "scheduler": "K_EULER_ANCESTRAL",
      "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.

Output

{
  "completed_at": "2022-11-22T23:34:36.399360Z",
  "created_at": "2022-11-22T23:34:31.934216Z",
  "data_removed": false,
  "error": null,
  "id": "4hwtj7bbmvetvgdah5o4xln3qy",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "multicolor hyperspace",
    "scheduler": "K_EULER_ANCESTRAL",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 63752\n  0%|          | 0/50 [00:00<?, ?it/s]\n  4%|▍         | 2/50 [00:00<00:03, 13.59it/s]\n  8%|▊         | 4/50 [00:00<00:03, 14.13it/s]\n 12%|█▏        | 6/50 [00:00<00:03, 14.41it/s]\n 16%|█▌        | 8/50 [00:00<00:02, 14.53it/s]\n 20%|██        | 10/50 [00:00<00:02, 14.27it/s]\n 24%|██▍       | 12/50 [00:00<00:02, 14.30it/s]\n 28%|██▊       | 14/50 [00:00<00:02, 14.22it/s]\n 32%|███▏      | 16/50 [00:01<00:02, 14.37it/s]\n 36%|███▌      | 18/50 [00:01<00:02, 14.43it/s]\n 40%|████      | 20/50 [00:01<00:02, 14.41it/s]\n 44%|████▍     | 22/50 [00:01<00:01, 14.39it/s]\n 48%|████▊     | 24/50 [00:01<00:01, 14.17it/s]\n 52%|█████▏    | 26/50 [00:01<00:01, 14.20it/s]\n 56%|█████▌    | 28/50 [00:01<00:01, 14.17it/s]\n 60%|██████    | 30/50 [00:02<00:01, 14.35it/s]\n 64%|██████▍   | 32/50 [00:02<00:01, 14.44it/s]\n 68%|██████▊   | 34/50 [00:02<00:01, 14.43it/s]\n 72%|███████▏  | 36/50 [00:02<00:00, 14.47it/s]\n 76%|███████▌  | 38/50 [00:02<00:00, 14.49it/s]\n 80%|████████  | 40/50 [00:02<00:00, 14.47it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.34it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.43it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.48it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.52it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.54it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.38it/s]",
  "metrics": {
    "predict_time": 4.430653,
    "total_time": 4.465144
  },
  "output": [
    "https://replicate.delivery/pbxt/DogdQ8movv5LNlAaHGXrRU9eS1hZMs0OZpIthOsyTbeL6wCQA/out-0.png"
  ],
  "started_at": "2022-11-22T23:34:31.968707Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4hwtj7bbmvetvgdah5o4xln3qy",
    "cancel": "https://api.replicate.com/v1/predictions/4hwtj7bbmvetvgdah5o4xln3qy/cancel"
  },
  "version": "27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478"
}

Generated in

4.4 seconds

Tweak it Share Report View full prediction

Prediction

stability-ai/stable-diffusion:0827b64897df7b6e8c04625167bbb275b9db0f14ab09e2454b9824141963c966

Model

stability-ai/stable-diffusion:0827b648

cxpa7egscbbtjbsx4r6623v5n4

Status

Succeeded

Source

Web

Hardware

–

Total duration

7.1s

Created

over 2 years ago by @bfirsh

Input

{
  "width": 768,
  "height": 768,
  "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
  "scheduler": "K_EULER",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:0827b64897df7b6e8c04625167bbb275b9db0f14ab09e2454b9824141963c966",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
      scheduler: "K_EULER",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:0827b64897df7b6e8c04625167bbb275b9db0f14ab09e2454b9824141963c966",
    input={
        "width": 768,
        "height": 768,
        "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:0827b64897df7b6e8c04625167bbb275b9db0f14ab09e2454b9824141963c966",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
      "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.

Output

{
  "completed_at": "2022-12-06T16:04:42.659888Z",
  "created_at": "2022-12-06T16:04:35.607941Z",
  "data_removed": false,
  "error": null,
  "id": "cxpa7egscbbtjbsx4r6623v5n4",
  "input": {
    "width": 768,
    "height": 768,
    "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
    "scheduler": "K_EULER",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 17203\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:07,  6.90it/s]\n  4%|▍         | 2/50 [00:00<00:06,  7.27it/s]\n  6%|▌         | 3/50 [00:00<00:05,  7.86it/s]\n  8%|▊         | 4/50 [00:00<00:05,  8.16it/s]\n 10%|█         | 5/50 [00:00<00:05,  8.34it/s]\n 12%|█▏        | 6/50 [00:00<00:05,  8.46it/s]\n 14%|█▍        | 7/50 [00:00<00:05,  8.53it/s]\n 16%|█▌        | 8/50 [00:00<00:04,  8.58it/s]\n 18%|█▊        | 9/50 [00:01<00:04,  8.62it/s]\n 20%|██        | 10/50 [00:01<00:04,  8.65it/s]\n 22%|██▏       | 11/50 [00:01<00:04,  8.67it/s]\n 24%|██▍       | 12/50 [00:01<00:04,  8.68it/s]\n 26%|██▌       | 13/50 [00:01<00:04,  8.68it/s]\n 28%|██▊       | 14/50 [00:01<00:04,  8.69it/s]\n 30%|███       | 15/50 [00:01<00:04,  8.69it/s]\n 32%|███▏      | 16/50 [00:01<00:03,  8.69it/s]\n 34%|███▍      | 17/50 [00:02<00:03,  8.69it/s]\n 36%|███▌      | 18/50 [00:02<00:03,  8.70it/s]\n 38%|███▊      | 19/50 [00:02<00:03,  8.70it/s]\n 40%|████      | 20/50 [00:02<00:03,  8.70it/s]\n 42%|████▏     | 21/50 [00:02<00:03,  8.70it/s]\n 44%|████▍     | 22/50 [00:02<00:03,  8.70it/s]\n 46%|████▌     | 23/50 [00:02<00:03,  8.70it/s]\n 48%|████▊     | 24/50 [00:02<00:02,  8.70it/s]\n 50%|█████     | 25/50 [00:02<00:02,  8.69it/s]\n 52%|█████▏    | 26/50 [00:03<00:02,  8.70it/s]\n 54%|█████▍    | 27/50 [00:03<00:02,  8.70it/s]\n 56%|█████▌    | 28/50 [00:03<00:02,  8.71it/s]\n 58%|█████▊    | 29/50 [00:03<00:02,  8.71it/s]\n 60%|██████    | 30/50 [00:03<00:02,  8.70it/s]\n 62%|██████▏   | 31/50 [00:03<00:02,  8.67it/s]\n 64%|██████▍   | 32/50 [00:03<00:02,  8.68it/s]\n 66%|██████▌   | 33/50 [00:03<00:01,  8.69it/s]\n 68%|██████▊   | 34/50 [00:03<00:01,  8.69it/s]\n 70%|███████   | 35/50 [00:04<00:01,  8.70it/s]\n 72%|███████▏  | 36/50 [00:04<00:01,  8.71it/s]\n 74%|███████▍  | 37/50 [00:04<00:01,  8.71it/s]\n 76%|███████▌  | 38/50 [00:04<00:01,  8.71it/s]\n 78%|███████▊  | 39/50 [00:04<00:01,  8.71it/s]\n 80%|████████  | 40/50 [00:04<00:01,  8.67it/s]\n 82%|████████▏ | 41/50 [00:04<00:01,  8.69it/s]\n 84%|████████▍ | 42/50 [00:04<00:00,  8.70it/s]\n 86%|████████▌ | 43/50 [00:04<00:00,  8.71it/s]\n 88%|████████▊ | 44/50 [00:05<00:00,  8.72it/s]\n 90%|█████████ | 45/50 [00:05<00:00,  8.72it/s]\n 92%|█████████▏| 46/50 [00:05<00:00,  8.72it/s]\n 94%|█████████▍| 47/50 [00:05<00:00,  8.72it/s]\n 96%|█████████▌| 48/50 [00:05<00:00,  8.72it/s]\n 98%|█████████▊| 49/50 [00:05<00:00,  8.72it/s]\n100%|██████████| 50/50 [00:05<00:00,  8.73it/s]\n100%|██████████| 50/50 [00:05<00:00,  8.63it/s]",
  "metrics": {
    "predict_time": 7.019087,
    "total_time": 7.051947
  },
  "output": [
    "https://replicate.delivery/pbxt/f4nlztv3uz1iFC4AEf2wBYQGTezdVeysvtZUtwfsvZOJDN6AC/out-0.png"
  ],
  "started_at": "2022-12-06T16:04:35.640801Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/cxpa7egscbbtjbsx4r6623v5n4",
    "cancel": "https://api.replicate.com/v1/predictions/cxpa7egscbbtjbsx4r6623v5n4/cancel"
  },
  "version": "0827b64897df7b6e8c04625167bbb275b9db0f14ab09e2454b9824141963c966"
}

Generated in

7.0 seconds

Tweak it Share Report View full prediction

Using seed: 17203
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Prediction

stability-ai/stable-diffusion:6359a0cab3ca6e4d3320c33d79096161208e9024d174b2311e5a21b6c7e1131c

Model

stability-ai/stable-diffusion:6359a0ca

3wztszjpcjbabi6iwgqukwyegq

Status

Succeeded

Source

Web

Hardware

–

Total duration

79.5s

Created

over 2 years ago by @bfirsh

Input

{
  "width": 768,
  "height": 768,
  "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
  "scheduler": "K_EULER",
  "num_outputs": "1",
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:6359a0cab3ca6e4d3320c33d79096161208e9024d174b2311e5a21b6c7e1131c",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
      scheduler: "K_EULER",
      num_outputs: "1",
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:6359a0cab3ca6e4d3320c33d79096161208e9024d174b2311e5a21b6c7e1131c",
    input={
        "width": 768,
        "height": 768,
        "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
        "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.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:6359a0cab3ca6e4d3320c33d79096161208e9024d174b2311e5a21b6c7e1131c",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
      "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.

Output

{
  "completed_at": "2022-12-09T02:48:58.002360Z",
  "created_at": "2022-12-09T02:47:38.477014Z",
  "data_removed": false,
  "error": null,
  "id": "3wztszjpcjbabi6iwgqukwyegq",
  "input": {
    "width": 768,
    "height": 768,
    "prompt": "an astronaut riding a horse on mars artstation, hd, dramatic lighting, detailed",
    "scheduler": "K_EULER",
    "num_outputs": "1",
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 57140\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:12,  3.84it/s]\n  4%|▍         | 2/50 [00:00<00:12,  3.84it/s]\n  6%|▌         | 3/50 [00:00<00:12,  3.84it/s]\n  8%|▊         | 4/50 [00:01<00:11,  3.84it/s]\n 10%|█         | 5/50 [00:01<00:11,  3.84it/s]\n 12%|█▏        | 6/50 [00:01<00:11,  3.83it/s]\n 14%|█▍        | 7/50 [00:01<00:11,  3.83it/s]\n 16%|█▌        | 8/50 [00:02<00:10,  3.83it/s]\n 18%|█▊        | 9/50 [00:02<00:10,  3.83it/s]\n 20%|██        | 10/50 [00:02<00:10,  3.83it/s]\n 22%|██▏       | 11/50 [00:02<00:10,  3.83it/s]\n 24%|██▍       | 12/50 [00:03<00:09,  3.82it/s]\n 26%|██▌       | 13/50 [00:03<00:09,  3.82it/s]\n 28%|██▊       | 14/50 [00:03<00:09,  3.82it/s]\n 30%|███       | 15/50 [00:03<00:09,  3.82it/s]\n 32%|███▏      | 16/50 [00:04<00:08,  3.82it/s]\n 34%|███▍      | 17/50 [00:04<00:08,  3.82it/s]\n 36%|███▌      | 18/50 [00:04<00:08,  3.83it/s]\n 38%|███▊      | 19/50 [00:04<00:08,  3.83it/s]\n 40%|████      | 20/50 [00:05<00:07,  3.83it/s]\n 42%|████▏     | 21/50 [00:05<00:07,  3.83it/s]\n 44%|████▍     | 22/50 [00:05<00:07,  3.83it/s]\n 46%|████▌     | 23/50 [00:06<00:07,  3.83it/s]\n 48%|████▊     | 24/50 [00:06<00:06,  3.83it/s]\n 50%|█████     | 25/50 [00:06<00:06,  3.83it/s]\n 52%|█████▏    | 26/50 [00:06<00:06,  3.83it/s]\n 54%|█████▍    | 27/50 [00:07<00:06,  3.83it/s]\n 56%|█████▌    | 28/50 [00:07<00:05,  3.83it/s]\n 58%|█████▊    | 29/50 [00:07<00:05,  3.83it/s]\n 60%|██████    | 30/50 [00:07<00:05,  3.83it/s]\n 62%|██████▏   | 31/50 [00:08<00:04,  3.83it/s]\n 64%|██████▍   | 32/50 [00:08<00:04,  3.83it/s]\n 66%|██████▌   | 33/50 [00:08<00:04,  3.84it/s]\n 68%|██████▊   | 34/50 [00:08<00:04,  3.84it/s]\n 70%|███████   | 35/50 [00:09<00:03,  3.84it/s]\n 72%|███████▏  | 36/50 [00:09<00:03,  3.84it/s]\n 74%|███████▍  | 37/50 [00:09<00:03,  3.84it/s]\n 76%|███████▌  | 38/50 [00:09<00:03,  3.84it/s]\n 78%|███████▊  | 39/50 [00:10<00:02,  3.84it/s]\n 80%|████████  | 40/50 [00:10<00:02,  3.84it/s]\n 82%|████████▏ | 41/50 [00:10<00:02,  3.83it/s]\n 84%|████████▍ | 42/50 [00:10<00:02,  3.83it/s]\n 86%|████████▌ | 43/50 [00:11<00:01,  3.83it/s]\n 88%|████████▊ | 44/50 [00:11<00:01,  3.83it/s]\n 90%|█████████ | 45/50 [00:11<00:01,  3.83it/s]\n 92%|█████████▏| 46/50 [00:12<00:01,  3.83it/s]\n 94%|█████████▍| 47/50 [00:12<00:00,  3.83it/s]\n 96%|█████████▌| 48/50 [00:12<00:00,  3.84it/s]\n 98%|█████████▊| 49/50 [00:12<00:00,  3.84it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.83it/s]\n100%|██████████| 50/50 [00:13<00:00,  3.83it/s]",
  "metrics": {
    "predict_time": 14.28122,
    "total_time": 79.525346
  },
  "output": [
    "https://replicate.delivery/pbxt/XdWzrhW73EIAIxuxl92iE5qVHdiFMnNh3axdwp56EdSGUBCE/out-0.png"
  ],
  "started_at": "2022-12-09T02:48:43.721140Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/3wztszjpcjbabi6iwgqukwyegq",
    "cancel": "https://api.replicate.com/v1/predictions/3wztszjpcjbabi6iwgqukwyegq/cancel"
  },
  "version": "6359a0cab3ca6e4d3320c33d79096161208e9024d174b2311e5a21b6c7e1131c"
}

Generated in

14.3 seconds

Tweak it Share Report View full prediction

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Prediction

stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf

Model

stability-ai/stable-diffusion:db21e45d

uvb7ynit4bhpjds3vn4bx7npeq

Status

Succeeded

Source

Web

Hardware

–

Total duration

2.0s

Created

about 2 years ago by @bfirsh

Input

{
  "prompt": "an astronaut riding a horse on mars, hd, dramatic lighting",
  "scheduler": "K_EULER",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "image_dimensions": "512x512",
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
  {
    input: {
      prompt: "an astronaut riding a horse on mars, hd, dramatic lighting",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 7.5,
      image_dimensions: "512x512",
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
    input={
        "prompt": "an astronaut riding a horse on mars, hd, dramatic lighting",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "image_dimensions": "512x512",
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run stability-ai/stable-diffusion 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": "stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
    "input": {
      "prompt": "an astronaut riding a horse on mars, hd, dramatic lighting",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "image_dimensions": "512x512",
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Generated in

1.9 seconds

Tweak it Share Report View full prediction

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